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

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

As of the 0.17.0 release, the sort method was deprecated in favor of sort_values. sort was completely removed in the 0.20.0 release. The arguments (and results) remain the same:

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

You can use the ascending argument of sort:

df.sort(['a', 'b'], ascending=[True, False])

For example:

In [11]: df1 = pd.DataFrame(np.random.randint(1, 5, (10,2)), columns=['a','b'])In [12]: df1.sort(['a', 'b'], ascending=[True, False])Out[12]:   a  b2  1  47  1  31  1  23  1  24  3  26  4  40  4  39  4  35  4  18  4  1

As commented by @renadeen

Sort isn't in place by default! So you should assign result of the sort method to a variable or add inplace=True to method call.

that is, if you want to reuse df1 as a sorted DataFrame:

df1 = df1.sort(['a', 'b'], ascending=[True, False])


df1.sort(['a', 'b'], ascending=[True, False], inplace=True)

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)

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 pdimport numpy as npnp.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'.