JIYIK CN >

We'll introduce pandas.DataFrame.sort_valuesthe method to DataFramesort values, as well as ascendingoptions like to specify the sort order, and na_positionto determine the positioning of NaNs in the sorted results.

Refer to the following DataFrame,

import pandas as pd

df = pd.DataFrame(
    {
        "col1": ["g", "t", "n", "w", "n", "g"],
        "col2": [5, 2, 5, 1, 3, 6],
        "col3": [0, 7, 2, 8, 1, 2],
    }
)
print(df)

If you run this code, you will get the following unsorted output.

    col1  col2  col3
0    g     5     0
1    t     2     7
2    n     5     2
3    w     1     8
4    n     3     1
5    g     6     2

DataFrameNow we can sort using the following code .

import pandas as pd

df = pd.DataFrame(
    {
        "col1": ["g", "t", "n", "w", "n", "g"],
        "col2": [5, 2, 5, 1, 3, 6],
        "col3": [0, 7, 2, 8, 1, 2],
    }
)
print(df.sort_values(by=["col1"]))

We have sorted col1by DataFrame. After running the above code, you will get the following output.

    col1  col2  col3
0    g     5     0
5    g     6     2
2    n     5     2
4    n     3     1
1    t     2     7
3    w     1     8

We can also sort using multiple columns, let us change the last line of the above code as follows,

print(df.sort_values(by=["col1", "col2"]))

After running the code, we will get the following output.

    col1  col2  col3
0    g     5     0
5    g     6     2
4    n     3     1
2    n     5     2
1    t     2     7
3    w     1     8

Now, further sort DataFrameby as well .col2

DataFrameSorting order - parameter Ascending

By default the sorting is in ascending order, to change it in descending order DataFramewe need to set the flag ascending=False.

print(df.sort_values(by=["col1", "col2"], ascending=False))

After running the code, we will get the following output.

    col1  col2  col3
3    w     1     8
1    t     2     7
2    n     5     2
4    n     3     1
5    g     6     2
0    g     5     0

DataFrameSort Order - Parametersna_position

na_positionThe position specified after sorting NaN. lastPuts NaNat the end of the sort. Its default value is firstto NaNput at the beginning of the sorted result.

Refer to the following DataFrame,

import numpy as np
import pandas as pd

s = pd.Series([np.nan, 2, 4, 10, 7])
print(s.sort_values(na_position="last"))

After running the code, we will get the following output.

1 2.0
2 4.0
4 7.0
3 10.0
0 NaN