Pandas DataFrame pivot_table() Method
In this tutorial, we will discuss and learn the Python pandas DataFrame.pivot_table()
method. This method can be used to aggregate and summarize the data of the DataFrame. When this method is applied to the DataFrame, it returns a spreadsheet-style pivot table as a DataFrame.
The below is the syntax of the DataFrame.pivot_table()
method.
Syntax
DataFrame.pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False)
Parameters
values: It represents the column to aggregate, which is optional.
index: It represents the column, Grouper, array, or list of the previous.
columns: It represents the column, Grouper, array, or list of the previous.
aggfunc: It represents the function, list of functions, dict, and the default is numpy.mean
Example: Aggregate the DataFrame using the DataFrame.pivot_table()
Method
By default, the DataFrame.pivot_table()
method aggregate the data of the DataFrame using the function np.mean
. Here, in this example, we reshaped the DataFrame by the 'Date' as index axis, 'state' as column axis and aggregate the data according to this using the DataFrame.pivot_table()
method. See the below example.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df=pd.DataFrame({'Date':['1/1/2021','1/1/2021','2/1/2021','2/1/2021','1/1/2021','1/1/2021','2/1/2021','2/1/2021'],
'state':['karnataka','karnataka','karnataka','karnataka','Tamilnadu','Tamilnadu','Tamilnadu','Tamilnadu'],
'Tempreture':[25,29,28,31,26,27,22,32],
'Humidity':[46,50,52,59,42,45,46,43]})
df.pivot_table(index='Date',columns='state')
![](data:image/png;base64,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)
Example: Apply function using the DataFrame.pivot_table()
Method
We can pass different functions to the DataFrame.pivot_table()
method using the aggfunc
parameter. See the below example.
The DataFrame.pivot_table()
method returns the DataFrame according to the specified function.
#importing pandas as pd
import pandas as pd
import numpy as np
