Time Series 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbec9b6e-9123-493d-9194-6ba95e2870ff",
   "metadata": {},
   "source": [
    "### Time series EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edd0136e-c28f-4c9b-94c8-363db0480f55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas-datareader in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (0.10.0)\n",
      "Requirement already satisfied: lxml in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas-datareader) (5.3.0)\n",
      "Requirement already satisfied: pandas>=0.23 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas-datareader) (2.2.3)\n",
      "Requirement already satisfied: requests>=2.19.0 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas-datareader) (2.32.3)\n",
      "Requirement already satisfied: numpy>=1.23.2 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2024.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2024.8.30)\n",
      "Requirement already satisfied: six>=1.5 in d:\\anaconda\\envs\\timeseries\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=0.23->pandas-datareader) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "## Install Pandas Data Reader\n",
    "\n",
    "!pip install pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58c17be6-f771-45c9-8ad8-b38853224fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                                  Version            Editable project location\n",
      "---------------------------------------- ------------------ ------------------------------------------\n",
      "aiobotocore                              2.12.3\n",
      "aiohttp                                  3.9.5\n",
      "aioitertools                             0.7.1\n",
      "aiosignal                                1.2.0\n",
      "alabaster                                0.7.16\n",
      "alembic                                  1.14.0\n",
      "altair                                   5.0.1\n",
      "anaconda-anon-usage                      0.4.3\n",
      "anaconda-catalogs                        0.2.0\n",
      "anaconda-client                          1.12.3\n",
      "anaconda-cloud-auth                      0.1.4\n",
      "anaconda-navigator                       2.5.2\n",
      "anaconda-project                         0.11.1\n",
      "androguard                               4.1.2\n",
      "annotated-types                          0.7.0\n",
      "anyio                                    4.2.0\n",
      "apkInspector                             1.3.2\n",
      "appdirs                                  1.4.4\n",
      "archspec                                 0.2.3\n",
      "argon2-cffi                              21.3.0\n",
      "argon2-cffi-bindings                     21.2.0\n",
      "arrow                                    1.2.3\n",
      "asn1crypto                               1.5.1\n",
      "astroid                                  2.14.2\n",
      "astropy                                  6.1.0\n",
      "astropy-iers-data                        0.2024.6.3.0.31.14\n",
      "asttokens                                2.0.5\n",
      "async-lru                                2.0.4\n",
      "atomicwrites                             1.4.0\n",
      "attrs                                    23.1.0\n",
      "Automat                                  20.2.0\n",
      "autopep8                                 2.0.4\n",
      "Babel                                    2.11.0\n",
      "backports.functools-lru-cache            1.6.4\n",
      "backports.tempfile                       1.0\n",
      "backports.weakref                        1.0.post1\n",
      "banal                                    1.0.6\n",
      "bcrypt                                   3.2.0\n",
      "beautifulsoup4                           4.12.3\n",
      "binaryornot                              0.4.4\n",
      "black                                    24.4.2\n",
      "bleach                                   4.1.0\n",
      "blinker                                  1.6.2\n",
      "bokeh                                    3.4.1\n",
      "boltons                                  23.0.0\n",
      "boto3                                    1.35.8\n",
      "botocore                                 1.34.69\n",
      "Bottleneck                               1.3.7\n",
      "Brotli                                   1.0.9\n",
      "cachetools                               5.3.3\n",
      "catboost                                 1.2.5\n",
      "certifi                                  2024.6.2\n",
      "cffi                                     1.16.0\n",
      "chardet                                  4.0.0\n",
      "charset-normalizer                       2.0.4\n",
      "click                                    8.1.7\n",
      "cloudpickle                              2.2.1\n",
      "clyent                                   1.2.2\n",
      "colorama                                 0.4.6\n",
      "colorcet                                 3.1.0\n",
      "comm                                     0.2.1\n",
      "conda                                    24.9.1\n",
      "conda-build                              24.9.0\n",
      "conda-content-trust                      0.2.0\n",
      "conda_index                              0.4.0\n",
      "conda-libmamba-solver                    24.1.0\n",
      "conda-pack                               0.6.0\n",
      "conda-package-handling                   2.2.0\n",
      "conda_package_streaming                  0.9.0\n",
      "conda-repo-cli                           1.0.75\n",
      "conda-token                              0.4.0\n",
      "conda-verify                             3.4.2\n",
      "constantly                               23.10.4\n",
      "contourpy                                1.2.0\n",
      "cookiecutter                             2.6.0\n",
      "cryptography                             42.0.5\n",
      "cssselect                                1.2.0\n",
      "cycler                                   0.11.0\n",
      "cytoolz                                  0.12.2\n",
      "dask                                     2024.5.0\n",
      "dask-expr                                1.1.0\n",
      "dataset                                  1.6.2\n",
      "datashader                               0.16.2\n",
      "debugpy                                  1.6.7\n",
      "decorator                                5.1.1\n",
      "defusedxml                               0.7.1\n",
      "Deprecated                               1.2.14\n",
      "deprecation                              2.1.0\n",
      "diff-match-patch                         20200713\n",
      "dill                                     0.3.8\n",
      "distributed                              2024.5.0\n",
      "distro                                   1.8.0\n",
      "dnspython                                2.6.1\n",
      "docstring-to-markdown                    0.11\n",
      "docutils                                 0.18.1\n",
      "dynaconf                                 3.2.6\n",
      "entrypoints                              0.4\n",
      "et-xmlfile                               1.1.0\n",
      "evidently                                0.2.8\n",
      "executing                                0.8.3\n",
      "Faker                                    28.0.0\n",
      "fastapi                                  0.112.2\n",
      "fastjsonschema                           2.16.2\n",
      "filelock                                 3.13.1\n",
      "flake8                                   7.0.0\n",
      "Flask                                    3.0.3\n",
      "fonttools                                4.51.0\n",
      "frida                                    16.5.6\n",
      "from-root                                1.3.0\n",
      "frozendict                               2.4.2\n",
      "frozenlist                               1.4.0\n",
      "fsspec                                   2024.3.1\n",
      "future                                   0.18.3\n",
      "gensim                                   4.3.2\n",
      "gitdb                                    4.0.7\n",
      "GitPython                                3.1.37\n",
      "gmpy2                                    2.1.2\n",
      "googleapis-common-protos                 1.65.0\n",
      "graphviz                                 0.20.3\n",
      "greenlet                                 3.0.1\n",
      "grpcio                                   1.66.0\n",
      "h11                                      0.14.0\n",
      "h2o                                      3.46.0.5\n",
      "h5py                                     3.11.0\n",
      "HeapDict                                 1.0.1\n",
      "holoviews                                1.19.0\n",
      "httpcore                                 1.0.5\n",
      "httptools                                0.6.1\n",
      "httpx                                    0.27.2\n",
      "hvplot                                   0.10.0\n",
      "hyperlink                                21.0.0\n",
      "idna                                     3.7\n",
      "imagecodecs                              2023.1.23\n",
      "imageio                                  2.33.1\n",
      "imagesize                                1.4.1\n",
      "imbalanced-learn                         0.12.3\n",
      "imblearn                                 0.0\n",
      "importlib-metadata                       7.0.1\n",
      "incremental                              22.10.0\n",
      "inflection                               0.5.1\n",
      "iniconfig                                1.1.1\n",
      "intake                                   0.7.0\n",
      "intervaltree                             3.1.0\n",
      "ipykernel                                6.28.0\n",
      "ipython                                  8.25.0\n",
      "ipython-genutils                         0.2.0\n",
      "ipywidgets                               7.8.1\n",
      "isort                                    5.13.2\n",
      "itemadapter                              0.3.0\n",
      "itemloaders                              1.1.0\n",
      "iterative-telemetry                      0.0.8\n",
      "itsdangerous                             2.2.0\n",
      "jaraco.classes                           3.2.1\n",
      "jedi                                     0.18.1\n",
      "jellyfish                                1.0.1\n",
      "Jinja2                                   3.1.4\n",
      "jmespath                                 1.0.1\n",
      "joblib                                   1.4.2\n",
      "json5                                    0.9.6\n",
      "jsonpatch                                1.32\n",
      "jsonpointer                              2.1\n",
      "jsonschema                               4.19.2\n",
      "jsonschema-specifications                2023.7.1\n",
      "jupyter                                  1.0.0\n",
      "jupyter_client                           8.6.0\n",
      "jupyter-console                          6.6.3\n",
      "jupyter_core                             5.7.2\n",
      "jupyter-events                           0.10.0\n",
      "jupyter-lsp                              2.2.0\n",
      "jupyter_server                           2.14.1\n",
      "jupyter_server_terminals                 0.4.4\n",
      "jupyterlab                               4.0.11\n",
      "jupyterlab-pygments                      0.1.2\n",
      "jupyterlab_server                        2.25.1\n",
      "jupyterlab-widgets                       1.0.0\n",
      "keyring                                  24.3.1\n",
      "kiwisolver                               1.4.4\n",
      "lazy_loader                              0.4\n",
      "lazy-object-proxy                        1.10.0\n",
      "lckr_jupyterlab_variableinspector        3.1.0\n",
      "libarchive-c                             2.9\n",
      "libmambapy                               1.5.8\n",
      "linkify-it-py                            2.0.0\n",
      "litestar                                 2.11.0\n",
      "llvmlite                                 0.42.0\n",
      "lmdb                                     1.4.1\n",
      "locket                                   1.0.0\n",
      "loguru                                   0.7.2\n",
      "lxml                                     5.2.1\n",
      "lz4                                      4.3.2\n",
      "Mako                                     1.3.6\n",
      "Markdown                                 3.4.1\n",
      "markdown-it-py                           2.2.0\n",
      "MarkupSafe                               2.1.3\n",
      "matplotlib                               3.8.4\n",
      "matplotlib-inline                        0.1.6\n",
      "mccabe                                   0.7.0\n",
      "mdit-py-plugins                          0.3.0\n",
      "mdurl                                    0.1.0\n",
      "menuinst                                 2.0.2\n",
      "mistune                                  2.0.4\n",
      "mkl-fft                                  1.3.8\n",
      "mkl-random                               1.2.4\n",
      "mkl-service                              2.4.0\n",
      "ml_project                               0.1.0              D:\\Projects\\Krish_Naik_YT\\END_END_ML_PLAY\n",
      "more-itertools                           10.1.0\n",
      "mpmath                                   1.3.0\n",
      "msal                                     1.31.0\n",
      "msgpack                                  1.0.3\n",
      "msgspec                                  0.18.6\n",
      "multidict                                6.0.4\n",
      "multipledispatch                         0.6.0\n",
      "munkres                                  1.1.4\n",
      "mutf8                                    1.0.6\n",
      "mypy                                     1.10.0\n",
      "mypy-boto3-s3                            1.35.2\n",
      "mypy-extensions                          1.0.0\n",
      "navigator-updater                        0.4.0\n",
      "nbclient                                 0.8.0\n",
      "nbconvert                                7.10.0\n",
      "nbformat                                 5.9.2\n",
      "nest-asyncio                             1.6.0\n",
      "networkx                                 3.2.1\n",
      "neuro-mf                                 0.0.5\n",
