test: matplotlib backend side-effects

tests/notebooks/notebook_plotting.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import pytest\n",
+ "\n",
+ "from pandas_profiling import ProfileReport\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import requests\n",
+ "from IPython.display import display\n",
+ "from IPython.utils.capture import capture_output, CapturedIO\n",
+ "import IPython.utils\n",
+ "\n",
+ "\n",
+ "import pandas_profiling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## import matplot lib after importing pandas profiling to recreate Issue #888, 837\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with capture_output() as out_matplot:\n",
+ " plt.plot([1, 2, 3, 4])\n",
+ " plt.ylabel('some numbers in a pyplot')\n",
+ " plt.show()\n",
+ " \n",
+ "assert len(out_matplot.outputs) == 1\n",
+ "assert \"<Figure\" in out_matplot.outputs[0].data[\"text/plain\"] \n",
+ "assert len(out_matplot.outputs[0].data[\"image/png\"]) > 0 #assert an image actually exists\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.utils import capture\n",
+ "from IPython.display import Image, display\n",
+ "\n",
+ "with capture.capture_output() as cap:\n",
+ " \n",
+ " df = pd.DataFrame(data={\"col1\": [1, 2]})\n",
+ " display(Image(df.plot(kind=\"line\", subplots=True, sharex=True, legend=True)))\n",
+ "assert len(cap.outputs[0].data) == 2\n",
+ "assert \"IPython.core.display.Image\" in cap.outputs[0].data[\"text/plain\"]\n",
+ "assert len(cap.outputs[0].data[\"image/png\"]) > 0 #assert an image actually exists"
+ ]
+ }
+ ],
+ "metadata": {
+ "file_extension": ".py",
+ "kernelspec": {
+ "display_name": "Python 3.9.9 64-bit ('venv': venv)",
+ "language": "python",
+ "name": "python39964bitvenvvenv9a8385474b4d41148e4fbaedabba2862"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.9"
+ },
+ "mimetype": "text/x-python",
+ "name": "python",
+ "npconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}