diff --git a/pandas/Practice.ipynb b/pandas/Practice.ipynb new file mode 100644 index 00000000..b7c94161 --- /dev/null +++ b/pandas/Practice.ipynb @@ -0,0 +1,4755 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (1.3.5)\n", + "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (1.22.0)\n", + "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2017.3 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from pandas) (2021.3)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\anonymous\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are using pip version 21.2.3; however, version 21.3.1 is available.\n", + "You should consider upgrading via the 'C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\python.exe -m pip install --upgrade pip' command.\n" + ] + } + ], + "source": [ + "pip install pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1.0\n", + "1 3.0\n", + "2 NaN\n", + "3 5.0\n", + "4 6.0\n", + "5 8.0\n", + "6 9.0\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Object Creation\n", + "\n", + "s= pd.Series([1,3,np.nan,5,6,8,9])\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n", + " '2013-01-05', '2013-01-06'],\n", + " dtype='datetime64[ns]', freq='D')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dates = pd.date_range(\"20130101\",periods=6)\n", + "dates" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + ".. ... ... ... ... ... ... ... ... ... \n", + "886 0 2 male 27.0 0 0 13.0000 S Second \n", + "887 1 1 female 19.0 0 0 30.0000 S First \n", + "888 0 3 female NaN 1 2 23.4500 S Third \n", + "889 1 1 male 26.0 0 0 30.0000 C First \n", + "890 0 3 male 32.0 0 0 7.7500 Q Third \n", + "\n", + " who adult_male deck embark_town alive alone \n", + "0 man True NaN Southampton no False \n", + "1 woman False C Cherbourg yes False \n", + "2 woman False NaN Southampton yes True \n", + "3 woman False C Southampton yes False \n", + "4 man True NaN Southampton no True \n", + ".. ... ... ... ... ... ... \n", + "886 man True NaN Southampton no True \n", + "887 woman False B Southampton yes True \n", + "888 woman False NaN Southampton no False \n", + "889 man True C Cherbourg yes True \n", + "890 man True NaN Queenstown no True \n", + "\n", + "[891 rows x 15 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kashti = sns.load_dataset(\"titanic\")\n", + "kashti" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_15132/2159104507.py:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " kashti.median()\n" + ] + }, + { + "data": { + "text/plain": [ + "survived 0.0000\n", + "pclass 3.0000\n", + "age 28.0000\n", + "sibsp 0.0000\n", + "parch 0.0000\n", + "fare 14.4542\n", + "adult_male 1.0000\n", + "alone 1.0000\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Saving dataframe into csv file\n", + "kashti.to_csv('kashti.csv')\n", + "#kashti.mean()\n", + "kashti.median()\n", + "#kashti.mode()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " survived pclass age sibsp parch fare\n", + "count 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000\n", + "mean 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208\n", + "std 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429\n", + "min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000\n", + "25% 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400\n", + "50% 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200\n", + "75% 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000\n", + "max 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# basic statistics or summary\n", + "kashti.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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003male22.0107.2500SThirdmanTrueSouthamptonFalse
111female38.01071.2833CFirstwomanFalseCherbourgFalse
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403male35.0008.0500SThirdmanTrueSouthamptonTrue
