我想在熊猫数据框中赢取几列数据。每列都有一些 NaN,这会影响获胜,因此需要删除它们。我知道如何做到这一点的唯一方法是为所有数据删除它们,而不是仅逐列删除它们。
女工程师:
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
以下是基线分布:
df.quantile([0, 0.01, 0.5, 0.99, 1])
输出:
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
这就是我获胜的方式:
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])
这给出了这个:
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
列four
是正确的,因为它没有NaN
但其他列不正确。第 99 个百分位数和最大值应该相同。两者的观测值计数相同:
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
这就是我"解决"它的方式,但代价是丢失了大量数据:
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])
输出:
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
如何在保持数据形状(即不删除行(的同时按列对非 NaN 的数据进行 winsorization?
正如经常发生的那样,简单地创建 MWE 有助于澄清。我需要将 clip(( 与 quantile(( 结合使用,如下所示:
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])
输出:
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
这些数字与上面的数字不同,因为我维护了每列中未丢失的所有数据 (NaN(。