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how to apply a function to multiple columns in pandas dataframe

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Darkstar answered on September 22, 2020 Popularity 10/10 Helpfulness 1/10

answer how to apply a function to multiple columns in pandas dataframe

related pandas apply function on two columns

related pandas pass two columns to function

related pandas set two columns on apply function

related apply on dataframe access multiple columns

related pandas assign multiple columns at once

related assign multiple columns pandas

related how to create multiple columns after applying a function in pandas column python

related pandas operations with multiple columns

related df multiple columns into one column

how to apply a function to multiple columns in pandas dataframe

Comment

-1

>>> print df
   A  B  C
0 -1  0  0
1 -4  3 -1
2 -1  0  2
3  0  3  2
4  1 -1  0
>>> print df.applymap(lambda x: x>1)
       A      B      C
0  False  False  False
1  False   True  False
2  False  False   True
3  False   True   True
4  False  False  False

Popularity 10/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: apply dataframe function multiple-columns pandas python

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Contributed on Oct 17 2021

Darkstar

0 Answers Avg Quality 2/10

Closely Related Answers

pandas apply function on two columns

Comment

def stream_half(col1, col2):
    if col1 == 'desktop':
        return col2/2
    else:
        return int(col2)
        
df['clean'] = df.apply(lambda row: stream_half(row['device'], 
  row['streams']), axis = 1)

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: function functio

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Contributed on Mar 30 2022

Kat

0 Answers Avg Quality 2/10

pandas pass two columns to function

Comment

#Method 1:
df["Delivery Charges"] = df[["Weight", "Package Size", "Delivery Mode"]].apply(
  lambda x : calculate_rate(*x), axis=1)

#Method 2:
df["Delivery Charges"] = df.apply(
  lambda x : calculate_rate(x["Weight"], 
  x["Package Size"], x["Delivery Mode"]), axis=1)

Popularity 10/10 Helpfulness 4/10 Language python

Source: codeforests.medium.com

Tags: function functio

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Contributed on May 14 2021

Exuberant Eland

0 Answers Avg Quality 2/10

pandas set two columns on apply function

Comment

This allows f to be a user-defined function with multiple input values and uses column names rather than numeric indices to access the columns

df['new_column'] = df.apply(lambda x: f(x.col_1, x.col_2), axis=1)

Popularity 7/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: apply function pandas pa

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Contributed on May 11 2023

FishBrawler

0 Answers Avg Quality 2/10

apply on dataframe access multiple columns

Comment

df['col_3'] = df.apply(lambda x: x.col_1 + x.col_2, axis=1)

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: apply appl

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Contributed on Mar 22 2022

Tirbo06

0 Answers Avg Quality 2/10

pandas assign multiple columns at once

Comment

df = pd.DataFrame({'col1':[0,1,2], 'col2':[0,1,2], 'col3':[0,1,2]})

df
   col1  col2  col3
0     0     0     0
1     1     1     1
2     2     2     2

df['col1'], df['col2'], df['col3'] = zip(*df.apply(lambda r: (1, 2, 3), axis=1))

df
   col1  col2  col3
0     1     2     3
1     1     2     3
2     1     2     3

Popularity 10/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: assign as

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Contributed on Jan 12 2022

serhii_rb

0 Answers Avg Quality 2/10

assign multiple columns pandas

Comment

import pandas as pd

df = {'col_1': [0, 1, 2, 3],
        'col_2': [4, 5, 6, 7]}
df = pd.DataFrame(df)

df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3]  #thought this wo

Popularity 10/10 Helpfulness 2/10 Language python

Source: www.edureka.co

Tags: assign as

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Contributed on Sep 22 2020

0 Answers Avg Quality 2/10

how to create multiple columns after applying a function in pandas column python

Comment

>>> df = pd.DataFrame([[i] for i in range(10)], columns=['num'])
>>> df
    num
0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9

>>> def powers(x):
>>>     return x, x**2, x**3, x**4, x**5, x**6

>>> df['p1'], df['p2'], df['p3'], df['p4'], df['p5'], df['p6'] = \
>>>     zip(*df['num'].map(powers))

>>> df
        num     p1      p2      p3      p4      p5      p6
0       0       0       0       0       0       0       0
1       1       1       1       1       1       1       1
2       2       2       4       8       16      32      64
3       3       3       9       27      81      243     729
4       4       4       16      64      256     1024    4096
5       5       5       25      125     625     3125    15625
6       6       6       36      216     1296    7776    46656
7       7       7       49      343     2401    16807   117649
8       8       8       64      512     4096    32768   262144
9       9       9       81      729     6561    59049   531441

Popularity 10/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: function functio

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Contributed on Mar 30 2022

Darkstar

0 Answers Avg Quality 2/10

pandas operations with multiple columns

Comment

df.query('A in @mylist')

Popularity 9/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: multiple-columns mult

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Contributed on Oct 08 2022

Real Raccoon

0 Answers Avg Quality 2/10

df multiple columns into one column

Comment

-1

df.stack().reset_index()

   level_0   level_1  0
0        0  Column 1  A
1        0  Column 2  E
2        1  Column 1  B
3        1  Column 2  F
4        2  Column 1  C
5        2  Column 2  G
6        3  Column 1  D
7        3  Column 2  H

Popularity 10/10 Helpfulness 1/10 Language python

Source: stackoverflow.com

Tags: multiple-columns mult

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Contributed on Jul 27 2021

Benja

0 Answers Avg Quality 2/10

how to apply a function to multiple columns in pandas dataframe

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how to apply a function to multiple columns in pandas dataframe

Closely Related Answers

pandas apply function on two columns

pandas pass two columns to function

pandas set two columns on apply function

apply on dataframe access multiple columns

pandas assign multiple columns at once

assign multiple columns pandas

how to create multiple columns after applying a function in pandas column python

pandas operations with multiple columns

df multiple columns into one column

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