将每个值放在 Pandas 的百分位数中 [英] Placing every value in its percentile in Pandas

问题描述

考虑具有以下百分位数的系列:

Consider a Series with the following percentiles:

> df['col_1'].describe(percentiles=np.linspace(0, 1, 20))

count      13859.000000
mean         421.772842
std        14665.298998
min            1.201755
0%             1.201755
5.3%           1.430695
10.5%          1.438417
15.8%          1.466462
21.1%          1.473050
26.3%          1.500834
31.6%          1.512218
36.8%          1.542935
42.1%          1.579845
47.4%          1.647162
50%            1.690612
52.6%          1.749047
57.9%          1.955589
63.2%          2.344475
68.4%          3.075641
73.7%          4.466094
78.9%          8.410964
84.2%         14.998738
89.5%         41.363612
94.7%        162.865079
100%     1511013.790233
max      1511013.790233
Name: col_1, dtype: float64

我想获得另一列 col_2，其中包含在上述计算中分配给每一行的百分位数.

I would like to get another column col_2 with the percentile each row was assigned to in the calculation made above.

我怎样才能在 Pandas 中做到这一点?

How can I do that in Pandas?

推荐答案

df2 = pd.DataFrame(range(1000))
df2.columns = ['a1']
df2['percentile'] = pd.qcut(df2.a1,100, labels=False)

或者省略标签以查看范围

Or leave out labels to see the range

请注意，在 Python 3 中，使用 Pandas 0.16.2(截至今天的最新版本)，您需要使用 list(range(1000)) 而不是 range(1000) 使上述工作.

Note that in Python 3, with Pandas 0.16.2 (latest version as of today), you need to use list(range(1000)) instead of range(1000) for the above to work.

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