From 91748f2c0b5f79aa7ada2b8284eff54091d6fa9b Mon Sep 17 00:00:00 2001 From: Dave Date: Tue, 3 May 2022 14:38:22 -0400 Subject: [PATCH] add testing (#4) * remove scratch notebook * ignore vscode/pytest_cache * initial updates for testing * add docstring back * testing might be interfering too much here * pass optional ipython shell arg to register/deregister * remove these other fixtures since we don't need them * revert register()/deregister() tests since they use the ipython shell instance in a better way --- .gitignore | 5 +- dx/formatters.py | 28 +- dx/tests/__init__.py | 0 dx/tests/conftest.py | 54 + dx/tests/test_formatters.py | 72 + examples/dx testing.ipynb | 12338 ---------------------------------- pytest.ini | 2 + 7 files changed, 155 insertions(+), 12344 deletions(-) create mode 100644 dx/tests/__init__.py create mode 100644 dx/tests/conftest.py create mode 100644 dx/tests/test_formatters.py delete mode 100644 examples/dx testing.ipynb create mode 100644 pytest.ini diff --git a/.gitignore b/.gitignore index ed0e08fc..2930ab24 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,7 @@ __pycache__/ *.pyc -dist/ \ No newline at end of file +dist/ + +.pytest_cache +.vscode \ No newline at end of file diff --git a/dx/formatters.py b/dx/formatters.py index 874546f7..48ef1647 100644 --- a/dx/formatters.py +++ b/dx/formatters.py @@ -1,9 +1,16 @@ import pandas as pd from IPython import get_ipython +from IPython.core.interactiveshell import InteractiveShell from IPython.core.formatters import DisplayFormatter from pandas.io.json import build_table_schema +from typing import Optional + +IN_IPYTHON_ENV = get_ipython() is not None + +DEFAULT_IPYTHON_DISPLAY_FORMATTER = None +if IN_IPYTHON_ENV: + DEFAULT_IPYTHON_DISPLAY_FORMATTER = get_ipython().display_formatter -DEFAULT_IPYTHON_DISPLAY_FORMATTER = get_ipython().display_formatter DX_MEDIA_TYPE = "application/vnd.dex.v1+json" @@ -32,16 +39,27 @@ def format_dx(df) -> tuple: return (payload, metadata) -def deregister() -> None: +def deregister(ipython_shell: Optional[InteractiveShell] = None) -> None: """Reverts IPython.display_formatter to its original state""" + if not IN_IPYTHON_ENV and ipython_shell is None: + return pd.options.display.max_rows = 60 - get_ipython().display_formatter = DEFAULT_IPYTHON_DISPLAY_FORMATTER + ipython = ipython_shell or get_ipython() + ipython.display_formatter = DEFAULT_IPYTHON_DISPLAY_FORMATTER -def register() -> None: +def register(ipython_shell: Optional[InteractiveShell] = None) -> None: """Overrides the default IPython display formatter to use DXDisplayFormatter""" + if not IN_IPYTHON_ENV and ipython_shell is None: + return pd.options.display.max_rows = 100_000 - get_ipython().display_formatter = DXDisplayFormatter() + + if ipython_shell is not None: + global DEFAULT_IPYTHON_DISPLAY_FORMATTER + DEFAULT_IPYTHON_DISPLAY_FORMATTER = ipython_shell.display_formatter + + ipython = ipython_shell or get_ipython() + ipython.display_formatter = DXDisplayFormatter() disable = deregister diff --git a/dx/tests/__init__.py b/dx/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/dx/tests/conftest.py b/dx/tests/conftest.py new file mode 100644 index 00000000..a8f265ab --- /dev/null +++ b/dx/tests/conftest.py @@ -0,0 +1,54 @@ +import random +import string + +import pandas as pd +import pytest +from IPython.core import formatters +from IPython.core.formatters import BaseFormatter, DisplayFormatter +from IPython.terminal.interactiveshell import TerminalInteractiveShell +from IPython.testing import tools + + +@pytest.fixture +def get_ipython() -> TerminalInteractiveShell: + if TerminalInteractiveShell._instance: + return TerminalInteractiveShell.instance() + + config = tools.default_config() + config.TerminalInteractiveShell.simple_prompt = True + shell = TerminalInteractiveShell.instance(config=config) + return shell + + +@pytest.fixture +def sample_dataframe() -> pd.DataFrame: + df = pd.DataFrame( + { + "col_1": list("aaa"), + "col_2": list("bbb"), + "col_3": list("ccc"), + } + ) + return df + + +@pytest.fixture +def sample_large_dataframe() -> pd.DataFrame: + """ + Generates a random assortment of values of different data types, + and returns a larger dataframe than the `sample_dataframe` fixture. + """ + n_rows = 100_000 + data = { + "float_col": [random.randint(0, 100) for _ in range(n_rows)], + "int_col": [random.random() for _ in range(n_rows)], + "bool_col": [random.choice([True, False]) for _ in range(n_rows)], + "str_col": [ + "".join(random.sample(string.ascii_uppercase, 3)) for _ in range(n_rows) + ], + "date_col": [ + pd.Timestamp("now") - pd.Timedelta(hours=random.randint(-100, 100)) + for _ in range(n_rows) + ], + } + return pd.DataFrame(data) diff --git a/dx/tests/test_formatters.py b/dx/tests/test_formatters.py new file mode 100644 index 00000000..8fd5ad32 --- /dev/null +++ b/dx/tests/test_formatters.py @@ -0,0 +1,72 @@ +from dx.formatters import ( + DX_MEDIA_TYPE, + DXDisplayFormatter, + deregister, + format_dx, + register, +) +from IPython.core.formatters import DisplayFormatter +from IPython.terminal.interactiveshell import TerminalInteractiveShell + + +def test_media_type(sample_dataframe): + payload, _ = format_dx(sample_dataframe) + assert DX_MEDIA_TYPE in payload + + +def test_data_structure(sample_dataframe): + """ + The transformed data needs to represent a list of lists, + each associated with a column in the dataframe, + including one for the dataframe's index. + """ + payload, _ = format_dx(sample_dataframe) + data = payload[DX_MEDIA_TYPE]["data"] + assert isinstance(data, list) + assert len(data) == 4 + assert isinstance(data[0], list) + + +def test_data_list_order(sample_dataframe): + """ + Ensure the payload contains lists as column values, + and not row values. + """ + payload, _ = format_dx(sample_dataframe) + data = payload[DX_MEDIA_TYPE]["data"] + assert data[0] == [0, 1, 2] # index values + assert data[1] == list("aaa") # "col_1" values + assert data[2] == list("bbb") # "col_2" values + assert data[3] == list("ccc") # "col_3" values + + +def test_fields_match_data_length(sample_dataframe): + """ + The number of fields in the schema needs to match + the number of lists in the data list. + """ + payload, _ = format_dx(sample_dataframe) + data = payload[DX_MEDIA_TYPE]["data"] + fields = payload[DX_MEDIA_TYPE]["schema"]["fields"] + assert len(data) == len(fields) + + +def test_register_ipython_display_formatter(get_ipython: TerminalInteractiveShell): + """ + Test that the display formatter for an IPython shell is + successfully registered as a DXDisplayFormatter. + """ + register(ipython_shell=get_ipython) + assert isinstance(get_ipython.display_formatter, DXDisplayFormatter) + + +def test_deregister_ipython_display_formatter(get_ipython: TerminalInteractiveShell): + """ + Test that the display formatter reverts to the default + `IPython.core.formatters.DisplayFormatter` after deregistering. + """ + register(ipython_shell=get_ipython) + assert isinstance(get_ipython.display_formatter, DXDisplayFormatter) + + deregister(ipython_shell=get_ipython) + assert isinstance(get_ipython.display_formatter, DisplayFormatter) diff --git a/examples/dx testing.ipynb b/examples/dx testing.ipynb deleted file mode 100644 index 77fb8ad3..00000000 --- a/examples/dx testing.ipynb +++ /dev/null @@ -1,12338 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5e2c36e4-c9e7-4571-8169-459829ba4957", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Intro\n", - "\n", - "This notebook is for testing out the interactions between `pandas` and `IPython.display` as a lead-in to `dx` package development.