#creating the DataFrame
df=pd.DataFrame({'Date':['1/1/2021','1/1/2021','2/1/2021','2/1/2021','1/1/2021','1/1/2021','2/1/2021','2/1/2021'],
'state':['karnataka','karnataka','karnataka','karnataka','Tamilnadu','Tamilnadu','Tamilnadu','Tamilnadu'],
'Tempreture':[25,29,28,31,26,27,22,32],
'Humidity':[46,50,52,59,42,45,46,43]})
df.pivot_table(index='Date',columns='state',aggfunc='max')
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbsAAACyCAYAAADbEWFiAAAcO0lEQVR4Ae2dS47buhZFNcMABmo4FwEKnkdarv6bQF6vMofXuJ00agRp80EkN3n4kWQ5dllSrQsEZUkUP+vsczalcnIHx38QgAAEIACBgxMYDr4+lgcBCEAAAhBwmB0igAAEIACBwxPA7A4fYhYIAQhAAAKYHRqAAAQgAIHDE8DsDh9iFggBCEAAApgdGoAABCAAgcMTwOwOH2IWCAEIQAACmB0agAAEIACBwxPA7A4fYhYIAQhAAAKYHRqAAAQgAIHDE8DsDh9iFggBCEAAApgdGoAABCAAgcMTwOwOH2IWCAEIQAACmB0agAAEIACBwxPA7A4fYhYIAQhAAAKYHRqAAAQgAIHDE8DsDh9iFggBCEAAApgdGoAABCAAgcMTwOwOH2IWCAEIQAACmB0agAAEIACBwxPA7A4fYhYIAQhAAAKYHRqAAAQgAIHDE8DsDh9iFggBCEAAApgdGoAABCAAgcMTwOzuFOKPt5MbhsGd3j6qHj/c5WXw186/qkv3Pvx9cadhcMPLxdWzyENpPid3+T2efXfn8Z7h7N5zIz59JQK/zl6fo377f6SVrwSFtR6NAGZ3p4gezuzGAviK/d1JHtvu5qhmN27+Zjd+2w4Ls7svAczuTjw3YXZXraV+smtv0lowu5bN8c8c5ElfBo7ZHV+yV64Qs7sS1FIzGcQ1rzHfX9vXmvX9anN6u8TXjOM98XWSXlfWryx1vkhwmVsY8/T2Hl+r6tVUWdw0j/w66+Qu/4mvuYonvfK+JT5c3wuBibjKPOKrzkLnuvZycZeo7fxK3+pPmgsspPHzL43Z5kVq8xZf0Q+D068Daq3qvNN8zGvZ8Zrad+cubZscSmtRPlX9Fv3sJbxfeJ6Y3Z2Cr0TKJhES1x4rGVMCm9/h6X4lkNrY+6c+6x5nEjX8zs4Wmno+KjwqNOF3dppHHmtsV7bxyJT4KhJ34kg3zyYwE2tjHqM+ku6khep61pDRnoxj/G2xMca6bZ0r+XrQbatTY5Sd+aw2O7uWUeOdPgsGzw4b4y8SwOwWEV3XYCr5cpLmHamSXAk9jqD7VUDUJn3ZREZmiozuSa8b1UYFRcd6IhwHSknbNzs7l9SvKUyas+an4+so0Wr7BGqzyxumHOuqTaOpfE+r3/xFKGkotTE607lem/ylKmnYuWajpzkpFzo55mOhdtq0pZzJ+epcXs8kg+0H9svPELO7kwRkPDKr3G2bKErgnDjTZpf7U4ExCT6VqErw+rqflOajftRvLkJaizW7ZJK+KLT35PXyad8E6tjq2DydpaeeqCHpTLozptXqN+uslwfJtOK3g9Um92OMLc3Dzi3235mTdF30pXaN2eV5ZnO14+iz8mjfUf8Ks8fs7hTlbiL5vmUueafYS+D6XH2cE84k4VSiqujU14v5KElVzHK/WkthdvavKHT7vRNIunkygVoPOlZxtz+XzS5v6NRP1pk0nttYIwvt5tvYuehz7F8aVS6YJzur60brerIz9+Xc0xj2p/LoyWFj+EUCmN0iousaKGmKXaO/ddrsctKpEOTfgyjJc39qk4