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jaiswalakshay508-maker/data visulisa.ipynb

Created May 3, 2026 12:59
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data visulisa.ipynb
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T06:58:19.539894Z",
"start_time": "2025-12-20T06:58:19.531434Z"
}
},
"cell_type": "code",
"source": [
"from cProfile import label\n",
"from random import random\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from unicodedata import category"
],
"id": "b8762d6005af7741",
"outputs": [],
"execution_count": 2
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Scatter Plot",
"id": "475ca88fe5e6e15e"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1.Plot a scatter plot showing the relation",
"id": "bbf9c0d3b395c709"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T12:38:05.066313Z",
"start_time": "2025-12-18T12:38:04.793728Z"
}
},
"cell_type": "code",
"source": [
"temperature=[10,20,30,40,50]\n",
"tea_sales=[250,200,150,100,50]\n",
"plt.grid()\n",
"plt.title(\"relationship between temperature and sales\")\n",
"plt.xlabel(\"Temperature\")\n",
"plt.ylabel(\"Tea Sales\")\n",
"plt.show()\n",
"print(\"above scatter plot shows that if temperature and sales and temperature and sales are the same\")"
],
"id": "c51a9756008334ba",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"above scatter plot shows that if temperature and sales and temperature and sales are the same\n"
]
}
],
"execution_count": 3
},
{
"metadata": {},
"cell_type": "markdown",
"source": "2. Using the list",
"id": "8506cb1d313eb905"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T13:11:12.687587Z",
"start_time": "2025-12-18T13:11:12.395324Z"
}
},
"cell_type": "code",
"source": [
"temp=[10,20,30,40,50,60]\n",
"ice=[20,50,80,90,100,300]\n",
"\n",
"plt.scatter(temp,ice)\n",
"plt.grid()\n",
"plt.legend([\"Temperature\",\"Ice\"])\n",
"plt.title(\"relationship between temperature and sales\")\n",
"plt.xlabel(\"Temperature\")\n",
"plt.ylabel(\"Ice\")\n",
"plt.show()"
],
"id": "28afa90a70793be8",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 4
},
{
"metadata": {},
"cell_type": "markdown",
"source": "3.Create a scatter plot between units sold & total sales",
"id": "2162ea733a14312f"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T13:20:56.773662Z",
"start_time": "2025-12-18T13:20:55.287278Z"
}
},
"cell_type": "code",
"source": [
"adidas_df=pd.read_excel(\"Adidas.xlsx\")\n",
"adidas_df['total sales']=adidas_df['units sold']\n",
"adidas_df.head(5)\n",
"plt.scatter(adidas_df['units sold'],adidas_df['total sales'])\n",
"plt.grid()\n",
"plt.legend([\"units sold\",\"total sales\"])\n",
"plt.title(\"relationship between units sold and total sales\")\n",
"plt.xlabel(\"units sold\")\n",
"plt.ylabel(\"total sales\")\n",
"plt.show()"
],
"id": "4bf96c28cbcd612e",
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'Adidas.xlsx'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mFileNotFoundError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[5]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m adidas_df=\u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_excel\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mAdidas.xlsx\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 2\u001B[39m adidas_df[\u001B[33m'\u001B[39m\u001B[33mtotal sales\u001B[39m\u001B[33m'\u001B[39m]=adidas_df[\u001B[33m'\u001B[39m\u001B[33munits sold\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m 3\u001B[39m adidas_df.head(\u001B[32m5\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:495\u001B[39m, in \u001B[36mread_excel\u001B[39m\u001B[34m(io, sheet_name, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, parse_dates, date_parser, date_format, thousands, decimal, comment, skipfooter, storage_options, dtype_backend, engine_kwargs)\u001B[39m\n\u001B[32m 493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(io, ExcelFile):\n\u001B[32m 494\u001B[39m should_close = \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m495\u001B[39m io = \u001B[43mExcelFile\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 496\u001B[39m \u001B[43m \u001B[49m\u001B[43mio\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 497\u001B[39m \u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 498\u001B[39m \u001B[43m \u001B[49m\u001B[43mengine\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 499\u001B[39m \u001B[43m \u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mengine_kwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 500\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 501\u001B[39m \u001B[38;5;28;01melif\u001B[39;00m engine \u001B[38;5;129;01mand\u001B[39;00m engine != io.engine:\n\u001B[32m 502\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 503\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mEngine should not be specified when passing \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 504\u001B[39m \u001B[33m\"\u001B[39m\u001B[33man ExcelFile - ExcelFile already has the engine set\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 505\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1550\u001B[39m, in \u001B[36mExcelFile.__init__\u001B[39m\u001B[34m(self, path_or_buffer, engine, storage_options, engine_kwargs)\u001B[39m\n\u001B[32m 1548\u001B[39m ext = \u001B[33m\"\u001B[39m\u001B[33mxls\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 1549\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1550\u001B[39m ext = \u001B[43minspect_excel_format\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1551\u001B[39m \u001B[43m \u001B[49m\u001B[43mcontent_or_path\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\n\u001B[32m 1552\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1553\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m ext \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 1554\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 1555\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mExcel file format cannot be determined, you must specify \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 1556\u001B[39m \u001B[33m\"\u001B[39m\u001B[33man engine manually.\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 1557\u001B[39m )\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\excel\\_base.py:1402\u001B[39m, in \u001B[36minspect_excel_format\u001B[39m\u001B[34m(content_or_path, storage_options)\u001B[39m\n\u001B[32m 1399\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(content_or_path, \u001B[38;5;28mbytes\u001B[39m):\n\u001B[32m 1400\u001B[39m content_or_path = BytesIO(content_or_path)\n\u001B[32m-> \u001B[39m\u001B[32m1402\u001B[39m \u001B[38;5;28;01mwith\u001B[39;00m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1403\u001B[39m \u001B[43m \u001B[49m\u001B[43mcontent_or_path\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mrb\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mis_text\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\n\u001B[32m 1404\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;28;01mas\u001B[39;00m handle:\n\u001B[32m 1405\u001B[39m stream = handle.handle\n\u001B[32m 1406\u001B[39m stream.seek(\u001B[32m0\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:882\u001B[39m, in \u001B[36mget_handle\u001B[39m\u001B[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[39m\n\u001B[32m 873\u001B[39m handle = \u001B[38;5;28mopen\u001B[39m(\n\u001B[32m 874\u001B[39m handle,\n\u001B[32m 875\u001B[39m ioargs.mode,\n\u001B[32m (...)\u001B[39m\u001B[32m 878\u001B[39m newline=\u001B[33m\"\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 879\u001B[39m )\n\u001B[32m 880\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 881\u001B[39m \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m882\u001B[39m handle = \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mioargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mmode\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 883\u001B[39m handles.append(handle)\n\u001B[32m 885\u001B[39m \u001B[38;5;66;03m# Convert BytesIO or file objects passed with an encoding\u001B[39;00m\n",
"\u001B[31mFileNotFoundError\u001B[39m: [Errno 2] No such file or directory: 'Adidas.xlsx'"
]
}
],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "markdown",
"source": "5.from the sleep dataset ,plot sleep duration",
"id": "2555a36c0c8d5b5"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T13:48:43.276987Z",
"start_time": "2025-12-18T13:48:43.052750Z"
}
},
"cell_type": "code",
"source": [
"df=pd.read_csv(\"sleep_health_and_lifestyle_dataset.csv\")\n",
"plt.scatter(df['sleep duration'],df['sleep duration'])\n",
"plt.grid()\n",
"plt.legend([\"sleep duration\",\"sleep duration\"])\n",
"plt.title(\"relationship between sleep duration vs quantity of sleep\")\n",
"plt.xlabel(\"sleep duration\")\n",
"plt.ylabel(\"quantity of sleep\")\n",
"plt.show()"
],
"id": "c4c59f1fa9c66bed",
"outputs": [
{
"ename": "KeyError",
"evalue": "'sleep duration'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mKeyError\u001B[39m Traceback (most recent call last)",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m 3811\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m3812\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_engine\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 3813\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:167\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/index.pyx:196\u001B[39m, in \u001B[36mpandas._libs.index.IndexEngine.get_loc\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7088\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/hashtable_class_helper.pxi:7096\u001B[39m, in \u001B[36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[31mKeyError\u001B[39m: 'sleep duration'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001B[31mKeyError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[18]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m 1\u001B[39m df=pd.read_csv(\u001B[33m\"\u001B[39m\u001B[33msleep_health_and_lifestyle_dataset.csv\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m plt.scatter(\u001B[43mdf\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43msleep duration\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m,df[\u001B[33m'\u001B[39m\u001B[33msleep duration\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 3\u001B[39m plt.grid()\n\u001B[32m 4\u001B[39m plt.legend([\u001B[33m\"\u001B[39m\u001B[33msleep duration\u001B[39m\u001B[33m\"\u001B[39m,\u001B[33m\"\u001B[39m\u001B[33msleep duration\u001B[39m\u001B[33m\"\u001B[39m])\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:4113\u001B[39m, in \u001B[36mDataFrame.__getitem__\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m 4111\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.columns.nlevels > \u001B[32m1\u001B[39m:\n\u001B[32m 4112\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m._getitem_multilevel(key)\n\u001B[32m-> \u001B[39m\u001B[32m4113\u001B[39m indexer = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mcolumns\u001B[49m\u001B[43m.\u001B[49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 4114\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[32m 4115\u001B[39m indexer = [indexer]\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3819\u001B[39m, in \u001B[36mIndex.get_loc\u001B[39m\u001B[34m(self, key)\u001B[39m\n\u001B[32m 3814\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[32m 3815\u001B[39m \u001B[38;5;28misinstance\u001B[39m(casted_key, abc.Iterable)\n\u001B[32m 3816\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[32m 3817\u001B[39m ):\n\u001B[32m 3818\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[32m-> \u001B[39m\u001B[32m3819\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m 3820\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[32m 3821\u001B[39m \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[32m 3822\u001B[39m \u001B[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[32m 3823\u001B[39m \u001B[38;5;66;03m# the TypeError.\u001B[39;00m\n\u001B[32m 3824\u001B[39m \u001B[38;5;28mself\u001B[39m._check_indexing_error(key)\n",
