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jaiswalakshay508-maker/matpolib.ipynb

Created May 3, 2026 12:57
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matpolib.ipynb
{
"cells": [
{
"cell_type": "code",
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2025-12-16T14:21:34.231778Z",
"start_time": "2025-12-16T14:21:29.217051Z"
}
},
"source": [
"from calendar import month\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
],
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T14:24:32.709002Z",
"start_time": "2025-12-16T14:24:32.195216Z"
}
},
"cell_type": "code",
"source": [
"month=['Jan','feb','mar','apr']\n",
"sale=[100,200,50,400]\n",
"plt.plot(month,sale)\n",
"plt.show"
],
"id": "45209886cdcd0542",
"outputs": [
{
"data": {
"text/plain": [
"<function matplotlib.pyplot.show(close=None, block=None)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"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": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "6f7455956459989d"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1.Sacatter plot",
"id": "e1950f999870239b"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T14:32:26.447122Z",
"start_time": "2025-12-16T14:32:26.358114Z"
}
},
"cell_type": "code",
"source": [
"month=['Jan','feb','mar','apr']\n",
"sale=[100,200,50,400]\n",
"plt.scatter(month,sale_color='red')\n",
"plt.title('Monthly sales')\n",
"plt.show(plt.xlabel('Month'))\n",
"plt.ylabel('Sales')\n",
"plt.grid()\n",
"plt.show()"
],
"id": "fa32a4478949b588",
"outputs": [
{
"ename": "TypeError",
"evalue": "scatter() missing 1 required positional argument: 'y'",
"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[10]\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 sale=[\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[43mscatter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmonth\u001B[49m\u001B[43m,\u001B[49m\u001B[43msale_color\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 4\u001B[39m plt.title(\u001B[33m'\u001B[39m\u001B[33mMonthly sales\u001B[39m\u001B[33m'\u001B[39m)\n\u001B[32m 5\u001B[39m plt.show(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\\_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[31mTypeError\u001B[39m: scatter() missing 1 required positional argument: 'y'"
]
}
],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T14:58:11.922207Z",
"start_time": "2025-12-16T14:58:11.634456Z"
}
},
"cell_type": "code",
"source": [
"month=['Jan','feb','mar','apr']\n",
"isale=[100,200,50,100]\n",
"ssale=[200,400,100,700]\n",
"plt.scatter(month,isale)\n",
"plt.title('Monthly sales')\n",
"plt.xlabel('Month')\n",
"plt.ylabel('Month')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
],
"id": "7ef7207e6ab4fe31",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_12700\\2908609655.py:9: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
" plt.legend()\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": 11
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T15:02:40.416481Z",
"start_time": "2025-12-16T15:02:40.140168Z"
}
},
"cell_type": "code",
"source": [
"temp=[10,20,30,40,50,60]\n",
"tea=[100,80,70,40,20,10]\n",
"ice=[20,50,80,90,100,300]\n",
"plt.scatter(temp,tea,label='Tea')\n",
"plt.title('Temperature')\n",
"plt.xlabel('Temperature')\n",
"plt.ylabel('Temperature')\n",
"plt.legend()\n",
"plt.show()"
],
"id": "8c297687a4591f32",
"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": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T15:13:13.268949Z",
"start_time": "2025-12-16T15:13:12.293407Z"
}
},
"cell_type": "code",
"source": "import pandas as pd",
"id": "2d938f1cdcf60f86",
"outputs": [],
"execution_count": 19
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-12-16T15:21:01.121203Z",
"start_time": "2025-12-16T15:21:00.735675Z"
}
},
"cell_type": "code",
"source": [
"df=pd.read_csv('Adidas US Sales Datasets.xlsx')\n",
"df['Total']=df['Price per unit']*df['Units sold']"
],
"id": "8986d0d4b1fc3e71",
"outputs": [
{
"ename": "UnicodeDecodeError",
"evalue": "'utf-8' codec can't decode byte 0xde in position 16: invalid continuation byte",
"output_type": "error",
"traceback": [
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
"\u001B[31mUnicodeDecodeError\u001B[39m Traceback (most recent call last)",
