Standardize columns of a dataframe

Is there a function which standardizes data in the columns of a dataframe? If not, can we introduce it?

Through standardization, one can re-scale the data to have a mean of zero and standard deviation of one. I use standardization regularly for two purposes

• To facilitate interpretation of regression estimates. Here is a related discussion on Cross Validated.
• To facilitate inspection of plots of time-series. Especially when I like to understand whether different series co-move over time, plotting them on the scale helps.

So far, I use a simple function:

def standardize(self, df, label):
    """
    standardizes a series with name ``label'' within the pd.DataFrame
    ``df''.
    """
    df = df.copy(deep=True)
    series = df.loc[:, label]
    avg = series.mean()
    stdv = series.std()
    series_standardized = (series - avg)/ stdv
return series_standardized

I thought if there could be a function standardize which can be used similarly to the rolling function, such as df.standardize().

[mwaskom]

For the record, the fact that pandas doesn’t handle using scipy.zstats properly here, and so the user needs to write a lambda (for an extremely common operation), remains incredibly annoying.

[jreback]

as I wrote before here

In [13]: standarize = lambda x: (x-x.mean()) / x.std()

In [14]: s = pd.Series(np.random.rand(10))
    ...: 
    ...: (s-s.mean())/s.std()
    ...: 
Out[14]: 
0    0.395159
1    0.611805
2   -1.976001
3    0.512755
4    0.954300
5   -0.873228
6   -0.988174
7   -0.099802
8    0.196835
9    1.266350
dtype: float64

In [15]: standarize = lambda x: (x-x.mean()) / x.std()

In [16]: s.pipe(standarize)
Out[16]: 
0    0.395159
1    0.611805
2   -1.976001
3    0.512755
4    0.954300
5   -0.873228
6   -0.988174
7   -0.099802
8    0.196835
9    1.266350
dtype: float64