concat() for stacking DataFrames in Pandas

Introduction

We use concat() to join two or more tables (DataFrames) together. It helps us combine data from different sources into one big table.

You have sales data from January and February in separate tables and want to see all months together.

You collected survey results from different groups and want to combine them into one list.

You want to add new rows of data to an existing table without changing the columns.

You want to stack data vertically or horizontally to compare or analyze together.

Syntax

Pandas

import pandas as pd

# Concatenate DataFrames vertically (stack rows)
combined_df = pd.concat([df1, df2], axis=0, ignore_index=True)

# Concatenate DataFrames horizontally (stack columns)
combined_df = pd.concat([df1, df2], axis=1)

axis=0 stacks rows (one below the other).

axis=1 stacks columns (side by side).

Examples

Stack two DataFrames with the same columns one below the other. ignore_index=True resets the row numbers.

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

# Vertical concat (stack rows)
result = pd.concat([df1, df2], axis=0, ignore_index=True)
print(result)

Stack two DataFrames side by side, adding columns.

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'B': [3, 4]})

# Horizontal concat (stack columns)
result = pd.concat([df1, df2], axis=1)
print(result)

Concatenating with an empty DataFrame just returns the non-empty one.

Pandas

import pandas as pd

# Edge case: empty DataFrame
empty_df = pd.DataFrame()
df = pd.DataFrame({'A': [1, 2]})

result = pd.concat([empty_df, df], axis=0, ignore_index=True)
print(result)

Concatenating DataFrames with only one row each stacks them correctly.

Pandas

import pandas as pd

df_single = pd.DataFrame({'A': [10]})
df_another = pd.DataFrame({'A': [20]})

# Concatenate single-row DataFrames
result = pd.concat([df_single, df_another], axis=0, ignore_index=True)
print(result)

Sample Program

This program creates two small tables for sales in January and February. Then it stacks them vertically to see all sales together.

Pandas

import pandas as pd

# Create two DataFrames with sales data
sales_jan = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Sales': [100, 150]
})

sales_feb = pd.DataFrame({
    'Product': ['Apple', 'Banana'],
    'Sales': [120, 130]
})

print('Sales January:')
print(sales_jan)

print('\nSales February:')
print(sales_feb)

# Combine sales data vertically (stack rows)
all_sales = pd.concat([sales_jan, sales_feb], axis=0, ignore_index=True)

print('\nAll Sales Combined:')
print(all_sales)

Important Notes

Time complexity: Concatenation takes time proportional to the total size of all DataFrames combined.

Space complexity: Requires memory to hold all combined data.

Common mistake: Forgetting ignore_index=True can keep old row numbers, causing duplicate indices.

Use concat() to stack data vertically or horizontally. For merging based on keys, use merge() instead.

Summary

concat() joins DataFrames by stacking rows or columns.

Use axis=0 to stack rows, axis=1 to stack columns.

Remember to reset index with ignore_index=True when stacking rows.

Practice

1. What does the concat() function do in pandas?

easy

A. Deletes rows from a DataFrame

B. Joins DataFrames by stacking rows or columns

C. Sorts the DataFrame by index

D. Filters rows based on a condition

Solution

1. Step 1: Understand the purpose of concat()

   concat() is used to combine multiple DataFrames either by stacking rows or columns.

2. Step 2: Compare with other DataFrame operations

   Deleting, sorting, or filtering are different operations and not related to concat().

3. Final Answer:

   Joins DataFrames by stacking rows or columns -> Option B

4. Quick Check:

   concat() stacks DataFrames [OK]

Hint: Remember: concat stacks rows or columns [OK]

Common Mistakes:

• Confusing concat with filtering or sorting
• Thinking concat deletes data
• Assuming concat only merges columns

2. Which of the following is the correct syntax to stack two DataFrames df1 and df2 vertically using concat()?

easy

A. pd.concat(df1 + df2)

B. pd.concat([df1, df2], axis=1)

C. pd.concat(df1, df2)

D. pd.concat([df1, df2], axis=0)

Solution

1. Step 1: Identify axis for vertical stacking

   Vertical stacking means stacking rows, which is done with axis=0.