      "nltk                                     3.8.1\n",
      "notebook                                 7.0.8\n",
      "notebook_shim                            0.2.3\n",
      "numba                                    0.59.1\n",
      "numexpr                                  2.8.7\n",
      "numpy                                    1.26.0\n",
      "numpydoc                                 1.7.0\n",
      "openpyxl                                 3.1.2\n",
      "opentelemetry-api                        1.26.0\n",
      "opentelemetry-exporter-otlp-proto-common 1.26.0\n",
      "opentelemetry-exporter-otlp-proto-grpc   1.26.0\n",
      "opentelemetry-exporter-otlp-proto-http   1.26.0\n",
      "opentelemetry-proto                      1.26.0\n",
      "opentelemetry-sdk                        1.26.0\n",
      "opentelemetry-semantic-conventions       0.47b0\n",
      "oscrypto                                 1.3.0\n",
      "overrides                                7.4.0\n",
      "packaging                                23.2\n",
      "pandas                                   2.2.2\n",
      "pandocfilters                            1.5.0\n",
      "panel                                    1.4.4\n",
      "param                                    2.1.0\n",
      "paramiko                                 2.8.1\n",
      "parsel                                   1.8.1\n",
      "parso                                    0.8.3\n",
      "partd                                    1.4.1\n",
      "pathlib                                  1.0.1\n",
      "pathspec                                 0.10.3\n",
      "patsy                                    0.5.6\n",
      "pexpect                                  4.8.0\n",
      "pickleshare                              0.7.5\n",
      "pillow                                   10.3.0\n",
      "pip                                      24.0\n",
      "pkce                                     1.0.3\n",
      "pkginfo                                  1.9.6\n",
      "platformdirs                             3.10.0\n",
      "plotly                                   5.22.0\n",
      "pluggy                                   1.0.0\n",
      "ply                                      3.11\n",
      "polyfactory                              2.16.2\n",
      "prometheus-client                        0.14.1\n",
      "prompt-toolkit                           3.0.43\n",
      "Protego                                  0.1.16\n",
      "protobuf                                 3.20.3\n",
      "psutil                                   5.9.0\n",
      "ptyprocess                               0.7.0\n",
      "pure-eval                                0.2.2\n",
      "py-cpuinfo                               9.0.0\n",
      "pyarrow                                  14.0.2\n",
      "pyasn1                                   0.4.8\n",
      "pyasn1-modules                           0.2.8\n",
      "pycodestyle                              2.11.1\n",
      "pycosat                                  0.6.6\n",
      "pycparser                                2.21\n",
      "pyct                                     0.5.0\n",
      "pycurl                                   7.45.2\n",
      "pydantic                                 1.10.18\n",
      "pydantic_core                            2.20.1\n",
      "pydeck                                   0.8.0\n",
      "PyDispatcher                             2.0.5\n",
      "pydocstyle                               6.3.0\n",
      "pydot                                    3.0.2\n",
      "pyerfa                                   2.0.1.4\n",
      "pyflakes                                 3.2.0\n",
      "Pygments                                 2.15.1\n",
      "PyJWT                                    2.4.0\n",
      "pylint                                   2.16.2\n",
      "pylint-venv                              3.0.3\n",
      "pyls-spyder                              0.4.0\n",
      "pymongo                                  4.8.0\n",
      "PyNaCl                                   1.5.0\n",
      "pyodbc                                   5.0.1\n",
      "pyOpenSSL                                24.0.0\n",
      "pyparsing                                3.0.9\n",
      "PyQt5                                    5.15.10\n",
      "PyQt5-sip                                12.13.0\n",
      "PyQtWebEngine                            5.15.6\n",
      "PySocks                                  1.7.1\n",
      "pytest                                   7.4.4\n",
      "python-dateutil                          2.9.0.post0\n",
      "python-dotenv                            0.21.0\n",
      "python-json-logger                       2.0.7\n",
      "python-lsp-black                         2.0.0\n",
      "python-lsp-jsonrpc                       1.1.2\n",
      "python-lsp-server                        1.10.0\n",
      "python-multipart                         0.0.9\n",
      "python-slugify                           5.0.2\n",
      "python-snappy                            0.6.1\n",
      "pytoolconfig                             1.2.6\n",
      "pytz                                     2024.1\n",
      "pyviz_comms                              3.0.2\n",
      "pywavelets                               1.5.0\n",
      "pywin32                                  305.1\n",
      "pywin32-ctypes                           0.2.2\n",
      "pywinpty                                 2.0.10\n",
      "PyYAML                                   6.0.1\n",
      "pyzmq                                    25.1.2\n",
      "QDarkStyle                               3.2.3\n",
      "qstylizer                                0.2.2\n",
      "QtAwesome                                1.2.2\n",
      "qtconsole                                5.5.1\n",
      "QtPy                                     2.4.1\n",
      "queuelib                                 1.6.2\n",
      "referencing                              0.30.2\n",
      "regex                                    2023.10.3\n",
      "requests                                 2.32.2\n",
      "requests-file                            1.5.1\n",
      "requests-toolbelt                        1.0.0\n",
      "rfc3339-validator                        0.1.4\n",
      "rfc3986-validator                        0.1.1\n",
      "rich                                     13.3.5\n",
      "rich-click                               1.8.3\n",
      "rope                                     1.12.0\n",
      "rpds-py                                  0.10.6\n",
      "Rtree                                    1.0.1\n",
      "ruamel.yaml                              0.17.21\n",
      "ruamel-yaml-conda                        0.17.21\n",
      "s3fs                                     2024.3.1\n",
      "s3transfer                               0.10.2\n",
      "scikit-image                             0.23.2\n",
      "scikit-learn                             1.2.0\n",
      "scipy                                    1.13.1\n",
      "Scrapy                                   2.11.1\n",
      "seaborn                                  0.13.2\n",
      "semver                                   2.13.0\n",
      "Send2Trash                               1.8.2\n",
      "service-identity                         18.1.0\n",
      "setuptools                               69.5.1\n",
      "shellingham                              1.5.4\n",
      "sip                                      6.7.12\n",
      "six                                      1.16.0\n",
      "smart-open                               5.2.1\n",
      "smmap                                    4.0.0\n",
      "sniffio                                  1.3.0\n",
      "snowballstemmer                          2.2.0\n",
      "sortedcontainers                         2.4.0\n",
      "soupsieve                                2.5\n",
      "Sphinx                                   7.3.7\n",
      "sphinxcontrib-applehelp                  1.0.2\n",
      "sphinxcontrib-devhelp                    1.0.2\n",
      "sphinxcontrib-htmlhelp                   2.0.0\n",
      "sphinxcontrib-jsmath                     1.0.1\n",
      "sphinxcontrib-qthelp                     1.0.3\n",
      "sphinxcontrib-serializinghtml            1.1.10\n",
      "spyder                                   5.5.1\n",
      "spyder-kernels                           2.5.0\n",
      "SQLAlchemy                               1.4.54\n",
      "stack-data                               0.2.0\n",
      "starlette                                0.38.2\n",
      "statsmodels                              0.14.2\n",
      "streamlit                                1.32.0\n",
      "sympy                                    1.12\n",
      "tables                                   3.9.2\n",
      "tabulate                                 0.9.0\n",
      "tblib                                    1.7.0\n",
      "tenacity                                 8.2.2\n",
      "termcolor                                2.5.0\n",
      "terminado                                0.17.1\n",
      "text-unidecode                           1.3\n",
      "textdistance                             4.2.1\n",
      "threadpoolctl                            2.2.0\n",
      "three-merge                              0.1.1\n",
      "tifffile                                 2023.4.12\n",
      "tinycss2                                 1.2.1\n",
      "tldextract                               3.2.0\n",
      "toml                                     0.10.2\n",
      "tomli                                    2.0.1\n",
      "tomlkit                                  0.11.1\n",
      "toolz                                    0.12.0\n",
      "tornado                                  6.4.1\n",
      "tqdm                                     4.66.4\n",
      "traitlets                                5.14.3\n",
      "truststore                               0.8.0\n",
      "Twisted                                  23.10.0\n",
      "twisted-iocpsupport                      1.0.2\n",
      "typer                                    0.12.5\n",
      "typing_extensions                        4.11.0\n",
      "typing-inspect                           0.9.0\n",
      "tzdata                                   2023.3\n",
      "tzlocal                                  2.1\n",
      "uc-micro-py                              1.0.1\n",
      "ujson                                    5.10.0\n",
      "unicodedata2                             15.1.0\n",
      "Unidecode                                1.2.0\n",
      "urllib3                                  2.2.2\n",
      "us-visa                                  0.0.0              D:\\Projects\\Prediction-of-US-Visa-Approval\n",
      "uvicorn                                  0.30.6\n",
      "validators                               0.18.2\n",
      "w3lib                                    2.1.2\n",
      "watchdog                                 4.0.1\n",
      "watchfiles                               0.24.0\n",
      "wcwidth                                  0.2.5\n",
      "webencodings                             0.5.1\n",
      "websocket-client                         1.8.0\n",
      "websockets                               13.0\n",
      "Werkzeug                                 3.0.3\n",
      "whatthepatch                             1.0.2\n",
      "wheel                                    0.43.0\n",
      "widgetsnbextension                       3.6.6\n",
      "win-inet-pton                            1.1.0\n",
      "win32-setctime                           1.1.0\n",
      "wrapt                                    1.14.1\n",
      "xarray                                   2023.6.0\n",
      "xgboost                                  2.1.1\n",
      "xlwings                                  0.31.4\n",
      "xyzservices                              2022.9.0\n",
      "yapf                                     0.40.2\n",
      "yarl                                     1.9.3\n",
      "zict                                     3.0.0\n",
      "zipp                                     3.17.0\n",
      "zope.interface                           5.4.0\n",
      "zstandard                                0.19.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b14eaf36-e401-4be4-a49f-ec4e638120ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas-datareaderNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "  Using cached pandas_datareader-0.10.0-py3-none-any.whl.metadata (2.9 kB)\n",
      "Requirement already satisfied: lxml in d:\\anaconda\\lib\\site-packages (from pandas-datareader) (5.2.1)\n",
      "Requirement already satisfied: pandas>=0.23 in d:\\anaconda\\lib\\site-packages (from pandas-datareader) (2.2.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in d:\\anaconda\\lib\\site-packages (from pandas-datareader) (2.32.2)\n",
      "Requirement already satisfied: numpy>=1.23.2 in d:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (1.26.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\anaconda\\lib\\site-packages (from pandas>=0.23->pandas-datareader) (2023.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\anaconda\\lib\\site-packages (from requests>=2.19.0->pandas-datareader) (2024.6.2)\n",
      "Requirement already satisfied: six>=1.5 in d:\\anaconda\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=0.23->pandas-datareader) (1.16.0)\n",
      "Using cached pandas_datareader-0.10.0-py3-none-any.whl (109 kB)\n",
      "Installing collected packages: pandas-datareader\n",
      "Successfully installed pandas-datareader-0.10.0\n"
     ]
    }
   ],
   "source": [
    "pip install pandas-datareader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e718a908-c387-4651-bb4c-6f1b2f57e356",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr\n",
    "import pandas as pd\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fce4c22a-b1bd-475b-9b70-2dc54545d0b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting yfinanceNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "  Downloading yfinance-0.2.50-py2.py3-none-any.whl.metadata (5.5 kB)\n",
      "Requirement already satisfied: pandas>=1.3.0 in d:\\anaconda\\lib\\site-packages (from yfinance) (2.2.2)\n",
      "Requirement already satisfied: numpy>=1.16.5 in d:\\anaconda\\lib\\site-packages (from yfinance) (1.26.0)\n",
      "Requirement already satisfied: requests>=2.31 in d:\\anaconda\\lib\\site-packages (from yfinance) (2.32.2)\n",
      "Collecting multitasking>=0.0.7 (from yfinance)\n",
