Reading Multiple Header Rows from an Excel Sheet Using Python Pandas

When working with Excel sheets in Python, pandas is often the preferred choice for data manipulation due to its ease of use, flexibility, and powerful features. One common challenge when reading Excel files using pandas is dealing with multiple header rows that have varying column sizes. In this article, we will explore how to dynamically read an Excel sheet with multiple header rows of different column size and split them into separate DataFrames.

Understanding the Problem

Let’s break down the problem further:

• We have an Excel file with multiple header rows.
• Each header row has a different number of columns.
• We want to create separate DataFrames, each corresponding to one of the header rows.

This can be achieved by using pandas’ various arguments to control how it reads the Excel sheet. Specifically, we will use:

• header: specifies which row(s) contains the column names.
• skiprows and skipfooter: skips a specified number of rows or feet (bottom lines) in the file.
• usecols: defines the columns to include when reading the data.

Using Pandas’ Built-in Functions

One approach is to use pandas’ built-in functions to read the Excel sheet while taking into account the varying column sizes. We can do this by iterating over each header row and using a windowing function to define the columns to include.

Here’s an example code snippet that demonstrates how to achieve this:

import pandas as pd

# Define the Excel file path
excel_file = 'example.xlsx'

# Read the Excel sheet into a DataFrame with multiple header rows
df = pd.read_excel(excel_file, header=None)

# Split the DataFrame into separate DataFrames for each header row
header_rows = [row for row in df.values]
dataframes = []

for i, header_row in enumerate(header_rows):
    # Use a windowing function to define the columns to include
    usecols = tuple(range(i + 1))  # Include columns up to the current header row
    dataframe = pd.read_excel(excel_file, usecols=usecols, skiprows=i)
    
    dataframes.append(dataframe)

# Store the DataFrames in a dictionary for easy access
dataframes_dict = {i+1: df for i, df in enumerate(dataframes)}

print(dataframes_dict)  # Print the resulting DataFrames

Using Concatenation to Combine Multiple DataFrames

Another approach is to use concatenation functions like concat, merge, and append to combine multiple DataFrames.

Here’s an example code snippet that demonstrates how to achieve this:

import pandas as pd

# Define the Excel file path
excel_file = 'example.xlsx'

# Read the Excel sheet into a DataFrame with multiple header rows
df = pd.read_excel(excel_file, header=None)

# Split the DataFrame into separate DataFrames for each header row
header_rows = [row for row in df.values]
dataframes = []

for i, header_row in enumerate(header_rows):
    # Use a windowing function to define the columns to include
    usecols = tuple(range(i + 1))  # Include columns up to the current header row
    
    dataframe = pd.read_excel(excel_file, usecols=usecols, skiprows=i)
    
    dataframes.append(dataframe)

# Combine multiple DataFrames using concatenation
combined_df = pd.concat(dataframes, ignore_index=True)

print(combined_df)  # Print the combined DataFrame

Merging and Joining DataFrames

In some cases, you may need to merge or join DataFrames based on common columns. Here’s an example code snippet that demonstrates how to achieve this:

import pandas as pd

# Define the Excel file path
excel_file = 'example.xlsx'

# Read the Excel sheet into a DataFrame with multiple header rows
df = pd.read_excel(excel_file, header=None)

# Split the DataFrame into separate DataFrames for each header row
header_rows = [row for row in df.values]
dataframes = []

for i, header_row in enumerate(header_rows):
    # Use a windowing function to define the columns to include
    usecols = tuple(range(i + 1))  # Include columns up to the current header row
    
    dataframe = pd.read_excel(excel_file, usecols=usecols, skiprows=i)
    
    dataframes.append(dataframe)

# Merge multiple DataFrames using merge
merged_df = pd.merge(dataframes[0], dataframes[1], on='common_column')

print(merged_df)  # Print the merged DataFrame

Conclusion

In conclusion, reading an Excel sheet with multiple header rows of different column sizes can be achieved by using pandas’ built-in functions like header, skiprows, and usecols. We can also use concatenation, merging, and joining DataFrames to combine or link separate DataFrames. By understanding how these functions work and applying them correctly, we can effectively split an Excel sheet into multiple DataFrames with varying column sizes.

Additional Tips

Here are some additional tips for working with Excel sheets using pandas:

• Use the header=None argument: When reading an Excel file, specify header=None to avoid assuming a header row exists.
• Specify skiprows and skipfooter arguments: Use these arguments to skip specific rows or feet in the Excel file.
• Define columns using usecols: Use the usecols argument to define which columns to include when reading data from an Excel sheet.

By following these tips and techniques, you can efficiently work with multiple header rows in an Excel sheet using pandas.

Last modified on 2023-07-10