"\u001B[31mKeyError\u001B[39m: 'sleep duration'"
]
}
],
"execution_count": 18
},
{
"metadata": {},
"cell_type": "markdown",
"source": "8.Plot a scatter plot showing iphone vs sumsung",
"id": "239dc370917a4fe6"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T14:21:34.307618Z",
"start_time": "2025-12-18T14:21:33.896609Z"
}
},
"cell_type": "code",
"source": [
"months=[\"Jan\",\"feb\",\"Mar\",\"Apr\",\"May\"]\n",
"iphone=[120,130,140,150,160,170]\n",
"samsung=[150,160,155,170,175,190]\n",
"plt.scatter(months,iphone,label='iphone')\n",
"plt.scatter(months,samsung,label='samsung')\n",
"plt.title(\"iphone vs samsung vs month\")\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"sales\")\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
],
"id": "a23a6bf76185df98",
"outputs": [
{
"ename": "ValueError",
"evalue": "x and y must be the same size",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[21]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m 2\u001B[39m iphone=[\u001B[32m120\u001B[39m,\u001B[32m130\u001B[39m,\u001B[32m140\u001B[39m,\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m170\u001B[39m]\n\u001B[32m 3\u001B[39m samsung=[\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m155\u001B[39m,\u001B[32m170\u001B[39m,\u001B[32m175\u001B[39m,\u001B[32m190\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mscatter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonths\u001B[49m\u001B[43m,\u001B[49m\u001B[43miphone\u001B[49m\u001B[43m,\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43miphone\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 5\u001B[39m plt.scatter(months,samsung,label=\u001B[33m'\u001B[39m\u001B[33msamsung\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 6\u001B[39m plt.title(\u001B[33m\"\u001B[39m\u001B[33miphone vs samsung vs month\u001B[39m\u001B[33m\"\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3948\u001B[39m, in \u001B[36mscatter\u001B[39m\u001B[34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, data, **kwargs)\u001B[39m\n\u001B[32m 3928\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.scatter)\n\u001B[32m 3929\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mscatter\u001B[39m(\n\u001B[32m 3930\u001B[39m x: \u001B[38;5;28mfloat\u001B[39m | ArrayLike,\n\u001B[32m (...)\u001B[39m\u001B[32m 3946\u001B[39m **kwargs,\n\u001B[32m 3947\u001B[39m ) -> PathCollection:\n\u001B[32m-> \u001B[39m\u001B[32m3948\u001B[39m __ret = \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mscatter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 3949\u001B[39m \u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3950\u001B[39m \u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3951\u001B[39m \u001B[43m \u001B[49m\u001B[43ms\u001B[49m\u001B[43m=\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3952\u001B[39m \u001B[43m \u001B[49m\u001B[43mc\u001B[49m\u001B[43m=\u001B[49m\u001B[43mc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3953\u001B[39m \u001B[43m \u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3954\u001B[39m \u001B[43m \u001B[49m\u001B[43mcmap\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcmap\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3955\u001B[39m \u001B[43m \u001B[49m\u001B[43mnorm\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnorm\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3956\u001B[39m \u001B[43m \u001B[49m\u001B[43mvmin\u001B[49m\u001B[43m=\u001B[49m\u001B[43mvmin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3957\u001B[39m \u001B[43m \u001B[49m\u001B[43mvmax\u001B[49m\u001B[43m=\u001B[49m\u001B[43mvmax\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3958\u001B[39m \u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[43m=\u001B[49m\u001B[43malpha\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3959\u001B[39m \u001B[43m \u001B[49m\u001B[43mlinewidths\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlinewidths\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3960\u001B[39m \u001B[43m \u001B[49m\u001B[43medgecolors\u001B[49m\u001B[43m=\u001B[49m\u001B[43medgecolors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3961\u001B[39m \u001B[43m \u001B[49m\u001B[43mcolorizer\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcolorizer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3962\u001B[39m \u001B[43m \u001B[49m\u001B[43mplotnonfinite\u001B[49m\u001B[43m=\u001B[49m\u001B[43mplotnonfinite\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3963\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3964\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3965\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 3966\u001B[39m sci(__ret)\n\u001B[32m 3967\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m __ret\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001B[39m, in \u001B[36m_preprocess_data.<locals>.inner\u001B[39m\u001B[34m(ax, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1521\u001B[39m \u001B[38;5;129m@functools\u001B[39m.wraps(func)\n\u001B[32m 1522\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34minner\u001B[39m(ax, *args, data=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs):\n\u001B[32m 1523\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1524\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1525\u001B[39m \u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1526\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1527\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1529\u001B[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001B[32m 1530\u001B[39m auto_label = (bound.arguments.get(label_namer)\n\u001B[32m 1531\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m bound.kwargs.get(label_namer))\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:4936\u001B[39m, in \u001B[36mAxes.scatter\u001B[39m\u001B[34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, **kwargs)\u001B[39m\n\u001B[32m 4934\u001B[39m y = np.ma.ravel(y)\n\u001B[32m 4935\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.size != y.size:\n\u001B[32m-> \u001B[39m\u001B[32m4936\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mx and y must be the same size\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 4938\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m s \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 4939\u001B[39m s = (\u001B[32m20\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m mpl.rcParams[\u001B[33m'\u001B[39m\u001B[33m_internal.classic_mode\u001B[39m\u001B[33m'\u001B[39m] \u001B[38;5;28;01melse\u001B[39;00m\n\u001B[32m 4940\u001B[39m mpl.rcParams[\u001B[33m'\u001B[39m\u001B[33mlines.markersize\u001B[39m\u001B[33m'\u001B[39m] ** \u001B[32m2.0\u001B[39m)\n",
"\u001B[31mValueError\u001B[39m: x and y must be the same size"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 21
},
{
"metadata": {},
"cell_type": "markdown",
"source": "9. Add grid=true to a scatter plot.",
"id": "3716418af8db789a"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T14:28:24.002090Z",
"start_time": "2025-12-18T14:28:23.790144Z"
}
},
"cell_type": "code",
"source": [
"hours=[1,2,3,4,5,6]\n",
"marks=[40,45,60,65,70,85]\n",
"plt.title(\"relationship between hours vs sales\")\n",
"plt.xlabel(\"hours\")\n",
"plt.ylabel(\"marks\")\n",
"plt.show()"
],
"id": "183e7aef57c55f72",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 22
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Line chart",
"id": "79b34f69fa1097a4"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1.plot a line chart for",
"id": "428b327ec116d614"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T14:36:00.480657Z",
"start_time": "2025-12-18T14:35:58.904142Z"
}
},
"cell_type": "code",
"source": [
"month=['jan','feb','mar','apr']\n",
"isale=[100,200,50,400]\n",
"plt.plot(month,isale,marker='0')\n",
"plt.grid()\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Isale\")\n",
"plt.title(\"Isale In Month\")\n",
"plt.show()\n"
],
"id": "28190e7f176434d8",
"outputs": [
{
"ename": "ValueError",
"evalue": "Unrecognized marker style '0'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\markers.py:326\u001B[39m, in \u001B[36mMarkerStyle._set_marker\u001B[39m\u001B[34m(self, marker)\u001B[39m\n\u001B[32m 325\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m326\u001B[39m \u001B[43mPath\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 327\u001B[39m \u001B[38;5;28mself\u001B[39m._marker_function = \u001B[38;5;28mself\u001B[39m._set_vertices\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\path.py:130\u001B[39m, in \u001B[36mPath.__init__\u001B[39m\u001B[34m(self, vertices, codes, _interpolation_steps, closed, readonly)\u001B[39m\n\u001B[32m 129\u001B[39m vertices = _to_unmasked_float_array(vertices)\n\u001B[32m--> \u001B[39m\u001B[32m130\u001B[39m \u001B[43m_api\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcheck_shape\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m2\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvertices\u001B[49m\u001B[43m=\u001B[49m\u001B[43mvertices\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 132\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m codes \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(vertices):\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\__init__.py:162\u001B[39m, in \u001B[36mcheck_shape\u001B[39m\u001B[34m(shape, **kwargs)\u001B[39m\n\u001B[32m 160\u001B[39m text_shape += \u001B[33m\"\u001B[39m\u001B[33m,\u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m--> \u001B[39m\u001B[32m162\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 163\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mk\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[33m must be \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(shape)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33mD with shape (\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mtext_shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m), \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 164\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mbut your input has shape \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mv.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m\n\u001B[32m 165\u001B[39m )\n",
