197 lines
4 KiB
Text
197 lines
4 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0aac5da7-745c-4eda-847a-3d0d07a1bb9b",
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"metadata": {
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"tags": []
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},
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"source": [
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"# This is a markdown cell\n",
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"\n",
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"This paragraph has\n",
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"With\n",
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"Many\n",
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"Lines. How we will he handle MR notes?\n",
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"\n",
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"But I can add another paragraph"
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]
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},
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{
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"cell_type": "raw",
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"id": "faecea5b-de0a-49fa-9a3a-61c2add652da",
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"metadata": {},
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"source": [
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"This is a raw cell\n",
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"With\n",
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"Multiple lines"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "893ca2c0-ab75-4276-9dad-be1c40e16e8a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "0d707fb5-226f-46d6-80bd-489ebfb8905c",
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(42)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "35467fcf-28b1-4c7b-bb09-4cb192c35293",
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"metadata": {
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"tags": [
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"senoid"
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]
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}, "outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x123e39370>]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "some_invalid_base64_image_here\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"x = np.linspace(0, 4*np.pi,50)\n",
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"y = np.sin(x)\n",
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"\n",
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"plt.plot(x, y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "dc1178cd-c46d-4da3-9ab5-08f000699884",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame({\"x\": x, \"y\": y})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "6e749b4f-b409-4700-870f-f68c39462490",
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"metadata": {
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"tags": [
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"some-table"
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]
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>x</th>\n",
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" <th>y</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.256457</td>\n",
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" <td>0.253655</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" x y\n",
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"0 0.000000 0.000000\n",
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"1 0.256457 0.253655"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df[:2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0ddef5ef-94a3-4afd-9c70-ddee9694f512",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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},
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"toc-showtags": true
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"nbformat": 4,
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"nbformat_minor": 5
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}
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