      "  Downloading multitasking-0.0.11-py3-none-any.whl.metadata (5.5 kB)\n",
      "Requirement already satisfied: lxml>=4.9.1 in d:\\anaconda\\lib\\site-packages (from yfinance) (5.2.1)\n",
      "Requirement already satisfied: platformdirs>=2.0.0 in d:\\anaconda\\lib\\site-packages (from yfinance) (3.10.0)\n",
      "Requirement already satisfied: pytz>=2022.5 in d:\\anaconda\\lib\\site-packages (from yfinance) (2024.1)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in d:\\anaconda\\lib\\site-packages (from yfinance) (2.4.2)\n",
      "Collecting peewee>=3.16.2 (from yfinance)\n",
      "  Downloading peewee-3.17.8.tar.gz (948 kB)\n",
      "     ---------------------------------------- 0.0/948.2 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/948.2 kB ? eta -:--:--\n",
      "      ------------------------------------ 20.5/948.2 kB 330.3 kB/s eta 0:00:03\n",
      "     - ----------------------------------- 30.7/948.2 kB 330.3 kB/s eta 0:00:03\n",
      "     ---- ------------------------------- 112.6/948.2 kB 819.2 kB/s eta 0:00:02\n",
      "     ---------- --------------------------- 256.0/948.2 kB 1.4 MB/s eta 0:00:01\n",
      "     ---------------- --------------------- 419.8/948.2 kB 1.9 MB/s eta 0:00:01\n",
      "     ------------------------ ------------- 604.2/948.2 kB 2.2 MB/s eta 0:00:01\n",
      "     --------------------------------- ---- 829.4/948.2 kB 2.6 MB/s eta 0:00:01\n",
      "     -------------------------------------  942.1/948.2 kB 2.8 MB/s eta 0:00:01\n",
      "     -------------------------------------- 948.2/948.2 kB 2.6 MB/s eta 0:00:00\n",
      "  Installing build dependencies: started\n",
      "  Installing build dependencies: finished with status 'done'\n",
      "  Getting requirements to build wheel: started\n",
      "  Getting requirements to build wheel: finished with status 'done'\n",
      "  Preparing metadata (pyproject.toml): started\n",
      "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in d:\\anaconda\\lib\\site-packages (from yfinance) (4.12.3)\n",
      "Collecting html5lib>=1.1 (from yfinance)\n",
      "  Downloading html5lib-1.1-py2.py3-none-any.whl.metadata (16 kB)\n",
      "Requirement already satisfied: soupsieve>1.2 in d:\\anaconda\\lib\\site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\n",
      "Requirement already satisfied: six>=1.9 in d:\\anaconda\\lib\\site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: webencodings in d:\\anaconda\\lib\\site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in d:\\anaconda\\lib\\site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\n",
      "Requirement already satisfied: tzdata>=2022.7 in d:\\anaconda\\lib\\site-packages (from pandas>=1.3.0->yfinance) (2023.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in d:\\anaconda\\lib\\site-packages (from requests>=2.31->yfinance) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in d:\\anaconda\\lib\\site-packages (from requests>=2.31->yfinance) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in d:\\anaconda\\lib\\site-packages (from requests>=2.31->yfinance) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in d:\\anaconda\\lib\\site-packages (from requests>=2.31->yfinance) (2024.6.2)\n",
      "Downloading yfinance-0.2.50-py2.py3-none-any.whl (102 kB)\n",
      "   ---------------------------------------- 0.0/102.2 kB ? eta -:--:--\n",
      "   ---------------------------------------- 102.2/102.2 kB 5.8 MB/s eta 0:00:00\n",
      "Downloading html5lib-1.1-py2.py3-none-any.whl (112 kB)\n",
      "   ---------------------------------------- 0.0/112.2 kB ? eta -:--:--\n",
      "   ---------------------------------------- 112.2/112.2 kB 2.2 MB/s eta 0:00:00\n",
      "Downloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)\n",
      "Building wheels for collected packages: peewee\n",
      "  Building wheel for peewee (pyproject.toml): started\n",
      "  Building wheel for peewee (pyproject.toml): finished with status 'done'\n",
      "  Created wheel for peewee: filename=peewee-3.17.8-py3-none-any.whl size=139012 sha256=6a310a1792684733adbc48753ab62d689d945b233f09dc0d886257fb125565da\n",
      "  Stored in directory: c:\\users\\danie\\appdata\\local\\pip\\cache\\wheels\\ff\\6c\\15\\506e25bc390de450a7fa53c155cd9b0fbd13ad3e84a9abc183\n",
      "Successfully built peewee\n",
      "Installing collected packages: peewee, multitasking, html5lib, yfinance\n",
      "Successfully installed html5lib-1.1 multitasking-0.0.11 peewee-3.17.8 yfinance-0.2.50\n"
     ]
    }
   ],
   "source": [
    "pip install yfinance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a43ea7a-d991-4ce3-9675-8169d039fdfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Price       Adj Close      Close       High        Low       Open     Volume\n",
      "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
      "Date                                                                        \n",
      "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
      "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
      "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
      "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
      "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "# Fetch Tesla stock data using yfinance\n",
    "df_tesla = yf.download('TSLA', start='2018-01-01', end='2023-01-01')\n",
    "\n",
    "# Check the type of the data to ensure it's a DataFrame\n",
    "print(type(df_tesla))  # Output: <class 'pandas.core.frame.DataFrame'>\n",
    "\n",
    "# Display the first few rows of the data\n",
    "print(df_tesla.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "638c6aab-7858-4128-ade1-d891cefc6ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-02 00:00:00\n",
      "2022-12-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df_tesla.index.min())  # Earliest date\n",
    "print(df_tesla.index.max())  # Latest date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ac96da0-b6b2-4050-8a58-412971a6463a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_tesla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5bf114f-67ee-4eab-9dba-4947d962da17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
       "Date                                                                        \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fee3a6f5-ca32-403c-b10f-0c013b77e3b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>123.150002</td>\n",
       "      <td>123.150002</td>\n",
       "      <td>128.619995</td>\n",
       "      <td>121.019997</td>\n",
       "      <td>126.370003</td>\n",
       "      <td>166989700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>109.099998</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>108.760002</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>208643400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>112.709999</td>\n",
       "      <td>112.709999</td>\n",
       "      <td>116.269997</td>\n",
       "      <td>108.239998</td>\n",
       "      <td>110.349998</td>\n",
       "      <td>221070500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>121.820000</td>\n",
       "      <td>121.820000</td>\n",
       "      <td>123.570000</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>120.389999</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>123.180000</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>124.480003</td>\n",
       "      <td>119.750000</td>\n",
       "      <td>119.949997</td>\n",
       "      <td>157777300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2022-12-23  123.150002  123.150002  128.619995  121.019997  126.370003   \n",
       "2022-12-27  109.099998  109.099998  119.669998  108.760002  117.500000   \n",
       "2022-12-28  112.709999  112.709999  116.269997  108.239998  110.349998   \n",
       "2022-12-29  121.820000  121.820000  123.570000  117.500000  120.389999   \n",
       "2022-12-30  123.180000  123.180000  124.480003  119.750000  119.949997   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2022-12-23  166989700  \n",
       "2022-12-27  208643400  \n",
       "2022-12-28  221070500  \n",
       "2022-12-29  221923300  \n",
       "2022-12-30  157777300  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "541c3d28-705c-4ea6-a56c-2eab7823cb1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tesla.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "992119d2-af1b-482b-a0ac-c785c85c9cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tesla['High'].plot(figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "640a462d-5987-4e75-8590-14617b12192a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and ylimit\n",
    "\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2022-01-01'],figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0ca8124b-1495-4319-a968-f93b21905e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and ylimit\n",
    "\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2022-01-01'],ylim=[0,500],figsize=(12,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e66559b-f16a-4a6e-b819-1bb431b79920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and ylimit and coloring\n",
    "\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2022-01-01'],ylim=[0,500],figsize=(12,4),c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ac38c758-ff08-4ca7-84a6-6cc53a9b8e2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## xlimit and ylimit and coloring\n",
    "\n",
    "df_tesla['High'].plot(xlim=['2020-01-01','2022-01-01'],ylim=[0,500],figsize=(12,4),ls='--',c='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5b543379-498c-4c70-b942-d1792903e846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n",
       "               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n",
       "               '2018-01-12', '2018-01-16',\n",
       "               ...\n",
       "               '2022-12-16', '2022-12-19', '2022-12-20', '2022-12-21',\n",
       "               '2022-12-22', '2022-12-23', '2022-12-27', '2022-12-28',\n",
       "               '2022-12-29', '2022-12-30'],\n",
       "              dtype='datetime64[ns]', name='Date', length=1259, freq=None)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##  dates corresponding to each row of stock data\n",
    "df_tesla.index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cfbd3eae-4aa8-4fc0-b6c6-efa3fc3a6f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>28.684000</td>\n",
       "      <td>28.684000</td>\n",
       "      <td>28.713333</td>\n",
       "      <td>28.114000</td>\n",
       "      <td>28.299999</td>\n",
       "      <td>142981500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>29.534000</td>\n",
       "      <td>29.534000</td>\n",
       "      <td>30.266666</td>\n",
       "      <td>29.128000</td>\n",
       "      <td>29.366667</td>\n",
       "      <td>266677500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>30.102667</td>\n",
       "      <td>30.102667</td>\n",
       "      <td>30.104000</td>\n",
       "      <td>29.333332</td>\n",
       "      <td>29.364668</td>\n",
       "      <td>151995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>31.270666</td>\n",
       "      <td>31.270666</td>\n",
       "      <td>31.441999</td>\n",
       "      <td>30.224001</td>\n",
       "      <td>30.760000</td>\n",
       "      <td>268231500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>32.809334</td>\n",
       "      <td>32.809334</td>\n",
       "      <td>33.232666</td>\n",
       "      <td>31.215334</td>\n",
       "      <td>31.580000</td>\n",
       "      <td>467164500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>364.646667</td>\n",
       "      <td>364.646667</td>\n",
       "      <td>372.333344</td>\n",
       "      <td>356.906677</td>\n",
       "      <td>357.890015</td>\n",
       "      <td>71145900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>362.823334</td>\n",
       "      <td>362.823334</td>\n",
       "      <td>373.000000</td>\n",
       "      <td>359.473328</td>\n",
       "      <td>369.829987</td>\n",
       "      <td>60324000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>362.063324</td>\n",
       "      <td>362.063324</td>\n",
       "      <td>368.000000</td>\n",
       "      <td>354.713318</td>\n",
       "      <td>366.213318</td>\n",
       "      <td>56154000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>356.779999</td>\n",
       "      <td>356.779999</td>\n",
       "      <td>365.183319</td>\n",
       "      <td>351.049988</td>\n",
       "      <td>353.776672</td>\n",
       "      <td>47040900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>352.260010</td>\n",
       "      <td>352.260010</td>\n",
       "      <td>360.666656</td>\n",
       "      <td>351.529999</td>\n",
       "      <td>357.813324</td>\n",
       "      <td>40733700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2020-01-02   28.684000   28.684000   28.713333   28.114000   28.299999   \n",
       "2020-01-03   29.534000   29.534000   30.266666   29.128000   29.366667   \n",
       "2020-01-06   30.102667   30.102667   30.104000   29.333332   29.364668   \n",
       "2020-01-07   31.270666   31.270666   31.441999   30.224001   30.760000   \n",
       "2020-01-08   32.809334   32.809334   33.232666   31.215334   31.580000   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-12-27  364.646667  364.646667  372.333344  356.906677  357.890015   \n",
       "2021-12-28  362.823334  362.823334  373.000000  359.473328  369.829987   \n",
       "2021-12-29  362.063324  362.063324  368.000000  354.713318  366.213318   \n",
       "2021-12-30  356.779999  356.779999  365.183319  351.049988  353.776672   \n",
       "2021-12-31  352.260010  352.260010  360.666656  351.529999  357.813324   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2020-01-02  142981500  \n",
       "2020-01-03  266677500  \n",
       "2020-01-06  151995000  \n",
       "2020-01-07  268231500  \n",
       "2020-01-08  467164500  \n",
       "...               ...  \n",
       "2021-12-27   71145900  \n",
       "2021-12-28   60324000  \n",
       "2021-12-29   56154000  \n",
       "2021-12-30   47040900  \n",
       "2021-12-31   40733700  \n",
       "\n",
       "[505 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.loc['2020-01-01': '2022-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fa06df5c-bd9c-4542-99df-b8e6a3ef60aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "index=df_tesla.loc['2020-01-01': '2022-01-01'].index\n",