\n", - "\n", - "Currently established functionality:\n", - "- [establishing formatter baseline / deregistering DEX mediatype formatter](#?cellID=4a8ff57f-5b5f-46b6-ac4a-c12bf1f410b4)" - ] - }, - { - "cell_type": "markdown", - "id": "5d4d6aea-7fea-480c-9707-d19d369258cd", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "> Resources\n", - "> ---\n", - "> - [**DEX Data Link Tech Design Doc**](https://docs.google.com/document/d/1KMJU2h7_cmL_wD84-SsGNVuIEI6-KmKchSEjKXnP85U/edit#)\n", - "> - [transport layer notebook](https://app.noteable-integration.us/f/a70232e4-4081-4f7a-86ad-fc59d8b21c4a)\n", - "> - [`geas` Data Processing Worker PR](https://github.com/noteable-io/geas/pull/1969)" - ] - }, - { - "cell_type": "markdown", - "id": "dae8f99a-567e-4462-b9b5-da51df1bc569", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "> References\n", - "> ---\n", - "> - `pandas`\n", - "> - [IPython formatter updating](https://github.com/pandas-dev/pandas/blob/ad190575aa75962d2d0eade2de81a5fe5a2e285b/pandas/io/formats/printing.py#L244)\n", - "> - [DataFrame rendering](https://github.com/pandas-dev/pandas/blob/a8968bfa696d51f73769c54f2630a9530488236a/pandas/io/formats/format.py#L1003)\n", - "> - [`DataFrame._repr_html_`](https://github.com/pandas-dev/pandas/blob/f9762d8f52e044d6588d6ee065952aa7e2d5362b/pandas/core/frame.py#L1025)\n", - "> - [`NotebookFormatter`](https://github.com/pandas-dev/pandas/blob/a8968bfa696d51f73769c54f2630a9530488236a/pandas/io/formats/html.py#L585)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5cbabd0a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/lib/python3.9/site-packages (1.4.2)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/lib/python3.9/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: numpy>=1.18.5 in /home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/lib/python3.9/site-packages (from pandas) (1.22.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/lib/python3.9/site-packages (from pandas) (2021.3)\n", - "Requirement already satisfied: six>=1.5 in /home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n", - "\u001b[33mWARNING: You are using pip version 22.0.3; however, version 22.0.4 is available.\n", - "You should consider upgrading via the '/home/djshoup/.pyenv/versions/3.9.7/envs/gate-3.9.7/bin/python -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pandas" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "57da3488-d66e-481c-9c24-65c45075136c", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T21:57:11.911Z", - "iopub.status.busy": "2022-04-22T21:57:11.887Z", - "iopub.status.idle": "2022-04-22T21:57:11.944Z", - "shell.execute_reply": "2022-04-22T21:57:11.861Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IN_NOTEBOOK_ENV=True\n", - "NOTEABLE_ENV=False\n" - ] - } - ], - "source": [ - "# there's probably a better way of checking here\n", - "try:\n", - " from IPython import get_ipython\n", - " if \"IPKernelApp\" in get_ipython().config:\n", - " IN_NOTEBOOK_ENV = True\n", - "except ImportError:\n", - " IN_NOTEBOOK_ENV = False\n", - "\n", - "\n", - "import os\n", - "from typing import Optional\n", - "\n", - "from IPython.core.formatters import BaseFormatter\n", - "from IPython.display import display as ipydisplay # because we want to test 'dx.display' later\n", - "\n", - "from pandas.io.json import build_table_schema\n", - "import pandas as pd\n", - "\n", - "\n", - "DEX_MEDIA_TYPE = \"application/vnd.dex.v1+json\"\n", - "DATARESOURCE_MEDIA_TYPE = \"application/vnd.dataresource+json\"\n", - "\n", - "# maybe we want to check this in `__init__.py` before updating IPython.display formatters?\n", - "NOTEABLE_ENV = 'NTBL_USER_ID' in os.environ\n", - "\n", - "print(f\"{IN_NOTEBOOK_ENV=}\")\n", - "print(f\"{NOTEABLE_ENV=}\")" - ] - }, - { - "cell_type": "markdown", - "id": "ae6518f4-c548-4fc7-93eb-748a27572e56", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Make some sample data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "869e81da-434a-4e5c-a708-6f6fefa0d6eb", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:00:17.037Z", - "iopub.status.busy": "2022-04-22T22:00:17.010Z", - "iopub.status.idle": "2022-04-22T22:00:17.073Z", - "shell.execute_reply": "2022-04-22T22:00:16.970Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['numcol_1', 