# 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])`

or

`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'`

.