How to concatenate pandas DataFrames

Monthly exports often arrive as separate tables even though every file describes the same kind of record. A single combined pandas DataFrame gives later filters, summaries, and exports one continuous set of rows.

The pandas.concat() function accepts a sequence of DataFrame objects and stacks rows along axis=0 by default. Collecting the frames before one call avoids copying an expanding result on every iteration.

This row-stacking workflow assumes that the monthly tables represent the same columns. ignore_index=True replaces reused source labels with a fresh integer index, while explicit assertions stop the program if the row count, index, or record order differs from the expected result.

1. Create concat_dataframes.py with the monthly order tables.

   concat_dataframes.py

   import pandas as pd
    
   january = pd.DataFrame(
       {
           "order_id": ["A100", "A101"],
           "region": ["EMEA", "APAC"],
           "total": [125.0, 98.5],
       }
   )
    
   february = pd.DataFrame(
       {
           "order_id": ["A102", "A103"],
           "region": ["EMEA", "AMER"],
           "total": [143.0, 87.0],
       }
   )

   Loaded extracts need the same column meanings, and their sequence determines the combined row order.

2. Add the concatenation section below the February table.

   monthly_orders = [january, february]
   orders = pd.concat(monthly_orders, ignore_index=True)

   ignore_index=True discards labels on the row axis and creates a fresh RangeIndex. pandas.merge() matches records by key values instead of stacking rows.

3. Append the result assertions after the concat call.

   expected_ids = ["A100", "A101", "A102", "A103"]
   assert orders.shape == (4, 3)
   assert orders.index.tolist() == [0, 1, 2, 3]
   assert orders["order_id"].tolist() == expected_ids

4. Append the result display after the assertions.

   print(orders)
   print(f"shape={orders.shape}")
   print(f"index={orders.index.tolist()}")
   print(f"order_ids={orders['order_id'].tolist()}")

5. Run the completed concat program to confirm the combined table.

   $ python3 concat_dataframes.py
     order_id region  total
   0     A100   EMEA  125.0
   1     A101   APAC   98.5
   2     A102   EMEA  143.0
   3     A103   AMER   87.0
   shape=(4, 3)
   index=[0, 1, 2, 3]
   order_ids=['A100', 'A101', 'A102', 'A103']