'numcol_2', 'numcol_3', 'bool_col', 'str_col'])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pprint\n", - "import random\n", - "import string\n", - "\n", - "n_rows = 100\n", - "data = {\n", - " 'numcol_1': [random.randint(0, 100) for _ in range(n_rows)],\n", - " 'numcol_2': [random.random() for _ in range(n_rows)],\n", - " 'numcol_3': [random.randint(-10, 10) * random.random() for _ in range(n_rows)],\n", - " 'bool_col': [random.choice([True, False]) for _ in range(n_rows)],\n", - " 'str_col': [''.join(random.sample(string.ascii_uppercase, 3)) for _ in range(n_rows)],\n", - "}\n", - "data.keys()" - ] - }, - { - "cell_type": "markdown", - "id": "06d59d71-e4fc-493f-9f8a-24bdceeeffc2", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Original `pd.DataFrame` formatting" - ] - }, - { - "cell_type": "markdown", - "id": "38145e47-8cad-4829-9cef-30061e1f2dcc", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "ideally we would run this:\n", - "\n", - "```python\n", - "from pandas.io.formats.printing import enable_data_resource_formatter\n", - "enable_data_resource_formatter(True)\n", - "```\n", - "\n", - "and make sure we see the following in `get_ipython().display_formatter.formatters`:\n", - "```python\n", - "'application/vnd.dataresource+json': .TableSchemaFormatter at 0x7f1b9983b850>\n", - "```\n", - "\n", - "but that doesn't work after `register_dx_formatter()` is called" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6f4a4cdf-5f71-41ae-a6cf-36e76ccb9672", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:01:47.374Z", - "iopub.status.busy": "2022-04-22T22:01:47.348Z", - "iopub.status.idle": "2022-04-22T22:01:48.359Z", - "shell.execute_reply": "2022-04-22T22:01:48.319Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "python" - } - }, - "outputs": [], - "source": [ - "def deregister_dx_formatting(media_type: str = DEX_MEDIA_TYPE) -> None:\n", - " \"\"\"Reverts IPython.display_formatter.formatters to original states\"\"\"\n", - " pd.options.display.html.table_schema = False\n", - " pd.options.display.max_rows = 50_001\n", - " \n", - " formatters = get_ipython().display_formatter.formatters\n", - " if media_type in formatters:\n", - " formatters.pop(media_type)\n", - " \n", - " # not sure why using `pandas.io.formats.printing.enable_data_resource_formatter(True)` doesn't work as expected here\n", - " from IPython.core.formatters import BaseFormatter\n", - " class TableSchemaFormatter(BaseFormatter):\n", - " print_method = \"_repr_data_resource_\"\n", - " _return_type = (dict,)\n", - " formatters[DATARESOURCE_MEDIA_TYPE] = TableSchemaFormatter()\n", - " formatters[DATARESOURCE_MEDIA_TYPE].enabled = True" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "26cc2355-37b1-4711-92df-9c07c6cf7558", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:01:48.434Z", - "iopub.status.busy": "2022-04-22T22:01:48.404Z", - "iopub.status.idle": "2022-04-22T22:01:49.384Z", - "shell.execute_reply": "2022-04-22T22:01:49.325Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [], - "source": [ - "# deregister_dx_formatting()\n", - "# get_ipython().display_formatter.formatters" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "921169ec-cb02-429b-9608-7740af79e1d1", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:01:49.445Z", - "iopub.status.busy": "2022-04-22T22:01:49.426Z", - "iopub.status.idle": "2022-04-22T22:01:49.468Z", - "shell.execute_reply": "2022-04-22T22:01:49.343Z" - }, - "noteable": { - "cell_type": "python" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " numcol_1 numcol_2 numcol_3 bool_col str_col\n", - "10 72 0.162265 2.933297 False BJO\n", - "21 4 0.508288 -0.494598 True VQE\n", - "64 27 0.631291 -1.341139 False ZJO\n", - "2 19 0.953691 0.000000 False WUL\n", - "75 47 0.461670 6.307645 False CUQ" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# if the output below doesn't show the default