I’m wondering if there’s any simpler way to assign multiple columns in Python, just like the := in R data.table.
For example, in Python I would have to write like this:
df['Col_A'] = df.A/df.B
df['Col_B'] = df.C/df.D
df['Col_C'] = df.E/df.F * 1000000
df['Col_D'] = df.G/df.H * 1000000
However, it’s just one line of code in R data.table:
df[, ':='(Col_A = A/B, Col_B = C/D, Col_C = E/F*1000000, Col_B = G/H*1000000)]
>Solution :
You can use DataFrame.assign to assign multiple columns:
df = df.assign(Col_A=df.A/df.B, Col_B=df.C/df.D, Col_C=df.E/df.F*1000000, Col_D=df.G/df.H*1000000)
Example:
df = pd.DataFrame(np.random.random((4, 8)), columns=list('ABCDEFGH'))
# A B ... H
# 0 0.771211 0.238201 ... 0.311904
# 1 0.197548 0.635218 ... 0.626639
# 2 0.332333 0.838589 ... 0.477978
# 3 0.929690 0.327412 ... 0.046179
df = df.assign(Col_A=df.A/df.B, Col_B=df.C/df.D, Col_C=df.E/df.F*1000000, Col_D=df.G/df.H*1000000)
# A B ... H Col_A Col_B Col_C Col_D
# 0 0.771211 0.238201 ... 0.311904 3.237647 1.547285 1.463586e+06 2.845234e+06
# 1 0.197548 0.635218 ... 0.626639 0.310993 1.385892 1.394466e+07 2.685293e+05
# 2 0.332333 0.838589 ... 0.477978 0.396300 0.078238 8.494174e+06 6.001031e+05
# 3 0.929690 0.327412 ... 0.046179 2.839514 0.852443 1.962892e+06 8.791233e+06
If you want column names with spaces, you can use a dict:
df = df.assign(**{'Col A': df.A/df.B, 'Col B': df.C/df.D, 'Col C': df.E/df.F*1000000, 'Col D': df.G/df.H*1000000})
# A B ... H Col A Col B Col C Col D
# 0 0.868320 0.086743 ... 0.505330 10.010311 6.680195 1.147554e+06 2.620416e+05
# 1 0.244341 0.908793 ... 0.389684 0.268863 2.388179 2.196769e+06 2.235063e+06
# 2 0.917949 0.248149 ... 0.710027 3.699188 0.453094 1.311617e+06 1.004200e+06
# 3 0.616655 0.498817 ... 0.703579 1.236235 2.186589 1.272981e+06 8.602272e+05