"\u001B[31mValueError\u001B[39m: 'vertices' must be 2D with shape (N, 2), but your input has shape ()",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[23]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m 1\u001B[39m month=[\u001B[33m'\u001B[39m\u001B[33mjan\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mfeb\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mmar\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mapr\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m 2\u001B[39m isale=[\u001B[32m100\u001B[39m,\u001B[32m200\u001B[39m,\u001B[32m50\u001B[39m,\u001B[32m400\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonth\u001B[49m\u001B[43m,\u001B[49m\u001B[43misale\u001B[49m\u001B[43m,\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 4\u001B[39m plt.grid()\n\u001B[32m 5\u001B[39m plt.xlabel(\u001B[33m\"\u001B[39m\u001B[33mMonth\u001B[39m\u001B[33m\"\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m 3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m 3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m 3832\u001B[39m *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m (...)\u001B[39m\u001B[32m 3836\u001B[39m **kwargs,\n\u001B[32m 3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 3839\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3840\u001B[39m \u001B[43m \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3841\u001B[39m \u001B[43m \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3842\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3843\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3844\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m 1536\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m 1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m 1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m 1779\u001B[39m \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m 295\u001B[39m this += args[\u001B[32m0\u001B[39m],\n\u001B[32m 296\u001B[39m args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 298\u001B[39m \u001B[43m \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 299\u001B[39m \u001B[43m \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m 300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:546\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m 544\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(result)\n\u001B[32m 545\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m--> \u001B[39m\u001B[32m546\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m[\u001B[49m\u001B[43ml\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43ml\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mresult\u001B[49m\u001B[43m]\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:539\u001B[39m, in \u001B[36m<genexpr>\u001B[39m\u001B[34m(.0)\u001B[39m\n\u001B[32m 534\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 535\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 536\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mlabel must be scalar or have the same length as the input \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 537\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mdata, but found \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(label)\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m for \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mn_datasets\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m datasets.\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m539\u001B[39m result = (\u001B[43mmake_artist\u001B[49m\u001B[43m(\u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mj\u001B[49m\u001B[43m \u001B[49m\u001B[43m%\u001B[49m\u001B[43m \u001B[49m\u001B[43mncx\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m[\u001B[49m\u001B[43m:\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mj\u001B[49m\u001B[43m \u001B[49m\u001B[43m%\u001B[49m\u001B[43m \u001B[49m\u001B[43mncy\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkw\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 540\u001B[39m \u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mlabel\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 541\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m j, label \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(labels))\n\u001B[32m 543\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m return_kwargs:\n\u001B[32m 544\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(result)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:338\u001B[39m, in \u001B[36m_process_plot_var_args._make_line\u001B[39m\u001B[34m(self, axes, x, y, kw, kwargs)\u001B[39m\n\u001B[32m 336\u001B[39m kw = {**kw, **kwargs} \u001B[38;5;66;03m# Don't modify the original kw.\u001B[39;00m\n\u001B[32m 337\u001B[39m \u001B[38;5;28mself\u001B[39m._setdefaults(\u001B[38;5;28mself\u001B[39m._getdefaults(kw), kw)\n\u001B[32m--> \u001B[39m\u001B[32m338\u001B[39m seg = \u001B[43mmlines\u001B[49m\u001B[43m.\u001B[49m\u001B[43mLine2D\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 339\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m seg, kw\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\lines.py:394\u001B[39m, in \u001B[36mLine2D.__init__\u001B[39m\u001B[34m(self, xdata, ydata, linewidth, linestyle, color, gapcolor, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001B[39m\n\u001B[32m 392\u001B[39m marker = \u001B[33m'\u001B[39m\u001B[33mnone\u001B[39m\u001B[33m'\u001B[39m \u001B[38;5;66;03m# Default.\u001B[39;00m\n\u001B[32m 393\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(marker, MarkerStyle):\n\u001B[32m--> \u001B[39m\u001B[32m394\u001B[39m \u001B[38;5;28mself\u001B[39m._marker = \u001B[43mMarkerStyle\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfillstyle\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 395\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[32m 396\u001B[39m \u001B[38;5;28mself\u001B[39m._marker = marker\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\markers.py:248\u001B[39m, in \u001B[36mMarkerStyle.__init__\u001B[39m\u001B[34m(self, marker, fillstyle, transform, capstyle, joinstyle)\u001B[39m\n\u001B[32m 246\u001B[39m \u001B[38;5;28mself\u001B[39m._user_joinstyle = JoinStyle(joinstyle) \u001B[38;5;28;01mif\u001B[39;00m joinstyle \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 247\u001B[39m \u001B[38;5;28mself\u001B[39m._set_fillstyle(fillstyle)\n\u001B[32m--> \u001B[39m\u001B[32m248\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_set_marker\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\markers.py:329\u001B[39m, in \u001B[36mMarkerStyle._set_marker\u001B[39m\u001B[34m(self, marker)\u001B[39m\n\u001B[32m 327\u001B[39m \u001B[38;5;28mself\u001B[39m._marker_function = \u001B[38;5;28mself\u001B[39m._set_vertices\n\u001B[32m 328\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[32m--> \u001B[39m\u001B[32m329\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[32m 330\u001B[39m \u001B[33mf\u001B[39m\u001B[33m'\u001B[39m\u001B[33mUnrecognized marker style \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmarker\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[33m'\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01merr\u001B[39;00m\n\u001B[32m 332\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(marker, MarkerStyle):\n\u001B[32m 333\u001B[39m \u001B[38;5;28mself\u001B[39m._marker = marker\n",
"\u001B[31mValueError\u001B[39m: Unrecognized marker style '0'"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 23
},
{
"metadata": {},
"cell_type": "markdown",
"source": "2.plot line charts for iphone vs samsung",
"id": "65d72ba128da9564"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T14:49:16.034748Z",
"start_time": "2025-12-18T14:49:15.652570Z"
}
},
"cell_type": "code",
"source": [
"months=[\"jan\",\"feb\",\"mar\",\"apr\",\"may\"]\n",
"iphone=[120,135,140,150,160,170]\n",
"samsung=[150,160,155,170,175,190]\n",
"plt.plot(months,samsung,marker='0',label='samsung')\n",
"plt.plot(months,iphone,marks='0',label='iphone')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Sales\")\n",
"plt.title(\"iphone vs samsung monthly sales\")\n",
"plt.show()"
],
"id": "c5c5815057f7046c",
"outputs": [
{
"ename": "ValueError",
"evalue": "x and y must have same first dimension, but have shapes (5,) and (6,)",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[24]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m 2\u001B[39m iphone=[\u001B[32m120\u001B[39m,\u001B[32m135\u001B[39m,\u001B[32m140\u001B[39m,\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m170\u001B[39m]\n\u001B[32m 3\u001B[39m samsung=[\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m155\u001B[39m,\u001B[32m170\u001B[39m,\u001B[32m175\u001B[39m,\u001B[32m190\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonths\u001B[49m\u001B[43m,\u001B[49m\u001B[43msamsung\u001B[49m\u001B[43m,\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43m0\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43msamsung\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 5\u001B[39m plt.plot(months,iphone,marks=\u001B[33m'\u001B[39m\u001B[33m0\u001B[39m\u001B[33m'\u001B[39m,label=\u001B[33m'\u001B[39m\u001B[33miphone\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 6\u001B[39m plt.grid()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3838\u001B[39m, in \u001B[36mplot\u001B[39m\u001B[34m(scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m 3830\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.plot)\n\u001B[32m 3831\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mplot\u001B[39m(\n\u001B[32m 3832\u001B[39m *args: \u001B[38;5;28mfloat\u001B[39m | ArrayLike | \u001B[38;5;28mstr\u001B[39m,\n\u001B[32m (...)\u001B[39m\u001B[32m 3836\u001B[39m **kwargs,\n\u001B[32m 3837\u001B[39m ) -> \u001B[38;5;28mlist\u001B[39m[Line2D]:\n\u001B[32m-> \u001B[39m\u001B[32m3838\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 3839\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3840\u001B[39m \u001B[43m \u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscalex\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3841\u001B[39m \u001B[43m \u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m=\u001B[49m\u001B[43mscaley\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3842\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3843\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3844\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1777\u001B[39m, in \u001B[36mAxes.plot\u001B[39m\u001B[34m(self, scalex, scaley, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1534\u001B[39m \u001B[38;5;250m\u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 1535\u001B[39m \u001B[33;03mPlot y versus x as lines and/or markers.\u001B[39;00m\n\u001B[32m 1536\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 1774\u001B[39m \u001B[33;03m(``'green'``) or hex strings (``'#008000'``).\u001B[39;00m\n\u001B[32m 1775\u001B[39m \u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 1776\u001B[39m kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)\n\u001B[32m-> \u001B[39m\u001B[32m1777\u001B[39m lines = [*\u001B[38;5;28mself\u001B[39m._get_lines(\u001B[38;5;28mself\u001B[39m, *args, data=data, **kwargs)]\n\u001B[32m 1778\u001B[39m \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines:\n\u001B[32m 1779\u001B[39m \u001B[38;5;28mself\u001B[39m.add_line(line)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:297\u001B[39m, in \u001B[36m_process_plot_var_args.__call__\u001B[39m\u001B[34m(self, axes, data, return_kwargs, *args, **kwargs)\u001B[39m\n\u001B[32m 295\u001B[39m this += args[\u001B[32m0\u001B[39m],\n\u001B[32m 296\u001B[39m args = args[\u001B[32m1\u001B[39m:]\n\u001B[32m--> \u001B[39m\u001B[32m297\u001B[39m \u001B[38;5;28;01myield from\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_plot_args\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 298\u001B[39m \u001B[43m \u001B[49m\u001B[43maxes\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mthis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m=\u001B[49m\u001B[43mambiguous_fmt_datakey\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 299\u001B[39m \u001B[43m \u001B[49m\u001B[43mreturn_kwargs\u001B[49m\u001B[43m=\u001B[49m\u001B[43mreturn_kwargs\u001B[49m\n\u001B[32m 300\u001B[39m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_base.py:494\u001B[39m, in \u001B[36m_process_plot_var_args._plot_args\u001B[39m\u001B[34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001B[39m\n\u001B[32m 491\u001B[39m axes.yaxis.update_units(y)\n\u001B[32m 493\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.shape[\u001B[32m0\u001B[39m] != y.shape[\u001B[32m0\u001B[39m]:\n\u001B[32m--> \u001B[39m\u001B[32m494\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y must have same first dimension, but \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 495\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mhave shapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n\u001B[32m 496\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m2\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m y.ndim > \u001B[32m2\u001B[39m:\n\u001B[32m 497\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mx and y can be no greater than 2D, but have \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 498\u001B[39m \u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mshapes \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mx.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00my.shape\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m\"\u001B[39m)\n",
