Data Merging Guide

Dataset is too large to merge using Excel due to size constraints, here are alternative approaches to manage and merge the data efficiently:

---

Option 1: Merge Using Power BI

Power BI can handle large datasets and is ideal for merging multiple files.

1. Load Data:

   • Open Power BI Desktop.

   • Load all five CSV files into Power BI by navigating to Home > Get Data > Text/CSV.

2. Transform Data:

   • Go to the Power Query Editor.

   • Use the Append Queries option to combine the files into one unified table.

   • Ensure columns align correctly across all datasets.

3. Process Data:

   • Perform preliminary preprocessing (e.g., removing duplicates, renaming columns).

   • Save the merged table as a single file using Export Data or save it in a database for further analysis.

---

Option 2: Merge Using Python

Python can handle large datasets and merge them efficiently without memory issues.

1. Install necessary libraries:

   pip install pandas  

2. Use the following script:

   import pandas as pd  
   import glob  
   
   # Define the path to your CSV files
   path = "path_to_your_csv_files/"
   all_files = glob.glob(path + "*.csv")
   
   # Read and concatenate all CSV files
   data_frames = [pd.read_csv(file) for file in all_files]
   merged_data = pd.concat(data_frames, ignore_index=True)
   
   # Save the merged dataset
   merged_data.to_csv("merged_pickup_data.csv", index=False)

   This will create a unified dataset as a CSV file for further processing.

---

Option 3: Use a Database (Recommended for Scalability)

Load the data into a database like MySQL, PostgreSQL, or SQLite, and merge the files using SQL queries.

1. Import Files into the Database: Use bulk import functionality of your database for each CSV file.

2. Merge Using SQL:

   CREATE TABLE merged_data AS
   SELECT * FROM city1
   UNION ALL
   SELECT * FROM city2
   UNION ALL
   SELECT * FROM city3
   UNION ALL
   SELECT * FROM city4
   UNION ALL
   SELECT * FROM city5;

3. Export the merged dataset for further processing.

---

Option 4: Divide and Conquer with Excel

If you must use Excel:

1. Split the data into manageable chunks for each file (e.g., by filtering rows).

2. Use Power Query to process chunks separately, then append the results incrementally.

3. Save each processed file as a part of the final dataset.

---

Recommendation

For your scenario, Power BI or Python would be the easiest and most efficient. If you’re limited to Excel, consider moving to Power Query or splitting the task into smaller steps.