Kaustdata

README.md

@@ -1,8 +1,13 @@
 # Data-Analysis
-Data Science Projects Using Python and a little R. 
+# Here we give you a brief about what this project stands for and means to us along with what purpose it fulfills and what problem it solves
+ This is a Data Science Project Using Python Programming and a little R Programming. 
 
-This repository is a place to share my code and notebooks for numerous data science projects.
+This repository is a place where I share my code and notebooks for numerous data science projects.
 
+# Little Plug
 Many of these projects are written up on my [Towards Data Science Medium page](https://medium.com/@williamkoehrsen). 
-
+# Little Plug Again;)
 Reach me on Twitter [@koehrsen_will](https://twitter.com/@koehrsen_will)
+
+Feel free to fork my repos and do some awesome changes!
+Best changes will get merged with my project and also with my heart!;)

bayesian drake equation/poisson-development.ipynb

@@ -61,17 +61,19 @@
    ],
    "source": [
     "# Standard Data Science Helpers\n",
+    "# The Basic Libraries we need to import to get started\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import scipy\n",
     "\n",
     "# Extra options\n",
     "pd.options.display.max_columns = 20\n",
     "\n",
-    "# Show all code cells outputs\n",
+    "#  Code To Show all code cells outputs\n",
     "from IPython.core.interactiveshell import InteractiveShell\n",
     "InteractiveShell.ast_node_interactivity = 'all'\n",
     "\n",
+    "# Importing some more libraries\n",
     "import plotly.plotly as py\n",
     "import plotly.graph_objs as go\n",
     "import cufflinks\n",
@@ -146,7 +148,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "print(\"Have a great day:)\");\n"
+   ]
   }
  ],
  "metadata": {

example_notebook/k.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "State notebook purpose here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports\n",
+    "Import libraries and write settings here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-03-01T01:57:57.838379Z",
+     "start_time": "2019-03-01T01:57:35.320667Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ],
+      "text/vnd.plotly.v1+html": [
+       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Data manipulation\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# Options for pandas\n",
+    "pd.options.display.max_columns = 50\n",
+    "pd.options.display.max_rows = 30\n",
+    "\n",
+    "# Display all cell outputs\n",
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "InteractiveShell.ast_node_interactivity = 'all'\n",
+    "\n",
+    "from IPython import get_ipython\n",
+    "ipython = get_ipython()\n",
+    "\n",
+    "# autoreload extension\n",
+    "if 'autoreload' not in ipython.extension_manager.loaded:\n",
+    "    %load_ext autoreload\n",
+    "\n",
+    "%autoreload 2\n",
+    "\n",
+    "# Visualizations\n",
+    "import plotly.plotly as py\n",
+    "import plotly.graph_objs as go\n",
+    "from plotly.offline import iplot, init_notebook_mode\n",
+    "init_notebook_mode(connected=True)\n",
+    "\n",
+    "import cufflinks as cf\n",
+    "cf.go_offline(connected=True)\n",
+    "cf.set_config_file(theme='white')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis/Modeling\n",
+    "Do work here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results\n",
+    "Show graphs and stats here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusions and Next Steps\n",
+    "Summarize findings here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"i love everyone\");"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

example_notebook/template-example.ipynb

@@ -113,7 +113,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "print(\"i love everyone\");"
+   ]
   }
  ],
  "metadata": {