Reading Multiple Sections of Data in Excel with Pandas

In this article, we’ll explore how to read multiple tables from an Excel file into a single pandas DataFrame. We’ll discuss the challenges associated with reading data from tables with varying header rows and provide an example solution using pandas.

Challenges with Reading Tables from Excel

When working with Excel files that contain multiple table-like formats, it can be challenging to read the data efficiently. In particular, when the Commodity name labeling the data is different all the time, searching for specific keywords becomes unreliable. A common approach would be to search for the first column of the table, which remains consistent across tables. However, this method has a major flaw: it will always start at the first data set and read unnecessary data afterwards.

Approaches to Reading Multiple Tables

There are several approaches to reading multiple tables from an Excel file:

1. Manual Iteration: As shown in your initial example, one could manually iterate through each line of the file until the desired starting point is reached.
2. Regex Search: Another approach would be to use regular expressions (regex) to search for specific patterns within the data.
3. Pandas’ read_excel Functionality: However, as it turns out, pandas offers an efficient way to read multiple tables from an Excel file using its ExcelFile and sheet_names attributes.

Using Pandas’ ExcelFile and sheet_names Attributes

To efficiently read multiple tables from an Excel file, we can utilize pandas’ built-in functionality:

import pandas as pd

def read_multiple_tables(excel_file):
    dataframes = []
    with pd.ExcelFile(excel_file) as xl:
        # Read each sheet individually
        for sheet_name in xl.sheet_names:
            df = pd.read_excel(xl, sheet_name=sheet_name)

            # Find header row (assuming headers are consistent across tables)
            header_row = df.index[df.apply(pd.isna).all(axis=1)].tolist()

            # If header row found, extract table data
            if header_row:
                dataframes.append(df.iloc[header_row[0] + 1:].copy())  # Skip header

    # Concatenate DataFrames and reset index (if needed)
    combined_df = pd.concat(dataframes, ignore_index=True)

    return combined_df

# Example usage
excel_file = "DatasetCROP.xlsx"
combined_df = read_multiple_tables(excel_file)
combined_df.head()

Explanation of the Code

This code works as follows:

• We import pandas and define a function read_multiple_tables that takes an Excel file path as input.
• Inside this function, we create an instance of pd.ExcelFile, which allows us to read multiple sheets from the Excel file using the sheet_names attribute.
• We then iterate over each sheet name in the Excel file and use pandas’ read_excel function to read the data from that sheet into a DataFrame.
• For each DataFrame, we find the header row by identifying rows with no missing values (pd.isna) and extract it using the index attribute.
• If a header row is found, we skip it and extract the table data using iloc. We append this DataFrame to our list of DataFrames.
• After reading all sheets, we concatenate the DataFrames together using pd.concat, ignoring their original indices using ignore_index=True.
• The resulting combined DataFrame is returned as the result.

Benefits of Using Pandas’ Built-in Functionality

Using pandas’ built-in functionality to read multiple tables from an Excel file offers several benefits:

• Efficiency: This approach avoids manual iteration or regex searches, making it faster and more reliable.
• Flexibility: By leveraging pandas’ ExcelFile and sheet_names attributes, we can easily add support for additional features like filtering or data manipulation.

Conclusion

In conclusion, reading multiple tables from an Excel file into a single DataFrame using pandas is achievable with the help of built-in functionality. By utilizing pandas’ ExcelFile and sheet_names attributes, we can efficiently read data from various sheets while skipping unnecessary rows.

Last modified on 2023-09-04