You can use the following basic syntax to set the first row of a pandas DataFrame as the header:

df.columns = df.iloc[0]
df = df[1:]

The following example shows how to use this syntax in practice.

Example: Set First Row as Header in Pandas

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'Bad Name 1': ['team', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'Bad Name 2': ['points', 18, 22, 19, 14, 14, 11, 20, 28],
                   'Bad Name 3': ['assists', 5, 7, 7, 9, 12, 9, 9, 4],
                   'Bad Name 4': ['rebounds', 11, 8, 10, 6, 6, 5, 9, 12]})

#view DataFrame
print(df)

  Bad Name 1 Bad Name 2 Bad Name 3 Bad Name 4
0       team     points    assists   rebounds
1          A         18          5         11
2          B         22          7          8
3          C         19          7         10
4          D         14          9          6
5          E         14         12          6
6          F         11          9          5
7          G         20          9          9
8          H         28          4         12

Suppose the first row contains the values that we actually want to use in the header.

To set the first row as the header, we can use the following syntax:

#set column names equal to values in row index position 0
df.columns = df.iloc[0]

#remove first row from DataFrame
df = df[1:]

#view updated DataFrameprint(df)

0 team points assists rebounds
1    A     18       5       11
2    B     22       7        8
3    C     19       7       10
4    D     14       9        6
5    E     14      12        6
6    F     11       9        5
7    G     20       9        9
8    H     28       4       12

Notice that the values in the first row are now used as the header.

If you’d like to reset the index of the DataFrame, use the following code:

#reset index values
df.reset_index(drop=True, inplace=True)

#view updated DataFrameprint(df)

0 team points assists rebounds
0    A     18       5       11
1    B     22       7        8
2    C     19       7       10
3    D     14       9        6
4    E     14      12        6
5    F     11       9        5
6    G     20       9        9
7    H     28       4       12

The index is now reset so that the first row has an index value of 0.

The following tutorials explain how to perform other common tasks in pandas: