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pandas set two columns on apply function

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FishBrawler answered on September 22, 2020 Popularity 8/10 Helpfulness 4/10

answer pandas set two columns on apply function

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related put two columns together in one column in pandas dataframe

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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)

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Source: Grepper

Tags: apply function pandas python set two-columns

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Contributed on May 11 2023

FishBrawler

0 Answers Avg Quality 2/10

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pandas apply function on two columns

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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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pandas pass two columns to function

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#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

combine multiple columns in pandas dataframe

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cols = ['foo', 'bar', 'new']
df['combined'] = df[cols].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)

Popularity 9/10 Helpfulness 4/10 Language python

Source: stackoverflow.com

Tags: combine

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Contributed on Jan 29 2023

Niclas Bruhns

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 3/10 Language python

Source: Grepper

Tags: apply appl

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Contributed on Mar 22 2022

Tirbo06

0 Answers Avg Quality 2/10

pandas combine two columns into one

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df["station"] = df["End station"].combine_first(df["Start station"])
df.drop(["End station", "Start station"], 1, inplace=True)

Popularity 9/10 Helpfulness 2/10 Language python

Source: stackoverflow.com

Tags: combine

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Contributed on Nov 23 2022

FishBrawler

0 Answers Avg Quality 2/10

put two columns together in one column in pandas dataframe

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# First dataframe with three columns
dlist=["vx","vy","vz"]
df=pd.DataFrame(columns=dlist)
df["vx"]=df1["v2x"]
df["vy"]=df1["v2y"]
df["vz"]=df1["v2z"]
# second dataframe with three columns
dlist=["vx","vy","vz"]
df0=pd.DataFrame(columns=dlist)
df0["vx"]=df2["v1x"]
df0["vy"]=df2["v1y"]
df0["vz"]=df2["v1z"]
# Here with concat we can create new dataframe with garther both in one
# YOU CAN PUT SOME VALUES IN EACH AND CHECK IT
# WHAT INSIDE THE CONCAT MUST BE A LIST OF DATAFRAME
v = pd.concat([df,df0])

Popularity 10/10 Helpfulness 2/10 Language python

Source: Grepper

Tags: dataframe data

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Contributed on Aug 05 2022

Itchy Iguana

0 Answers Avg Quality 2/10

pandas assign multiple columns at once

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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

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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

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 set two columns on apply function

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pandas set two columns on apply function

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