tF+bRlCoUSVYm78jVm029EoDnp1WxRpK7DRKvNE6h11uq3WULHWKSVrJG6X/M7O+nVGlI81/Yzjq6+ZoxG2p/p276eTLnYuc+2y+tpKHBi4wQwuzsFaI3Zqa1Mw/6UuSnJdZwTfIXZmd812DHCZxUKFY5Ov/41kdrZ3/eNO1vT/k4M6WYLBJb0YJ5qZCR/a3ad15EyFeWBjkVoMoea15Fhvv5+zbMz3rzZ1drvMNDE+LlZApjdnUKj5MvmpI77O2MlsTeel4t7r/4FFl3P/c0UoTrBVYT8FPL441hLf/UgzFpjmUJR96UxtUx+HoSAYl9tZmqjsBrTNXNO+s0m1fab25Qazffkpz97TqCVc2kjV2iy7FN5VN5zdu+au+7tPtnFEdVWZmnWqznxc7sEMLvtxmaDM8sFpFd8NjhhprRhAtnsNjxJpnYYApjdYUL5CQtJO9tq1/8JQzPE8QhgdseL6ZZXhNltOTqbmVt+okv/istm5sZE9koAs9tr5PY5b8xun3Fj1hCAAAQgsIIAZrcCFk0hAAEIQGCfBDC7fcaNWUMAAhCAwAoCmN0KWDSFAAQgAIF9EsDs9hk3Zg0BCEAAAisIYHYrYNEUAhCAAAT2SQCz22fcmDUEIAABCKwggNmtgEVTCEAAAhDYJwHMbp9xY9YQgAAEILCCAGa3AhZNIQABCEBgnwQwu33GjVlDAAIQgMAKApjdClg0hQAEIACBfRLA7PYZN2YNAQhAAAIrCGB2K2DRFAIQgAAE9kkAs9tn3Jg1BCAAAQisIIDZrYBFUwhAAAIQ2CcBzG6fcWPWEIAABCCwggBmtwLWbNNfZzcMw8yfk7v8nu3hpov1/+25Pr6p06tu0v+9/DHr6k3h4+3k+Z7ePnqX93nu98WdRt28XNzTVoV2H64dtPtwxIsD3MnsxsK3vuiNhfn8a3GO+2hAwXh4nHZRMEbzWmNcmF2qAWzUHp5C8wPsUbvzKyqu3sHs3t3ZP9GsMTs9FRzI7AqsYnJ278X5xx9QMB7PeHIEbXj2ZnbFgtBugeNOB5vfqB1Cu/PBusrsVEDTa7qUzEoM8/ruNZZ3wTOv9sJTXDa6tr/62uebxTyua6+KSzn/huNgrovXy8VdXjPP8MrOcik3FepTT8j1sU0yXfPcUwzjmjR+Ey+t2c5hcKe3d3d5GeeZ56P+NZfxTju+err1Z9NXmnOeg0vnMsM8nxyXS3wlOvgY5PPvetKKHPK9zrnq2sgxvVKdGVdckt5t3NVniofmYvp2Jftu/G6F2tyn8Y02nXOza9Da0W5DUyfQbs7HZ72yXzS7VuRx0t7UlBhmIeN5JbApnCHRxwSaStzOeX9/mXQSz7Z/ikueu8SeC57l6FyvSDdtxTMVxlyEVJQVLx1PjmsL9Wy8RtJTsRnXkI2mHtvfecffs2kt3mDSnPP487obZ6O4GL16lp3zYp2MaapNHF8FP90X3lpozk0stSnUOvw8Mudkol2jCfO3be6XD1on2lXs7sG56EsxN7mDdu+n4KmerjY7Fc+2IyWHKTpNo7pNTurUr4qFKeSFQJo+t3xC680Fo5ltvV4dpwTIjNJOKCVJ7rc2mPpYDMMTTJiF2gwquM3kNP8Y0zSuiXEz39Z4x241/l0Lxus5fKljWHoNXq3DmF05H7UzT1O9NReccnzmNFzcMh6Im3SucV7O7uyflAc3HZfQm5gutWvGvuqEWGSNNbfVa9Ax2m1Q6YRidkK74Ut8k7VHxO7/c9HsUnJWO9Y8FSWHKYTpoq5pJ602baGQGJod8DjuE8CkJdz0QevuFIxUGCITFT2d17HZ0efC3PYr41LBrY8TV8tQY9lzfp3qv4pXt71iqJh+ntlljeSxyzBNrCOZXX2f2tvz7frCGDovRsZwxcnEMM1L15RHaiOz03n7xJ1ujiOnV69x7CZ+1Q03HYoF2lXu5Py7Cai/SX2h3Udqdz4+y2an+6eStVdAlMBK6KZNLhgq0q0YcjE5gtlpfSlxxFOM6