How to read certain columns from Excel using Pandas - Python

How to read certain columns from Excel using Pandas - Python

To read specific columns from an Excel file using Pandas in Python, you can use the pandas.read_excel() function and pass the usecols parameter to specify which columns you want to read. Here's how you can do it:

import pandas as pd # Specify the columns you want to read columns_to_read = ['Column1', 'Column3', 'Column5'] # Read the Excel file and select specific columns data = pd.read_excel('your_file.xlsx', usecols=columns_to_read) print(data)

Replace 'your_file.xlsx' with the path to your Excel file and adjust the columns_to_read list to contain the names of the columns you want to read.

This will read only the specified columns from the Excel file and create a DataFrame containing only those columns.

Keep in mind that the usecols parameter accepts either column indices (integers) or column names (strings). If you're using column indices, you can provide a list of integers instead of column names.

Additionally, make sure you have the pandas library installed (pip install pandas) before using it for reading Excel files.

Examples

  1. How to read specific columns from an Excel file using Pandas in Python?

    • Description: This query focuses on extracting only certain columns from an Excel file using Pandas library in Python. Limiting the data to required columns can improve processing efficiency.
    • Code Implementation:
      import pandas as pd # Specify columns to be read from the Excel file columns_to_read = ['Column1', 'Column2', 'Column5'] df = pd.read_excel('file.xlsx', usecols=columns_to_read)

  2. How to ignore certain columns while reading an Excel file with Pandas in Python?

    • Description: Users might want to exclude specific columns from being read when importing data from an Excel file using Pandas. This query addresses how to achieve that.
    • Code Implementation:
      import pandas as pd # Specify columns to be ignored while reading the Excel file columns_to_ignore = ['Column3', 'Column4'] df = pd.read_excel('file.xlsx', usecols=lambda x: x not in columns_to_ignore)

  3. How to read columns with specific data types from Excel using Pandas in Python?

    • Description: This query explores how to filter columns by their data types when reading an Excel file with Pandas. Filtering by data type ensures that only relevant columns are imported.
    • Code Implementation:
      import pandas as pd # Specify columns with specific data types to be read from the Excel file dtype_mapping = {'Column1': str, 'Column2': float} df = pd.read_excel('file.xlsx', dtype=dtype_mapping)

  4. How to read columns with headers containing specific keywords in Excel using Pandas?

    • Description: Users may want to select columns based on headers that contain certain keywords or phrases. This query demonstrates how to accomplish that with Pandas.
    • Code Implementation:
      import pandas as pd # Select columns with headers containing specific keywords df = pd.read_excel('file.xlsx', usecols=lambda col: 'keyword' in col.lower())

  5. How to read Excel columns by index using Pandas in Python?

    • Description: Sometimes, users may prefer to select columns by their index positions rather than by their names. This query addresses how to achieve this with Pandas.
    • Code Implementation:
      import pandas as pd # Specify column indices to be read from the Excel file column_indices = [0, 2, 4] df = pd.read_excel('file.xlsx', usecols=column_indices)

  6. How to read columns from a specific sheet of an Excel file using Pandas?

    • Description: Users might want to extract columns from a particular sheet within a multi-sheet Excel file. This query demonstrates how to read columns from a specific sheet using Pandas.
    • Code Implementation:
      import pandas as pd # Read specific columns from a specific sheet df = pd.read_excel('file.xlsx', sheet_name='Sheet1', usecols=['Column1', 'Column2'])

  7. How to read Excel columns with headers spanning multiple rows using Pandas?

    • Description: Excel files may have headers that span multiple rows, which can complicate data import. This query addresses how to handle such scenarios with Pandas.
    • Code Implementation:
      import pandas as pd # Specify the rows containing headers and skip others df = pd.read_excel('file.xlsx', header=[0, 1], usecols=[0, 1, 2])

  8. How to rename columns while reading an Excel file with Pandas in Python?

    • Description: Users might need to rename columns during the import process to ensure consistency or clarity. This query demonstrates how to rename columns using Pandas.
    • Code Implementation:
      import pandas as pd # Rename columns while reading the Excel file df = pd.read_excel('file.xlsx', usecols=['OldName1', 'OldName2']) df.rename(columns={'OldName1': 'NewName1', 'OldName2': 'NewName2'}, inplace=True)

  9. How to read columns from an Excel file with a custom separator using Pandas?

    • Description: Excel files may use custom separators for data organization. This query addresses how to read columns from such files using Pandas, specifying a custom separator.
    • Code Implementation:
      import pandas as pd # Read columns from Excel file with a custom separator (e.g., tab) df = pd.read_excel('file.xlsx', sep='\t', usecols=['Column1', 'Column2'])

  10. How to handle duplicate column names when reading an Excel file with Pandas?

    • Description: Excel files may contain duplicate column names, which can cause issues during data import. This query demonstrates how to handle duplicate column names effectively with Pandas.
    • Code Implementation:
      import pandas as pd # Read Excel file with duplicate column names, rename them to avoid conflicts df = pd.read_excel('file.xlsx', usecols=['Name', 'Name.1']) df.columns = ['Name1', 'Name2'] # Rename duplicate columns

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