I have a fairly big dataframe with around 20 columns containing floats, and one column with a factor.

I want to multiply every value in each row with the factor for that row.

How can I do this fairly fast?

I have considered df.iterrows() to loop and try to multiply all values with row['Factor'], but this has not been successful.

Any suggestion is appreciated.

CodePudding user response：

For in place modification, you could use filter to select the columns, mul to perform the multiplication, and update to update the DataFrame with the new values:

df.update(df.filter(like='col').mul(df['factor'], axis=0))

NB. with update, the modification is in place, there is no output, df is modified directly.

resulting df:

  index     col1     col2  factor
0  row1    5.252   10.605    1.01
1  row2  120.393  184.210    1.30
2  row3    5.225  390.564    1.90

CodePudding user response：

Use Index.difference for columns names for multiple by column with DataFrame.mul:

cols = df.columns.difference(['factor'])
df[cols] = df[cols].mul(df['factor'], axis=0)
print (df)
         col1     col2  factor
row1    5.252   10.605    1.01
row2  120.393  184.210    1.30
row3    5.225  390.564    1.90