mNjdmKUX8PlQlSbW33cjDvGU2OpWC7Fq24f0nfyd3ZpjWYN9pwktfan1pILRrURWlpH0mHmF+bQK/DSqAywbqPr82anOaf1i6Xirjkbs7NP4eP8pmL6mLyo19l5Oq/XUB935ox2w1+bQbuRg2rP2iLwF+2vN7s0iJJBRUBJr+OcnCnBU0K3bVIh7yRMGnJ3H8RIRVXHef0qgukVZWf9dZF7VMHQOJPx6sQvGWZ6dZXjnouw1m1eD/5FLMUszFO6y2azuI6/MDuN3Vtb0rA4ycjSeDNxr+5pxtH19LvDHue/gNrcqpih3fAtc7Srvx+ctCl9S5vxOF2Xken6p2m3EXNxYsHsckEpdiTjLlQLrr+88Pqefk/T3NMrjKmv6bFSMSmmvuWDumBMry1xfKLZJZEWTxfjk7WK9Mz8U5v8BNDG/REFw3w7sk62yXXUcZGGeue15shA8Wn6zmZbf8ng/Et9mLcUul/5o4Kg45RPYq+5dfpQUdEy7vJT48nsrliD2KQ1ZEPOuVv327bRZkX3SJdpEzauT2PFtatNqznxm5k/2k1/x/EY2p1PgAWzCzdLhFlQSoRwvRCcF3wpsFGsapOEK9H65Fd/5X3jeBL+/DK2drVN7FJMYyJqrVUxfULBqL9t2Y1XKsKh6Pb+6sEYhUIrLxf3/qhvY8aQa7ygKzHVHGvddeLi++mdV18qmtXaxmIrDSfT0T15/MW4N2ZnCrq0oHFSrvTme68c6PStOfrx0e4tpJv6Z3IF7d5CdP09V5nd+m65AwIQgAAEILAdApjddmLBTCAAAQhA4EEEMLsHgaVbCEAAAhDYDgHMbjuxYCYQgAAEIPAgApjdg8DSLQQgAAEIbIcAZredWDATCEAAAhB4EAHM7kFg6RYCEIAABLZDALPbTiyYCQQgAAEIPIgAZvcgsHQLAQhAAALbIYDZbScWzAQCEIAABB5EALN7EFi6hQAEIACB7RDA7LYTC2YCAQhAAAIPIoDZPQgs3UIAAhCAwHYIYHbbiQUzgQAEIACBBxHA7B4Elm4hAAEIQGA7BDC77cSCmUAAAhCAwIMIYHYPAku3EIAABCCwHQKY3XZiwUwgAAEIQOBBBDC7B4GlWwhAAAIQ2A4BzG47sWAmEIAABCDwIAIbMrt3dx4GN/T+vFzcx4MA0C0EIAABCByfwD7Mzhvg2b2vjMf76+DOv1beRHMIQAACEDgcgQ2aXW1q+Ynv9Hbt892Hu7yEp0TM7nCaZUEQgAAEVhPYgdk5535f3Gl8urOvM3+dm1eewdiy0aVXoum++lptrKv5cQMEIAABCOyAwD7MzunpLpqTzK/5/d54vTY0mWTn/I2vR3cQV6YIAQhAAAKGwM7M7uQuv83s00eZoa5nY0uvMfUkmJ7ynPt4O/mnw+tfj6YB+QABCEAAAjsisHOzk8npW5zTZidjS6827VPh69qvvuwowkwVAhCAAATcPswuvbasXmOmpzSZHmaHpiEAAQhAoCWwA7PLryT1unH8KwXjE5qO0xdYBpmdc2oz9xqzxcEZCEAAAhA4IoENmp1eSdY/8zcnp19JtmbnX1v6J8BsmvWrzGSIR4wwa4IABCAAgS2+xqxNbnBD8zu10rjGJzwZYHra0xdSim9clveNpofRkQUQgAAEjk9gQ092x4fNCiEAAQhA4DkEMLvncGdUCEAAAhD4RAKY3SfCZigIQAACEHgOAczuOdwZFQIQgAAEPpEAZveJsBkKAhCAAASeQwCzew53RoUABCAAgU8kgNl9ImyGggAEIACB5xDA7J7DnVEhAAEIQOATCWB2nwiboSAAAQhA4DkEMLvncGdUCEAAAhD4RAJD/e9Ectz558rs/w6Iz83/IR7NoBk0gAYeoYF7euHw588fxx8YoAE0gAbQwNY0gNlh0GxQ0AAaQAOH1wBmh8gPL/Kt7TCZD089aODzNYDZYXaYHRpAA2jg8BrA7BD54UXOLvrzd9Ewh/nWNIDZYXaYHRpAA2jg8BrA7BD