"\u001B[31mValueError\u001B[39m: x and y must have same first dimension, but have shapes (5,) and (6,)"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGiCAYAAAA1LsZRAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAFbRJREFUeJzt3X+sV3X9wPHXBeUiswsmeq8QRlZGhoDxK1TW3G6yZTRabURNiKmN1pjBXIAoSJa3Mhx/gDFIVv8wqGauBcMZizUnG/MSWyyhkAzm4lcMLmFdEu5353x3b1y5F/kg8Ore+3hsZ9xz7jmfz7nXP+7T93mf86lqaWlpCQCAJL2y3hgAoCBGAIBUYgQASCVGAIBUYgQASCVGAIBUYgQASCVGAIBUYgQASCVGAICuFSO///3vY/LkyTFo0KCoqqqKF1544V2P2bJlS3zyk5+M6urq+MhHPhI//elPL/Z8AYCeHiMnT56MkSNHxooVKy5o/7/+9a9x3333xT333BM7duyIb33rW/Hggw/Giy++eDHnCwB0M1Xv5YPyipGRX/3qVzFlypRO95k3b15s2LAhdu7c2bbty1/+chw7diw2bdp0sW8NAHQTV13uN9i6dWvU19e32zZp0qRyhKQzzc3N5dLqzJkzcfTo0bj++uvLAAIA/vcV4x0nTpwop3b06tUrL0YOHDgQtbW17bYV601NTfGvf/0rrrnmmnOOaWhoiCVLllzuUwMAroD9+/fHBz7wgbwYuRgLFiyIuXPntq0fP348br755vKHqampST03AODCFAMPQ4YMife9733n3e+yx0hdXV0cPHiw3bZivYiKjkZFCsVdN8XyTsUxYgQAupZ3m2Jx2Z8zMmHChNi8eXO7bS+99FK5HQCg4hj55z//Wd6iWyytt+4WX+/bt6/tEsv06dPb9p81a1bs3bs3vv3tb8euXbvi2WefjZ///OcxZ84cv30AoPIYefXVV+OOO+4ol0Ixt6P4etGiReX63//+97YwKXzoQx8qb+0tRkOK55MsXbo0fvKTn5R31AAAvKfnjFzJCTD9+/cvJ7KaMwIAXcOF/v322TQAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQCoxAgCkEiMAQNeLkRUrVsTQoUOjb9++MX78+Ni2bdt591+2bFl87GMfi2uuuSaGDBkSc+bMiX//+98Xe84AQE+OkfXr18fcuXNj8eLFsX379hg5cmRMmjQpDh061OH+a9eujfnz55f7v/baa/Hcc8+Vr/Hoo49eivMHAHpajDzzzDPx0EMPxcyZM+O2226LlStXRr9+/WLNmjUd7v/KK6/EXXfdFV/5ylfK0ZR77703pk2b9q6jKQBAz1BRjJw6dSoaGxujvr7+vy/Qq1e5vnXr1g6PufPOO8tjWuNj7969sXHjxvjsZz/b6fs0NzdHU1NTuwUA6J6uqmTnI0eOxOnTp6O2trbd9mJ9165dHR5TjIgUx919993R0tISb7/9dsyaNeu8l2kaGhpiyZIllZwaANBFXfa7abZs2RJPPfVUPPvss+Uck+effz42bNgQTz75ZKfHLFiwII4fP9627N+//3KfJgDQFUZGBg4cGL17946DBw+2216s19XVdXjM448/Hvfff388+OCD5frtt98eJ0+ejK9//euxcOHC8jLPO1VXV5cLAND9VTQy0qdPnxg9enRs3ry5bduZM2fK9QkTJnR4zFtvvXVOcBRBUygu2wAAPVtFIyOF4rbeGTNmxJgxY2LcuHHlM0SKkY7i7prC9OnTY/DgweW8j8LkyZPLO3DuuOOO8pkke/bsKUdLiu2tUQIA9FwVx8jUqVPj8OHDsWjRojhw4ECMGjUqNm3a1Dapdd++fe1GQh577LGoqqoq/33zzTfjhhtuKEPke9/73qX9SQCALqmqpQtcKylu7e3fv385mbWmpib7dACAS/j322fTAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAABdL0ZWrFgRQ4cOjb59+8b48eNj27Zt593/2LFj8c1vfjNuuummqK6ujltvvTU2btx4secMAHQjV1V6wPr162Pu3LmxcuXKMkSWLVsWkyZNit27d8eNN954zv6nTp2Kz3zmM+X3fvnLX8bgwYPjb3/7WwwYMOBS/QwAQBdW1dLS0lLJAUWAjB07NpYvX16unzlzJoYMGRKzZ8+O+fPnn7N/ES1PP/107Nq1K66++uqLOsmmpqbo379/HD9+PGpqai7qNQCAK+tC/35XdJmmGOVobGyM+vr6/75Ar17l+tatWzs85te//nVMmDChvExTW1sbw4cPj6eeeipOnz7d6fs0NzeXP8DZCwDQPVUUI0eOHCkjooiKsxXrBw4c6PCYvXv3lpdniuOKeSKPP/54LF26NL773e92+j4NDQ1lSbUuxcgLANA9Xfa7aYrLOMV8kVWrVsXo0aNj6tSpsXDhwvLyTWcWLFhQDum0Lvv377/cpwkAdIUJrAMHDozevXvHwYMH220v1uvq6jo8priDppgrUhzX6uMf/3g5klJc9unTp885xxR33BQLAND9VTQyUoRDMbqxefPmdiMfxXoxL6Qjd911V+zZs6fcr9Wf//znMlI6ChEAoGep+DJNcVvv6tWr42c/+1m89tpr8Y1vfCNOnjwZM2fOLL8/ffr08jJLq+L7R48ejYcffriMkA0bNpQTWIsJrQAAFT9npJjzcfjw4Vi0aFF5qWXUqFGxadOmtkmt+/btK++waVVMPn3xxRdjzpw5MWLEiPI5I0WYzJs3z28fAKj8OSMZPGcEALqey/KcEQCAS02MAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgAkEqMAACpxAgA0PViZMWKFTF06NDo27dvjB8/PrZt23ZBx61bty6qqqpiypQpF/O2AEA3VHGMrF+/PubOnRuLFy+O7du3x8iRI2PSpElx6NCh8x73xhtvxCOPPBITJ058L+cLAPT0GHnmmWfioYceipkzZ8Ztt90WK1eujH79+sWaNWs6Peb06dPx1a9+NZYsWRK33HLLu75Hc3NzNDU1tVsAgO6pohg5depUNDY2Rn19/X9foFevcn3r1q2dHved73wnbrzxxnjggQcu6H0aGhqif//+bcuQIUMqOU0AoLvGyJEjR8pRjtra2nbbi/UDBw50eMzLL78czz33XKxevfqC32fBggVx/PjxtmX//v2VnCYA0IVcdTlf/MSJE3H//feXITJw4MALPq66urpcAIDur6IYKYKid+/ecfDgwXbbi/W6urpz9n/99dfLiauTJ09u23bmzJn/f+Orrordu3fHhz/84Ys/ewCgZ12m6dOnT4wePTo2b97cLi6K9QkTJpyz/7Bhw+KPf/xj7Nixo235/Oc/H/fcc0/5tbkgAEDFl2mK23pnzJgRY8aMiXHjxsWyZcvi5MmT5d01henTp8fgwYPLSajFc0iGDx/e7vgBAwaU/75zOwDQM1UcI1OnTo3Dhw/HokWLykmro0aNik2bNrVNat23b195hw0AwIWoamlpaYn/ccVzRopbfIs7a2pqarJPBwC4hH+/DWEAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAKnECACQSowAAF0vRlasWBFDhw6Nvn37xvjx42Pbtm2d7rt69eqYOHFiXHfddeVSX19/3v0BgJ6l4hhZv359zJ07NxYvXhzbt2+PkSNHxqRJk+LQoUMd7r9ly5aYNm1a/O53v4utW7fGkCFD4t57740333zzUpw/ANDFVbW0tLRUckAxEjJ27NhYvnx5uX7mzJkyMGbPnh3z589/1+NPnz5djpAUx0+fPr3DfZqbm8ulVVNTU/kex48fj5qamkpOFwBIUvz97t+//7v+/a5oZOTUqVPR2NhYXmppe4Fevcr1YtTjQrz11lvxn//8J97//vd3uk9DQ0N58q1LESIAQPdUUYwcOXKkHNmora1tt71YP3DgwAW9xrx582LQoEHtguadFixYUFZU67J///5KThMA6EKuupJv9v3vfz/WrVtXziMpJr92prq6ulwAgO6vohgZOHBg9O7dOw4ePNhue7FeV1d33mN/9KMflTHy29/+NkaMGHFxZwsA9OzLNH369InRo0fH5s2b27YVE1iL9QkTJnR63A9/+MN48sknY9OmTTFmzJj3dsYAQM++TFPc1jtjxowyKsaNGxfLli2LkydPxsyZM8vvF3fIDB48uJyEWvjBD34QixYtirVr15bPJmmdW3LttdeWCwDQs1UcI1OnTo3Dhw+XgVGExahRo8oRj9ZJrfv27SvvsGn14x//uLwL50tf+lK71ymeU/LEE09cip8BAOhJzxn5X75PGQDo5s8ZAQC41MQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAqcQIAJBKjAAAXS9GVqxYEUOHDo2+ffvG+PHjY9u2befd/xe/+EUMGzas3P/222+PjRs3Xuz5AgA9PUbWr18fc+fOjcWLF8f27dtj5MiRMWnSpDh06FCH+7/yyisxbdq0eOCBB+IPf/hDTJkypVx27tx5Kc4fAOjiqlpaWloqOaAYCRk7dmwsX768XD9z5kwMGTIkZs+eHfPnzz9n/6lTp8bJkyfjN7/5Tdu2T33qUzFq1KhYuXJlh+/R3NxcLq2OHz8eN998c+zfvz9qamoqOV0AIElTU1PZCMeOHYv+/ft3ut9VlbzoqVOnorGxMRYsWNC2rVevXlFfXx9bt27t8JhiezGScrZiJOWFF17o9H0aGhpiyZIl52wvfiAAoGs5ceLEpYuRI0eOxOnTp6O2trbd9mJ9165dHR5z4MCBDvcvtnemiJ2zA6YYfTl69Ghcf/31UVVVVckpA13k/5yMfEL3U1x8KUJk0KBB592vohi5Uqqrq8vlbAMGDEg7H+DyKy7BugwL3c/5RkQuagLrwIEDo3fv3nHw4MF224v1urq6Do8ptleyPwDQs1QUI3369InRo0fH5s2b211CKdYnTJjQ4THF9rP3L7z00kud7g8A9CwVX6Yp5nLMmDEjxowZE+PGjYtly5aVd8vMnDmz/P706dNj8ODB5STUwsMPPxyf/vSnY+nSpXHffffFunXr4tVXX41Vq1Zd+p8G6HKKS7LFowLeeWkW6DkqjpHiVt3Dhw/HokWLykmoxS26mzZtapukum/fvvIOm1Z33nlnrF27Nh577LF49NFH46Mf/Wh5J83w4cMv7U8CdElFhDzxxBPZpwF0peeMAABcSj6bBgBIJUYAgFRiBABIJUaAy+prX/ta+eGYAJ0xgRW4rIoPuizmyXuKMtAZMQIApHKZBrhil2mKZxLdfffd5ShJ8cGXn/vc5+L1119v2/eNN94oPwzz+eefj3vuuSf69esXI0eO7PRTwYHuQYwAV0zxtObiKc7FU5iLj4koHpD4hS98ofxYibMtXLgwHnnkkdixY0fceuutMW3atHj77bf9l4Ju6n/yU3uB7umLX/xiu/U1a9bEDTfcEH/605/aPZW5CJHi4yMKS5YsiU984hOxZ8+eGDZs2BU/Z+DyMzICXDF/+ctfylGOW265JWpqamLo0KFtHyNxthEjRrR9fdNNN5X/Hjp0yH8p6KaMjABXzOTJk+ODH/xgrF69OgYNGlRenilGRE6dOtVuv6uvvrrt62IOSeGdl3KA7kOMAFfEP/7xj9i9e3cZIhMnTiy3vfzyy377gBgBrozrrruuvINm1apV5aWX4tLM/Pnz/foBc0aAK6O4c2bdunXR2NhYXpqZM2dOPP300379gJER4PJqbm6Oa6+9tvy6vr6+vHPmbMXTWVsVE1rPXi8UzyR55zage3E3DXBZFM8FKcKjeGBZcWsuQGfECHBZ7Ny5M8aMGVOGyKxZs/yWgU75bBoAIJWREQAglRgBAFKJEQAglRgBAFKJEQAglRgBAFKJEQAglRgBACLT/wFxe37JoZOHugAAAABJRU5ErkJggg=="
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 24
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Bar chart",
"id": "b978133c3e4ef6bc"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1.Create a bar chart of state-wise revenue",
"id": "8bae693a4f2ae1b7"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T15:02:05.012234Z",
"start_time": "2025-12-18T15:02:04.925280Z"
}
},
"cell_type": "code",
"source": [
"revenue_des=pd['revenue'].sort_value(ascending=True)\n",
"plt.figure(figsize=(22,4))\n",
"plt.bar(df['state'],'revenue_desc')\n",
"plt.xlabel(\"state\")\n",
"plt.ylabel(\"revenue\")\n",
"plt.title(\"state-wise revenue in Adidas ,sorted by state\")\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
],
"id": "7f366bfb9dc349e",
"outputs": [
{
"ename": "TypeError",
"evalue": "'module' object is not subscriptable",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mTypeError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[30]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m revenue_des=\u001B[43mpd\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mrevenue\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m]\u001B[49m.sort_value(ascending=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 2\u001B[39m plt.figure(figsize=(\u001B[32m22\u001B[39m,\u001B[32m4\u001B[39m))\n\u001B[32m 3\u001B[39m plt.bar(df[\u001B[33m'\u001B[39m\u001B[33mstate\u001B[39m\u001B[33m'\u001B[39m],\u001B[33m'\u001B[39m\u001B[33mrevenue_desc\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[31mTypeError\u001B[39m: 'module' object is not subscriptable"
]
}
],
"execution_count": 30
},
{
"metadata": {},
"cell_type": "markdown",
"source": "plot",
"id": "bc2728245894d442"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-18T15:05:49.050362Z",
"start_time": "2025-12-18T15:05:48.204639Z"
}
},
"cell_type": "code",
"source": [
"city=['delhi','pune','agra','bangalore']\n",
"isale=[100,200,50,400]\n",
"plt.bar(city,isale,color='red')\n",
"plt.title(\"city and its isale\")\n",
"plt.show()"
],
"id": "aa20747d4a6a497b",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 31
},
{
"metadata": {},
"cell_type": "markdown",
"source": "create a bar chart of product-wise total",
"id": "cd675622e78e542"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T06:58:51.933131Z",
"start_time": "2025-12-20T06:58:51.927406Z"
}
},
"cell_type": "code",
"source": "import matplotlib.pyplot as plt",
"id": "31f58d575efcf3b4",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:17:24.042311Z",
"start_time": "2025-12-19T07:17:23.976433Z"
}
},
"cell_type": "code",
"source": [
"mdf=df.groupby('Product')['Unit Sold'].sum\n",
"plt.bar(mdf['Product'],mdf['Units Sold'])\n",
"plt.xlabel('Product')\n",
"plt.ylabel('Total units sold')\n",
"plt.title()\n",
"plt.xticks(rotation=45)\n",
"plt.show()"
],
"id": "d7db351e34e8e32c",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[7]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m mdf=\u001B[43mdf\u001B[49m.groupby(\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m)[\u001B[33m'\u001B[39m\u001B[33mUnit Sold\u001B[39m\u001B[33m'\u001B[39m].sum\n\u001B[32m 2\u001B[39m plt.bar(mdf[\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m],mdf[\u001B[33m'\u001B[39m\u001B[33mUnits Sold\u001B[39m\u001B[33m'\u001B[39m])\n\u001B[32m 3\u001B[39m plt.xlabel(\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[31mNameError\u001B[39m: name 'df' is not defined"
]
}
],
"execution_count": 7
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Make a grouped bar chart comparing iphone & samsung",
"id": "ec669e05852ebb1a"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:07:56.325485Z",
"start_time": "2025-12-19T07:07:55.927816Z"
}
},
"cell_type": "code",
