I'd like to winsorize several columns of data in a pandas Data Frame. Each column has some NaN, which affects the winsorization, so they need to be removed. The only way I know how to do this is to remove them for all of the data, rather than remove them only column-by-column.
MWE:
import numpy as np
import pandas as pd
from scipy.stats.mstats import winsorize
# Create Dataframe
N, M, P = 10**5, 4, 10**2
dates = pd.date_range('2001-01-01', periods=N//P, freq='D').repeat(P)
df = pd.DataFrame(np.random.random((N, M))
, index=dates)
df.index.names = ['DATE']
df.columns = ['one','two','three','four']
# Now scale them differently so you can see the winsorization
df['four'] = df['four']*(10**5)
df['three'] = df['three']*(10**2)
df['two'] = df['two']*(10**-1)
df['one'] = df['one']*(10**-4)
# Create NaN
df.loc[df.index.get_level_values(0).year == 2002,'three'] = np.nan
df.loc[df.index.get_level_values(0).month == 2,'two'] = np.nan
df.loc[df.index.get_level_values(0).month == 1,'one'] = np.nan
Here is the baseline distribution:
df.quantile([0, 0.01, 0.5, 0.99, 1])
output:
one two three four
0.00 2.336618e-10 2.294259e-07 0.002437 2.305353
0.01 9.862626e-07 9.742568e-04 0.975807 1003.814520
0.50 4.975859e-05 4.981049e-02 50.290946 50374.548980
0.99 9.897463e-05 9.898590e-02 98.978263 98991.438985
1.00 9.999983e-05 9.999966e-02 99.996793 99999.437779
This is how I'm winsorizing:
def using_mstats(s):
return winsorize(s, limits=[0.01, 0.01])
wins = df.apply(using_mstats, axis=0)
wins.quantile([0, 0.01, 0.25, 0.5, 0.75, 0.99, 1])
Which gives this:
Out[356]:
one two three four
0.00 0.000001 0.001060 1.536882 1003.820149
0.01 0.000001 0.001060 1.536882 1003.820149
0.25 0.000025 0.024975 25.200378 25099.994780
0.50 0.000050 0.049810 50.290946 50374.548980
0.75 0.000075 0.074842 74.794537 75217.343920
0.99 0.000099 0.098986 98.978263 98991.436957
1.00 0.000100 0.100000 99.996793 98991.436957
Column four is correct because it has no NaN but the others are incorrect. The 99th percentile and Max should be the same. The observations counts are identical for both:
In [357]: df.count()
Out[357]:
one 90700
two 91600
three 63500
four 100000
dtype: int64
In [358]: wins.count()
Out[358]:
one 90700
two 91600
three 63500
four 100000
dtype: int64
This is how I can 'solve' it, but at the cost of losing a lot of my data:
wins2 = df.loc[df.notnull().all(axis=1)].apply(using_mstats, axis=0)
wins2.quantile([0, 0.01, 0.25, 0.5, 0.75, 0.99, 1])
Output:
Out[360]:
one two three four
0.00 9.686203e-07 0.000928 0.965702 1005.209503
0.01 9.686203e-07 0.000928 0.965702 1005.209503
0.25 2.486052e-05 0.024829 25.204032 25210.837443
0.50 4.980946e-05 0.049894 50.299004 50622.227179
0.75 7.492750e-05 0.075059 74.837900 75299.906415
0.99 9.895563e-05 0.099014 98.972310 99014.311761
1.00 9.895563e-05 0.099014 98.972310 99014.311761
In [361]: wins2.count()
Out[361]:
one 51700
two 51700
three 51700
four 51700
dtype: int64
How can I winsorize the data, by column, that is not NaN, while maintaining the data shape (i.e. not removing rows)?
As often happens, simply creating the MWE helped clarify. I need to use clip() in combination with quantile() as below:
df2 = df.clip(lower=df.quantile(0.01), upper=df.quantile(0.99), axis=1)
df2.quantile([0, 0.01, 0.25, 0.5, 0.75, 0.99, 1])
Output:
one two three four
0.00 9.862626e-07 0.000974 0.975807 1003.814520
0.01 9.862666e-07 0.000974 0.975816 1003.820092
0.25 2.485043e-05 0.024975 25.200378 25099.994780
0.50 4.975859e-05 0.049810 50.290946 50374.548980
0.75 7.486737e-05 0.074842 74.794537 75217.343920
0.99 9.897462e-05 0.098986 98.978245 98991.436977
1.00 9.897463e-05 0.098986 98.978263 98991.438985
In [384]: df2.count()
Out[384]:
one 90700
two 91600
three 63500
four 100000
dtype: int64
The numbers are different from above because I have maintained all of the data in each column that is not missing (NaN).
来源：https://stackoverflow.com/questions/50612095/winsorizing-data-by-column-in-pandas-with-nan