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.

Practice

1. What is the main reason to use pandas.concat() instead of DataFrame.append()?

easy

A. concat() is faster for single DataFrame addition only

B. Because append() is deprecated and concat() can join multiple DataFrames at once

C. append() works only for columns, not rows

D. concat() automatically sorts columns alphabetically

Solution

1. Step 1: Understand the deprecation of append()

   The append() method is deprecated in pandas and will be removed in future versions.

2. Step 2: Recognize concat() advantages

   concat() can join multiple DataFrames at once, not just one, making it more flexible.

3. Final Answer:

   Because append() is deprecated and concat() can join multiple DataFrames at once -> Option B

4. Quick Check:

   append() deprecated, concat() multi-join [OK]

Hint: Remember: concat replaces append and handles many DataFrames [OK]

Common Mistakes:

• Thinking append is still recommended
• Believing concat only works for columns
• Assuming append is faster than concat

2. Which of the following is the correct syntax to replace df1.append(df2, ignore_index=True) using concat?

easy

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

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

C. pd.concat(df1, df2, ignore_index=True)

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

Solution

1. Step 1: Identify the correct function call

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

2. Step 2: Use ignore_index=True to reset row numbers

   To mimic append(ignore_index=True), pass ignore_index=True to concat(). The default axis is 0 (rows), so no need to specify.

3. Final Answer:

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

4. Quick Check:

   List of DataFrames + ignore_index=True = correct concat syntax [OK]

Hint: Use list of DataFrames and ignore_index=True with concat [OK]

Common Mistakes:

• Passing DataFrames without list brackets
• Using axis=1 to add rows (wrong axis)
• Omitting ignore_index when needed

3. Given the code:

import pandas as pd

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

What will be the output?

medium

A. A
0 1
1 2
0 3
1 4

B. A
0 3
1 4

C. A
0 1
1 2
2 3
3 4

D. Error because ignore_index is not valid

Solution

1. Step 1: Understand concat with ignore_index=True

   concat joins rows from both DataFrames and resets the index to 0,1,2,...

2. Step 2: Combine rows and reset index

   Rows from df1 (1,2) and df2 (3,4) stack vertically with new index 0 to 3.

3. Final Answer:

   A 0 1 1 2 2 3 3 4 -> Option C

4. Quick Check:

   concat stacks rows and resets index with ignore_index=True [OK]

Hint: concat stacks rows; ignore_index resets numbering [OK]

Common Mistakes:

• Expecting original indexes to remain
• Confusing axis parameter
• Thinking concat drops rows

4. What is wrong with this code snippet?

import pandas as pd

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

medium

A. The DataFrames should be passed as a list inside concat

B. ignore_index is not a valid argument for concat

C. concat cannot join DataFrames with same columns

D. The code will run without errors and print combined DataFrame

Solution

1. Step 1: Check 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

   Correct usage is pd.concat([df1, df2], ignore_index=True) with square brackets around DataFrames.

3. Final Answer:

   The DataFrames should be passed as a list inside concat -> Option A

4. Quick Check:

   concat needs list of DataFrames [OK]

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

Common Mistakes:

• Passing DataFrames as separate arguments
• Forgetting brackets around DataFrames
• Misusing ignore_index parameter

5. You have three DataFrames:

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

You want to combine them vertically using concat so that missing columns fill with NaN and the index resets. Which code achieves this?

hard

A. pd.concat(df1, df2, df3, ignore_index=True)

B. pd.concat([df1, df2, df3], axis=1, ignore_index=True)

C. pd.concat([df1, df2, df3], join='inner', ignore_index=True)

D. pd.concat([df1, df2, df3], ignore_index=True)

Solution

1. Step 1: Combine DataFrames vertically with concat

   Use pd.concat() with a list of DataFrames to stack rows.

2. Step 2: Handle missing columns and reset index

   Default join='outer' fills missing columns with NaN. Use ignore_index=True to reset row numbers.

3. Final Answer:

   pd.concat([df1, df2, df3], ignore_index=True) -> Option D

4. Quick Check:

   concat list, default outer join, ignore_index=True [OK]

Hint: Use concat list with ignore_index=True for clean vertical join [OK]

Common Mistakes:

• Using axis=1 to join rows (actually joins columns)
• Using join='inner' which drops columns
• Passing DataFrames without list brackets