54UW+tR0m8+GpBw18vgYwO8wOs0MDaAANHF4DmB0iP7zI2UV//i4a5jDfmgYwO8wOs0MDaAANHF4DTzS7n+67/+eyvruftdD+98N9Gwb3/b/l7ujfH9/8Py/17ce/TWDCtdyX2uqfnan7+hPH0PVenz//sf9szzf343/lfMLOZWYd9bo4buKWdn8+HhXjKkYhVlUbmLZM//u988+w5dwYmZfaLq+lmMC2ZSsmtTZPP9y/utblH2vJPz+n+9T9X/nnHFfP5V/342TqsuW+wO1pZpeTrU20YFRVUTMQWmOKAOLCa6OToWXDk0EZaJW55vnZNtWciqLRroOi0dsc9M5JwBVfXzTgulZHXv8zRSDkR+bqtT7Tfu34h28fa1GuJ3HzMMfQa7nS90JxPjzHev2LXMs6P/JZ0rpl+ASzU2GTieSk08Sa5Kt2Sq3ZBfMK53P/EmMyrrirSmaoXZb6T2KVGUqcuc88dj4XzLRdh9bDz57B5XNBsN/ct0G8w7U1QoZx5un1Lm3XBeVP0LZyI3Drncv9wbZk0dWlL9SlfjM3W5/KvnIbzi9y7TIOdbjUc5/lJ5udMYjTWNxGw6tNIrZRssqIhm/uW3x8zYYTF2V3BPFz0a/6iH3K/DIgmVuci9on84s7iGFwtaEOk+voA0fcFRfP+rv72RHyfNGu+mmK+le9HvKnyRHx6XAeNTmynrxH9/Jz+hXkBFexHUwtoQasyE3LVbWi0uG12n2K2XmT8YvomF08n4xoXGAUikyqTkq/I2hMMwPVfaFPGa7dhfXO5fv//Kmvh+PZdVQBQeCWpz6bJworas9OzPUGYPxpY6Y++FlqSxs3y81sKGcKxqANJtqdNrUJNpM1qK5nE/eXMUTT4lFwbWrEyCnWiSu0+8lmZ4IYRVA8gY1C8MnYL2oyrdrs/PmJnVOANSTDTHCKwqnCOj9utxhMrQNRLxYMHzeJtBFyLNq6vqANJceX/xn1aHOkKBiY3aIuV2vIM22/UDf249lP1KbV43y1mtJwjXXa8oxturW54rU5swuGZnaiZsJ9s+sUxXhPMrriqa9nbL1zwZg1pp4uG4FidrcVj7roNmZnNkZJAzFO1gDTtV57zgW9hhzxBlhzj/yKjQdMr9d0LLZ2c5FrRNBr/xrazJw6LCa5qlbHNxf//AzfLL6iJmzM7OaLmYynEE80m/TaMyZq3+gCVPWT79Grn9Jk1W7S6MaxMLvrC4Mpoolt5//WXsTX3DMmh7/vCmHPJlLV5/HbmqI7sakYuc5xPz6jTsFd0slkQY59TbCG5QLrJa5FXIy2i/PtGBszO7MD7UxcBbJISg+mev0YYU39jicZoYqm2pvH49SmeCpsAWJ2HSad2C0meF0Y6mPf5/XCXhzvljnu4R6v5XLT9qf4BmbIsbzRG+PXO3enuO6B2S1zjDWj5Fgx68aianPL2Ee+Z5ZrR6fdOtFnvC2z8wutjMsEtmd2/pwxKSWu/m5d8VPm5gHZX+CHz0m4E9fHvgqjHeeW2tYFpg/8yxZhE8cug0a07VN+2IDAucsv8W03jHWO1Bzr6/P9o2vlfKoXiX3JxnMualN5Hc4Vj1hL57iWWm1rxBzTTZldnYT1xP1CC8PpLDbuDAqT06symV1hUsHorImFebRmiNlV4pxI8jpuVx03ZjeOFeOr+C09Zd9zPrvuKxheyoFOwVUuhTZsIK7SaNREya6sE7ZQ+3a25uxaUw/M/VVcq5qwgu/zzI7A3/S7rjVJSdvHJyiMYYwG9qEBzA7TxXTRABpAA4fXAGaHyA8vcnbe+9h5Eyfi9EgNYHaYHWaHBtAAGji8BjA7RH54kT9yt0jfPI2ggX1oALPD7DA7NIAG0MDhNYDZIfLDi5yd9z523sSJOD1SA5gdZofZoQE0gAYOrwHMDpEfXuSP3C3SN08jaGAfGsDsMDvMDg2gATRweA3c1ezu2Rl9QQACEIAABLZIYNjipJgTBCAAAQhA4J4EMLt70qQvCEAAAhDYJAHMbpNhYVIQgAAEIHBPApjdPWnSFwQgAAEIbJIAZrfJsDApCEAAAhC4JwHM7p406QsCEIAABDZJALPbZFiYFAQgAAEI3JMAZndPmvQFAQhAAAKbJIDZbTIsTAoCEIAABO5J4Gqz+3g7uWEY0p/zr2oavy/uNAyuPq/7Tm8f1Q3OhWtn9x6vqK3GqftycQxd7/X5/prnOAwnd/ndDOuce3dnv5Y8dq8V5xYI+Hi0jMs4ttcXev2al3+dU25J38Ng9fnhLi9G2y8X12bU10R3/aorhq+qPOpBdUGcLX+14WdLYIlrvsPXhoZ7vv7IT1eZXVm8JITS2EKbqrAZc2qNKQKKSbs8Ri3EMA9riKXRaZ7VnEarS4aImG8XlwRe8g1sM9cQ13x8+3jHvtNzmjSwMldGEvPtj83qttVFhqnQhuNcl0J9ycftZvy2cY9+1xLXvP5U41MM8rXP+HSF2cXFmKe2ZBZm0v6cTdZqp2pFFBZmxbU8RgNK/acxZYYqvrnPPHY+F3bPFOFbRRaK7cmd7NNz9+k+xMVuSm4d88j3+fwx+VSstfsEHbQM14LU9IFnWOX7WENUP3w9qa7HN0AwnsYa3rZV3CxXf6vq7smdxrcTUzqfGeYel5bNLj2dmQXJaNKk42J0rOtDXNwwuGw4cdq2MF4xhgw2C0/mFuelMSVe7X7T2AI+uOFlLNLjk59Z0z1ofpU+POuze/dx0+bCOafzX4XD3dYZtNnkiPqf4DrmxOQ9upef0wQmuOYb2KhlFis+1VzH41iXZzd1K4a4pemy2XV6bYzHGtfYvl5cMpzcWXhSmzabcgwZlSmsrncu9++a66G9N8ueudpb+TxDwBSAyuzC097FfXixT79Gnun8i17Sxk3Mqo1YxTlAivrXBvOLkrt92YH57GbB69jWnNtH+zp3znPdldml14nmCSrs6PuikGnVovLnbR9GDe0YPWPrncudaNzuIzNml0Gt/FSItSrCKW6mAIdzfW2sHPq4zaMebY4EbtoMRq3bfNGGwrA+LqB7rkx1o9pQ1EN0YlI34dgSuI5rUT/s7Z/wedWTXSpm1eu/YCxKzHLWMh2byOnbkJ1E7Y8hkLZo9s6FsTWmHp3LGTmXv9XZn3PTnhOBgC+whlnP7GxB9nfFOHViDdY5AvUOWXqPT3+v7+GLVnCdgzh/berJLRpdd6M83yNXRwJTXPXlwCdp9mqz65vQuLL5YibjKcwuiin//i1oZHqM/A3KfI9e/ZjiK5jj7+Oaomt0KDFXpm1a8LFDQLHMX43Pr93G+Pr4dbj7+54k8M4ydnIq5FWRN8XMl64XjTnoEugwVG1Ar11i153scI03PrMWXGd2emViv3mXVl3vQNMF/0EFskjanvPPjqGvAZtv8qi9Ka5zZlnMSoLG7Aosqw88R/O07Y/LzYc2Q0X8Vw908Bu8lmtuIa/C5s5+jixq9gdH9NfL6zKuinKsC2h1Be1ruJruNm52eoLKu/i0sx93P36xpuCZhY0fe2bnzxmTSq81zV9aL8YYO4pCTOdj2/SkN3F9bN+IN7WtC0w1eQ7nCXiOZexrMYcNSNlmvtOveLXdMNY5Uh6HIs1rtjVaiXXMPLEFbaoGtDFY0/vXbbvEtSRT14fy6mOPlp/svJl1jG40m9f35l9BqafrF1cYTidRF8ZIfSaTCvOxJpaf6tq52na+r9SPhJ5G4MMaAh2zG29XzMPGBKO7DmksGtrwFZvBsYeYN7puivZ1/dOq3VTn/F9VP0BZEai0O/PGbNtmVy2LQwhAAAIQgMDeCCw/2e1tRcwXAhCAAAQgUBHA7CogHEIAAhCAwPEIYHbHiykrggAEIACBigBmVwHhEAIQgAAEjkcAszteTFkRBCAAAQhUBDC7CgiHEIAABCBwPAKY3fFiyoogAAEIQKAigNlVQDiEAAQgAIHjEcDsjhdTVgQBCEAAAhUBzK4CwiEEIAABCByPAGZ3vJiyIghAAAIQqAhgdhUQDiEAAQhA4HgEMLvjxZQVQQACEIBARQCzq4BwCAEIQAACxyOA2R0vpqwIAhCAAAQqAphdBYRDCEAAAhA4HoH/A+hHczwNUrDrAAAAAElFTkSuQmCC)
