DataFrame: add column whose values are the quantile number/rank of an existing column?

I discovered it is quite easy:

df['quantile'] = pd.qcut(df['b'], 2, labels=False)

   a    b  quantile
0  1    1         0
1  2   10         0
2  3  100         1
3  4  100         1

Interesting to know "difference between pandas.qcut and pandas.cut"

You can use DataFrame.quantile with q=[0.25, 0.5, 0.75] on the existing column to produce a quartile column.

Then, you can DataFrame.rank on that quartile column.

See below for an example of adding a quartile column:

import pandas as pd

d = {'one' : pd.Series([40., 45., 50., 55, 60, 65], index=['val1', 'val2', 'val3', 'val4', 'val5', 'val6'])}
df = pd.DataFrame(d)

quantile_frame = df.quantile(q=[0.25, 0.5, 0.75])
quantile_ranks = []
for index, row in df.iterrows():
    if (row['one'] <= quantile_frame.ix[0.25]['one']):
        quantile_ranks.append(1)
    elif (row['one'] > quantile_frame.ix[0.25]['one'] and row['one'] <= quantile_frame.ix[0.5]['one']):
        quantile_ranks.append(2)
    elif (row['one'] > quantile_frame.ix[0.5]['one'] and row['one'] <= quantile_frame.ix[0.75]['one']):
        quantile_ranks.append(3)
    else:
        quantile_ranks.append(4)

df['quartile'] = quantile_ranks

Note: There's probably a more idiomatic way to accomplish this with Pandas... but it's beyond me

df['quantile'] = pd.qcut(df['b'], 2, labels=False) seems to tend to throw a SettingWithCopyWarning.

The only general way I have found of doing this without complaints is like:

quantiles = pd.qcut(df['b'], 2, labels=False)
df = df.assign(quantile=quantiles.values)

This will assign the quantile rank values as a new DataFrame column df['quantile'].

A solution for a more generalized case, in which one wants to partition the cut by multiple columns, is given here.