    "share_open=df_tesla.loc['2020-01-01': '2022-01-01','Open']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c5ed94b6-acad-4c19-828c-2a06e36c31ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>28.299999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>29.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>29.364668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>30.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>31.580000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>357.890015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>369.829987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>366.213318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>353.776672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>357.813324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker            TSLA\n",
       "Date                  \n",
       "2020-01-02   28.299999\n",
       "2020-01-03   29.366667\n",
       "2020-01-06   29.364668\n",
       "2020-01-07   30.760000\n",
       "2020-01-08   31.580000\n",
       "...                ...\n",
       "2021-12-27  357.890015\n",
       "2021-12-28  369.829987\n",
       "2021-12-29  366.213318\n",
       "2021-12-30  353.776672\n",
       "2021-12-31  357.813324\n",
       "\n",
       "[505 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "22a45852-f758-4b36-b629-bdd481e777b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2020-01-02', '2020-01-03', '2020-01-06', '2020-01-07',\n",
       "               '2020-01-08', '2020-01-09', '2020-01-10', '2020-01-13',\n",
       "               '2020-01-14', '2020-01-15',\n",
       "               ...\n",
       "               '2021-12-17', '2021-12-20', '2021-12-21', '2021-12-22',\n",
       "               '2021-12-23', '2021-12-27', '2021-12-28', '2021-12-29',\n",
       "               '2021-12-30', '2021-12-31'],\n",
       "              dtype='datetime64[ns]', name='Date', length=505, freq=None)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "81e92509-4274-4acf-bfb4-d10989f78d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d40d2e04-fd4d-42dc-895a-326c15cb51f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x211a9cb8410>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure,axis=plt.subplots()\n",
    "plt.tight_layout()\n",
    "## Preventing overlapping\n",
    "figure.autofmt_xdate()\n",
    "axis.plot(index,share_open)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "09408ef1-dedc-4115-8ad8-aa2b179ecfd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.datetimes.DatetimeIndex'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "(505,)\n",
      "(505, 1)\n"
     ]
    }
   ],
   "source": [
    "print(type(index))        # Should be pandas.Series, numpy.ndarray, or similar\n",
    "print(type(share_open))   # Same as above\n",
    "print(index.shape)        # Ensure it matches share_open.shape\n",
    "print(share_open.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7f8006a6-ec71-4d6a-8efa-3381a4d14f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Datetime Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2cf62680-76a1-45b1-b72d-a14cb206d2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1259 entries, 2018-01-02 to 2022-12-30\n",
      "Data columns (total 6 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   (Adj Close, TSLA)  1259 non-null   float64\n",
      " 1   (Close, TSLA)      1259 non-null   float64\n",
      " 2   (High, TSLA)       1259 non-null   float64\n",
      " 3   (Low, TSLA)        1259 non-null   float64\n",
      " 4   (Open, TSLA)       1259 non-null   float64\n",
      " 5   (Volume, TSLA)     1259 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 101.1 KB\n"
     ]
    }
   ],
   "source": [
    "df_tesla.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d200344b-aaa5-4fc4-817c-e4f782395e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>2022-12-23</td>\n",
       "      <td>123.150002</td>\n",
       "      <td>123.150002</td>\n",
       "      <td>128.619995</td>\n",
       "      <td>121.019997</td>\n",
       "      <td>126.370003</td>\n",
       "      <td>166989700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>108.760002</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>208643400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>112.709999</td>\n",
       "      <td>112.709999</td>\n",
       "      <td>116.269997</td>\n",
       "      <td>108.239998</td>\n",
       "      <td>110.349998</td>\n",
       "      <td>221070500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>121.820000</td>\n",
       "      <td>121.820000</td>\n",
       "      <td>123.570000</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>120.389999</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>124.480003</td>\n",
       "      <td>119.750000</td>\n",
       "      <td>119.949997</td>\n",
       "      <td>157777300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Date   Adj Close       Close        High         Low        Open  \\\n",
       "Ticker                   TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "0      2018-01-02   21.368668   21.368668   21.474001   20.733334   20.799999   \n",
       "1      2018-01-03   21.150000   21.150000   21.683332   21.036667   21.400000   \n",
       "2      2018-01-04   20.974667   20.974667   21.236668   20.378668   20.858000   \n",
       "3      2018-01-05   21.105333   21.105333   21.149332   20.799999   21.108000   \n",
       "4      2018-01-08   22.427334   22.427334   22.468000   21.033333   21.066668   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "1254   2022-12-23  123.150002  123.150002  128.619995  121.019997  126.370003   \n",
       "1255   2022-12-27  109.099998  109.099998  119.669998  108.760002  117.500000   \n",
       "1256   2022-12-28  112.709999  112.709999  116.269997  108.239998  110.349998   \n",
       "1257   2022-12-29  121.820000  121.820000  123.570000  117.500000  120.389999   \n",
       "1258   2022-12-30  123.180000  123.180000  124.480003  119.750000  119.949997   \n",
       "\n",
       "Price      Volume  \n",
       "Ticker       TSLA  \n",
       "0        65283000  \n",
       "1        67822500  \n",
       "2       149194500  \n",
       "3        68868000  \n",
       "4       147891000  \n",
       "...           ...  \n",
       "1254    166989700  \n",
       "1255    208643400  \n",
       "1256    221070500  \n",
       "1257    221923300  \n",
       "1258    157777300  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a43e3206-22ea-48a5-a3c0-903d2b421c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla=df_tesla.reset_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ef5fd3a7-827b-4c99-89be-8a7a15ea0096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1259 entries, 0 to 1258\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   (Date, )           1259 non-null   datetime64[ns]\n",
      " 1   (Adj Close, TSLA)  1259 non-null   float64       \n",
      " 2   (Close, TSLA)      1259 non-null   float64       \n",
      " 3   (High, TSLA)       1259 non-null   float64       \n",
      " 4   (Low, TSLA)        1259 non-null   float64       \n",
      " 5   (Open, TSLA)       1259 non-null   float64       \n",
      " 6   (Volume, TSLA)     1259 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
      "memory usage: 69.0 KB\n"
     ]
    }
   ],
   "source": [
    "df_tesla.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b1db7e8f-372c-46a5-8b3a-8a2647e6aca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2018-01-02\n",
       "1      2018-01-03\n",
       "2      2018-01-04\n",
       "3      2018-01-05\n",
       "4      2018-01-08\n",
       "          ...    \n",
       "1254   2022-12-23\n",
       "1255   2022-12-27\n",
       "1256   2022-12-28\n",
       "1257   2022-12-29\n",
       "1258   2022-12-30\n",
       "Name: Date, Length: 1259, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## This is a Pandas function that converts a column (or array-like object) into datetime64[ns] format, which is Pandas' built-in datetime type.\n",
    "\n",
    "pd.to_datetime(df_tesla['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "211d2a52-e25e-4e34-93b7-5036022086c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Date</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>2022-12-23</td>\n",
       "      <td>123.150002</td>\n",
       "      <td>123.150002</td>\n",
       "      <td>128.619995</td>\n",
       "      <td>121.019997</td>\n",
       "      <td>126.370003</td>\n",
       "      <td>166989700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>119.669998</td>\n",
       "      <td>108.760002</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>208643400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>112.709999</td>\n",
       "      <td>112.709999</td>\n",
       "      <td>116.269997</td>\n",
       "      <td>108.239998</td>\n",
       "      <td>110.349998</td>\n",
       "      <td>221070500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>121.820000</td>\n",
       "      <td>121.820000</td>\n",
       "      <td>123.570000</td>\n",
       "      <td>117.500000</td>\n",
       "      <td>120.389999</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>123.180000</td>\n",
       "      <td>124.480003</td>\n",
       "      <td>119.750000</td>\n",
       "      <td>119.949997</td>\n",
       "      <td>157777300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Price            Date   Adj Close       Close        High         Low  \\\n",
       "Ticker                       TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                    \n",
       "2018-01-02 2018-01-02   21.368668   21.368668   21.474001   20.733334   \n",
       "2018-01-03 2018-01-03   21.150000   21.150000   21.683332   21.036667   \n",
       "2018-01-04 2018-01-04   20.974667   20.974667   21.236668   20.378668   \n",
       "2018-01-05 2018-01-05   21.105333   21.105333   21.149332   20.799999   \n",
       "2018-01-08 2018-01-08   22.427334   22.427334   22.468000   21.033333   \n",
       "...               ...         ...         ...         ...         ...   \n",
       "2022-12-23 2022-12-23  123.150002  123.150002  128.619995  121.019997   \n",
       "2022-12-27 2022-12-27  109.099998  109.099998  119.669998  108.760002   \n",
       "2022-12-28 2022-12-28  112.709999  112.709999  116.269997  108.239998   \n",
       "2022-12-29 2022-12-29  121.820000  121.820000  123.570000  117.500000   \n",
       "2022-12-30 2022-12-30  123.180000  123.180000  124.480003  119.750000   \n",
       "\n",
       "Price             Open     Volume  \n",
       "Ticker            TSLA       TSLA  \n",
       "Date                               \n",
       "2018-01-02   20.799999   65283000  \n",
       "2018-01-03   21.400000   67822500  \n",
       "2018-01-04   20.858000  149194500  \n",
       "2018-01-05   21.108000   68868000  \n",
       "2018-01-08   21.066668  147891000  \n",
       "...                ...        ...  \n",
       "2022-12-23  126.370003  166989700  \n",
       "2022-12-27  117.500000  208643400  \n",
       "2022-12-28  110.349998  221070500  \n",
       "2022-12-29  120.389999  221923300  \n",
       "2022-12-30  119.949997  157777300  \n",
       "\n",
       "[1259 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.set_index(df_tesla['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a82ff0fc-853e-4c3f-983b-1907c3e87d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop Date\n",
    "df_tesla=df_tesla.set_index(['Date'],drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fdc94354-df10-4c47-b371-a8fce9658bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
       "Date                                                                        \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4646804d-3127-4975-865d-637447138e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Datetime\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f8090291-117e-4268-9538-a355f0040180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 11, 21, 0, 0)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime(2020,11,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "555c88a7-7cf7-4637-a147-c208273da305",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2024, 12, 14, 19, 8, 25, 112326)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Time of now\n",
    "datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4a0427ca-b363-4f76-a607-ee36f8676ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_num(num1,num2):\n",
    "    return num1+num2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2a437ed2-61f7-4e99-8d49-c9ae365884d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:00.024863\n"
     ]
    }
   ],
   "source": [
    "start_time=datetime.now()\n",
    "\n",
    "num1=20\n",
    "num2=30\n",
    "for i in [1,2,3,4,5]:\n",
    "    \n",
    "    add_num(num1,num2)\n",
    "end_time=datetime.now()\n",
    "print(end_time-start_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b2350508-8dee-4ba8-a437-fb49e1ce4f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "date=datetime(2021,11,21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4ef45ad6-d83b-4900-a91a-428be9a68bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 11, 21, 0, 0)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7d619581-0945-4a2c-8bd7-a715d909c4a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2021, 11, 21)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "981626e4-fff3-4705-be85-b48dc8c09239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4251e725-252e-4ce7-9106-d82b22f423fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "22d3c3e1-b5de-402c-8a06-0c875c432589",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "edcc3b1d-7390-4a03-90ec-9e4a3bce4051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358f32ee-5b70-453a-8656-739b86f676c4",
   "metadata": {},
   "source": [
    "## Time resampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "76c0ffcc-b374-4e59-8a35-12ae3e41c7b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
       "Date                                                                        \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7c820442-09eb-44e3-ad8a-07310dd341a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x00000211AABE9C10>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='YE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "aff7c2eb-bd15-44f9-8f87-6d9e4fa92732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>16.704000</td>\n",
       "      <td>16.704000</td>\n",
       "      <td>17.355333</td>\n",
       "      <td>16.306000</td>\n",
       "      <td>16.851999</td>\n",