Example: Pass list
of Functions to the pivot_table() Method
We can pass the list
of functions to the DataFrame.pivot_table()
method using the aggfunc
parameter. The DataFrame.pivot_table()
method returns the DataFrame consists of hierarchical columns where function names are at the top level. See the below example.
#importing pandas as pd
import pandas as pd
import numpy as np
#creating the DataFrame
df=pd.DataFrame({'Date':['1/1/2021','1/1/2021','2/1/2021','2/1/2021','1/1/2021','1/1/2021','2/1/2021','2/1/2021'],
'state':['karnataka','karnataka','karnataka','karnataka','Tamilnadu','Tamilnadu','Tamilnadu','Tamilnadu'],
'Tempreture':[25,29,28,31,26,27,22,32],
'Humidity':[46,50,52,59,42,45,46,43]})
df.pivot_table(index='Date',columns='state',aggfunc=['sum','count'])
![](data:image/png;base64,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)
Example: Set margins=True
to the DataFrame.pivot_table()
Method
If the parameter margins=True
in the DataFrame.pivot_table()
method, it adds the 'All'
row and column in the resulted DataFrame that consists of the aggregate of the values. See the below example.
#importing pandas as pd
import pandas as pd
#creating the DataFrame
df=pd.DataFrame({'Date':['1/1/2021','1/1/2021','2/1/2021','2/1/2021','1/1/2021','1/1/2021','2/1/2021','2/1/2021'],
'state':['karnataka','karnataka','karnataka','karnataka','Tamilnadu','Tamilnadu','Tamilnadu','Tamilnadu'],
'Tempreture':[25,29,28,31,26,27,22,32],
'Humidity':[46,50,52,59,42,45,46,43]})
df.pivot_table(index='Date',columns='state',margins=True)
![](https://lh6.googleusercontent.com/Yd-1YzobFlJ7L_I4Fg45v3OcmnxBxUg4U-mj46vCj7OvrDldrc6OOiaHL1UP-eUqX0nN0QZPEqaC2duyy1z644TGGbXEUlAnxN_XzzSrqs8d1VJ17QGVZVSczxoeavbKIcEXirip)
Conclusion
In this tutorial, we learned the Python pandas DataFrame.pivot_table() method. We learned syntax, parameters, and solved examples by applying this method on the DataFrame.