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apply on dataframe access multiple columns

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Tirbo06 answered on September 22, 2020 Popularity 10/10 Helpfulness 4/10

answer apply on dataframe access multiple columns

related pandas apply function on two columns

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related Apply multiple columns

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apply on dataframe access multiple columns

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

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Source: Grepper

Tags: apply dataframe multiple-columns python

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Contributed on Mar 22 2022

Tirbo06

0 Answers Avg Quality 2/10

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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

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select multiple columns in pandas dataframe

Comment

df1 = df[['a', 'b']]

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Source: stackoverflow.com

Tags: dataframe data

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Contributed on Aug 25 2021

Zealous Zebra

0 Answers Avg Quality 2/10

pandas set two columns on apply function

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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 8/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: apply function func

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Contributed on May 11 2023

FishBrawler

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Apply multiple columns

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def f(x):
    d = {}
    d['a_sum'] = x['a'].sum()
    d['a_max'] = x['a'].max()
    d['b_mean'] = x['b'].mean()
    d['c_d_prodsum'] = (x['c'] * x['d']).sum()
    return pd.Series(d, index=['a_sum', 'a_max', 'b_mean', 'c_d_prodsum'])

df.groupby('group').apply(f)

         a_sum     a_max    b_mean  c_d_prodsum
group                                           
0      0.864569  0.446069  0.466054     0.173711
1      1.478872  0.843026  0.687672     0.630494

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Source: stackoverflow.com

Tags: apply multiple-columns multipl

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Contributed on Aug 20 2022

Wicked Wasp

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

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Tags: assign as

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Contributed on Jan 12 2022

serhii_rb

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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

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Source: www.edureka.co

Tags: assign as

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Contributed on Sep 22 2020

0 Answers Avg Quality 2/10

pandas select multiple columns

Comment

#Example, in a df with about 20 columns
#If you want to select columns 1-3, 7, 8, 12-15
# Use numpy's np.r_ to slice the columns and parse it into pandas iloc[] slicer

df.iloc[:, np.r_[1:3, 7, 8, 12:15]]
#This selects all rows "df.iloc[:," and then these selected columns "np.r_[1:3, 7, 8, 12:15]]"
#Remember to import numpy ;)

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Source: stackoverflow.com

Tags: multiple-columns mult

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Contributed on May 26 2022

Kwams

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

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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

apply on dataframe access multiple columns

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apply on dataframe access multiple columns

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pandas apply function on two columns

select multiple columns in pandas dataframe

pandas set two columns on apply function

Apply multiple columns

pandas assign multiple columns at once

assign multiple columns pandas

pandas select multiple columns

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

pandas operations with multiple columns

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