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?
3 Answers
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 b 2 1 4 7 1 3 1 1 2 3 1 2 4 3 2 6 4 4 0 4 3 9 4 3 5 4 1 8 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 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'.
