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

## Question:

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?

## 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'`

.