You can use the following basic syntax to set the column names of a DataFrame when importing an Excel file into pandas:
colnames = ['col1', 'col2', 'col3'] df = pd.read_excel('my_data.xlsx', names=colnames)
The names argument takes a list of names that you’d like to use for the columns in the DataFrame.
By using this argument, you also tell pandas to use the first row in the Excel file as the first row in the DataFrame instead of using it as the header row.
The following example shows how to use this syntax in practice.
Example: Set Column Names when Importing Excel File into Pandas
Suppose we have the following Excel file called players_data.xlsx:
From the file we can see that the first row does not contain any column names.
If we import the Excel file using the read_excel() function, pandas will attempt to use the values in the first row as the column names for the DataFrame:
import pandas as pd #import Excel file df = pd.read_excel('players_data.xlsx') #view resulting DataFrame print(df) A 22 10 0 B 14 9 1 C 29 6 2 D 30 2 3 E 22 9 4 F 31 10
However, we can use the names argument to specify our own column names when importing the Excel file:
import pandas as pd #specify column names colnames = ['team', 'points', 'rebounds'] #import Excel file and use specified column names df = pd.read_excel('players_data.xlsx', names=colnames) #view resulting DataFrame print(df) team points rebounds 0 B 14 9 1 C 29 6 2 D 30 2 3 E 22 9 4 F 31 10
Notice that the first row in the Excel file is no longer used as the header row.
Instead, the column names that we specified using the names argument are now used as the column names.
Note: You can find the complete documentation for the pandas read_excel() function here.
The following tutorials explain how to perform other common tasks in pandas:
Pandas: How to Read Excel File with Merged Cells
Pandas: Skip Specific Columns when Importing Excel File
Pandas: How to Specify dtypes when Importing Excel File
Pandas: How to Skip Rows when Reading Excel File