pandas->DEX view, something is wrong \n", - "# and you may need to run the deregister function above\n", - "df = pd.DataFrame(data)\n", - "df.sample(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "21208693-1258-4350-b02c-9d1b1191b7d6", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T21:26:21.954Z", - "iopub.status.busy": "2022-04-22T21:26:21.880Z", - "iopub.status.idle": "2022-04-22T21:26:21.979Z", - "shell.execute_reply": "2022-04-22T21:26:21.833Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [], - "source": [ - "# save off a file in case we want to try rendering direct-from-file later\n", - "df.to_csv(\"dx_sample_data.csv\", index=False)" - ] - }, - { - "cell_type": "markdown", - "id": "9d9f5d46-2201-4f2e-843a-64a83f525ea6", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Register the new IPython formatter and media type\n", - "\n", - "we should see something like this included in `get_ipython().display_formatter.formatters`:\n", - "```python\n", - "'application/vnd.dex.v1+json': <__main__.DEXDataFrameFormatter at 0x7f1b9963ac40>\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "80e27507-7f36-4fcd-866e-9234c0482d64", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:01:57.390Z", - "iopub.status.busy": "2022-04-22T22:01:57.367Z", - "iopub.status.idle": "2022-04-22T22:01:57.409Z", - "shell.execute_reply": "2022-04-22T22:01:57.336Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'text/plain': ,\n", - " 'text/html': ,\n", - " 'text/markdown': ,\n", - " 'image/svg+xml': ,\n", - " 'image/png': ,\n", - " 'application/pdf': ,\n", - " 'image/jpeg': ,\n", - " 'text/latex': ,\n", - " 'application/json': ,\n", - " 'application/javascript': ,\n", - " 'application/vnd.dataresource+json': .TableSchemaFormatter at 0x7fe22e4cc2b0>,\n", - " 'application/vnd.dex.v1+json': <__main__.DEXDataFrameFormatter at 0x7fe22e4cc370>}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "class DEXDataFrameFormatter(BaseFormatter):\n", - " print_method = \"_repr_data_resource_\"\n", - " _return_type = (dict,)\n", - " \n", - " \n", - "def register_dx_formatter(media_type: str = DEX_MEDIA_TYPE) -> None:\n", - " \"\"\"Registers a mediatype for IPython display formatting\"\"\" \n", - " pd.options.display.html.table_schema = True\n", - " pd.options.display.max_rows = 100_000\n", - " \n", - " formatters = get_ipython().display_formatter.formatters \n", - " # add new DEX formatter\n", - " formatters[media_type] = DEXDataFrameFormatter()\n", - " formatters[media_type].enabled = False # this breaks default Dataframe._repl_html_ if enabled\n", - "\n", - " \n", - "register_dx_formatter()\n", - "get_ipython().display_formatter.formatters " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ab8741b6-e622-4d1f-b6ac-e2de3b7c1d1a", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:07:09.371Z", - "iopub.status.busy": "2022-04-22T22:07:09.355Z", - "iopub.status.idle": "2022-04-22T22:07:09.395Z", - "shell.execute_reply": "2022-04-22T22:07:09.326Z" - }, - "noteable": { - "cell_type": "python" - } - }, - "outputs": [ - { - "data": { - "application/vnd.dataresource+json": { - "data": [ - { - "bool_col": false, - "index": 0, - "numcol_1": 74, - 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"98 63 0.695356 -0.867379 True UEO\n", - "99 99 0.090516 -1.585039 True KNG" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# if we configured everything above properly, \n", - "# the default pandas display should be using the dex.v1 media type\n", - "df" - ] - }, - { - "cell_type": "markdown", - "id": "2a2165d3-d664-4e6b-b84a-0460c58534de", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Subclass `pd.DataFrame` and start providing convenience methods for DEX displays\n", - "\n", - "this is definitely a work in progress and subject to tons of change" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "4ed4eef5-070b-4686-8f69-a19104cd19b6", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:08:46.648Z", - "iopub.status.busy": "2022-04-22T22:08:46.628Z", - "iopub.status.idle": "2022-04-22T22:08:46.685Z", - "shell.execute_reply": "2022-04-22T22:08:46.585Z" - }, - "noteable": { - "cell_type": "python" - } - }, - "outputs": [], - "source": [ - "# toggle this to show/hide the payloads sent to `IPython.display`\n", - "SHOW_PAYLOADS = False" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "d31c6f72-ea07-4834-b9d6-f121bb6937c7", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:15:24.505Z", - "iopub.status.busy": "2022-04-22T22:15:24.478Z", - "iopub.status.idle": "2022-04-22T22:15:24.526Z", - "shell.execute_reply": "2022-04-22T22:15:24.446Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [], - "source": [ - "from IPython.display import Markdown\n", - "\n", - "class DEXDataFrame(pd.DataFrame):\n", - " \"\"\"Subclass DataFrame to provide DEX-focused convenience methods\"\"\"\n", - " \n", - " _display_index = False\n", - " media_type = DEX_MEDIA_TYPE\n", - " \n", - " def display(self, media_type: Optional[str] = None, index: bool = False):\n", - " \"\"\"Render DEXDataFrame based on provided media type.\"\"\"\n", - " media_type = media_type or self.media_type\n", - " self._display_index = index\n", - " \n", - " if media_type != self.media_type:\n", - " # non-default media type; use default orient\n", - " data = self.to_dict(orient=\"records\")\n", - " else:\n", - " # we can't use `.to_dict(orient='list')` here since that would return a dictionary of {column: [values]} pairs\n", - " transform = self.transpose\n", - " if self._display_index:\n", - " transform = self.reset_index().transpose\n", - " data = transform().values.tolist()\n", - " \n", - " payload = {\n", - " \"schema\": self.table_schema,\n", - " \"data\": data,\n", - " #\"summary_statistics\": {},\n", - " #\"dx-seed\": {},\n", - " }\n", - " \n", - " ### ONLY FOR TESTING - REMOVE THIS LATER ###\n", - " if SHOW_PAYLOADS:\n", - " ipydisplay(Markdown(f\"\"\"sending the following to `IPython.display.display` :\n", - "```python\n", - "{pprint.pformat({media_type: payload})}\n", - "```\"\"\"))\n", - " ### --- ###\n", - " \n", - " ipydisplay({media_type: payload}, raw=True)\n", - " return\n", - " \n", - " @property\n", - " def table_schema(self):\n", - " return build_table_schema(self, index=self._display_index)\n", - " \n", - " \n", - "def display(df: pd.DataFrame, media_type: Optional[str] = None, index: bool = False) -> None:\n", - " \"\"\"Convenience function to allow calling `dx.display(df)` on a pandas Dataframe.\"\"\"\n", - " \n", - " # TODO: handle when `df` isn't an instance of `pd.DataFrame`\n", - " # - read files? \n", - " # - pd.json_normalize()?\n", - " # - ...other data parsing?\n", - " # should be imported from `dx/__init__.py` to allow `dx.display(df)`\n", - " \n", - " return DEXDataFrame(df).display(media_type=media_type, index=index)" - ] - }, - { - "cell_type": "markdown", - "id": "48244d4c-114a-4a71-9ea8-fb167f15505c", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "# Now let's see if `display(df)` works" - ] - }, - { - "cell_type": "markdown", - "id": "f7845e9d-6bfb-402f-b706-97bec75ff930", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "## first test a `pd.DataFrame`" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "129b8f94-fc52-4aff-a37b-c756a5e020a7", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:04:29.768Z", - "iopub.status.busy": "2022-04-22T22:04:29.748Z", - "iopub.status.idle": "2022-04-22T22:04:29.793Z", - "shell.execute_reply": "2022-04-22T22:04:29.701Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "data": { - "application/vnd.dataresource+json": { - "data": [ - { - "bool_col": false, - "index": 0, - "numcol_1": 74, - "numcol_2": 0.1913603692, - "numcol_3": 5.6958215179, - "str_col": "JPR" - }, - { - "bool_col": true, - "index": 1, - "numcol_1": 64, - "numcol_2": 0.1012414715, - "numcol_3": -5.320791875, - 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} - ], - "source": [ - "display(df) # media_type=DEX_MEDIA_TYPE by default" - ] - }, - { - "cell_type": "markdown", - "id": "7bf3ac23-42d6-4f07-ab2a-5a0dc86c726d", - "metadata": { - "noteable": { - "cell_type": "markdown" - } - }, - "source": [ - "## test with `DEXDataFrame` subclass\n", - "...which ultimately shouldn't make any difference for now" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6ed67317-5747-4ae6-883c-2e33cb798a43", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:15:39.771Z", - "iopub.status.busy": "2022-04-22T22:15:39.752Z", - "iopub.status.idle": "2022-04-22T22:15:39.792Z", - "shell.execute_reply": "2022-04-22T22:15:39.685Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "data": { - "application/vnd.dataresource+json": { - "data": [ - { - "bool_col": false, - "index": 0, - "numcol_1": 74, - "numcol_2": 0.1913603692, - "numcol_3": 5.6958215179, - 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0740.1913605.695822FalseJPR
1640.101241-5.320792TrueEWM
2190.9536910.000000FalseWUL
3680.9794030.408580FalseVFB
4900.594331-8.534415TrueEBG
5200.789158-0.651252FalsePYX
640.9899740.095438FalseSVN
790.045342-3.168897TrueZOH
8800.4917790.843663FalseLRA
9660.0257866.974413FalseEHI
10720.1622652.933297FalseBJO
11150.5719930.000000FalseIWL
12810.4728692.494679TrueIAB
13910.9559283.187540TrueUPM
14470.2992664.390156TrueKPH
15300.3431860.670349FalseCAV
16310.6648660.188203FalseXLJ
17480.650348-2.807652TrueGVI
18760.771272-8.966495FalseZED
19460.538051-8.505276TrueKMA
20770.8875990.544548TruePMG
2140.508288-0.494598TrueVQE
22440.7348162.478881TrueKZV
23870.1114970.295398FalsePDI
24590.2538470.080263FalseDKS
25110.8860850.053753FalseDVC
26800.052328-7.935371TrueGZM
27210.4747455.841084TrueLXF
28820.829580-0.898644TrueBTN
29370.996615-2.951766TrueCXJ
30760.3874455.002675TrueKGO
31530.874072-4.905907FalseMSP
32670.4705297.847990TrueQMV
33400.487268-0.931058FalseCWD
34160.7986822.278133TrueLDJ
35730.2303121.979176FalseYRD
36770.1530763.288681FalseIDF
37630.230537-0.960627TrueRSC
38730.166375-2.006404FalseGPB
39410.3420094.387755TrueXBD
40250.6181534.088533FalseTDA
41910.864688-8.360162TruePQM
42270.0006600.461835TrueIOY
43290.4454330.016063TrueGQT
44750.175075-3.698835TrueMLN
45860.7006604.739397TrueADN
46530.8947393.832789FalseXFY
47650.0897041.458376TrueGAE
48510.3420333.255475TrueIFY
49500.698682-0.686319TrueUZJ
50710.572828-2.036156TrueAHJ
51840.1831652.375698TrueNJR
52230.599238-1.022665TrueLRW
53700.506683-0.734115TrueGPZ
54830.994871-0.525338FalseBKJ
5510.5570221.829048FalseQZS
56780.141443-0.202524TrueISU
57530.3912045.005505TrueRXL
58750.053264-6.254004TrueFUJ
59380.5660241.119520FalsePKQ
60500.7943250.404127FalsePFI
61430.7432676.386115FalseYCR
62750.9263980.407851TrueBNA
63280.131768-3.610070FalseGPU
64270.631291-1.341139FalseZJO
65200.024985-5.815052TrueDUM
6640.7661951.221918TrueYES
67410.507810-8.372200TrueEWI
68210.622641-0.865017TrueRZO
69390.0128161.722463FalsePTV
70170.162465-0.681979TrueAWF
7170.7084180.000000TrueJSF
7250.742636-1.559058TrueTRV
73410.4835390.000000TrueXNA
74960.2078440.004276TrueMYX
75470.4616706.307645FalseCUQ
76210.6435188.773289TrueBCU
77650.928854-1.488954FalseQNB
78160.0000513.139456TrueAYL
79940.0187633.950667TrueXBF
80970.342332-2.341112TrueWPZ
81490.3429891.693290TrueWEM
82920.4173290.000000TrueXIS
83380.1810044.505464FalsePDK
84330.349696-4.793207FalseWTC
85760.852470-8.959847FalseZMC
86250.6828800.296378FalseVDL
87630.1337212.490155TrueMOC
88890.785527-2.977299FalseENL
89470.466288-0.102588TrueWSE
90610.8693902.515040TrueTKZ
91740.963925-0.323752FalseLGU
92720.709322-1.387102FalseKVC
9310.8635910.096374FalseDEG
9400.3057297.854363FalseGSH
95140.5818093.963542TrueXBV
96840.400504-0.810713FalseLMH
97910.0727410.747695TrueYFU
98630.695356-0.867379TrueUEO
99990.090516-1.585039TrueKNG
