How to sort a Pandas DataFrame by index?

Question

When there is a DataFrame like the following:

import pandas as pd
df = pd.DataFrame(1, index=[100, 29, 234, 1, 150], columns=['A'])

How can I sort this dataframe by index with each combination of index and column value intact?

Answer 1

Dataframes have a sort_index method which returns a copy by default. Pass inplace=True to operate in place.

import pandas as pd
df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], columns=['A'])
df.sort_index(inplace=True)
print(df.to_string())

Gives me:

     A
1    4
29   2
100  1
150  5
234  3

Answer 2

Slightly more compact:

df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], columns=['A'])
df = df.sort_index()
print(df)

Note:

• sort has been deprecated, replaced by sort_index for this scenario
• preferable not to use inplace as it is usually harder to read and prevents chaining. See explanation in answer here: Pandas: peculiar performance drop for inplace rename after dropna

Answer 3

If the DataFrame index has name, then you can use sort_values() to sort by the name as well. For example, if the index is named lvl_0, you can sort by this name. This particular case is common if the dataframe is obtained from a groupby or a pivot_table operation.

df = df.sort_values('lvl_0')

If the index has name(s), you can even sort by both index and a column value. For example, the following sorts by both the index and the column A values:

df = df.sort_values(['lvl_0', 'A'])

If you have a MultiIndex dataframe, then, you can sort by the index level by using the level= parameter. For example, if you want to sort by the second level in descending order and the first level in ascending order, you can do so by the following code.

df = df.sort_index(level=[1, 0], ascending=[False, True])

If the indices have names, again, you can call sort_values(). For example, the following sorts by indexes 'lvl_1' and 'lvl_2'.

df = df.sort_values(['lvl_1', 'lvl_2'])