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" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + "\n", + " who adult_male embark_town alone \n", + "0 man True Southampton False \n", + "1 woman False Cherbourg False \n", + "2 woman False Southampton True \n", + "3 woman False Southampton False \n", + "4 man True Southampton True " + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_kashti = kashti.drop(['deck','alive'],axis=1)\n", + "new_kashti.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_6824/3332994036.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " kashti.mean()\n" + ] + }, + { + "data": { + "text/plain": [ + "survived 0.383838\n", + "pclass 2.308642\n", + "age 29.699118\n", + "sibsp 0.523008\n", + "parch 0.381594\n", + "fare 32.204208\n", + "adult_male 0.602694\n", + "alone 0.602694\n", + "dtype: float64" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kashti.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclassagesibspparchfareadult_malealone
sexclass
femaleFirst0.9680851.034.6117650.5531910.457447106.1257980.0000000.361702
Second0.9210532.028.7229730.4868420.60526321.9701210.0000000.421053
Third0.5000003.021.7500000.8958330.79861116.1188100.0000000.416667
maleFirst0.3688521.041.2813860.3114750.27868967.2261270.9754100.614754
Second0.1574072.030.7407070.3425930.22222219.7417820.9166670.666667
Third0.1354473.026.5075890.4985590.22478412.6616330.9193080.760807
\n", + "
" + ], + "text/plain": [ + " survived pclass age sibsp parch fare \\\n", + "sex class \n", + "female First 0.968085 1.0 34.611765 0.553191 0.457447 106.125798 \n", + " Second 0.921053 2.0 28.722973 0.486842 0.605263 21.970121 \n", + " Third 0.500000 3.0 21.750000 0.895833 0.798611 16.118810 \n", + "male First 0.368852 1.0 41.281386 0.311475 0.278689 67.226127 \n", + " Second 0.157407 2.0 30.740707 0.342593 0.222222 19.741782 \n", + " Third 0.135447 3.0 26.507589 0.498559 0.224784 12.661633 \n", + "\n", + " adult_male alone \n", + "sex class \n", + "female First 0.000000 0.361702 \n", + " Second 0.000000 0.421053 \n", + " Third 0.000000 0.416667 \n", + "male First 0.975410 0.614754 \n", + " Second 0.916667 0.666667 \n", + " Third 0.919308 0.760807 " + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kashti.groupby(['sex','class']).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "survived\n", + "0 549\n", + "1 342\n", + "dtype: int64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kashti.value_counts(['survived'])" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclassagesibspparchfareadult_malealone
sexclass
femaleFirst0.8750001.014.1250000.5000000.875000104.0833370.0000000.125000
Second1.0000002.08.3333330.5833331.08333326.2416670.0000000.166667
Third0.5428573.08.4285711.5714291.05714318.7279770.0000000.228571
maleFirst1.0000001.08.2300000.5000002.000000116.0729000.2500000.000000
Second0.8181822.04.7572730.7272731.00000025.6594730.1818180.181818
Third0.2325583.09.9632562.0697671.00000022.7525230.3488370.232558
\n", + "
" + ], + "text/plain": [ + " survived pclass age sibsp parch fare \\\n", + "sex class \n", + "female First 0.875000 1.0 14.125000 0.500000 0.875000 104.083337 \n", + " Second 1.000000 2.0 8.333333 0.583333 1.083333 26.241667 \n", + " Third 0.542857 3.0 8.428571 1.571429 1.057143 18.727977 \n", + "male First 1.000000 1.0 8.230000 0.500000 2.000000 116.072900 \n", + " Second 0.818182 2.0 4.757273 0.727273 1.000000 25.659473 \n", + " Third 0.232558 3.0 9.963256 2.069767 1.000000 22.752523 \n", + "\n", + " adult_male alone \n", + "sex class \n", + "female First 0.000000 0.125000 \n", + " Second 0.000000 0.166667 \n", + " Third 0.000000 0.228571 \n", + "male First 0.250000 0.000000 \n", + " Second 0.181818 0.181818 \n", + " Third 0.348837 0.232558 " + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cuz children and women are first\n", + "#Children\n", + "kashti[kashti['age']<18].groupby(['sex','class']).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graphs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The syntax of the command is incorrect.\n" + ] + } + ], + "source": [ + "pip install plotly|" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "pak = pd.read_csv(\"Pakistan.csv\")\n", + "#ind = pd.read_csv(\"India.csv\")\n", + "\n", + "\n", + "#plot lineplot\n", + "\n", + "sns.relplot(data=pak,x=\"Year Code\",y=\"Value\",hue=\"Area Code\",size=\"Element Code\",col=\"Item Code\",kind=\"line\",size_order=[\"T1\",\"T2\"],height=5,aspect=.75,facet_kws=dict(sharex=False))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{hovertext}

Year Code=%{x}
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