{ "metadata": { "orig_nbformat": 4, "language_info": { "codemirror_mode": { "name": "python", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8" }, "kernelspec": { "name": "python", "display_name": "Pyolite", "language": "python" } }, "nbformat_minor": 4, "nbformat": 4, "cells": [ { "cell_type": "code", "source": "import micropip\nawait micropip.install('ipywidgets')\nawait micropip.install('requests')\nfrom ipywidgets import interact, interactive, fixed, interact_manual\nimport ipywidgets as widgets\nimport requests # Import the requests library\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nprint(\"Done\")\n", "metadata": { "trusted": true }, "execution_count": 1, "outputs": [ { "name": "stderr", "text": "/lib/python3.9/site-packages/pandas/compat/__init__.py:117: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n warnings.warn(msg)\n", "output_type": "stream" }, { "name": "stdout", "text": "Done\n", "output_type": "stream" } ] }, { "cell_type": "code", "source": "import json\nfrom pyodide import to_js\nfrom IPython.display import JSON\nfrom js import Object, fetch\nprint(\"Done\")", "metadata": { "trusted": true }, "execution_count": 2, "outputs": [ { "name": "stdout", "text": "Done\n", "output_type": "stream" } ] }, { "cell_type": "code", "source": "from IPython.core.display import display, HTML\ndisplay(HTML(\"<style>.toggle-button { display :none !important; }</style>\"))\ndisplay(HTML(\"<style>.toggle-button-hidden { display :none !important; }</style>\"))\ndisplay(HTML(\"<style>.button.toggle-button { display :none !important; }</style>\"))\ndisplay(HTML(\"<style>.fas fa-download { display :none !important; }</style>\"))\n\nresp = await fetch('http://localhost:5000/api/districtstotal?startdate=2021-08-31&enddate=2021-09-31',\n method=\"GET\",\n headers=Object.fromEntries(to_js({ \"Content-Type\": \"application/json\" })),\n)\nres = await resp.text()\npayload =json.loads(res)\n#print(payload)\n", "metadata": { "tags": [ "hide_input", "hide_output" ], "trusted": true }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": "data=pd.json_normalize(payload['data'])\nselected=data[[\"datetext\", \"counttext\",\"location.formattedAddress\"]]\nprint('Dataset feactched as selected dataframe')\n#print(selected)\n#list(selected.columns.values)", "metadata": { "trusted": true }, "execution_count": 4, "outputs": [ { "name": "stdout", "text": "Dataset feactched as selected dataframe\n", "output_type": "stream" } ] }, { "cell_type": "code", "source": "filtedVals= selected[selected['location.formattedAddress'].str.contains('Nuwara Eliya, Sri Lanka')]\nprint('By defult Nuwara Eliya District selected')\nprint('For other district select it form the dropdown below >>>')\n\n\n\ndef f(x):\n print(\"District changed to %s\" % x)\n filtedVals=selected[selected['location.formattedAddress'].str.contains(x)]\n print(filtedVals)\n pivoted = pd.DataFrame(filtedVals.pivot_table(values='counttext', index='datetext', columns='location.formattedAddress', aggfunc='sum'))\n return pivoted\n\ninteract(f, x=['Nuwara Eliya, Sri Lanka', 'Badulla, Sri Lanka', 'Kurunegala, Sri Lanka']);\n\n", "metadata": { "trusted": true }, "execution_count": 5, "outputs": [ { "name": "stdout", "text": "By defult Nuwara Eliya District selected\nFor other district select it form the dropdown below >>>\n", "output_type": "stream" }, { "output_type": "display_data", "data": { "text/plain": "interactive(children=(Dropdown(description='x', options=('Nuwara Eliya, Sri Lanka', 'Badulla, Sri Lanka', 'Kur…", "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "74bec51337a640919336c1c1689650d7" } }, "metadata": {} } ] }, { "cell_type": "code", "source": "", "metadata": {}, "execution_count": null, "outputs": [] } ] }