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"23 87 0.111497 0.295398 False PDI\n", - "24 59 0.253847 0.080263 False DKS\n", - "25 11 0.886085 0.053753 False DVC\n", - "26 80 0.052328 -7.935371 True GZM\n", - "27 21 0.474745 5.841084 True LXF\n", - "28 82 0.829580 -0.898644 True BTN\n", - "29 37 0.996615 -2.951766 True CXJ\n", - "30 76 0.387445 5.002675 True KGO\n", - "31 53 0.874072 -4.905907 False MSP\n", - "32 67 0.470529 7.847990 True QMV\n", - "33 40 0.487268 -0.931058 False CWD\n", - "34 16 0.798682 2.278133 True LDJ\n", - "35 73 0.230312 1.979176 False YRD\n", - "36 77 0.153076 3.288681 False IDF\n", - "37 63 0.230537 -0.960627 True RSC\n", - "38 73 0.166375 -2.006404 False GPB\n", - "39 41 0.342009 4.387755 True XBD\n", - "40 25 0.618153 4.088533 False TDA\n", - "41 91 0.864688 -8.360162 True PQM\n", - "42 27 0.000660 0.461835 True IOY\n", - "43 29 0.445433 0.016063 True GQT\n", - "44 75 0.175075 -3.698835 True MLN\n", - "45 86 0.700660 4.739397 True ADN\n", - "46 53 0.894739 3.832789 False XFY\n", - "47 65 0.089704 1.458376 True GAE\n", - "48 51 0.342033 3.255475 True IFY\n", - "49 50 0.698682 -0.686319 True UZJ\n", - "50 71 0.572828 -2.036156 True AHJ\n", - "51 84 0.183165 2.375698 True NJR\n", - "52 23 0.599238 -1.022665 True LRW\n", - "53 70 0.506683 -0.734115 True GPZ\n", - "54 83 0.994871 -0.525338 False BKJ\n", - "55 1 0.557022 1.829048 False QZS\n", - "56 78 0.141443 -0.202524 True ISU\n", - "57 53 0.391204 5.005505 True RXL\n", - "58 75 0.053264 -6.254004 True FUJ\n", - "59 38 0.566024 1.119520 False PKQ\n", - "60 50 0.794325 0.404127 False PFI\n", - "61 43 0.743267 6.386115 False YCR\n", - "62 75 0.926398 0.407851 True BNA\n", - "63 28 0.131768 -3.610070 False GPU\n", - "64 27 0.631291 -1.341139 False ZJO\n", - "65 20 0.024985 -5.815052 True DUM\n", - "66 4 0.766195 1.221918 True YES\n", - "67 41 0.507810 -8.372200 True EWI\n", - "68 21 0.622641 -0.865017 True RZO\n", - "69 39 0.012816 1.722463 False PTV\n", - "70 17 0.162465 -0.681979 True AWF\n", - "71 7 0.708418 0.000000 True JSF\n", - "72 5 0.742636 -1.559058 True TRV\n", - "73 41 0.483539 0.000000 True XNA\n", - "74 96 0.207844 0.004276 True MYX\n", - "75 47 0.461670 6.307645 False CUQ\n", - "76 21 0.643518 8.773289 True BCU\n", - "77 65 0.928854 -1.488954 False QNB\n", - "78 16 0.000051 3.139456 True AYL\n", - "79 94 0.018763 3.950667 True XBF\n", - "80 97 0.342332 -2.341112 True WPZ\n", - "81 49 0.342989 1.693290 True WEM\n", - "82 92 0.417329 0.000000 True XIS\n", - "83 38 0.181004 4.505464 False PDK\n", - "84 33 0.349696 -4.793207 False WTC\n", - "85 76 0.852470 -8.959847 False ZMC\n", - "86 25 0.682880 0.296378 False VDL\n", - "87 63 0.133721 2.490155 True MOC\n", - "88 89 0.785527 -2.977299 False ENL\n", - "89 47 0.466288 -0.102588 True WSE\n", - "90 61 0.869390 2.515040 True TKZ\n", - "91 74 0.963925 -0.323752 False LGU\n", - "92 72 0.709322 -1.387102 False KVC\n", - "93 1 0.863591 0.096374 False DEG\n", - "94 0 0.305729 7.854363 False GSH\n", - "95 14 0.581809 3.963542 True XBV\n", - "96 84 0.400504 -0.810713 False LMH\n", - "97 91 0.072741 0.747695 True YFU\n", - "98 63 0.695356 -0.867379 True UEO\n", - "99 99 0.090516 -1.585039 True KNG" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# ...and using the `dx.display()` should render the same\n", - "ddf = DEXDataFrame(data)\n", - "ddf" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "2f95c589-0b01-4285-875a-24e252dfc271", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-22T22:15:39.847Z", - "iopub.status.busy": "2022-04-22T22:15:39.828Z", - "iopub.status.idle": "2022-04-22T22:15:39.900Z", - "shell.execute_reply": "2022-04-22T22:15:39.704Z" - }, - "jupyter": { - "outputs_hidden": false, - "source_hidden": false - }, - "noteable": { - "cell_type": "code" - } - }, - "outputs": [ - { - "data": { - "text/markdown": [ - "sending the following to `IPython.display.display` :\n", - "```python\n", - "{'application/vnd.dataresource+json': {'data': [{'bool_col': False,\n", - " 'numcol_1': 74,\n", - " 'numcol_2': 0.19136036918636945,\n", - 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