[python] How to sort a dataFrame in python pandas by two or more columns?

Suppose I have a dataframe with columns a, b and c, I want to sort the dataframe by column b in ascending order, and by column c in descending order, how do I do this?

For large dataframes of numeric data, you may see a significant performance improvement via numpy.lexsort, which performs an indirect sort using a sequence of keys:

import pandas as pd
import numpy as np

np.random.seed(0)

df1 = pd.DataFrame(np.random.randint(1, 5, (10,2)), columns=['a','b'])
df1 = pd.concat([df1]*100000)

def pdsort(df1):
    return df1.sort_values(['a', 'b'], ascending=[True, False])

def lex(df1):
    arr = df1.values
    return pd.DataFrame(arr[np.lexsort((-arr[:, 1], arr[:, 0]))])

assert (pdsort(df1).values == lex(df1).values).all()

%timeit pdsort(df1)  # 193 ms per loop
%timeit lex(df1)     # 143 ms per loop

One peculiarity is that the defined sorting order with numpy.lexsort is reversed: (-'b', 'a') sorts by series a first. We negate series b to reflect we want this series in descending order.

Be aware that np.lexsort only sorts with numeric values, while pd.DataFrame.sort_values works with either string or numeric values. Using np.lexsort with strings will give: TypeError: bad operand type for unary -: 'str'.

As of pandas 0.17.0, DataFrame.sort() is deprecated, and set to be removed in a future version of pandas. The way to sort a dataframe by its values is now is DataFrame.sort_values

As such, the answer to your question would now be

df.sort_values(['b', 'c'], ascending=[True, False], inplace=True)