       "      <td>46210500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>11.931333</td>\n",
       "      <td>11.931333</td>\n",
       "      <td>12.445333</td>\n",
       "      <td>11.799333</td>\n",
       "      <td>12.073333</td>\n",
       "      <td>36984000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>24.081333</td>\n",
       "      <td>24.081333</td>\n",
       "      <td>26.990667</td>\n",
       "      <td>23.367332</td>\n",
       "      <td>24.980000</td>\n",
       "      <td>52073100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>187.666672</td>\n",
       "      <td>187.666672</td>\n",
       "      <td>188.736664</td>\n",
       "      <td>179.830002</td>\n",
       "      <td>184.183334</td>\n",
       "      <td>29401800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>109.099998</td>\n",
       "      <td>109.099998</td>\n",
       "      <td>116.269997</td>\n",
       "      <td>108.239998</td>\n",
       "      <td>110.349998</td>\n",
       "      <td>41864700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2018-12-31   16.704000   16.704000   17.355333   16.306000   16.851999   \n",
       "2019-12-31   11.931333   11.931333   12.445333   11.799333   12.073333   \n",
       "2020-12-31   24.081333   24.081333   26.990667   23.367332   24.980000   \n",
       "2021-12-31  187.666672  187.666672  188.736664  179.830002  184.183334   \n",
       "2022-12-31  109.099998  109.099998  116.269997  108.239998  110.349998   \n",
       "\n",
       "Price         Volume  \n",
       "Ticker          TSLA  \n",
       "Date                  \n",
       "2018-12-31  46210500  \n",
       "2019-12-31  36984000  \n",
       "2020-12-31  52073100  \n",
       "2021-12-31  29401800  \n",
       "2022-12-31  41864700  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='YE').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4b0ee54f-8e95-4d66-9df1-58e8edc3bf78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.830667</td>\n",
       "      <td>24.474667</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>504745500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>28.729334</td>\n",
       "      <td>28.729334</td>\n",
       "      <td>29.020666</td>\n",
       "      <td>28.423332</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>450091500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>235.223328</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>239.573334</td>\n",
       "      <td>230.373337</td>\n",
       "      <td>233.330002</td>\n",
       "      <td>914082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>409.970001</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>411.470001</td>\n",
       "      <td>268189500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>399.926666</td>\n",
       "      <td>399.926666</td>\n",
       "      <td>402.666656</td>\n",
       "      <td>378.679993</td>\n",
       "      <td>396.516663</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2018-12-31   25.304667   25.304667   25.830667   24.474667   25.000000   \n",
       "2019-12-31   28.729334   28.729334   29.020666   28.423332   29.000000   \n",
       "2020-12-31  235.223328  235.223328  239.573334  230.373337  233.330002   \n",
       "2021-12-31  409.970001  409.970001  414.496674  405.666656  411.470001   \n",
       "2022-12-31  399.926666  399.926666  402.666656  378.679993  396.516663   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2018-12-31  504745500  \n",
       "2019-12-31  450091500  \n",
       "2020-12-31  914082000  \n",
       "2021-12-31  268189500  \n",
       "2022-12-31  221923300  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.resample(rule='YE').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5ea63f7d-6d76-4804-a94d-3b33240d5ce3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## year and frequency\n",
    "df_tesla.resample(rule='YE').max()['Open'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "097a5049-8e5f-4b43-b8ec-92542c57f44d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>23.827999</td>\n",
       "      <td>23.827999</td>\n",
       "      <td>24.033333</td>\n",
       "      <td>23.490667</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>315021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>24.722000</td>\n",
       "      <td>24.722000</td>\n",
       "      <td>24.915333</td>\n",
       "      <td>23.633333</td>\n",
       "      <td>24.344000</td>\n",
       "      <td>335211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.830667</td>\n",
       "      <td>24.474667</td>\n",
       "      <td>24.606001</td>\n",
       "      <td>504745500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>25.119333</td>\n",
       "      <td>25.119333</td>\n",
       "      <td>25.299334</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>411382500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>23.153999</td>\n",
       "      <td>23.153999</td>\n",
       "      <td>23.466667</td>\n",
       "      <td>22.943333</td>\n",
       "      <td>23.080667</td>\n",
       "      <td>362262000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>19.454000</td>\n",
       "      <td>19.454000</td>\n",
       "      <td>19.744667</td>\n",
       "      <td>19.144667</td>\n",
       "      <td>19.219999</td>\n",
       "      <td>398206500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>17.658667</td>\n",
       "      <td>17.658667</td>\n",
       "      <td>17.738001</td>\n",
       "      <td>17.210667</td>\n",
       "      <td>17.278000</td>\n",
       "      <td>336274500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>28.729334</td>\n",
       "      <td>28.729334</td>\n",
       "      <td>29.020666</td>\n",
       "      <td>28.423332</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>450091500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>61.161331</td>\n",
       "      <td>61.161331</td>\n",
       "      <td>64.599335</td>\n",
       "      <td>60.068001</td>\n",
       "      <td>61.566666</td>\n",
       "      <td>914082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>71.987335</td>\n",
       "      <td>71.987335</td>\n",
       "      <td>72.512665</td>\n",
       "      <td>66.915337</td>\n",
       "      <td>67.518669</td>\n",
       "      <td>487977000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>166.106674</td>\n",
       "      <td>166.106674</td>\n",
       "      <td>167.496674</td>\n",
       "      <td>156.836670</td>\n",
       "      <td>167.380005</td>\n",
       "      <td>584781000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>235.223328</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>239.573334</td>\n",
       "      <td>230.373337</td>\n",
       "      <td>233.330002</td>\n",
       "      <td>666378600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>294.363342</td>\n",
       "      <td>294.363342</td>\n",
       "      <td>300.133331</td>\n",
       "      <td>290.533325</td>\n",
       "      <td>297.126678</td>\n",
       "      <td>268189500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>254.106674</td>\n",
       "      <td>254.106674</td>\n",
       "      <td>260.263336</td>\n",
       "      <td>244.203339</td>\n",
       "      <td>256.899994</td>\n",
       "      <td>147052200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>263.786682</td>\n",
       "      <td>263.786682</td>\n",
       "      <td>266.333344</td>\n",
       "      <td>258.333344</td>\n",
       "      <td>262.399994</td>\n",
       "      <td>100847400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>409.970001</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>411.470001</td>\n",
       "      <td>188556300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>399.926666</td>\n",
       "      <td>399.926666</td>\n",
       "      <td>402.666656</td>\n",
       "      <td>378.679993</td>\n",
       "      <td>396.516663</td>\n",
       "      <td>151565700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>381.816681</td>\n",
       "      <td>381.816681</td>\n",
       "      <td>384.290009</td>\n",
       "      <td>362.433319</td>\n",
       "      <td>378.766663</td>\n",
       "      <td>144973200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>309.320007</td>\n",
       "      <td>309.320007</td>\n",
       "      <td>314.666656</td>\n",
       "      <td>305.579987</td>\n",
       "      <td>311.666656</td>\n",
       "      <td>142032300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>249.440002</td>\n",
       "      <td>249.440002</td>\n",
       "      <td>257.500000</td>\n",
       "      <td>242.009995</td>\n",
       "      <td>254.500000</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2018-01-01   23.827999   23.827999   24.033333   23.490667   24.000000   \n",
       "2018-04-01   24.722000   24.722000   24.915333   23.633333   24.344000   \n",
       "2018-07-01   25.304667   25.304667   25.830667   24.474667   24.606001   \n",
       "2018-10-01   25.119333   25.119333   25.299334   24.450001   25.000000   \n",
       "2019-01-01   23.153999   23.153999   23.466667   22.943333   23.080667   \n",
       "2019-04-01   19.454000   19.454000   19.744667   19.144667   19.219999   \n",
       "2019-07-01   17.658667   17.658667   17.738001   17.210667   17.278000   \n",
       "2019-10-01   28.729334   28.729334   29.020666   28.423332   29.000000   \n",
       "2020-01-01   61.161331   61.161331   64.599335   60.068001   61.566666   \n",
       "2020-04-01   71.987335   71.987335   72.512665   66.915337   67.518669   \n",
       "2020-07-01  166.106674  166.106674  167.496674  156.836670  167.380005   \n",
       "2020-10-01  235.223328  235.223328  239.573334  230.373337  233.330002   \n",
       "2021-01-01  294.363342  294.363342  300.133331  290.533325  297.126678   \n",
       "2021-04-01  254.106674  254.106674  260.263336  244.203339  256.899994   \n",
       "2021-07-01  263.786682  263.786682  266.333344  258.333344  262.399994   \n",
       "2021-10-01  409.970001  409.970001  414.496674  405.666656  411.470001   \n",
       "2022-01-01  399.926666  399.926666  402.666656  378.679993  396.516663   \n",
       "2022-04-01  381.816681  381.816681  384.290009  362.433319  378.766663   \n",
       "2022-07-01  309.320007  309.320007  314.666656  305.579987  311.666656   \n",
       "2022-10-01  249.440002  249.440002  257.500000  242.009995  254.500000   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2018-01-01  315021000  \n",
       "2018-04-01  335211000  \n",
       "2018-07-01  504745500  \n",
       "2018-10-01  411382500  \n",
       "2019-01-01  362262000  \n",
       "2019-04-01  398206500  \n",
       "2019-07-01  336274500  \n",
       "2019-10-01  450091500  \n",
       "2020-01-01  914082000  \n",
       "2020-04-01  487977000  \n",
       "2020-07-01  584781000  \n",
       "2020-10-01  666378600  \n",
       "2021-01-01  268189500  \n",
       "2021-04-01  147052200  \n",
       "2021-07-01  100847400  \n",
       "2021-10-01  188556300  \n",
       "2022-01-01  151565700  \n",
       "2022-04-01  144973200  \n",
       "2022-07-01  142032300  \n",
       "2022-10-01  221923300  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Quaterly Start Frequency\n",
    "df_tesla.resample(rule='QS').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ccee39f4-4e3b-4a1d-8a15-cecf6a1cb20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Quaterly Start Frequency\n",
    "df_tesla.resample(rule='QS').max()['High'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "270bf4e0-4b60-4af7-bd88-a4fb956cdfb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Business End Frequency\n",
    "df_tesla.resample(rule='BYE').max()['High'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d54ec901-7566-45b7-b7c5-5a632f1ccba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.830667</td>\n",
       "      <td>24.474667</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>504745500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>28.729334</td>\n",
       "      <td>28.729334</td>\n",
       "      <td>29.020666</td>\n",
       "      <td>28.423332</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>450091500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>235.223328</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>239.573334</td>\n",
       "      <td>230.373337</td>\n",
       "      <td>233.330002</td>\n",
       "      <td>914082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>409.970001</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>411.470001</td>\n",
       "      <td>268189500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>399.926666</td>\n",
       "      <td>399.926666</td>\n",
       "      <td>402.666656</td>\n",
       "      <td>378.679993</td>\n",
       "      <td>396.516663</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2018-12-31   25.304667   25.304667   25.830667   24.474667   25.000000   \n",
       "2019-12-31   28.729334   28.729334   29.020666   28.423332   29.000000   \n",
       "2020-12-31  235.223328  235.223328  239.573334  230.373337  233.330002   \n",
       "2021-12-31  409.970001  409.970001  414.496674  405.666656  411.470001   \n",
       "2022-12-30  399.926666  399.926666  402.666656  378.679993  396.516663   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2018-12-31  504745500  \n",
       "2019-12-31  450091500  \n",
       "2020-12-31  914082000  \n",
       "2021-12-31  268189500  \n",
       "2022-12-30  221923300  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Business End Frequency\n",
    "df_tesla.resample(rule='BYE').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "57b16c57-9e80-45b3-9dea-6deb15d49424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>23.827999</td>\n",
       "      <td>23.827999</td>\n",
       "      <td>24.033333</td>\n",
       "      <td>23.490667</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>315021000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <td>24.722000</td>\n",
       "      <td>24.722000</td>\n",
       "      <td>24.915333</td>\n",
       "      <td>23.633333</td>\n",
       "      <td>24.344000</td>\n",
       "      <td>335211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-02</th>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.304667</td>\n",
       "      <td>25.830667</td>\n",
       "      <td>24.474667</td>\n",
       "      <td>24.606001</td>\n",
       "      <td>504745500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>25.119333</td>\n",
       "      <td>25.119333</td>\n",
