append equivalent with concat in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to combine two DataFrames using pandas concat.

Pandas

import pandas as pd

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

Drag options to blanks, or click blank then click option'

Amerge

Bappend

Cconcat

Djoin

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'append' which is deprecated in recent pandas versions.

Using 'merge' which is for joining on keys, not simple stacking.

pandas concat combines DataFrames along a particular axis. It is the recommended way to append DataFrames.

2fill in blank

medium

Complete the code to reset the index after concatenating two DataFrames.

Pandas

import pandas as pd

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

Drag options to blanks, or click blank then click option'

Aignore_index

Bdrop

Creset_index

Dinplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'reset_index' as a parameter to concat (it is a method on DataFrame).

Using 'drop' which is unrelated here.

The ignore_index=True option resets the index in the concatenated DataFrame to a default integer index.

3fill in blank

hard

Fix the error in the code to concatenate two DataFrames along columns.

Pandas

import pandas as pd

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

Drag options to blanks, or click blank then click option'

A-1

B0

C2

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using axis=0 which stacks rows instead of columns.

Using invalid axis values like 2 or -1.

Using axis=1 concatenates DataFrames side-by-side (columns). axis=0 stacks them vertically (rows).

4fill in blank

hard

Fill both blanks to create a concatenated DataFrame with a new index and drop the old index.

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]}, index=[10, 20])
df2 = pd.DataFrame({'A': [3, 4]}, index=[30, 40])
result = pd.concat([df1, df2], [1]=True).[2](drop=True)

Drag options to blanks, or click blank then click option'

Aignore_index

Breset_index

Csort

Ddrop

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'drop' as a parameter to concat (it is a method parameter).

Confusing 'sort' with index resetting.

ignore_index=True resets the index during concat, and reset_index(drop=True) removes the old index column if it exists.

5fill in blank

hard

Fill all three blanks to concatenate two DataFrames vertically, reset the index, and sort columns.

Pandas

import pandas as pd

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

Drag options to blanks, or click blank then click option'

Aignore_index

Breset_index

Csort_index

Dsort_values

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values instead of sort_index for sorting columns.

Not dropping the old index after reset.

ignore_index=True resets the index during concat, reset_index(drop=True) removes the old index, and sort_index(axis=1) sorts columns alphabetically.

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