"source": [
"months=[120,135,140,150]\n",
"samsung=[150,160,155,170]\n",
"plt.bar(months,samsung,label='samsung')\n",
"plt.bar(months,iphone,label='iphone')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.xlabel(\"Months\")\n",
"plt.ylabel(\"Sales\")\n",
"plt.title(\"iphone vs samsung monthly sales\")\n",
"plt.show()"
],
"id": "22cf562f711db273",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'iphone' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[5]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m 2\u001B[39m samsung=[\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m155\u001B[39m,\u001B[32m170\u001B[39m]\n\u001B[32m 3\u001B[39m plt.bar(months,samsung,label=\u001B[33m'\u001B[39m\u001B[33msamsung\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m plt.bar(months,\u001B[43miphone\u001B[49m,label=\u001B[33m'\u001B[39m\u001B[33miphone\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 5\u001B[39m plt.grid()\n\u001B[32m 6\u001B[39m plt.legend()\n",
"\u001B[31mNameError\u001B[39m: name 'iphone' is not defined"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "markdown",
"source": "use abar chart to compare categories",
"id": "b7f9d6e35f2c1f82"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:23:49.967994Z",
"start_time": "2025-12-19T07:23:49.904466Z"
}
},
"cell_type": "code",
"source": [
"category_counts=df[df['Product'].isin([\"Men's street footwear\",\"Women's street footwear\"])['Product']].value_counts()\n",
"plt.bar(category_counts.index,category_counts)\n",
"plt.title('Product category distribution')\n",
"plt.xlabel('Product')\n",
"plt.ylabel('Count')\n",
"plt.xticks(rotations=45)\n",
"plt.show()\n"
],
"id": "887a9c7f90699c75",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[8]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m category_counts=\u001B[43mdf\u001B[49m[df[\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m].isin([\u001B[33m\"\u001B[39m\u001B[33mMen\u001B[39m\u001B[33m'\u001B[39m\u001B[33ms street footwear\u001B[39m\u001B[33m\"\u001B[39m,\u001B[33m\"\u001B[39m\u001B[33mWomen\u001B[39m\u001B[33m'\u001B[39m\u001B[33ms street footwear\u001B[39m\u001B[33m\"\u001B[39m])[\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m]].value_counts()\n\u001B[32m 2\u001B[39m plt.bar(category_counts.index,category_counts)\n\u001B[32m 3\u001B[39m plt.title(\u001B[33m'\u001B[39m\u001B[33mProduct category distribution\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[31mNameError\u001B[39m: name 'df' is not defined"
]
}
],
"execution_count": 8
},
{
"metadata": {},
"cell_type": "markdown",
"source": "pie chart",
"id": "6238eda4c5131c54"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "pie chart of revenue contribution",
"id": "cb76c09cb54a3cfb"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:29:45.948393Z",
"start_time": "2025-12-19T07:29:45.866445Z"
}
},
"cell_type": "code",
"source": [
"state_revenue=df.groupby('State')['Revenue'].sum()\n",
"plt.pie(state_revenue,labels=state_revenue)\n",
"plt.title('Top 5 States by Revenue Contribution')\n",
"plt.xlabel('State')\n",
"plt.ylabel('Revenue')\n",
"plt.grid()\n",
"plt.show()"
],
"id": "a20dfebe12fe8aa8",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[9]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m state_revenue=\u001B[43mdf\u001B[49m.groupby(\u001B[33m'\u001B[39m\u001B[33mState\u001B[39m\u001B[33m'\u001B[39m)[\u001B[33m'\u001B[39m\u001B[33mRevenue\u001B[39m\u001B[33m'\u001B[39m].sum()\n\u001B[32m 2\u001B[39m plt.pie(state_revenue,labels=state_revenue)\n\u001B[32m 3\u001B[39m plt.title(\u001B[33m'\u001B[39m\u001B[33mTop 5 States by Revenue Contribution\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[31mNameError\u001B[39m: name 'df' is not defined"
]
}
],
"execution_count": 9
},
{
"metadata": {},
"cell_type": "markdown",
"source": "stylish pie chart using",
"id": "9acabbe1fd7e6ede"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:35:16.929742Z",
"start_time": "2025-12-19T07:35:16.810642Z"
}
},
"cell_type": "code",
"source": [
"quantity=[13.3,2.2,8.7,5.6]\n",
"plt.pie(quantity,\n",
" autopct='%1.1f%%',\n",
" explode=[0.1,0,0,0],\n",
" shadow=True)\n",
"plt.show()"
],
"id": "addb4ee6e0437dd6",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:39:38.985212Z",
"start_time": "2025-12-19T07:39:38.977484Z"
}
},
"cell_type": "code",
"source": "import pandas as pd",
"id": "379c0f10270330ef",
"outputs": [],
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:39:41.482251Z",
"start_time": "2025-12-19T07:39:41.426879Z"
}
},
"cell_type": "code",
"source": [
"pd.read_csv('Sleep_health_and_lifestyle_dataset.csv')\n",
"df"
],
"id": "567d2a6976ddf1b6",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[15]\u001B[39m\u001B[32m, line 2\u001B[39m\n\u001B[32m 1\u001B[39m pd.read_csv(\u001B[33m'\u001B[39m\u001B[33mSleep_health_and_lifestyle_dataset.csv\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m----> \u001B[39m\u001B[32m2\u001B[39m \u001B[43mdf\u001B[49m\n",
"\u001B[31mNameError\u001B[39m: name 'df' is not defined"
]
}
],
"execution_count": 15
},
{
"metadata": {},
"cell_type": "markdown",
"source": "pie chart showing product category distribution",
"id": "1ff5a8ae196091c5"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:43:58.391642Z",
"start_time": "2025-12-19T07:43:58.336840Z"
}
},
"cell_type": "code",
"source": [
"category_counts=df['Product'].value_counts()\n",
"plt.pie(category_counts,\n",
" labels=category_counts.index,\n",
" auctopct='%1.1f%%',\n",
" shadow=True)\n",
"plt.title('Product category distribution')\n",
"plt.show()"
],
"id": "34eb58cb31cde5e6",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mNameError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[16]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m category_counts=\u001B[43mdf\u001B[49m[\u001B[33m'\u001B[39m\u001B[33mProduct\u001B[39m\u001B[33m'\u001B[39m].value_counts()\n\u001B[32m 2\u001B[39m plt.pie(category_counts,\n\u001B[32m 3\u001B[39m labels=category_counts.index,\n\u001B[32m 4\u001B[39m auctopct=\u001B[33m'\u001B[39m\u001B[38;5;132;01m%1.1f\u001B[39;00m\u001B[38;5;132;01m%%\u001B[39;00m\u001B[33m'\u001B[39m,\n\u001B[32m 5\u001B[39m shadow=\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[32m 6\u001B[39m plt.title(\u001B[33m'\u001B[39m\u001B[33mProduct category distribution\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[31mNameError\u001B[39m: name 'df' is not defined"
]
}
],
"execution_count": 16
},
{
"metadata": {},
"cell_type": "markdown",
"source": "create any pie chart that represent parts",
"id": "11eb0cc70ee60bf4"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:50:26.879724Z",
"start_time": "2025-12-19T07:50:25.375951Z"
}
},
"cell_type": "code",
"source": [
"value=[25,15,30,10,20]\n",
"label=['Apples','Bananas','Cherries','Dates','Elderberries']\n",
"plt.pie('values',labels='labels',autopct='%1.1f%%')\n",
"plt.show()"
],
"id": "e80160072fef0c44",
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: 'values'",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[21]\u001B[39m\u001B[32m, line 3\u001B[39m\n\u001B[32m 1\u001B[39m value=[\u001B[32m25\u001B[39m,\u001B[32m15\u001B[39m,\u001B[32m30\u001B[39m,\u001B[32m10\u001B[39m,\u001B[32m20\u001B[39m]\n\u001B[32m 2\u001B[39m label=[\u001B[33m'\u001B[39m\u001B[33mApples\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mBananas\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mCherries\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mDates\u001B[39m\u001B[33m'\u001B[39m,\u001B[33m'\u001B[39m\u001B[33mElderberries\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m3\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpie\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mvalues\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mlabels\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mautopct\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[38;5;132;43;01m%1.1f\u001B[39;49;00m\u001B[38;5;132;43;01m%%\u001B[39;49;00m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 4\u001B[39m plt.show()\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3806\u001B[39m, in \u001B[36mpie\u001B[39m\u001B[34m(x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch, data)\u001B[39m\n\u001B[32m 3783\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.pie)\n\u001B[32m 3784\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mpie\u001B[39m(\n\u001B[32m 3785\u001B[39m x: ArrayLike,\n\u001B[32m (...)\u001B[39m\u001B[32m 3804\u001B[39m data=\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[32m 3805\u001B[39m ) -> \u001B[38;5;28mtuple\u001B[39m[\u001B[38;5;28mlist\u001B[39m[Wedge], \u001B[38;5;28mlist\u001B[39m[Text]] | \u001B[38;5;28mtuple\u001B[39m[\u001B[38;5;28mlist\u001B[39m[Wedge], \u001B[38;5;28mlist\u001B[39m[Text], \u001B[38;5;28mlist\u001B[39m[Text]]:\n\u001B[32m-> \u001B[39m\u001B[32m3806\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mpie\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 3807\u001B[39m \u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3808\u001B[39m \u001B[43m \u001B[49m\u001B[43mexplode\u001B[49m\u001B[43m=\u001B[49m\u001B[43mexplode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3809\u001B[39m \u001B[43m \u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3810\u001B[39m \u001B[43m \u001B[49m\u001B[43mcolors\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcolors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3811\u001B[39m \u001B[43m \u001B[49m\u001B[43mautopct\u001B[49m\u001B[43m=\u001B[49m\u001B[43mautopct\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3812\u001B[39m \u001B[43m \u001B[49m\u001B[43mpctdistance\u001B[49m\u001B[43m=\u001B[49m\u001B[43mpctdistance\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3813\u001B[39m \u001B[43m \u001B[49m\u001B[43mshadow\u001B[49m\u001B[43m=\u001B[49m\u001B[43mshadow\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3814\u001B[39m \u001B[43m \u001B[49m\u001B[43mlabeldistance\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlabeldistance\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3815\u001B[39m \u001B[43m \u001B[49m\u001B[43mstartangle\u001B[49m\u001B[43m=\u001B[49m\u001B[43mstartangle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3816\u001B[39m \u001B[43m \u001B[49m\u001B[43mradius\u001B[49m\u001B[43m=\u001B[49m\u001B[43mradius\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3817\u001B[39m \u001B[43m \u001B[49m\u001B[43mcounterclock\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcounterclock\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3818\u001B[39m \u001B[43m \u001B[49m\u001B[43mwedgeprops\u001B[49m\u001B[43m=\u001B[49m\u001B[43mwedgeprops\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3819\u001B[39m \u001B[43m \u001B[49m\u001B[43mtextprops\u001B[49m\u001B[43m=\u001B[49m\u001B[43mtextprops\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3820\u001B[39m \u001B[43m \u001B[49m\u001B[43mcenter\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcenter\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3821\u001B[39m \u001B[43m \u001B[49m\u001B[43mframe\u001B[49m\u001B[43m=\u001B[49m\u001B[43mframe\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3822\u001B[39m \u001B[43m \u001B[49m\u001B[43mrotatelabels\u001B[49m\u001B[43m=\u001B[49m\u001B[43mrotatelabels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3823\u001B[39m \u001B[43m \u001B[49m\u001B[43mnormalize\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnormalize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3824\u001B[39m \u001B[43m \u001B[49m\u001B[43mhatch\u001B[49m\u001B[43m=\u001B[49m\u001B[43mhatch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3825\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3826\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001B[39m, in \u001B[36m_preprocess_data.