       "      <td>25.299334</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>411382500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>23.153999</td>\n",
       "      <td>23.153999</td>\n",
       "      <td>23.466667</td>\n",
       "      <td>22.943333</td>\n",
       "      <td>23.080667</td>\n",
       "      <td>362262000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>19.454000</td>\n",
       "      <td>19.454000</td>\n",
       "      <td>19.744667</td>\n",
       "      <td>19.144667</td>\n",
       "      <td>19.219999</td>\n",
       "      <td>398206500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>17.658667</td>\n",
       "      <td>17.658667</td>\n",
       "      <td>17.738001</td>\n",
       "      <td>17.210667</td>\n",
       "      <td>17.278000</td>\n",
       "      <td>336274500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>28.729334</td>\n",
       "      <td>28.729334</td>\n",
       "      <td>29.020666</td>\n",
       "      <td>28.423332</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>450091500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>61.161331</td>\n",
       "      <td>61.161331</td>\n",
       "      <td>64.599335</td>\n",
       "      <td>60.068001</td>\n",
       "      <td>61.566666</td>\n",
       "      <td>914082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>71.987335</td>\n",
       "      <td>71.987335</td>\n",
       "      <td>72.512665</td>\n",
       "      <td>66.915337</td>\n",
       "      <td>67.518669</td>\n",
       "      <td>487977000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>166.106674</td>\n",
       "      <td>166.106674</td>\n",
       "      <td>167.496674</td>\n",
       "      <td>156.836670</td>\n",
       "      <td>167.380005</td>\n",
       "      <td>584781000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>235.223328</td>\n",
       "      <td>235.223328</td>\n",
       "      <td>239.573334</td>\n",
       "      <td>230.373337</td>\n",
       "      <td>233.330002</td>\n",
       "      <td>666378600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>294.363342</td>\n",
       "      <td>294.363342</td>\n",
       "      <td>300.133331</td>\n",
       "      <td>290.533325</td>\n",
       "      <td>297.126678</td>\n",
       "      <td>268189500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>254.106674</td>\n",
       "      <td>254.106674</td>\n",
       "      <td>260.263336</td>\n",
       "      <td>244.203339</td>\n",
       "      <td>256.899994</td>\n",
       "      <td>147052200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>263.786682</td>\n",
       "      <td>263.786682</td>\n",
       "      <td>266.333344</td>\n",
       "      <td>258.333344</td>\n",
       "      <td>262.399994</td>\n",
       "      <td>100847400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-01</th>\n",
       "      <td>409.970001</td>\n",
       "      <td>409.970001</td>\n",
       "      <td>414.496674</td>\n",
       "      <td>405.666656</td>\n",
       "      <td>411.470001</td>\n",
       "      <td>188556300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>399.926666</td>\n",
       "      <td>399.926666</td>\n",
       "      <td>402.666656</td>\n",
       "      <td>378.679993</td>\n",
       "      <td>396.516663</td>\n",
       "      <td>151565700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-01</th>\n",
       "      <td>381.816681</td>\n",
       "      <td>381.816681</td>\n",
       "      <td>384.290009</td>\n",
       "      <td>362.433319</td>\n",
       "      <td>378.766663</td>\n",
       "      <td>144973200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>309.320007</td>\n",
       "      <td>309.320007</td>\n",
       "      <td>314.666656</td>\n",
       "      <td>305.579987</td>\n",
       "      <td>311.666656</td>\n",
       "      <td>142032300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-03</th>\n",
       "      <td>249.440002</td>\n",
       "      <td>249.440002</td>\n",
       "      <td>257.500000</td>\n",
       "      <td>242.009995</td>\n",
       "      <td>254.500000</td>\n",
       "      <td>221923300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price        Adj Close       Close        High         Low        Open  \\\n",
       "Ticker            TSLA        TSLA        TSLA        TSLA        TSLA   \n",
       "Date                                                                     \n",
       "2018-01-01   23.827999   23.827999   24.033333   23.490667   24.000000   \n",
       "2018-04-02   24.722000   24.722000   24.915333   23.633333   24.344000   \n",
       "2018-07-02   25.304667   25.304667   25.830667   24.474667   24.606001   \n",
       "2018-10-01   25.119333   25.119333   25.299334   24.450001   25.000000   \n",
       "2019-01-01   23.153999   23.153999   23.466667   22.943333   23.080667   \n",
       "2019-04-01   19.454000   19.454000   19.744667   19.144667   19.219999   \n",
       "2019-07-01   17.658667   17.658667   17.738001   17.210667   17.278000   \n",
       "2019-10-01   28.729334   28.729334   29.020666   28.423332   29.000000   \n",
       "2020-01-01   61.161331   61.161331   64.599335   60.068001   61.566666   \n",
       "2020-04-01   71.987335   71.987335   72.512665   66.915337   67.518669   \n",
       "2020-07-01  166.106674  166.106674  167.496674  156.836670  167.380005   \n",
       "2020-10-01  235.223328  235.223328  239.573334  230.373337  233.330002   \n",
       "2021-01-01  294.363342  294.363342  300.133331  290.533325  297.126678   \n",
       "2021-04-01  254.106674  254.106674  260.263336  244.203339  256.899994   \n",
       "2021-07-01  263.786682  263.786682  266.333344  258.333344  262.399994   \n",
       "2021-10-01  409.970001  409.970001  414.496674  405.666656  411.470001   \n",
       "2022-01-03  399.926666  399.926666  402.666656  378.679993  396.516663   \n",
       "2022-04-01  381.816681  381.816681  384.290009  362.433319  378.766663   \n",
       "2022-07-01  309.320007  309.320007  314.666656  305.579987  311.666656   \n",
       "2022-10-03  249.440002  249.440002  257.500000  242.009995  254.500000   \n",
       "\n",
       "Price          Volume  \n",
       "Ticker           TSLA  \n",
       "Date                   \n",
       "2018-01-01  315021000  \n",
       "2018-04-02  335211000  \n",
       "2018-07-02  504745500  \n",
       "2018-10-01  411382500  \n",
       "2019-01-01  362262000  \n",
       "2019-04-01  398206500  \n",
       "2019-07-01  336274500  \n",
       "2019-10-01  450091500  \n",
       "2020-01-01  914082000  \n",
       "2020-04-01  487977000  \n",
       "2020-07-01  584781000  \n",
       "2020-10-01  666378600  \n",
       "2021-01-01  268189500  \n",
       "2021-04-01  147052200  \n",
       "2021-07-01  100847400  \n",
       "2021-10-01  188556300  \n",
       "2022-01-03  151565700  \n",
       "2022-04-01  144973200  \n",
       "2022-07-01  142032300  \n",
       "2022-10-03  221923300  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Business End Frequency\n",
    "df_tesla.resample(rule='BQS').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e111524f-4057-404f-a27b-cf5a1c3f4a6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting\n",
    "\n",
    "df_tesla['Open'].resample(rule='YE').mean().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "2be4ad0f-abbc-4f97-8629-5109e9fd9bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>22.459206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>22.421123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>21.161873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>19.231492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>19.279939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>22.330921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>20.908635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>22.003015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>19.262772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>19.095275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>22.852286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>23.067930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>21.112730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>20.479789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>18.591397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>17.801175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>14.702424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>14.218700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>16.082000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>15.063030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>15.804700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>17.596348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>22.587134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>24.979746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>34.869683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>52.983614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>37.595031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>44.352985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>53.270667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>63.879576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>97.662666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>118.170762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>138.094286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>143.496818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>154.757665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>210.501363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>275.725791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>265.857542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>220.130724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>236.427620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>206.239336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>208.752122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>219.840953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>234.811213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>250.604284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>289.953491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>372.588890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>341.477726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>339.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>292.789474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>303.253333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30</th>\n",
       "      <td>337.054169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>256.985554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>234.472539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-31</th>\n",
       "      <td>248.886668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-31</th>\n",
       "      <td>297.223042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>287.885710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31</th>\n",
       "      <td>224.126190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-30</th>\n",
       "      <td>193.479049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>155.897143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker            TSLA\n",
       "Date                  \n",
       "2018-01-31   22.459206\n",
       "2018-02-28   22.421123\n",
       "2018-03-31   21.161873\n",
       "2018-04-30   19.231492\n",
       "2018-05-31   19.279939\n",
       "2018-06-30   22.330921\n",
       "2018-07-31   20.908635\n",
       "2018-08-31   22.003015\n",
       "2018-09-30   19.262772\n",
       "2018-10-31   19.095275\n",
       "2018-11-30   22.852286\n",
       "2018-12-31   23.067930\n",
       "2019-01-31   21.112730\n",
       "2019-02-28   20.479789\n",
       "2019-03-31   18.591397\n",
       "2019-04-30   17.801175\n",
       "2019-05-31   14.702424\n",
       "2019-06-30   14.218700\n",
       "2019-07-31   16.082000\n",
       "2019-08-31   15.063030\n",
       "2019-09-30   15.804700\n",
       "2019-10-31   17.596348\n",
       "2019-11-30   22.587134\n",
       "2019-12-31   24.979746\n",
       "2020-01-31   34.869683\n",
       "2020-02-29   52.983614\n",
       "2020-03-31   37.595031\n",
       "2020-04-30   44.352985\n",
       "2020-05-31   53.270667\n",
       "2020-06-30   63.879576\n",
       "2020-07-31   97.662666\n",
       "2020-08-31  118.170762\n",
       "2020-09-30  138.094286\n",
       "2020-10-31  143.496818\n",
       "2020-11-30  154.757665\n",
       "2020-12-31  210.501363\n",
       "2021-01-31  275.725791\n",
       "2021-02-28  265.857542\n",
       "2021-03-31  220.130724\n",
       "2021-04-30  236.427620\n",
       "2021-05-31  206.239336\n",
       "2021-06-30  208.752122\n",
       "2021-07-31  219.840953\n",
       "2021-08-31  234.811213\n",
       "2021-09-30  250.604284\n",
       "2021-10-31  289.953491\n",
       "2021-11-30  372.588890\n",
       "2021-12-31  341.477726\n",
       "2022-01-31  339.770000\n",
       "2022-02-28  292.789474\n",
       "2022-03-31  303.253333\n",
       "2022-04-30  337.054169\n",
       "2022-05-31  256.985554\n",
       "2022-06-30  234.472539\n",
       "2022-07-31  248.886668\n",
       "2022-08-31  297.223042\n",
       "2022-09-30  287.885710\n",
       "2022-10-31  224.126190\n",
       "2022-11-30  193.479049\n",
       "2022-12-31  155.897143"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Mean\n",
    "df_tesla['Open'].resample(rule='ME').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b03e6ab9-4ce6-4115-8a17-bcc677cdc780",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\danie\\AppData\\Local\\Temp\\ipykernel_8456\\3609332289.py:3: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  df_tesla['Open'].resample(rule='M').max()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-28</th>\n",
       "      <td>23.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>23.000668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-30</th>\n",
       "      <td>20.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-31</th>\n",
       "      <td>20.513332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>24.344000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-31</th>\n",
       "      <td>24.004667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>24.606001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>20.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>22.497999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>23.756001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>23.080667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>21.306000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>20.462667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-30</th>\n",
