Placing every value in its percentile in Pandas

Question

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

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

How can I do that in Pandas?

Answer 1

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

Or leave out labels to see the range

---

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.