<locals>.inner\u001B[39m\u001B[34m(ax, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1521\u001B[39m \u001B[38;5;129m@functools\u001B[39m.wraps(func)\n\u001B[32m 1522\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34minner\u001B[39m(ax, *args, data=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs):\n\u001B[32m 1523\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1524\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1525\u001B[39m \u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1526\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1527\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1529\u001B[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001B[32m 1530\u001B[39m auto_label = (bound.arguments.get(label_namer)\n\u001B[32m 1531\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m bound.kwargs.get(label_namer))\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:3358\u001B[39m, in \u001B[36mAxes.pie\u001B[39m\u001B[34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch)\u001B[39m\n\u001B[32m 3355\u001B[39m \u001B[38;5;28mself\u001B[39m.set_aspect(\u001B[33m'\u001B[39m\u001B[33mequal\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 3356\u001B[39m \u001B[38;5;66;03m# The use of float32 is \"historical\", but can't be changed without\u001B[39;00m\n\u001B[32m 3357\u001B[39m \u001B[38;5;66;03m# regenerating the test baselines.\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m3358\u001B[39m x = \u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43masarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mfloat32\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 3359\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.ndim > \u001B[32m1\u001B[39m:\n\u001B[32m 3360\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mx must be 1D\u001B[39m\u001B[33m\"\u001B[39m)\n",
"\u001B[31mValueError\u001B[39m: could not convert string to float: 'values'"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 21
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Generate 100 random values",
"id": "308431e7a538f813"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T07:55:07.580616Z",
"start_time": "2025-12-19T07:55:07.572899Z"
}
},
"cell_type": "code",
"source": "import numpy as np",
"id": "58a765c28cd59f14",
"outputs": [],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T08:29:26.502010Z",
"start_time": "2025-12-19T08:29:26.195750Z"
}
},
"cell_type": "code",
"source": [
"random_values=np.random.randint(0,101,size=100)\n",
"mean_random=np.mean(random_values)\n",
"std_random=np.std(random_values)\n",
"plt.hist(random_values,edgecolor='black')\n",
"plt.title(\"100 random values\")\n",
"plt.xlabel(\"Random values\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.show()"
],
"id": "9a9d7f8a6ed91d50",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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KlSTuymWxSY6cuSTyt72EGQBIRwQZ+D1tidEQE9xmsGQLDhEbXDsbLWeXfmjKTqsMAKQfggysoSEmqFg5XxcDAOBHGOwLAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWMunQWb9+vXStm1bKVGihAQEBMiSJUvc265duybDhg2TatWqSe7cuc0+3bp1k2PHjvmyyAAAwI/4NMhcunRJatSoIVOnTk227fLly7J9+3YZMWKEuV20aJFERkbKE0884ZOyAgAA/xPoyydv1aqVWVKSP39+WbFihce6KVOmSL169SQqKkpCQ0MzqJQAAMBf+TTI3KkLFy6YLqgCBQrcdJ/4+HizuMTExGRQ6QAAQEazZrBvXFycGTPTqVMnyZcv3033Cw8PN605riUkJCRDywkAADKOFUFGB/4+++yz4jiOTJs27Zb7Dh8+3LTcuJbo6OgMKycAAMhYgbaEmCNHjsjq1atv2RqjgoKCzAIAAO5+gTaEmP3798uaNWskODjY10UCAAB+xKdBJjY2Vg4cOOC+f+jQIdm5c6cUKlRIihcvLs8884yZer106VJJSEiQEydOmP10e/bs2X1YcgAAIJk9yEREREjTpk3d9wcNGmRuw8LC5J133pFvv/3W3K9Zs6bH47R1pkmTJhlcWgAA4G98GmQ0jOgA3pu51TYAAAArZi0BAACkhCADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsFagrwtgs6ioKDlz5ozYpnDhwhIaGurrYgBes3fvXqtqMz4+XoKCgsQmfG7AXxFk/kKIqVCxksRduSy2yZEzl0T+tpcwA+slxJ4XCQiQLl26iFUCsog4N8QmfG7AXxFk0khbYjTEBLcZLNmCQ8QW185Gy9mlH5ry0yoD292IjxVxHKv+H145GCEXNsy1qsx8bsCfEWT+Iv0gCipWzju/DQB3/f9DDQW2lRnwZwz2BQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKzl0yCzfv16adu2rZQoUUICAgJkyZIlHtsdx5G3335bihcvLjlz5pTmzZvL/v37fVZeAADgX3waZC5duiQ1atSQqVOnprh93Lhx8tFHH8n06dNl8+bNkjt3bmnZsqXExcVleFkBAID/CfTlk7dq1cosKdHWmIkTJ8rf/vY3adeunVk3Z84cKVq0qGm56dixYwaXFgAA+Bu/HSNz6NAhOXHihOlOcsmfP7/Ur19fNm7ceNPHxcfHS0xMjMcCAADuTn4bZDTEKG2BSUzvu7alJDw83AQe1xISEpLuZQUAAL7ht0EmrYYPHy4XLlxwL9HR0b4uEgAAyGxBplixYub25MmTHuv1vmtbSoKCgiRfvnweCwAAuDv5bZApU6aMCSyrVq1yr9PxLjp7qUGDBj4tGwAA8A8+nbUUGxsrBw4c8Bjgu3PnTilUqJCEhobKwIED5f3335fy5cubYDNixAhzzpn27dv7stgAAMBP+DTIRERESNOmTd33Bw0aZG7DwsJk9uzZ8vrrr5tzzfTu3Vv+/PNPady4sXz//feSI0cOH5YaAAD4C58GmSZNmpjzxdyMnu33vffeMwsAAIA1Y2QAAABuhyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAADJXkDl48KD3SwIAAJARQaZcuXLmYo9z586VuLi4tBwCAADAN0Fm+/btUr16dXO16mLFislLL70kW7Zs+eulAQAASO8gU7NmTZk0aZIcO3ZMZs6cKcePH5fGjRtL1apVZcKECXL69Om0HBYAACDjBvsGBgbKU089JV9++aWMHTtWDhw4IEOGDJGQkBDp1q2bCTgAAAB+GWQiIiKkb9++Urx4cdMSoyHm999/lxUrVpjWmnbt2nmvpAAAAEkEShpoaJk1a5ZERkbK448/LnPmzDG3WbL8by4qU6aMzJ49W0qXLp2WwwN3jb1794otbCorcLeKioqSM2fOiE0KFy4soaGhdgWZadOmSc+ePaV79+6mNSYlRYoUkU8//fSvlg+wUkLseZGAAOnSpYuviwLAohBToWIlibtyWWySI2cuifxtr8/CTJqCzP79+2+7T/bs2SUsLCwthwesdyM+VsRxJLjNYMkWHCI2uHIwQi5smOvrYgCZlrbEaIix6XPj2tloObv0Q1N2q4KMdivlyZNHOnTo4LFeB/1evnyZAAP8f/phFFSsnDUfSAB8z6bPDWsH+4aHh5s+sZS6k0aPHu2NcgEAAKRPkNF+PB3Qm1SpUqXMNgAAAL8NMtrysnv37mTrd+3aJcHBwd4oFwAAQPoEmU6dOkn//v1lzZo1kpCQYJbVq1fLgAEDpGPHjmk5JAAAQMYM9h01apQcPnxYmjVrZs7uq27cuGHO5ssYGQAA4NdBRqdWf/755ybQaHdSzpw5pVq1amaMDAAAgF8HGZf777/fLAAAANYEGR0To5cgWLVqlZw6dcp0KyWm42UAAAD8MsjooF4NMq1bt5aqVatKQECA90sGAACQHkFm4cKF8sUXX5gLRQIAAFg1/VoH+5Yrx+mTAQCAhUFm8ODBMmnSJHEcx/slAgAASM+upR9++MGcDG/ZsmVSpUoVyZYtm8f2RYsWpeWwAAAA6R9kChQoIE8++WRaHgoAAODbIDNr1izvlQAAACAjx8io69evy8qVK+Xjjz+WixcvmnXHjh2T2NjYtB4SAAAg/YPMkSNHzCUJ2rVrJ/369ZPTp0+b9WPHjpUhQ4aIt+iJ90aMGCFlypQxl0EoW7asuSwCg4wBAMBfOiHeAw88YK6zFBwc7F6v42Z69erltZrVYDRt2jT57LPPzKDiiIgI6dGjh+TPn99cfRsAAGRuaQoyGzZskJ9++smcTyax0qVLy9GjR71VNvMc2uqjZxB2HX/BggWyZcsWrz0HAADIZEFGr62k3T5J/fHHH5I3b17xloYNG8qMGTNk37595uKU2gKkU78nTJhw08fEx8ebxSUmJsZr5QEA2CMqKkrOnDkjtti7d6+vi5B5gkyLFi1k4sSJJmQovdaSDvIdOXKkVy9b8MYbb5ggUrFiRcmaNasJTx988IF07tz5po8JDw+Xd99912tlAADYGWIqVKwkcVcu+7oo8Mcg8+GHH0rLli2lcuXKEhcXJ88//7zs379fChcubLp+vEWv5zRv3jyZP3++GSOzc+dOGThwoJQoUULCwsJSfMzw4cNl0KBB7vsahEJCQrxWJgCA/9OWGA0xwW0GS7ZgO74DrhyMkAsb5vq6GJkjyJQsWdJ08+jFI3fv3m1aY1544QXTUqKzi7xl6NChplWmY8eO5r7OlNIZU9rqcrMgExQUZBYAADTEBBWz49qA185G+7oImSfImAcGBkqXLl0kPV2+fFmyZPGcIa5dTDpGBwAAIE1BZs6cObfc3q1bN6/UbNu2bc2YmNDQUNO1tGPHDjPQt2fPnl45PgAAyKTnkUns2rVrpvVEp2PnypXLa0Fm8uTJ5oR4ffv2lVOnTpmxMS+99JK8/fbbXjk+AADIhEHm/PnzydbpYN+XX37ZjGvxFp3KrbOjdAEAAPDatZaSKl++vIwZMyZZaw0AAIDfBxnXAGC9cCQAAIDfdi19++23Hvf1Io7Hjx+XKVOmSKNGjbxVNgAAAO8Hmfbt23vc1zP73nPPPfLoo4+ak+UBAAD49bWWAAAA7qoxMgAAAH7fIpP4Wka3c6srVQMAAGR4kNEz7OqiJ8KrUKGCWbdv3z5z+YDatWt7jJ0BAADwqyCjlw7Qk9V99tlnUrBgQfdJ8nr06CEPPfSQDB482NvlBAAA8M4YGZ2ZpFegdoUYpT+///77zFoCAAD+HWRiYmLk9OnTydbruosXL3qjXAAAAOkTZJ588knTjbRo0SL5448/zPL111/LCy+8IE899VRaDgkAAJAxY2SmT58uQ4YMkeeff94M+DUHCgw0QWb8+PFpOSQAAEDGBJlcuXLJP/7xDxNafv/9d7OubNmykjt37rQcDgAAIONPiKfXV9JFr3ytIUavuQQAAODXLTJnz56VZ599VtasWWPOFbN//3657777TNeSzl7iekv+b+/evWILm8oKALAgyLz22muSLVs2iYqKkkqVKrnXP/fcc+asvwQZ/5UQe17PVChdunTxdVEAAPBNkPnPf/4jy5cvl5IlS3qs1y6mI0eO/PVSId3ciI8VcRwJbjNYsgWHWFHTVw5GyIUNc31dDADA3RJkLl26ZAb8JnXu3DkJCgryRrmQzjTEBBUrZ0U9Xzsb7esiAADupsG+ehmCOXPmuO/rOJkbN27IuHHjpGnTpt4sHwAAgHdbZDSwNGvWTCIiIuTq1avy+uuvyy+//GJaZH788ce0HBIAACBjWmSqVq1qrnbduHFjadeunelq0jP66hWx9XwyAAAAftkio2fyfeyxx8zZfd966630KRUAAEB6tMjotOvdu3ff6cMAAAD8o2tJz0Hy6aefer80AAAA6T3Y9/r16zJz5kxZuXKl1KlTJ9k1liZMmJCWwwIAAKRfkDl48KCULl1a9uzZI7Vr1zbrdNBvYjoVGwAAwO+CjJ65Vy8SqddYcl2S4KOPPpKiRYumV/kAAAC8M0Ym6dWtly1bZqZeAwAAWDPY92bBBgAAwG+DjI5/SToGhjExAADAijEy2gLTvXt394Uh4+LipE+fPslmLS1atMi7pQQAAPirQSYsLCzZ+WQAAACsCDKzZs1Kv5IAAABk5GBfAAAAXyLIAAAAa/l9kDl69KgZixMcHCw5c+aUatWqSUREhK+LBQAAbL3WUkY5f/68NGrUSJo2bWpOvnfPPffI/v37pWDBgr4uGgAA8AN+HWTGjh0rISEhHoOMy5Qp49MyAQAA/+HXQebbb7+Vli1bSocOHWTdunVy7733St++faVXr143fUx8fLxZXGJiYjKotABwd9u7d6/Ywqay4i4OMnq17WnTpsmgQYPkzTfflK1bt0r//v0le/bsyc5p4xIeHi7vvvtuhpcVAO5WCbHn9TTunDsMfsmvg8yNGzfkgQcekNGjR5v7tWrVkj179sj06dNvGmSGDx9ugk/iFhntngIApPGzOD5WT+0uwW0GS7ZgOz5PrxyMkAsb5vq6GMjsQaZ48eJSuXJlj3WVKlWSr7/++qaP0csnuC6hAADwHg0xQcXKWVGl185G+7oIyCB+Pf1aZyxFRkZ6rNu3b5+UKlXKZ2UCAAD+w6+DzGuvvSabNm0yXUsHDhyQ+fPny4wZM6Rfv36+LhoAAPADfh1k6tatK4sXL5YFCxZI1apVZdSoUTJx4kTp3Lmzr4sGAAD8gF+PkVFt2rQxCwAAgFUtMgAAALdCkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtawKMmPGjJGAgAAZOHCgr4sCAAD8gDVBZuvWrfLxxx9L9erVfV0UAADgJ6wIMrGxsdK5c2f55JNPpGDBgr4uDgAA8BNWBJl+/fpJ69atpXnz5rfdNz4+XmJiYjwWAABwdwoUP7dw4ULZvn276VpKjfDwcHn33XfTvVwAAMD3/LpFJjo6WgYMGCDz5s2THDlypOoxw4cPlwsXLrgXPQYAALg7+XWLzLZt2+TUqVNSu3Zt97qEhARZv369TJkyxXQjZc2a1eMxQUFBZgEAAHc/vw4yzZo1k59//tljXY8ePaRixYoybNiwZCEGAABkLn4dZPLmzStVq1b1WJc7d24JDg5Oth4AAGQ+fj1GBgAAwNoWmZSsXbvW10UAAAB+ghYZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFjLr4NMeHi41K1bV/LmzStFihSR9u3bS2RkpK+LBQAA/IRfB5l169ZJv379ZNOmTbJixQq5du2atGjRQi5duuTrogEAAD8QKH7s+++/97g/e/Zs0zKzbds2efjhh31WLgAA4B/8ukUmqQsXLpjbQoUK+booAADAD/h1i0xiN27ckIEDB0qjRo2katWqN90vPj7eLC4xMTEZVEIAAJDRrGmR0bEye/bskYULF952gHD+/PndS0hISIaVEQAAZCwrgswrr7wiS5culTVr1kjJkiVvue/w4cNNF5RriY6OzrByAgCAjOXXXUuO48irr74qixcvlrVr10qZMmVu+5igoCCzAACAu1+gv3cnzZ8/X7755htzLpkTJ06Y9dpllDNnTl8XDwAA+Jhfdy1NmzbNdA81adJEihcv7l4+//xzXxcNAAD4Ab/vWgIAALCyRQYAAOBWCDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAADWIsgAAABrEWQAAIC1CDIAAMBaBBkAAGAtggwAALAWQQYAAFiLIAMAAKxFkAEAANYiyAAAAGsRZAAAgLUIMgAAwFoEGQAAYC2CDAAAsJYVQWbq1KlSunRpyZEjh9SvX1+2bNni6yIBAAA/4PdB5vPPP5dBgwbJyJEjZfv27VKjRg1p2bKlnDp1ytdFAwAAPub3QWbChAnSq1cv6dGjh1SuXFmmT58uuXLlkpkzZ/q6aAAAwMf8OshcvXpVtm3bJs2bN3evy5Ili7m/ceNGn5YNAAD4XqD4sTNnzkhCQoIULVrUY73e/+2331J8THx8vFlcLly4YG5jYmK8WrbY2Nj/fb4TB+TG1TixxbWz0daVmzJTz7w3+D/I54Z/unbuD/d3ore/Z13Hcxzn1js6fuzo0aNaeuenn37yWD906FCnXr16KT5m5MiR5jEs1AHvAd4DvAd4D/AeEOvrIDo6+pZZwa9bZAoXLixZs2aVkydPeqzX+8WKFUvxMcOHDzeDg11u3Lgh586dk+DgYAkICPBqUgwJCZHo6GjJly+f144L6tpXeE9Tz3cT3s/217O2xFy8eFFKlChxy/38Oshkz55d6tSpI6tWrZL27du7g4nef+WVV1J8TFBQkFkSK1CgQLqVUX9xBJmMQV1Tz3cT3s/U890kXzp9F+bPn/+2+/h1kFHauhIWFiYPPPCA1KtXTyZOnCiXLl0ys5gAAEDm5vdB5rnnnpPTp0/L22+/LSdOnJCaNWvK999/n2wAMAAAyHz8Psgo7Ua6WVeSr2j3lZ6kL2k3FqhrW/Gepp7vJryfM089B+iIX589OwAAwN16QjwAAIBbIcgAAABrEWQAAIC1CDIAAMBaBJk0mjp1qpQuXVpy5Mgh9evXly1btnj3N5PJhIeHS926dSVv3rxSpEgRcwLEyMhIj33i4uKkX79+5izNefLkkaeffjrZWZ9xZ8aMGWPOeD1w4EDq2cuOHj0qXbp0Me/XnDlzSrVq1SQiIsK9XedZ6GklihcvbrbrxXD379/v7WLc1fRafCNGjJAyZcqYOixbtqyMGjXK49o81HParF+/Xtq2bWvOqqufEUuWLPHYnpp61bPqd+7c2ZwoT09M+8ILL7ivU+hV3rw2UmaxcOFCJ3v27M7MmTOdX375xenVq5dToEAB5+TJk74umrVatmzpzJo1y9mzZ4+zc+dO5/HHH3dCQ0Od2NhY9z59+vRxQkJCnFWrVjkRERHOgw8+6DRs2NCn5bbZli1bnNKlSzvVq1d3BgwY4F5PPf91586dc0qVKuV0797d2bx5s3Pw4EFn+fLlzoEDB9z7jBkzxsmfP7+zZMkSZ9euXc4TTzzhlClTxrly5YoXSpA5fPDBB05wcLCzdOlS59ChQ86XX37p5MmTx5k0aZJ7H+o5bf797387b731lrNo0SJzvaPFixd7bE9NvT722GNOjRo1nE2bNjkbNmxwypUr53Tq1MnxNoJMGugFK/v16+e+n5CQ4JQoUcIJDw/35u8mUzt16pT5z7Nu3Tpz/88//3SyZctmPqhc9u7da/bZuHGjD0tqp4sXLzrly5d3VqxY4TzyyCPuIEM9e8ewYcOcxo0b33T7jRs3nGLFijnjx493r9O6DwoKchYsWOClUtz9Wrdu7fTs2dNj3VNPPeV07tzZ/Ew9e0fSIJOaev3111/N47Zu3ereZ9myZU5AQIC5ILQ30bV0h65evSrbtm0zzWguWbJkMfc3btzo7QazTOvChQvmtlChQuZW6/zatWse9V6xYkUJDQ2l3tNAu+hat27tUZ/Us/d8++235rIqHTp0MF2ltWrVkk8++cS9/dChQ+ZM5YnrX68po93UfI6kXsOGDc219/bt22fu79q1S3744Qdp1aoV9ZyOUvP+1VvtTtL/By66v35fbt68OfOd2defnDlzxvTLJr1Egt7/7bfffFauu4leGFTHbDRq1EiqVq1q1ul/Gr2IaNILgGq96zak3sKFC2X79u2ydevWZNuoZ+84ePCgTJs2zVwr7s033zR13b9/f/Me1mvHud6zKX2O8H5OvTfeeMNcfVn/qMmaNav5bP7ggw/MuAzX+5l69r7U1KveaohPLDAw0Pxx6u33OEEGftlasGfPHvOXFbwrOjpaBgwYICtWrDAD1ZF+YVz/Eh09erS5ry0y+p6ePn26CTLwji+++ELmzZsn8+fPlypVqsjOnTvNH0E6QJV6zjzoWrpDhQsXNsk/6WwZvV+sWDFv/m4yJb2m1tKlS2XNmjVSsmRJ93qtW+3W+/PPPz32p97vjHbRnTp1SmrXrm3+OtJl3bp18tFHH5mf9S8q6vmv05kclStX9lhXqVIliYqKMj+7Piv4HPlrhg4dalplOnbsaGaFde3aVV577TUzC5J6Tj+pef/qrX7WJHb9+nUzk8nb35UEmTukTcN16tQx/bKJ//rS+w0aNPDqLycz0fFkGmIWL14sq1evNtMpE9M6z5Ytm0e96/Rs/WKg3lOvWbNm8vPPP5u/XF2LthxoU7zrZ+r5r9Nu0aSnD9BxHKVKlTI/6/tbP8wTv5+1i0THDvB+Tr3Lly+bMReJ6R+a+plMPaef1Lx/9Vb/8NQ/nlz0s11/NzqWxqu8OnQ4E02/1tHZs2fPNiOze/fubaZfnzhxwtdFs9bLL79spvKtXbvWOX78uHu5fPmyx7RgnZK9evVqM/26QYMGZsFfk3jWEvXsvantgYGBZnrw/v37nXnz5jm5cuVy5s6d6zF9VT83vvnmG2f37t1Ou3btmH59h8LCwpx7773XPf1apwoXLlzYef3116lnL8xs3LFjh1k0KkyYMMH8fOTIkVS/f3X6da1atcwpCH744QczU5Lp135k8uTJ5ktVzyej07F1njzSTv+jpLTouWVc9D9I3759nYIFC5ovhSeffNKEHXg3yFDP3vGvf/3LqVq1qvmjp2LFis6MGTM8tusU1hEjRjhFixY1+zRr1syJjIz00rNnDjExMea9q5/FOXLkcO677z5z7pP4+Hj3PtRz2qxZsybFz2QNj6mt17Nnz5rgouf2yZcvn9OjRw8TkLwtQP/xbhsPAABAxmCMDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZABkmICBAlixZYmWNly5dWiZOnOjrYgBIgiADZCLdu3c3YUIXvaaSXjPl9ddfl7i4OF8XDQDSJDBtDwNgq8cee0xmzZol165dMxd0CwsLM8Fm7Nixvi4aANwxWmSATCYoKMhcuTYkJETat28vzZs3lxUrVri3nz17Vjp16iT33nuv5MqVS6pVqyYLFizwOEaTJk2kf//+pjWnUKFC5njvvPOOxz779++Xhx9+WHLkyCGVK1f2eA4XvRL3o48+Kjlz5pTg4GDp3bu3xMbGerQgaRlHjx4tRYsWlQIFCsh7770n169fl6FDh5rnLlmypAlmNzNjxgwpUaKE+4rILu3atZOePXuan3///XdzX58jT548UrduXVm5cuVNj3n48GET/vSK4S56pV9dt3btWve6PXv2SKtWrcwx9dhdu3aVM2fOuLd/9dVXpn5dr19/F5cuXbrp8wJIjiADZGL6RfvTTz9J9uzZ3eu0m6lOnTry3Xffme0aLvQLeMuWLR6P/eyzzyR37tyyefNmGTdunAkYrrCioeGpp54yx9Xt06dPl2HDhnk8Xr+wW7ZsKQULFpStW7fKl19+acLDK6+84rHf6tWr5dixY7J+/XqZMGGCjBw5Utq0aWMep8fu06ePvPTSS/LHH3+k+Bo7dOhgwtmaNWvc686dOyfff/+9dO7c2dzX8PT444/LqlWrZMeOHabVqm3bthIVFZXmutVgoyGtVq1aEhERYZ7v5MmT8uyzz5rtx48fN4FRw9TevXtNANI64/J3wB3y+mUoAfgtvXJt1qxZndy5c5sr1upHQJYsWZyvvvrqlo9r3bq1M3jwYI8rZjdu3Nhjn7p16zrDhg0zPy9fvtwJDAx0jh496t6+bNky83yLFy829/Vq0Hol89jYWPc+3333nSnPiRMn3OUtVaqUk5CQ4N6nQoUKzkMPPeS+f/36dfN6FixYcNPyt2vXzunZs6f7/scff+yUKFHC47hJValSxVzl3kXL8fe//938fOjQIfNaduzY4d5+/vx5s06vGqxGjRrltGjRwuOY0dHRZh+9SvC2bdvMz4cPH75pGQDcHi0yQCbTtGlT0yWirRk6PqZHjx7y9NNPu7cnJCTIqFGjTJeHdt1ot8jy5cuTtU5Ur17d437x4sXl1KlT5mdtYdCuK+3ScWnQoIHH/rpPjRo1TKuOS6NGjUxrTmRkpHtdlSpVJEuW//uo0i4aLZtL1qxZTbeM67lToi0vX3/9tcTHx5v78+bNk44dO7qPqy0yQ4YMkUqVKpnuK33NWr6/0iKza9cu0wqkx3ItFStWdHdl6Wtv1qyZeS3aavTJJ5/I+fPn0/x8QGZFkAEyGQ0O5cqVM1+kM2fONIHm008/dW8fP368TJo0yXQF6Rexhh7tArp69arHcXTWU2I6PiTpOBRvSOl57vS5tZtIu2y0uyw6Olo2bNjg7lZSGmIWL15sxuLoNn3NGjCSvmYXVwBK3A2kg6cT03Ckz6vHSry4xg5pANOuuGXLlpkxRJMnT5YKFSrIoUOH7rCGgMyNIANkYvqF/Oabb8rf/vY3uXLliln3448/moGvXbp0MWHnvvvuk3379t3RcbVlQwODjgNx2bRpU7J9tNUi8eBWfW4tk36he5MOONbxJ9oSowOX9fi1a9f2eF4dWPzkk0+aAKODl3VA783cc8895jbx60s88Ffp8X/55Rdz/hkNjokXVyuUBjBthXr33XfN2BwdU6SBCkDqEWSATE67NbR1YOrUqeZ++fLlTUuBDgLW7hUdSKuDVO+Ezr65//77TdeVhhVt5Xjrrbc89tEWEQ0Yuo8OKtbWn1dffdUMLNbuI2/T59MWGW2FStwa43rNixYtMmFEy/v888/fsoVHZxk9+OCDMmbMGFNH69atM2EwsX79+plBxTqgVwcza3eSdtFpV55232lLmLYA6UBg7cLS5z99+rQJeABSjyADZHKBgYFmppDOPNLWEf1C1tYE7U7SadbaOqFToO+Etqpoy4K28tSrV09efPFF+eCDDzz20and+sWuX/Y63fmZZ54xY0amTJki6UFnEOmYHx1/o0ElMZ0NpbOgGjZsaLqD9LUnbrFJiQYinQauM7wGDhwo77//vsd2HR+kLT0aWlq0aGFaenQ/HYOj9ZMvXz4zE0tnS2no03r/8MMPzXRtAKkXoCN+72B/AAAAv0GLDAAAsBZBBgAAWIsgAwAArEWQAQAA1iLIAAAAaxFkAACAtQgyAADAWgQZAABgLYIMAACwFkEGAABYiyADAACsRZABAABiq/8HIXSr5Mwry7EAAAAASUVORK5CYII="
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 23
},
{
"metadata": {},
"cell_type": "markdown",
"source": "create 3 list that produce:normal distribution",
"id": "22984a7e6feeadfb"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T08:43:33.224719Z",
"start_time": "2025-12-19T08:43:32.983881Z"
}
},
"cell_type": "code",
"source": [
"normal_dist=[38,42,45,47,48,49,50,52,54,55,56,57,58,60]\n",
"right_skew=[2,3,4,5,6,7,8,12,18,20,22,24,26,28]\n",
"left_skew=[98,94,93,92,91,90,88,85,82,70,67,64,61]\n",
"plt.hist(normal_dist,edgecolor='black')\n",
"plt.title(\"Normal distribution\")\n",
"plt.xlabel(\"Values\")\n",
"plt.ylabel(\"Frequency\")\n",
"mean_normal_distribution=np.mean(normal_dist)\n",
"median_normal_distribution=np.median(normal_dist)\n",
"print()\n",
"print(\"mean and median values are equal:\",mean_normal_distribution,median_normal_distribution)\n",
"plt.show()"
],
"id": "c153fd324083de15",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"mean and median values are equal: 50.785714285714285 51.0\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 25
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T08:49:24.809904Z",
"start_time": "2025-12-19T08:49:24.588558Z"
}
},
"cell_type": "code",
"source": [
"plt.hist(right_skew,edgecolor='black')\n",
"plt.title(\"Right skew distribution\")\n",
"plt.xlabel(\"Values\")\n",
"plt.ylabel(\"Frequency\")\n",
"mean_right_skew=np.mean(right_skew)\n",
"median_right_skew=np.median(right_skew)\n",
"print()\n",
"print(\"mean is greater than median:\",mean_right_skew,median_right_skew)\n",
"plt.show()"
],
"id": "b3969d13aa0a418b",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"mean is greater than median: 13.214285714285714 10.0\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 26
},
{
"metadata": {},
"cell_type": "markdown",
"source": "plot a histogram using 100 random integers",
"id": "54f498135dadba9c"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T06:59:25.395630Z",
"start_time": "2025-12-20T06:59:25.388112Z"
}
},
"cell_type": "code",
"source": "import numpy as np",
"id": "fd677e37e34682b5",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-19T08:55:00.516779Z",
"start_time": "2025-12-19T08:55:00.257202Z"
}
},
"cell_type": "code",
"source": [
"random_values=np.random.randint(0,501,size=100)\n",
"plt.hist(random_values,edgecolor='black')\n",
"plt.title(\"100 random values\")\n",
"plt.xlabel(\"Random values\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.show()"
],
"id": "f3bb92aaa9e9bb1d",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 28
},
{
"metadata": {},
"cell_type": "markdown",
"source": "boxplot",
"id": "d035d24693cc459c"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "boxplot of units sold to detect outliers",
"id": "82dacdce89b5f483"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "stackplot",
"id": "efd0c254196a7c84"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Create a stackplot using iphone and sumsung",
"id": "43604c0561da8906"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Add label + customer color to a stackolot",
"id": "4a217566564b6991"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T07:12:01.386863Z",
"start_time": "2025-12-20T07:12:00.341798Z"
}
},
"cell_type": "code",
"source": [
"months=['jan','feb','mar','apr','may','jun']\n",
"iphone=[120,135,140,150,160,170]\n",
"samsung=[150,160,155,170,175,190]\n",
"plt.stackplot(months,samsung,iphone,label)\n",
"plt.xlabel(\"Months\")\n",
"plt.ylabel(\"Sales\")\n",
"plt.title(\"iphone vs samsung monthly sales\")\n",
"plt.legend()\n",
"plt.show()"
],
"id": "aaa7f8eee2e755",
"outputs": [
{
"ename": "ValueError",
"evalue": "all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 6 and the array at index 2 has size 1",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[5]\u001B[39m\u001B[32m, line 4\u001B[39m\n\u001B[32m 2\u001B[39m iphone=[\u001B[32m120\u001B[39m,\u001B[32m135\u001B[39m,\u001B[32m140\u001B[39m,\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m170\u001B[39m]\n\u001B[32m 3\u001B[39m samsung=[\u001B[32m150\u001B[39m,\u001B[32m160\u001B[39m,\u001B[32m155\u001B[39m,\u001B[32m170\u001B[39m,\u001B[32m175\u001B[39m,\u001B[32m190\u001B[39m]\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstackplot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonths\u001B[49m\u001B[43m,\u001B[49m\u001B[43msamsung\u001B[49m\u001B[43m,\u001B[49m\u001B[43miphone\u001B[49m\u001B[43m,\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 5\u001B[39m plt.xlabel(\u001B[33m\"\u001B[39m\u001B[33mMonths\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 6\u001B[39m plt.ylabel(\u001B[33m\"\u001B[39m\u001B[33mSales\u001B[39m\u001B[33m\"\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:4061\u001B[39m, in \u001B[36mstackplot\u001B[39m\u001B[34m(x, labels, colors, hatch, baseline, data, *args, **kwargs)\u001B[39m\n\u001B[32m 4057\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.stackplot)\n\u001B[32m 4058\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mstackplot\u001B[39m(\n\u001B[32m 4059\u001B[39m x, *args, labels=(), colors=\u001B[38;5;28;01mNone\u001B[39;00m, hatch=\u001B[38;5;28;01mNone\u001B[39;00m, baseline=\u001B[33m\"\u001B[39m\u001B[33mzero\u001B[39m\u001B[33m\"\u001B[39m, data=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs\n\u001B[32m 4060\u001B[39m ):\n\u001B[32m-> \u001B[39m\u001B[32m4061\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mstackplot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 4062\u001B[39m \u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4063\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4064\u001B[39m \u001B[43m \u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlabels\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4065\u001B[39m \u001B[43m \u001B[49m\u001B[43mcolors\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcolors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4066\u001B[39m \u001B[43m \u001B[49m\u001B[43mhatch\u001B[49m\u001B[43m=\u001B[49m\u001B[43mhatch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4067\u001B[39m \u001B[43m \u001B[49m\u001B[43mbaseline\u001B[49m\u001B[43m=\u001B[49m\u001B[43mbaseline\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4068\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4069\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 4070\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001B[39m, in \u001B[36m_preprocess_data.<locals>.inner\u001B[39m\u001B[34m(ax, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1521\u001B[39m \u001B[38;5;129m@functools\u001B[39m.wraps(func)\n\u001B[32m 1522\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34minner\u001B[39m(ax, *args, data=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs):\n\u001B[32m 1523\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1524\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1525\u001B[39m \u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1526\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1527\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1529\u001B[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001B[32m 1530\u001B[39m auto_label = (bound.arguments.get(label_namer)\n\u001B[32m 1531\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m bound.kwargs.get(label_namer))\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\stackplot.py:83\u001B[39m, in \u001B[36mstackplot\u001B[39m\u001B[34m(axes, x, labels, colors, hatch, baseline, *args, **kwargs)\u001B[39m\n\u001B[32m 18\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mstackplot\u001B[39m(axes, x, *args,\n\u001B[32m 19\u001B[39m labels=(), colors=\u001B[38;5;28;01mNone\u001B[39;00m, hatch=\u001B[38;5;28;01mNone\u001B[39;00m, baseline=\u001B[33m'\u001B[39m\u001B[33mzero\u001B[39m\u001B[33m'\u001B[39m,\n\u001B[32m 20\u001B[39m **kwargs):\n\u001B[32m 21\u001B[39m \u001B[38;5;250m \u001B[39m\u001B[33;03m\"\"\"\u001B[39;00m\n\u001B[32m 22\u001B[39m \u001B[33;03m Draw a stacked area plot or a streamgraph.