       "      <td>19.219999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-31</th>\n",
       "      <td>17.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>15.248000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-31</th>\n",
       "      <td>17.278000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-31</th>\n",
       "      <td>16.176666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>16.513332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-31</th>\n",
       "      <td>21.836000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>42.666668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>61.566666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>53.666668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-30</th>\n",
       "      <td>57.012669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>55.633331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>67.518669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>111.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>153.007996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>167.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>151.479996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>200.736664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>233.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-31</th>\n",
       "      <td>297.126678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28</th>\n",
       "      <td>292.339996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>239.426666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>256.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-31</th>\n",
       "      <td>234.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>229.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-31</th>\n",
       "      <td>228.773331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>244.333328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>262.399994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>360.619995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>411.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>386.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>396.516663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>311.736664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>369.329987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-30</th>\n",
       "      <td>378.766663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>313.006653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>251.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-31</th>\n",
       "      <td>280.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-31</th>\n",
       "      <td>311.666656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>308.290009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31</th>\n",
       "      <td>254.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-30</th>\n",
       "      <td>234.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>197.080002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker            TSLA\n",
       "Date                  \n",
       "2018-01-31   24.000000\n",
       "2018-02-28   23.750000\n",
       "2018-03-31   23.000668\n",
       "2018-04-30   20.240000\n",
       "2018-05-31   20.513332\n",
       "2018-06-30   24.344000\n",
       "2018-07-31   24.004667\n",
       "2018-08-31   24.606001\n",
       "2018-09-30   20.860001\n",
       "2018-10-31   22.497999\n",
       "2018-11-30   23.756001\n",
       "2018-12-31   25.000000\n",
       "2019-01-31   23.080667\n",
       "2019-02-28   21.306000\n",
       "2019-03-31   20.462667\n",
       "2019-04-30   19.219999\n",
       "2019-05-31   17.120001\n",
       "2019-06-30   15.248000\n",
       "2019-07-31   17.278000\n",
       "2019-08-31   16.176666\n",
       "2019-09-30   16.513332\n",
       "2019-10-31   21.836000\n",
       "2019-11-30   24.000000\n",
       "2019-12-31   29.000000\n",
       "2020-01-31   42.666668\n",
       "2020-02-29   61.566666\n",
       "2020-03-31   53.666668\n",
       "2020-04-30   57.012669\n",
       "2020-05-31   55.633331\n",
       "2020-06-30   67.518669\n",
       "2020-07-31  111.930000\n",
       "2020-08-31  153.007996\n",
       "2020-09-30  167.380005\n",
       "2020-10-31  151.479996\n",
       "2020-11-30  200.736664\n",
       "2020-12-31  233.330002\n",
       "2021-01-31  297.126678\n",
       "2021-02-28  292.339996\n",
       "2021-03-31  239.426666\n",
       "2021-04-30  256.899994\n",
       "2021-05-31  234.600006\n",
       "2021-06-30  229.860001\n",
       "2021-07-31  228.773331\n",
       "2021-08-31  244.333328\n",
       "2021-09-30  262.399994\n",
       "2021-10-31  360.619995\n",
       "2021-11-30  411.470001\n",
       "2021-12-31  386.899994\n",
       "2022-01-31  396.516663\n",
       "2022-02-28  311.736664\n",
       "2022-03-31  369.329987\n",
       "2022-04-30  378.766663\n",
       "2022-05-31  313.006653\n",
       "2022-06-30  251.720001\n",
       "2022-07-31  280.700012\n",
       "2022-08-31  311.666656\n",
       "2022-09-30  308.290009\n",
       "2022-10-31  254.500000\n",
       "2022-11-30  234.050003\n",
       "2022-12-31  197.080002"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Montly max\n",
    "\n",
    "df_tesla['Open'].resample(rule='M').max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "924b5813-9e29-4c16-806c-ed35b1145ae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\danie\\AppData\\Local\\Temp\\ipykernel_8456\\1142611372.py:3: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  df_tesla['Open'].resample(rule='M').max().plot(kind='bar')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Monthly Max \n",
    "\n",
    "df_tesla['Open'].resample(rule='M').max().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "55ffd4e6-3e94-45b7-a70f-481695358a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\danie\\AppData\\Local\\Temp\\ipykernel_8456\\2724159595.py:3: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  df_tesla['Open'].resample(rule='M').max().plot(kind='bar',figsize=(16,6))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting Monthly Max \n",
    "\n",
    "df_tesla['Open'].resample(rule='M').max().plot(kind='bar',figsize=(16,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "dde67786-4942-4cd8-b9ac-70cde04443bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
       "Date                                                                        \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e4633008-20d0-475c-a7e8-e7858caaa8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.474001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.683332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>21.236668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.149332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.468000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker           TSLA\n",
       "Date                 \n",
       "2018-01-02  21.474001\n",
       "2018-01-03  21.683332\n",
       "2018-01-04  21.236668\n",
       "2018-01-05  21.149332\n",
       "2018-01-08  22.468000"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla['High'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "67780509-d666-4bd7-baec-3316da4e7150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>154.618999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>148.848999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>142.970998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>139.165999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>135.521000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker            TSLA\n",
       "Date                  \n",
       "2018-01-02         NaN\n",
       "2018-01-03         NaN\n",
       "2018-01-04         NaN\n",
       "2018-01-05         NaN\n",
       "2018-01-08         NaN\n",
       "...                ...\n",
       "2022-12-23  154.618999\n",
       "2022-12-27  148.848999\n",
       "2022-12-28  142.970998\n",
       "2022-12-29  139.165999\n",
       "2022-12-30  135.521000\n",
       "\n",
       "[1259 rows x 1 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Mean of High Column\n",
    "\n",
    "df_tesla['High'].rolling(10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e3f668b7-9f58-4a8e-b654-63db00e76987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA\n",
       "Date                                                                        \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000\n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500\n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500\n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000\n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "4f535bac-610b-4159-bd91-dda6d209cf89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>22.174600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>22.353867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>22.534200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>22.747800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>23.018400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>23.174933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>23.281267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>23.362600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>23.357200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>23.426800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>23.448600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker           TSLA\n",
       "Date                 \n",
       "2018-01-02        NaN\n",
       "2018-01-03        NaN\n",
       "2018-01-04        NaN\n",
       "2018-01-05        NaN\n",
       "2018-01-08        NaN\n",
       "2018-01-09        NaN\n",
       "2018-01-10        NaN\n",
       "2018-01-11        NaN\n",
       "2018-01-12        NaN\n",
       "2018-01-16  22.174600\n",
       "2018-01-17  22.353867\n",
       "2018-01-18  22.534200\n",
       "2018-01-19  22.747800\n",
       "2018-01-22  23.018400\n",
       "2018-01-23  23.174933\n",
       "2018-01-24  23.281267\n",
       "2018-01-25  23.362600\n",
       "2018-01-26  23.357200\n",
       "2018-01-29  23.426800\n",
       "2018-01-30  23.448600"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ## Mean of High Column\n",
    "\n",
    "df_tesla['High'].rolling(10).mean().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "acac443b-82ee-4558-b579-d4e77aa3f484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>22.273879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>22.456848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>22.610424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>22.848485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>23.110667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>23.218121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>23.281152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>23.323576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>23.360182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>23.407818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker           TSLA\n",
       "Date                 \n",
       "2018-01-02        NaN\n",
       "2018-01-03        NaN\n",
       "2018-01-04        NaN\n",
       "2018-01-05        NaN\n",
       "2018-01-08        NaN\n",
       "2018-01-09        NaN\n",
       "2018-01-10        NaN\n",
       "2018-01-11        NaN\n",
       "2018-01-12        NaN\n",
       "2018-01-16        NaN\n",
       "2018-01-17  22.273879\n",
       "2018-01-18  22.456848\n",
       "2018-01-19  22.610424\n",
       "2018-01-22  22.848485\n",
       "2018-01-23  23.110667\n",
       "2018-01-24  23.218121\n",
       "2018-01-25  23.281152\n",
       "2018-01-26  23.323576\n",
       "2018-01-29  23.360182\n",
       "2018-01-30  23.407818"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ## Mean of High Column\n",
    "\n",
    "df_tesla['High'].rolling(11).mean().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bc71e834-5a5f-4513-9f86-adc78bf8d782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>23.266666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>23.486668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>23.486668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>23.855333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>24.033333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ticker           TSLA\n",
       "Date                 \n",
       "2018-01-02        NaN\n",
       "2018-01-03        NaN\n",
       "2018-01-04        NaN\n",
       "2018-01-05        NaN\n",
       "2018-01-08        NaN\n",
       "2018-01-09        NaN\n",
       "2018-01-10        NaN\n",
       "2018-01-11        NaN\n",
       "2018-01-12        NaN\n",
       "2018-01-16        NaN\n",
       "2018-01-17  23.266666\n",
       "2018-01-18  23.486668\n",
       "2018-01-19  23.486668\n",
       "2018-01-22  23.855333\n",
       "2018-01-23  24.033333\n",
       "2018-01-24  24.033333\n",
       "2018-01-25  24.033333\n",
       "2018-01-26  24.033333\n",
       "2018-01-29  24.033333\n",
       "2018-01-30  24.033333"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ## Mean of High Column\n",
    "\n",
    "df_tesla['High'].rolling(11).max().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d72444cd-70d6-4854-87e7-d35f293bef46",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tesla['Open:30 days rolling']=df_tesla['Open'].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c29339d1-9320-4de2-a486-50fb48591d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Open:30 days roling</th>\n",
       "      <th>Open:30 days rolling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume  \\\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA   \n",
       "Date                                                                           \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000   \n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500   \n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500   \n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000   \n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000   \n",
       "\n",
       "Price      Open:30 days roling Open:30 days rolling  \n",
       "Ticker                                               \n",
       "Date                                                 \n",
       "2018-01-02                 NaN                  NaN  \n",
       "2018-01-03                 NaN                  NaN  \n",
       "2018-01-04                 NaN                  NaN  \n",
       "2018-01-05                 NaN                  NaN  \n",
       "2018-01-08                 NaN                  NaN  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3a84158e-a9f0-4445-b51b-3a9beae3000c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Open:30 days roling</th>\n",
       "      <th>Open:30 days rolling</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>TSLA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.368668</td>\n",
       "      <td>21.474001</td>\n",
       "      <td>20.733334</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>65283000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.150000</td>\n",
       "      <td>21.683332</td>\n",
       "      <td>21.036667</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>67822500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>20.974667</td>\n",
       "      <td>20.974667</td>\n",
       "      <td>21.236668</td>\n",
       "      <td>20.378668</td>\n",
       "      <td>20.858000</td>\n",
       "      <td>149194500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.105333</td>\n",
       "      <td>21.149332</td>\n",
       "      <td>20.799999</td>\n",
       "      <td>21.108000</td>\n",
       "      <td>68868000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.427334</td>\n",
       "      <td>22.468000</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>21.066668</td>\n",
       "      <td>147891000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>22.246000</td>\n",
       "      <td>22.246000</td>\n",
       "      <td>22.586666</td>\n",
       "      <td>21.826668</td>\n",
       "      <td>22.344000</td>\n",
       "      <td>107199000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>22.320000</td>\n",
       "      <td>22.320000</td>\n",
       "      <td>22.466667</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.146667</td>\n",
       "      <td>64648500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>22.530001</td>\n",
       "      <td>22.530001</td>\n",
       "      <td>22.987333</td>\n",
       "      <td>22.217333</td>\n",
       "      <td>22.349333</td>\n",
       "      <td>99682500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>22.414667</td>\n",
       "      <td>22.414667</td>\n",
       "      <td>22.694000</td>\n",
       "      <td>22.244667</td>\n",
       "      <td>22.575333</td>\n",
       "      <td>72376500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>22.670668</td>\n",
       "      <td>22.670668</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>22.320000</td>\n",
       "      <td>22.502666</td>\n",
       "      <td>97114500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>23.143999</td>\n",
       "      <td>23.143999</td>\n",
       "      <td>23.266666</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>22.698000</td>\n",
       "      <td>106552500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>22.971333</td>\n",
       "      <td>22.971333</td>\n",
       "      <td>23.486668</td>\n",
       "      <td>22.916000</td>\n",
       "      <td>23.044666</td>\n",
       "      <td>85287000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>23.334667</td>\n",
       "      <td>23.334667</td>\n",
       "      <td>23.372667</td>\n",
       "      <td>22.840000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>73324500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>23.437332</td>\n",
       "      <td>23.437332</td>\n",
       "      <td>23.855333</td>\n",
       "      <td>23.280001</td>\n",
       "      <td>23.293333</td>\n",
       "      <td>93156000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>23.519333</td>\n",
       "      <td>23.519333</td>\n",
       "      <td>24.033333</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>81981000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>23.059334</td>\n",
       "      <td>23.059334</td>\n",
       "      <td>23.650000</td>\n",
       "      <td>22.901333</td>\n",
       "      <td>23.638666</td>\n",
       "      <td>79312500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>22.509333</td>\n",
       "      <td>22.509333</td>\n",
       "      <td>23.280001</td>\n",
       "      <td>22.426666</td>\n",
       "      <td>23.218000</td>\n",
       "      <td>101104500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>22.856667</td>\n",
       "      <td>22.856667</td>\n",
       "      <td>22.933332</td>\n",
       "      <td>22.380667</td>\n",
       "      <td>22.766666</td>\n",
       "      <td>68091000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>23.302000</td>\n",
       "      <td>23.302000</td>\n",
       "      <td>23.389999</td>\n",
       "      <td>22.552000</td>\n",
       "      <td>22.656668</td>\n",
       "      <td>71206500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>23.054667</td>\n",
       "      <td>23.054667</td>\n",
       "      <td>23.218000</td>\n",
       "      <td>22.811333</td>\n",
       "      <td>23.009333</td>\n",
       "      <td>70765500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>23.620667</td>\n",
       "      <td>23.620667</td>\n",
       "      <td>23.746000</td>\n",
       "      <td>23.012667</td>\n",
       "      <td>23.167334</td>\n",
       "      <td>93211500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>23.283333</td>\n",
       "      <td>23.283333</td>\n",
       "      <td>23.977333</td>\n",
       "      <td>23.242001</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>62965500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>22.916668</td>\n",
       "      <td>22.916668</td>\n",
       "      <td>23.463333</td>\n",
       "      <td>22.700666</td>\n",
       "      <td>23.229334</td>\n",
       "      <td>55572000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>22.208668</td>\n",
       "      <td>22.208668</td>\n",
       "      <td>22.964666</td>\n",
       "      <td>22.200001</td>\n",
       "      <td>22.531334</td>\n",
       "      <td>66961500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>22.264668</td>\n",
       "      <td>22.264668</td>\n",
       "      <td>22.414667</td>\n",
       "      <td>21.566668</td>\n",
       "      <td>21.680668</td>\n",
       "      <td>76326000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.066668</td>\n",
       "      <td>22.377333</td>\n",
       "      <td>22.599333</td>\n",
       "      <td>104538000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>21.015333</td>\n",
       "      <td>21.015333</td>\n",
       "      <td>23.241333</td>\n",
       "      <td>20.973333</td>\n",
       "      <td>22.887333</td>\n",
       "      <td>154719000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>20.694668</td>\n",
       "      <td>20.694668</td>\n",
       "      <td>21.398666</td>\n",
       "      <td>19.650667</td>\n",
       "      <td>21.328667</td>\n",
       "      <td>194005500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>21.048668</td>\n",
       "      <td>21.048668</td>\n",
       "      <td>21.205334</td>\n",
       "      <td>20.416668</td>\n",
       "      <td>21.075333</td>\n",
       "      <td>93417000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>21.577333</td>\n",
       "      <td>21.577333</td>\n",
       "      <td>21.612667</td>\n",
       "      <td>20.834000</td>\n",
       "      <td>21.001333</td>\n",
       "      <td>68403000</td>\n",
       "      <td>22.379222</td>\n",
       "      <td>22.379222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>21.487333</td>\n",
       "      <td>21.487333</td>\n",
       "      <td>21.744667</td>\n",
       "      <td>21.234667</td>\n",
       "      <td>21.389334</td>\n",
       "      <td>59260500</td>\n",
       "      <td>22.398867</td>\n",
       "      <td>22.398867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Price       Adj Close      Close       High        Low       Open     Volume  \\\n",
       "Ticker           TSLA       TSLA       TSLA       TSLA       TSLA       TSLA   \n",
       "Date                                                                           \n",
       "2018-01-02  21.368668  21.368668  21.474001  20.733334  20.799999   65283000   \n",
       "2018-01-03  21.150000  21.150000  21.683332  21.036667  21.400000   67822500   \n",
       "2018-01-04  20.974667  20.974667  21.236668  20.378668  20.858000  149194500   \n",
       "2018-01-05  21.105333  21.105333  21.149332  20.799999  21.108000   68868000   \n",
       "2018-01-08  22.427334  22.427334  22.468000  21.033333  21.066668  147891000   \n",
       "2018-01-09  22.246000  22.246000  22.586666  21.826668  22.344000  107199000   \n",
       "2018-01-10  22.320000  22.320000  22.466667  22.000000  22.146667   64648500   \n",
       "2018-01-11  22.530001  22.530001  22.987333  22.217333  22.349333   99682500   \n",
       "2018-01-12  22.414667  22.414667  22.694000  22.244667  22.575333   72376500   \n",
       "2018-01-16  22.670668  22.670668  23.000000  22.320000  22.502666   97114500   \n",
       "2018-01-17  23.143999  23.143999  23.266666  22.650000  22.698000  106552500   \n",
       "2018-01-18  22.971333  22.971333  23.486668  22.916000  23.044666   85287000   \n",
       "2018-01-19  23.334667  23.334667  23.372667  22.840000  23.000000   73324500   \n",
       "2018-01-22  23.437332  23.437332  23.855333  23.280001  23.293333   93156000   \n",
       "2018-01-23  23.519333  23.519333  24.033333  23.400000  24.000000   81981000   \n",
       "2018-01-24  23.059334  23.059334  23.650000  22.901333  23.638666   79312500   \n",
       "2018-01-25  22.509333  22.509333  23.280001  22.426666  23.218000  101104500   \n",
       "2018-01-26  22.856667  22.856667  22.933332  22.380667  22.766666   68091000   \n",
       "2018-01-29  23.302000  23.302000  23.389999  22.552000  22.656668   71206500   \n",
       "2018-01-30  23.054667  23.054667  23.218000  22.811333  23.009333   70765500   \n",
       "2018-01-31  23.620667  23.620667  23.746000  23.012667  23.167334   93211500   \n",
       "2018-02-01  23.283333  23.283333  23.977333  23.242001  23.400000   62965500   \n",
       "2018-02-02  22.916668  22.916668  23.463333  22.700666  23.229334   55572000   \n",
       "2018-02-05  22.208668  22.208668  22.964666  22.200001  22.531334   66961500   \n",
       "2018-02-06  22.264668  22.264668  22.414667  21.566668  21.680668   76326000   \n",
       "2018-02-07  23.000000  23.000000  23.066668  22.377333  22.599333  104538000   \n",
       "2018-02-08  21.015333  21.015333  23.241333  20.973333  22.887333  154719000   \n",
       "2018-02-09  20.694668  20.694668  21.398666  19.650667  21.328667  194005500   \n",
       "2018-02-12  21.048668  21.048668  21.205334  20.416668  21.075333   93417000   \n",
       "2018-02-13  21.577333  21.577333  21.612667  20.834000  21.001333   68403000   \n",
       "2018-02-14  21.487333  21.487333  21.744667  21.234667  21.389334   59260500   \n",
       "\n",
       "Price      Open:30 days roling Open:30 days rolling  \n",
       "Ticker                                               \n",
       "Date                                                 \n",
       "2018-01-02                 NaN                  NaN  \n",
       "2018-01-03                 NaN                  NaN  \n",
       "2018-01-04                 NaN                  NaN  \n",
       "2018-01-05                 NaN                  NaN  \n",
       "2018-01-08                 NaN                  NaN  \n",
       "2018-01-09                 NaN                  NaN  \n",
       "2018-01-10                 NaN                  NaN  \n",
       "2018-01-11                 NaN                  NaN  \n",
       "2018-01-12                 NaN                  NaN  \n",
       "2018-01-16                 NaN                  NaN  \n",
       "2018-01-17                 NaN                  NaN  \n",
       "2018-01-18                 NaN                  NaN  \n",
       "2018-01-19                 NaN                  NaN  \n",
       "2018-01-22                 NaN                  NaN  \n",
       "2018-01-23                 NaN                  NaN  \n",
       "2018-01-24                 NaN                  NaN  \n",
       "2018-01-25                 NaN                  NaN  \n",
       "2018-01-26                 NaN                  NaN  \n",
       "2018-01-29                 NaN                  NaN  \n",
       "2018-01-30                 NaN                  NaN  \n",
       "2018-01-31                 NaN                  NaN  \n",
       "2018-02-01                 NaN                  NaN  \n",
       "2018-02-02                 NaN                  NaN  \n",
       "2018-02-05                 NaN                  NaN  \n",
       "2018-02-06                 NaN                  NaN  \n",
       "2018-02-07                 NaN                  NaN  \n",
       "2018-02-08                 NaN                  NaN  \n",
       "2018-02-09                 NaN                  NaN  \n",
       "2018-02-12                 NaN                  NaN  \n",
       "2018-02-13           22.379222            22.379222  \n",
       "2018-02-14           22.398867            22.398867  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tesla.head(31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "6fb8a299-1a8a-4026-99ed-1692b49892ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(           'Adj Close', 'TSLA'),\n",
      "            (               'Close', 'TSLA'),\n",
      "            (                'High', 'TSLA'),\n",
      "            (                 'Low', 'TSLA'),\n",
      "            (                'Open', 'TSLA'),\n",
      "            (              'Volume', 'TSLA'),\n",
      "            ( 'Open:30 days roling',     ''),\n",
      "            ('Open:30 days rolling',     '')],\n",
      "           names=['Price', 'Ticker'])\n"
     ]
    }
   ],
   "source": [
    "print(df_tesla.columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "3aa6f7e1-fcd4-4271-8293-2062440f61f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the 'Open' price and its 30-day rolling average\n",
    "df_tesla[['Open', 'Open:30 days rolling']].plot(figsize=(12, 5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c09d2789-0ea9-4a37-8f55-8d75b40db1c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}