apply a function to multiple columns in pandas

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Caleb
15 Apr 2018
1#Method 1:
2df["Delivery Charges"] = df[["Weight", "Package Size", "Delivery Mode"]].apply(
3  lambda x : calculate_rate(*x), axis=1)
4
5#Method 2:
6df["Delivery Charges"] = df.apply(
7  lambda x : calculate_rate(x["Weight"], 
8  x["Package Size"], x["Delivery Mode"]), axis=1)
source
Lilas
20 May 2016
1# Basic syntax:
2df[['new_column_1_name', 'new_column_2_name']] = pd.DataFrame([[np.nan, 'word']], index=df.index)
3# Where the columns you're adding have to be pandas dataframes
4
5# Example usage:
6# Define example dataframe:
7import pandas as pd
8import numpy as np
9df = pd.DataFrame({
10    'col_1': [0, 1, 2, 3],
11    'col_2': [4, 5, 6, 7]
12})
13
14print(df)
15   col_1  col_2
160      0      4
171      1      5
182      2      6
193      3      7
20
21# Add several columns simultaneously:
22df[['new_col_1', 'new_col_2', 'new_col_3']] = pd.DataFrame([[np.nan, 42, 'wow']], index=df.index)
23print(df)
24   col_1  col_2  new_col_1  new_col_2 new_col_3
250      0      4        NaN         42       wow
261      1      5        NaN         42       wow
272      2      6        NaN         42       wow
283      3      7        NaN         42       wow
29
30# Note, this isn't much more efficient than simply doing three
31#	separate assignments, e.g.:
32df['new_col_1'] = np.nan
33df['new_col_2'] = 42
34df['new_col_3'] = 'wow'
source
Giacomo
06 Apr 2019
1import pandas as pd
2
3df = {'col_1': [0, 1, 2, 3],
4        'col_2': [4, 5, 6, 7]}
5df = pd.DataFrame(df)
6
7df[[ 'column_new_1', 'column_new_2','column_new_3']] = [np.nan, 'dogs',3]  #thought this wo
source
Mitchell
31 Nov 2020
1In [49]: df
2Out[49]: 
3          0         1
40  1.000000  0.000000
51 -0.494375  0.570994
62  1.000000  0.000000
73  1.876360 -0.229738
84  1.000000  0.000000
9
10In [50]: def f(x):    
11   ....:  return x[0] + x[1]  
12   ....:  
13
14In [51]: df.apply(f, axis=1) #passes a Series object, row-wise
15Out[51]: 
160    1.000000
171    0.076619
182    1.000000
193    1.646622
204    1.000000
21
source
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