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set dtype for multiple columns pandas
import pandas as pd
df = pd. DataFrame( { 'id' : [ 'a1' , 'a2' , 'a3' , 'a4' ] ,
'A' : [ '0' , '1' , '2' , '3' ] ,
'B' : [ '1' , '1' , '1' , '1' ] ,
'C' : [ '0' , '1' , '1' , '0' ] } )
df[ [ 'A' , 'B' , 'C' ] ] = df[ [ 'A' , 'B' , 'C' ] ] . apply ( pd. to_numeric, axis = 1 )
pandas assign multiple columns at once
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
assign multiple columns pandas
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 ]
df multiple columns into one column
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
how to create multiple columns after applying a function in pandas column python
>> > 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
pandas assign multiple columns
In [ 1069 ] : df. assign( ** { 'col_new_1' : np. nan, 'col2_new_2' : 'dogs' , 'col3_new_3' : 3 } )
Out[ 1069 ] :
col_1 col_2 col2_new_2 col3_new_3 col_new_1
0 0 4 dogs 3 NaN
1 1 5 dogs 3 NaN
2 2 6 dogs 3 NaN
3 3 7 dogs 3 NaN
insert multiple column pandas
df[ 'column_new_1' ] , df[ 'column_new_2' ] , df[ 'column_new_3' ] = [ np. nan, 'dogs' , 3 ]
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