Working with Multiple Excel(xls) Workbooks in Pandas

When working with large datasets from various Excel files, it’s common to need to combine these files into a single, unified dataset. In this article, we’ll explore how to achieve this using pandas, focusing on exporting and reading multiple Excel(xls) workbooks into one DataFrame.

Understanding the Problem

The problem at hand involves taking 3 months of Excel files with varying dates, capturing them using pandas, and combining them into a single DataFrame. This requires understanding how to:

Identify and locate all Excel files in a specified directory
Read and parse each Excel file into a pandas DataFrame
Combine the DataFrames into a single, unified dataset

Step 1: Setting Up the Environment

Before we begin, make sure you have the necessary libraries installed. The most crucial ones for this task are:

pandas for data manipulation and analysis
glob for pattern matching in file paths
matplotlib and/or other visualization tools (optional)

You can install these libraries via pip:

pip install pandas glob matplotlib

Step 2: Reading Excel Files

To start, we’ll use the glob library to identify all Excel files within a specified directory. We’ll assume that the files are named “EOD RATES [date].xls” and have unique dates.

## Step 1: Setting up file paths
import glob

# Specify the directory path containing Excel files
directory_path = '/path/to/excel/files'

# Use glob to find all Excel files in the directory
files = glob.glob(directory_path + '/*.xls')

## Step 2: Reading Excel Files into DataFrames
import pandas as pd

# Initialize an empty list to store DataFrames
dfs = []

for file in files:
    # Read each Excel file into a DataFrame
    df = pd.read_excel(file)
    
    # Append the DataFrame to the list
    dfs.append(df)

## Step 3: Combining DataFrames into a Single Dataset
# Use pandas.concat() to combine all DataFrames into one
df_combined = pd.concat(dfs, ignore_index=True)

print (df_combined)

Understanding the Code

Here’s what each section of code does:

We use glob.glob() to find all Excel files within the specified directory.
For each file found, we read it into a pandas DataFrame using pd.read_excel().
The DataFrames are stored in an empty list called dfs.
After reading and parsing all files, we combine them into a single DataFrame using pd.concat().

Advanced Techniques: Handling Multiple Sheets and Encoding

In some cases, you may need to handle multiple sheets within the same Excel file or deal with text encoding issues. Here are some advanced techniques for addressing these situations:

Multiple Sheets: To read all sheets in an Excel file at once, use pd.read_excel() with the sheet_name parameter set to 'ALL'.

Reading multiple sheets

Specify sheet name as ‘ALL’

df = pd.read_excel(file, sheet_name=‘ALL’)

Or, specify a list of sheet names

sheet_names = [‘Sheet1’, ‘Sheet2’] dfs = [] for sheet in sheet_names: df = pd.read_excel(file, sheet_name=sheet) dfs.append(df)


*   **Encoding Issues**: If you encounter encoding issues (e.g., Unicode characters), consider specifying the `encoding` parameter when reading Excel files.

    ```markdown
## Specifying encoding

# Specify the file encoding
df = pd.read_excel(file, encoding='latin1')

## Or, use 'utf-8' or another encoding depending on your needs

Step 3: Visualizing and Saving DataFrames

After combining the data into a single DataFrame, you may want to visualize or save it for future reference. Here’s how:

Saving to CSV: Use df.to_csv() to export your DataFrame as a comma-separated values file.

Saving DataFrame as CSV

Specify output file path and name

output_path = ‘/path/to/output.csv’

Save DataFrame to CSV

df.to_csv(output_path)


*   **Visualization Tools**: To visualize the data, consider using libraries like `matplotlib`, `seaborn`, or `plotly`. For example:

    ```markdown
## Basic bar plot

import matplotlib.pyplot as plt

# Plot a simple bar chart
plt.bar(df['Column Name'])
plt.show()

Conclusion

In this article, we explored the process of combining multiple Excel files into one unified dataset using pandas. We covered basic file reading and sheet handling techniques, provided guidance on encoding and other advanced topics, and demonstrated visualization tools for saving your data. By following these steps, you should be able to effectively work with large datasets from various Excel files.

Additional Tips:

When working with large datasets, it’s essential to optimize memory usage by using the chunksize parameter when reading Excel files.
Consider adding additional error handling and logging mechanisms to improve the robustness of your code.

For more information on pandas or other data manipulation libraries, please refer to their official documentation. Happy coding!

Last modified on 2024-05-11