2. Step 2: Check syntax correctness

   pd.concat() requires a list of DataFrames as first argument, so [df1, df2] is correct.

3. Final Answer:

   pd.concat([df1, df2], axis=0) -> Option D

4. Quick Check:

   Vertical stack uses axis=0 [OK]

Hint: Use axis=0 to stack rows vertically [OK]

Common Mistakes:

• Using axis=1 for vertical stacking
• Passing DataFrames without a list
• Trying to add DataFrames with + inside concat

3. Given the following code:

import pandas as pd
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
result = pd.concat([df1, df2], ignore_index=True)
print(result)

What is the output?

medium

A. DataFrame with 4 rows, original indexes 0,1,0,1

B. DataFrame with 2 rows, columns stacked horizontally

C. DataFrame with 4 rows, index reset from 0 to 3

D. Error because ignore_index is invalid

Solution

1. Step 1: Understand concat with ignore_index=True

   Concatenating df1 and df2 vertically stacks rows, resulting in 4 rows total. ignore_index=True resets the index to 0,1,2,3.

2. Step 2: Confirm output structure

   The columns remain the same ('A' and 'B'), rows are stacked, and index is reset.

3. Final Answer:

   DataFrame with 4 rows, index reset from 0 to 3 -> Option C

4. Quick Check:

   ignore_index=True resets index [OK]

Hint: ignore_index=True resets row numbers after stacking [OK]

Common Mistakes:

• Forgetting ignore_index resets index
• Thinking columns stack horizontally by default
• Assuming error with ignore_index parameter

4. What is wrong with this code snippet?

import pandas as pd
df1 = pd.DataFrame({'X': [1, 2]})
df2 = pd.DataFrame({'Y': [3, 4]})
result = pd.concat(df1, df2, axis=0)
print(result)

medium

A. pd.concat requires a list of DataFrames as first argument

B. axis=0 stacks columns instead of rows

C. DataFrames must have same columns to concat

D. ignore_index=True is missing

Solution

1. Step 1: Check pd.concat argument format

   pd.concat() expects a list or iterable of DataFrames as the first argument, but here two DataFrames are passed as separate arguments.

2. Step 2: Identify correct usage

   The correct call is pd.concat([df1, df2], axis=0) with a list.

3. Final Answer:

   pd.concat requires a list of DataFrames as first argument -> Option A

4. Quick Check:

   pd.concat([df1, df2]) needs list [OK]

Hint: Always pass DataFrames as a list to concat [OK]

Common Mistakes:

• Passing DataFrames as separate arguments
• Confusing axis parameter meaning
• Assuming columns must match exactly

5. You have two DataFrames:

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'B': [5, 6], 'C': [7, 8]})

You want to stack them vertically and keep all columns, filling missing values with NaN. Which code will do this correctly?

hard

A. pd.concat([df1, df2], axis=0, ignore_index=True)

B. pd.concat([df1, df2], axis=1)

C. pd.concat([df1, df2], axis=0).fillna(0)

D. pd.concat([df1, df2], axis=0, join='inner')

Solution

1. Step 1: Understand vertical stacking with different columns

   Stacking vertically with axis=0 combines rows. Since columns differ, missing columns get NaN by default.

2. Step 2: Check parameters for desired behavior

   ignore_index=True resets index. Default join='outer' keeps all columns and fills missing with NaN.

3. Step 3: Evaluate other options

   pd.concat([df1, df2], axis=1) stacks columns horizontally, not vertically. pd.concat([df1, df2], axis=0).fillna(0) fills NaN with 0, not requested. pd.concat([df1, df2], axis=0, join='inner') uses join='inner' which keeps only common columns.

4. Final Answer:

   pd.concat([df1, df2], axis=0, ignore_index=True) -> Option A

5. Quick Check:

   axis=0 + default join='outer' keeps all columns [OK]

Hint: Use axis=0 and default join to keep all columns [OK]

Common Mistakes:

• Using axis=1 to stack rows
• Using join='inner' and losing columns
• Filling NaN when not needed