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'
.