\u001B[39;00m\n\u001B[32m 23\u001B[39m \n\u001B[32m (...)\u001B[39m\u001B[32m 80\u001B[39m \u001B[33;03m stacked area plot.\u001B[39;00m\n\u001B[32m 81\u001B[39m \u001B[33;03m \"\"\"\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m83\u001B[39m y = \u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mvstack\u001B[49m\u001B[43m(\u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 85\u001B[39m labels = \u001B[38;5;28miter\u001B[39m(labels)\n\u001B[32m 86\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m colors \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\numpy\\_core\\shape_base.py:292\u001B[39m, in \u001B[36mvstack\u001B[39m\u001B[34m(tup, dtype, casting)\u001B[39m\n\u001B[32m 290\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(arrs, \u001B[38;5;28mtuple\u001B[39m):\n\u001B[32m 291\u001B[39m arrs = (arrs,)\n\u001B[32m--> \u001B[39m\u001B[32m292\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_nx\u001B[49m\u001B[43m.\u001B[49m\u001B[43mconcatenate\u001B[49m\u001B[43m(\u001B[49m\u001B[43marrs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m=\u001B[49m\u001B[43mdtype\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcasting\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcasting\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[31mValueError\u001B[39m: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 6 and the array at index 2 has size 1"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 5
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Create a stackplot using any two numeric",
"id": "f8fe199ac2bdf460"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "python lists",
"id": "2fa1e26ca9c9a991"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Generate a list of 100 random integers",
"id": "19ab6ef2c92630de"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T07:16:07.921893Z",
"start_time": "2025-12-20T07:16:07.914161Z"
}
},
"cell_type": "code",
"source": "import numpy as np",
"id": "f1d259e2bc801867",
"outputs": [],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T07:19:55.276461Z",
"start_time": "2025-12-20T07:19:55.018839Z"
}
},
"cell_type": "code",
"source": [
"random_values=np.random.randint(0,501,size=100)\n",
"plt.hist(random_values,edgecolor='black')\n",
"plt.title(\"100 random values\")\n",
"plt.xlabel(\"Random values\")\n",
"plt.ylabel(\"frequency\")\n",
"plt.show()"
],
"id": "1c2a4d7a3194bd32",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 7
},
{
"metadata": {},
"cell_type": "markdown",
"source": "using : temp=[...] tea =[...] ice=[...]",
"id": "ac055b41c4dfea3"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-20T07:38:53.525355Z",
"start_time": "2025-12-20T07:38:52.412829Z"
}
},
"cell_type": "code",
"source": [
"temp=[20,25,30,40]\n",
"tea=[50,45,35,50,65]\n",
"ice=[10,20,35,50,65]\n",
"plt.figure(figsize=(10,6))\n",
"plt.scatter(months,temp,label='temp',color='red')\n",
"plt.scatter(months,tea,label='tea',color='blue')\n",
"plt.scatter(months,ice,label='ice',color='green')\n",
"plt.grid()\n",
"plt.title(\"Monthly sales scatter plot\")\n",
"plt.xlabel(\"sales\")\n",
"plt.legend(loc='upper left')\n",
"plt.show()"
],
"id": "292689a14afacbdc",
"outputs": [
{
"ename": "ValueError",
"evalue": "x and y must be the same size",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[8]\u001B[39m\u001B[32m, line 5\u001B[39m\n\u001B[32m 3\u001B[39m ice=[\u001B[32m10\u001B[39m,\u001B[32m20\u001B[39m,\u001B[32m35\u001B[39m,\u001B[32m50\u001B[39m,\u001B[32m65\u001B[39m]\n\u001B[32m 4\u001B[39m plt.figure(figsize=(\u001B[32m10\u001B[39m,\u001B[32m6\u001B[39m))\n\u001B[32m----> \u001B[39m\u001B[32m5\u001B[39m \u001B[43mplt\u001B[49m\u001B[43m.\u001B[49m\u001B[43mscatter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonths\u001B[49m\u001B[43m,\u001B[49m\u001B[43mtemp\u001B[49m\u001B[43m,\u001B[49m\u001B[43mlabel\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mtemp\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mcolor\u001B[49m\u001B[43m=\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mred\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 6\u001B[39m plt.scatter(months,tea,label=\u001B[33m'\u001B[39m\u001B[33mtea\u001B[39m\u001B[33m'\u001B[39m,color=\u001B[33m'\u001B[39m\u001B[33mblue\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 7\u001B[39m plt.scatter(months,ice,label=\u001B[33m'\u001B[39m\u001B[33mice\u001B[39m\u001B[33m'\u001B[39m,color=\u001B[33m'\u001B[39m\u001B[33mgreen\u001B[39m\u001B[33m'\u001B[39m)\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\pyplot.py:3948\u001B[39m, in \u001B[36mscatter\u001B[39m\u001B[34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, data, **kwargs)\u001B[39m\n\u001B[32m 3928\u001B[39m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes.scatter)\n\u001B[32m 3929\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mscatter\u001B[39m(\n\u001B[32m 3930\u001B[39m x: \u001B[38;5;28mfloat\u001B[39m | ArrayLike,\n\u001B[32m (...)\u001B[39m\u001B[32m 3946\u001B[39m **kwargs,\n\u001B[32m 3947\u001B[39m ) -> PathCollection:\n\u001B[32m-> \u001B[39m\u001B[32m3948\u001B[39m __ret = \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m.\u001B[49m\u001B[43mscatter\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 3949\u001B[39m \u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3950\u001B[39m \u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3951\u001B[39m \u001B[43m \u001B[49m\u001B[43ms\u001B[49m\u001B[43m=\u001B[49m\u001B[43ms\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3952\u001B[39m \u001B[43m \u001B[49m\u001B[43mc\u001B[49m\u001B[43m=\u001B[49m\u001B[43mc\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3953\u001B[39m \u001B[43m \u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m=\u001B[49m\u001B[43mmarker\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3954\u001B[39m \u001B[43m \u001B[49m\u001B[43mcmap\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcmap\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3955\u001B[39m \u001B[43m \u001B[49m\u001B[43mnorm\u001B[49m\u001B[43m=\u001B[49m\u001B[43mnorm\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3956\u001B[39m \u001B[43m \u001B[49m\u001B[43mvmin\u001B[49m\u001B[43m=\u001B[49m\u001B[43mvmin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3957\u001B[39m \u001B[43m \u001B[49m\u001B[43mvmax\u001B[49m\u001B[43m=\u001B[49m\u001B[43mvmax\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3958\u001B[39m \u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[43m=\u001B[49m\u001B[43malpha\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3959\u001B[39m \u001B[43m \u001B[49m\u001B[43mlinewidths\u001B[49m\u001B[43m=\u001B[49m\u001B[43mlinewidths\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3960\u001B[39m \u001B[43m \u001B[49m\u001B[43medgecolors\u001B[49m\u001B[43m=\u001B[49m\u001B[43medgecolors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3961\u001B[39m \u001B[43m \u001B[49m\u001B[43mcolorizer\u001B[49m\u001B[43m=\u001B[49m\u001B[43mcolorizer\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3962\u001B[39m \u001B[43m \u001B[49m\u001B[43mplotnonfinite\u001B[49m\u001B[43m=\u001B[49m\u001B[43mplotnonfinite\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3963\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mdata\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3964\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 3965\u001B[39m \u001B[43m \u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 3966\u001B[39m sci(__ret)\n\u001B[32m 3967\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m __ret\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\_api\\deprecation.py:453\u001B[39m, in \u001B[36mmake_keyword_only.<locals>.wrapper\u001B[39m\u001B[34m(*args, **kwargs)\u001B[39m\n\u001B[32m 447\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) > name_idx:\n\u001B[32m 448\u001B[39m warn_deprecated(\n\u001B[32m 449\u001B[39m since, message=\u001B[33m\"\u001B[39m\u001B[33mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[33m \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 450\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[33m; the \u001B[39m\u001B[33m\"\u001B[39m\n\u001B[32m 451\u001B[39m \u001B[33m\"\u001B[39m\u001B[33mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[33m.\u001B[39m\u001B[33m\"\u001B[39m,\n\u001B[32m 452\u001B[39m name=name, obj_type=\u001B[33mf\u001B[39m\u001B[33m\"\u001B[39m\u001B[33mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc.\u001B[34m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[33m()\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m--> \u001B[39m\u001B[32m453\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43m*\u001B[49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\__init__.py:1524\u001B[39m, in \u001B[36m_preprocess_data.<locals>.inner\u001B[39m\u001B[34m(ax, data, *args, **kwargs)\u001B[39m\n\u001B[32m 1521\u001B[39m \u001B[38;5;129m@functools\u001B[39m.wraps(func)\n\u001B[32m 1522\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34minner\u001B[39m(ax, *args, data=\u001B[38;5;28;01mNone\u001B[39;00m, **kwargs):\n\u001B[32m 1523\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1524\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[32m 1525\u001B[39m \u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1526\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[32m 1527\u001B[39m \u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mcbook\u001B[49m\u001B[43m.\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m.\u001B[49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1529\u001B[39m bound = new_sig.bind(ax, *args, **kwargs)\n\u001B[32m 1530\u001B[39m auto_label = (bound.arguments.get(label_namer)\n\u001B[32m 1531\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m bound.kwargs.get(label_namer))\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:4936\u001B[39m, in \u001B[36mAxes.scatter\u001B[39m\u001B[34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, colorizer, plotnonfinite, **kwargs)\u001B[39m\n\u001B[32m 4934\u001B[39m y = np.ma.ravel(y)\n\u001B[32m 4935\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m x.size != y.size:\n\u001B[32m-> \u001B[39m\u001B[32m4936\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[33m\"\u001B[39m\u001B[33mx and y must be the same size\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m 4938\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m s \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[32m 4939\u001B[39m s = (\u001B[32m20\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m mpl.rcParams[\u001B[33m'\u001B[39m\u001B[33m_internal.classic_mode\u001B[39m\u001B[33m'\u001B[39m] \u001B[38;5;28;01melse\u001B[39;00m\n\u001B[32m 4940\u001B[39m mpl.rcParams[\u001B[33m'\u001B[39m\u001B[33mlines.markersize\u001B[39m\u001B[33m'\u001B[39m] ** \u001B[32m2.0\u001B[39m)\n",
"\u001B[31mValueError\u001B[39m: x and y must be the same size"
]
},
{
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data",
"jetTransient": {
"display_id": null
}
}
],
"execution_count": 8
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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