append equivalent with concat in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of concat equivalent to append

What is the output of this code that uses pd.concat to append one DataFrame to another?

Pandas

import pandas as pd

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

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

A

   A  B
0  1  3
1  2  4
2  NaN NaN

B

   A  B
0  1  3
1  2  4
2  5  6

C

   A  B
0  5  6
1  1  3
2  2  4

D

   A  B
0  1  3
1  2  4

💡 Hint

Think about how concat stacks DataFrames vertically and what ignore_index=True does.

pd.concat stacks the two DataFrames vertically and resets the index with ignore_index=True, so the new row from df2 is added at the bottom with a new index.

❓ data_output

intermediate

Number of rows after concat equivalent to append

Given two DataFrames df1 with 3 rows and df2 with 2 rows, what is the number of rows in pd.concat([df1, df2])?

A 3

B 6

C 5

D 2

💡 Hint

Concat stacks rows from both DataFrames.

Concat combines all rows from both DataFrames, so total rows = 3 + 2 = 5.

🔧 Debug

advanced

Why does this concat code raise an error?

This code raises an error. What is the cause?

import pandas as pd

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

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

A Raises a TypeError due to incompatible data types

B Raises a KeyError because columns differ

C No common columns to concatenate, so columns are combined with NaNs

D Raises no error; outputs DataFrame with columns A and B, NaNs where missing

💡 Hint

Try running the code to see what happens when columns differ.

pd.concat stacks rows and aligns columns by name. Missing columns get NaN values. No error is raised.

🚀 Application

advanced

Using concat to append with keys

How can you use pd.concat to append two DataFrames and add keys to identify their origin?

Pandas

import pandas as pd

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

result = pd.concat([df1, df2], keys=['first', 'second'])
print(result)

A Creates a MultiIndex with 'first' and 'second' as keys for rows

B Raises an error because keys parameter is invalid

C Ignores keys and concatenates normally

D Adds a new column named 'keys' with values 'first' and 'second'

💡 Hint

Keys create a hierarchical index to label rows from each DataFrame.

The keys parameter creates a MultiIndex where the first level is the key label for each DataFrame's rows.

🧠 Conceptual

expert

Why is concat preferred over append in pandas 1.4+?

Pandas deprecated the append method for DataFrames. Why is pd.concat preferred instead?

A Concat is more flexible and efficient for combining multiple DataFrames at once

B Concat automatically sorts columns while append does not

C Append does not work with DataFrames containing missing values

D Append modifies DataFrames in place, concat does not

💡 Hint

Think about performance and flexibility when combining many DataFrames.

Concat can combine many DataFrames efficiently in one call, while append is slower and less flexible.