Python: Scaling numbers column by column with pandas

Question

I have a Pandas data frame \'df\' in which I\'d like to perform some scalings column by column.

• In column \'a\', I need the maximum number to be 1, the minimum

Answer 1

This is not very elegant but the following works for this two column case:

#Create dataframe
df = pd.DataFrame({'A':[14,90,90,96,91], 'B':[103,107,110,114,114]})

#Apply operates on each row or column with the lambda function
#axis = 0 -> act on columns, axis = 1 act on rows
#x is a variable for the whole row or column
#This line will scale minimum = 0 and maximum = 1 for each column
df2 = df.apply(lambda x:(x.astype(float) - min(x))/(max(x)-min(x)), axis = 0)

#Want to now invert the order on column 'B'
#Use apply function again, reverse numbers in column, select column 'B' only and 
#reassign to column 'B' of original dataframe
df2['B'] = df2.apply(lambda x: 1-x, axis = 1)['B']

If I find a more elegant way (for example, using the column index: (0 or 1)mod 2 - 1 to select the sign in the apply operation so it can be done with just one apply command, I'll let you know.

Answer 2

This is how you can do it using sklearn and the preprocessing module. Sci-Kit Learn has many pre-processing functions for scaling and centering data.

In [0]: from sklearn.preprocessing import MinMaxScaler

In [1]: df = pd.DataFrame({'A':[14,90,90,96,91],
                           'B':[103,107,110,114,114]}).astype(float)

In [2]: df
Out[2]:
    A    B
0  14  103
1  90  107
2  90  110
3  96  114
4  91  114

In [3]: scaler = MinMaxScaler()

In [4]: df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)

In [5]: df_scaled
Out[5]:
          A         B
0  0.000000  0.000000
1  0.926829  0.363636
2  0.926829  0.636364
3  1.000000  1.000000
4  0.939024  1.000000

Answer 3

I think Acumenus' comment in this answer, should be mentioned explicitly as an answer, as it is a one-liner.

>>> import pandas as pd
>>> from sklearn.preprocessing import minmax_scale
>>> df = pd.DataFrame({'A':[14,90,90,96,91], 'B':[103,107,110,114,114]})
>>> minmax_scale(df)
array([[0.        , 0.        ],
       [0.92682927, 0.36363636],
       [0.92682927, 0.63636364],
       [1.        , 1.        ],
       [0.93902439, 1.        ]])

Answer 4

You could subtract by the min, then divide by the max (beware 0/0). Note that after subtracting the min, the new max is the original max - min.

In [11]: df
Out[11]:
    a    b
A  14  103
B  90  107
C  90  110
D  96  114
E  91  114

In [12]: df -= df.min()  # equivalent to df = df - df.min()

In [13]: df /= df.max()  # equivalent to df = df / df.max()

In [14]: df
Out[14]:
          a         b
A  0.000000  0.000000
B  0.926829  0.363636
C  0.926829  0.636364
D  1.000000  1.000000
E  0.939024  1.000000

To switch the order of a column (from 1 to 0 rather than 0 to 1):

In [15]: df['b'] = 1 - df['b']

An alternative method is to negate the b columns first (df['b'] = -df['b']).

Answer 5

given a data frame

df = pd.DataFrame({'A':[14,90,90,96,91], 'B':[103,107,110,114,114]})

scale with mean 0 and var 1

df.apply(lambda x: (x - np.mean(x)) / np.std(x), axis=0)

scale with range between 0 and 1

df.apply(lambda x: x / np.max(x), axis=0)

Answer 6

In case you want to scale only one column in the dataframe, you can do the following:

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df['Col1_scaled'] = scaler.fit_transform(df['Col1'].values.reshape(-1,1))