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[24]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m df=\u001B[43mpd\u001B[49m\u001B[43m.\u001B[49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[33;43m'\u001B[39;49m\u001B[33;43mAdidas US Sales Datasets.xlsx\u001B[39;49m\u001B[33;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[32m 2\u001B[39m df[\u001B[33m'\u001B[39m\u001B[33mTotal\u001B[39m\u001B[33m'\u001B[39m]=df[\u001B[33m'\u001B[39m\u001B[33mPrice per unit\u001B[39m\u001B[33m'\u001B[39m]*df[\u001B[33m'\u001B[39m\u001B[33mUnits sold\u001B[39m\u001B[33m'\u001B[39m]\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001B[39m, in \u001B[36mread_csv\u001B[39m\u001B[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001B[39m\n\u001B[32m 1013\u001B[39m kwds_defaults = _refine_defaults_read(\n\u001B[32m 1014\u001B[39m dialect,\n\u001B[32m 1015\u001B[39m delimiter,\n\u001B[32m (...)\u001B[39m\u001B[32m 1022\u001B[39m dtype_backend=dtype_backend,\n\u001B[32m 1023\u001B[39m )\n\u001B[32m 1024\u001B[39m kwds.update(kwds_defaults)\n\u001B[32m-> \u001B[39m\u001B[32m1026\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001B[39m, in \u001B[36m_read\u001B[39m\u001B[34m(filepath_or_buffer, kwds)\u001B[39m\n\u001B[32m 617\u001B[39m _validate_names(kwds.get(\u001B[33m\"\u001B[39m\u001B[33mnames\u001B[39m\u001B[33m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[32m 619\u001B[39m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[32m--> \u001B[39m\u001B[32m620\u001B[39m parser = \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 622\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[32m 623\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m parser\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001B[39m, in \u001B[36mTextFileReader.__init__\u001B[39m\u001B[34m(self, f, engine, **kwds)\u001B[39m\n\u001B[32m 1617\u001B[39m \u001B[38;5;28mself\u001B[39m.options[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m] = kwds[\u001B[33m\"\u001B[39m\u001B[33mhas_index_names\u001B[39m\u001B[33m\"\u001B[39m]\n\u001B[32m 1619\u001B[39m \u001B[38;5;28mself\u001B[39m.handles: IOHandles | \u001B[38;5;28;01mNone\u001B[39;00m = \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m-> \u001B[39m\u001B[32m1620\u001B[39m \u001B[38;5;28mself\u001B[39m._engine = \u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[36mFile \u001B[39m\u001B[32m~\\PycharmProjects\\JupyterProject\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1898\u001B[39m, in \u001B[36mTextFileReader._make_engine\u001B[39m\u001B[34m(self, f, engine)\u001B[39m\n\u001B[32m 1895\u001B[39m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(msg)\n\u001B[32m 1897\u001B[39m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[32m-> \u001B[39m\u001B[32m1898\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmapping\u001B[49m\u001B[43m[\u001B[49m\u001B[43mengine\u001B[49m\u001B[43m]\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m.\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 1899\u001B[39m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m:\n\u001B[32m 1900\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m.handles \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\\pandas\\io\\parsers\\c_parser_wrapper.py:93\u001B[39m, in \u001B[36mCParserWrapper.__init__\u001B[39m\u001B[34m(self, src, **kwds)\u001B[39m\n\u001B[32m 90\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m kwds[\u001B[33m\"\u001B[39m\u001B[33mdtype_backend\u001B[39m\u001B[33m\"\u001B[39m] == \u001B[33m\"\u001B[39m\u001B[33mpyarrow\u001B[39m\u001B[33m\"\u001B[39m:\n\u001B[32m 91\u001B[39m \u001B[38;5;66;03m# Fail here loudly instead of in cython after reading\u001B[39;00m\n\u001B[32m 92\u001B[39m import_optional_dependency(\u001B[33m\"\u001B[39m\u001B[33mpyarrow\u001B[39m\u001B[33m\"\u001B[39m)\n\u001B[32m---> \u001B[39m\u001B[32m93\u001B[39m \u001B[38;5;28mself\u001B[39m._reader = \u001B[43mparsers\u001B[49m\u001B[43m.\u001B[49m\u001B[43mTextReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43msrc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m*\u001B[49m\u001B[43m*\u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m 95\u001B[39m \u001B[38;5;28mself\u001B[39m.unnamed_cols = \u001B[38;5;28mself\u001B[39m._reader.unnamed_cols\n\u001B[32m 97\u001B[39m \u001B[38;5;66;03m# error: Cannot determine type of 'names'\u001B[39;00m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/parsers.pyx:574\u001B[39m, in \u001B[36mpandas._libs.parsers.TextReader.__cinit__\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/parsers.pyx:663\u001B[39m, in \u001B[36mpandas._libs.parsers.TextReader._get_header\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/parsers.pyx:874\u001B[39m, in \u001B[36mpandas._libs.parsers.TextReader._tokenize_rows\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/parsers.pyx:891\u001B[39m, in \u001B[36mpandas._libs.parsers.TextReader._check_tokenize_status\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32mpandas/_libs/parsers.pyx:2053\u001B[39m, in \u001B[36mpandas._libs.parsers.raise_parser_error\u001B[39m\u001B[34m()\u001B[39m\n",
"\u001B[36mFile \u001B[39m\u001B[32m<frozen codecs>:325\u001B[39m, in \u001B[36mdecode\u001B[39m\u001B[34m(self, input, final)\u001B[39m\n",
"\u001B[31mUnicodeDecodeError\u001B[39m: 'utf-8' codec can't decode byte 0xde in position 16: invalid continuation byte"
]
}
],
"execution_count": 24
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "2127dde713fa01f1"
}
],
"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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