Pandas, Multiply All The Numeric Values In The Data Frame By A Constant

How to multiply all the numeric values in the data frame by a constant without having to specify column names explicitly? Example: In [13]: df = pd.DataFrame({'col1': ['A','B','C']

Solution 1:

you can use select_dtypes() including number dtype or excluding all columns of object and datetime64 dtypes:

Demo:

In[162]: dfOut[162]:
  col1col2col3date0A1302016-01-011B2102016-01-022C3202016-01-03In[163]: df.dtypesOut[163]:
col1objectcol2int64col3int64datedatetime64[ns]dtype: objectIn[164]: df.select_dtypes(exclude=['object', 'datetime']) * 3Out[164]:
   col2col3039016302960

or a much better solution (c) ayhan:

df[df.select_dtypes(include=['number']).columns] *= 3

From docs:

To select all numeric types use the numpy dtype numpy.number

Solution 2:

The other answer specifies how to multiply only numeric columns. Here's how to update it:

df = pd.DataFrame({'col1': ['A','B','C'], 'col2':[1,2,3], 'col3': [30, 10,20]})

s = df.select_dtypes(include=[np.number])*3

df[s.columns] = s

print (df)

  col1  col2  col3
0    A     3    90
1    B     6    30
2    C     9    60

Solution 3:

One way would be to get the dtypes, match them against object and datetime dtypes and exclude them with a mask, like so -

df.ix[:,~np.in1d(df.dtypes,['object','datetime'])] *= 3

Sample run -

In [273]: df
Out[273]: 
  col1  col2  col3
0    A     1    30
1    B     2    10
2    C     3    20

In [274]: df.ix[:,~np.in1d(df.dtypes,['object','datetime'])] *= 3

In [275]: df
Out[275]: 
  col1  col2  col3
0    A     3    90
1    B     6    30
2    C     9    60

Solution 4:

This should work even over mixed types within columns but is likely slow over large dataframes.

defmul(x, y):
    try:
        return pd.to_numeric(x) * y
    except:
        return x

df.applymap(lambda x: mul(x, 3))