concat() for stacking DataFrames in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of vertical stacking with concat()

What is the output DataFrame after stacking df1 and df2 vertically using pd.concat([df1, df2])?

Data Analysis Python

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  B
0  1  3
1  2  4
NaN  5  7
NaN  6  8

B

   A  B
0  1  3
1  2  4
2  5  7
3  6  8

C

   A  B
0  1  3
1  2  4
0  5  7
1  6  8

D

   A  B
0  1  3
1  2  4
0  5  7
2  6  8

💡 Hint

Remember that concat keeps original indices by default when stacking vertically.

When stacking vertically with pd.concat, the default behavior keeps the original row indices from both DataFrames, so the result has duplicate indices 0 and 1.

❓ Predict Output

intermediate

Output of horizontal stacking with concat()

What is the output DataFrame after stacking df1 and df2 horizontally using pd.concat([df1, df2], axis=1)?

Data Analysis Python

import pandas as pd

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

A

   A  B
0  1  3
1  2  4
0  NaN 3

B

   A  B
0  1  3
1  2  4

C

   A  B
0  1  3
1  2  4
2  NaN 4

D

   A  B
0  1  3
1  2  NaN
2  NaN 4

💡 Hint

Horizontal stacking aligns rows by index.

Concatenating along axis=1 stacks columns side by side, matching rows by their index. Since both DataFrames have indices 0 and 1, the result has those rows with columns A and B.

❓ data_output

advanced

Result of concat() with ignore_index=True

What is the output DataFrame after stacking df1 and df2 vertically using pd.concat([df1, df2], ignore_index=True)?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df2 = pd.DataFrame({'X': [50, 60], 'Y': [70, 80]})
result = pd.concat([df1, df2], ignore_index=True)
print(result)

A

    X   Y
0  10  30
1  20  40
2  NaN 70
3  NaN 80

B

    X   Y
0  10  30
1  20  40
0  50  70
1  60  80

C

    X   Y
0  10  30
1  20  40
2  50  NaN
3  60  NaN

D

    X   Y
0  10  30
1  20  40
2  50  70
3  60  80

💡 Hint

ignore_index resets the row indices to a continuous range.

Using ignore_index=True resets the row indices in the concatenated DataFrame to 0,1,2,... ignoring the original indices.

🔧 Debug

advanced

Identify the error in concat() usage

What error will this code raise when trying to concatenate df1 and df2 with mismatched columns without specifying join?

Data Analysis Python

import pandas as pd

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

A No error, output has columns A and B with NaNs where missing

B ValueError: columns overlap but no join specified

C KeyError: 'B' not found in df1

D TypeError: cannot concatenate DataFrames with different columns

💡 Hint

Check how concat handles different columns by default.

By default, concat stacks rows and includes all columns from both DataFrames, filling missing values with NaN. No error is raised.

🚀 Application

expert

Stacking DataFrames with different columns and resetting index

You have two DataFrames with different columns. Which option correctly stacks them vertically, includes all columns, fills missing values with NaN, and resets the index?

Data Analysis Python

import pandas as pd

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

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

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

C pd.concat([df1, df2], join='inner', ignore_index=True)

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

💡 Hint

Think about vertical stacking with all columns and resetting index.

Option A stacks vertically (default axis=0), includes all columns (default join='outer'), fills missing with NaN, and resets index with ignore_index=True. Other options either stack horizontally or limit columns.