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Matplotlib/demo_dash.py

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+# from dash import Dash, html, dcc, callback, Output, Input
+import plotly.express as px
+import pandas as pd
+
+df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
+
+app = Dash(__name__)
+
+app.layout = html.Div([
+    html.H1(children='year wise population', style={'textAlign':'center'}),
+    dcc.Dropdown(df.country.unique(), 'Brazil', id='dropdown-selection'),
+    dcc.Graph(id='graph-content')
+])
+
+@callback(
+    Output('graph-content', 'figure'),
+    Input('dropdown-selection', 'value')
+)
+def update_graph(value):
+    dff = df[df.country==value]
+    return px.line(dff, x='year', y='pop')
+
+if __name__ == '__main__':
+    app.run(debug=True)

Matplotlib/scatter_plot.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# scatter(x, y)\n",
+    "\n",
+    "See `~matplotlib.axes.Axes.scatter`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "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": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.style.use('_mpl-gallery')\n",
+    "\n",
+    "# make the data\n",
+    "np.random.seed(3)\n",
+    "x = 4 + np.random.normal(0, 2, 24)\n",
+    "y = 4 + np.random.normal(0, 2, len(x))\n",
+    "# size and color:\n",
+    "sizes = np.random.uniform(15, 80, len(x))\n",
+    "colors = np.random.uniform(15, 80, len(x))\n",
+    "\n",
+    "# plot\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.scatter(x, y, s=sizes, c=colors, vmin=0, vmax=100)\n",
+    "\n",
+    "ax.set(xlim=(0, 8), xticks=np.arange(1, 8),\n",
+    "       ylim=(0, 8), yticks=np.arange(1, 8))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}