concat() for stacking DataFrames in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of vertical stacking with concat()?

Given two DataFrames df1 and df2, what is the output of pd.concat([df1, df2])?

Pandas

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])
print(result)

A {'A': [1, 2], 'B': [3, 4]} only

B {'A': [1, 2, 5, 6], 'B': [3, 4, 7, 8]} with index [0, 1, 0, 1]

C {'A': [1, 2, 5, 6], 'B': [3, 4, 7, 8]} with index [0, 1, 2, 3]

D Raises a KeyError

💡 Hint

By default, concat keeps the original indices when stacking vertically.

When stacking vertically with pd.concat, the rows from df2 keep their original indices, so the result has duplicate indices 0 and 1.

❓ Predict Output

intermediate

What does the 'ignore_index=True' option do in concat()?

What is the output of pd.concat([df1, df2], ignore_index=True) given the same df1 and df2 as before?

Pandas

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)

A {'A': [1, 2, 5, 6], 'B': [3, 4, 7, 8]} with index [0, 1, 2, 3]

B Raises a TypeError

C {'A': [1, 2, 5, 6], 'B': [3, 4, 7, 8]} with index [0, 1, 0, 1]

D Returns an empty DataFrame

💡 Hint

ignore_index=True resets the index to a continuous range.

Setting ignore_index=True makes concat reset the index so the rows are numbered 0 to n-1 in the result.

❓ data_output

advanced

What is the shape of the DataFrame after horizontal concat?

Given df1 and df2 below, what is the shape of the DataFrame after pd.concat([df1, df2], axis=1)?

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2, 3]})
df2 = pd.DataFrame({'B': [4, 5]})
result = pd.concat([df1, df2], axis=1)
print(result.shape)

A (3, 1)

B (2, 2)

C (3, 2)

D (2, 1)

💡 Hint

Concat with axis=1 stacks columns side by side, aligning by index.

The result has 3 rows (from df1) and 2 columns (A and B). Missing values fill where df2 has fewer rows.

🔧 Debug

advanced

Why does this concat code raise an error?

What error does the following code raise and why?

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'B': [3, 4]})
result = pd.concat([df1, df2], axis=0, join='inner')
print(result)

A Runs successfully and returns only the common columns

B Raises a TypeError because join must be a boolean

C Raises a ValueError because join='inner' is invalid with axis=0

D Raises a KeyError due to missing columns

💡 Hint

join='inner' keeps only columns present in all DataFrames.

When concatenating vertically with join='inner', pandas keeps only columns common to all DataFrames. Here, no columns are common, so the result is empty with correct shape.

🚀 Application

expert

How to stack multiple DataFrames with different columns and keep all data?

You have three DataFrames with different columns. Which concat call stacks them vertically and keeps all columns, filling missing values with NaN?

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1], 'B': [2]})
df2 = pd.DataFrame({'B': [3], 'C': [4]})
df3 = pd.DataFrame({'A': [5], 'C': [6]})

A pd.concat([df1, df2, df3], axis=0, join='inner')

B pd.concat([df1, df2, df3], axis=1, join='inner')

C pd.concat([df1, df2, df3], axis=1, join='outer')

D pd.concat([df1, df2, df3], axis=0, join='outer')

💡 Hint

To keep all columns when stacking vertically, use join='outer'.

join='outer' keeps all columns from all DataFrames, filling missing values with NaN. axis=0 stacks rows vertically.