Why combining DataFrames matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to combine two DataFrames vertically using pandas.

Pandas

import pandas as pd

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

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

Ajoin

Bmerge

Cappend

Dconcat

Attempts:

3 left

💡 Hint

Common Mistakes

Using merge instead of concat for vertical stacking.

Using join which is for columns.

Using append which is deprecated in recent pandas versions.

The concat function stacks DataFrames vertically by default, combining rows.

2fill in blank

medium

Complete the code to merge two DataFrames on column 'key'.

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['K0', 'K1'], 'A': [1, 2]})
df2 = pd.DataFrame({'key': ['K0', 'K1'], 'B': [3, 4]})
merged = pd.merge(df1, df2, on=[1])
print(merged)

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

A'key'

B'A'

C'B'

D'index'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in both DataFrames.

Using 'index' which is not a column name here.

The on parameter specifies the column to join on, here it is 'key'.

3fill in blank

hard

Fix the error in the code to join two DataFrames on their indexes.

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2]}, index=['a', 'b'])
df2 = pd.DataFrame({'B': [3, 4]}, index=['a', 'b'])
joined = df1.[1](df2, how='inner')
print(joined)

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

Amerge

Bconcat

Cjoin

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using merge which requires specifying columns.

Using concat which stacks DataFrames.

Using append which adds rows.

The join method joins DataFrames on their indexes by default.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

words = ['apple', 'bat', 'car', 'door']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

Alen(word)

Blen(word) > 3

Cword

Dlen(word) < 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using wrong condition like less than 3.

The dictionary maps each word to its length using len(word). The condition keeps words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 3.

Pandas

words = ['apple', 'bat', 'car', 'door']
result = { [1]: [2] for word in words if [3] }
print(result)

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

Aword.upper()

Blen(word)

Clen(word) > 3

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using wrong condition or value.

The dictionary keys are uppercase words using word.upper(). Values are lengths with len(word). The condition filters words longer than 3.

Practice

1. Why do we combine DataFrames in pandas?
pd.concat and pd.merge are common methods. What is the main reason to combine DataFrames?

easy

A. To get a complete view by joining or stacking data

B. To delete duplicate rows automatically

C. To convert DataFrames into lists

D. To change the data types of columns

Solution

1. Step 1: Understand the purpose of combining DataFrames

   Combining DataFrames helps to bring together data from different sources or parts to analyze them as one.

2. Step 2: Identify the main reason

   Using pd.concat stacks data vertically or horizontally, and pd.merge joins data based on matching columns, both to get a fuller dataset.

3. Final Answer:

   To get a complete view by joining or stacking data -> Option A

4. Quick Check:

   Combining DataFrames = Complete view [OK]

Hint: Combining means joining or stacking data for full info [OK]

Common Mistakes:

• Thinking combining deletes duplicates automatically
• Confusing combining with data type changes
• Believing combining converts DataFrames to lists

2. Which of the following is the correct syntax to stack two DataFrames df1 and df2 vertically using pandas?

easy

A. pd.merge(df1, df2, axis=0)

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

C. df1.append(df2, axis=1)

D. pd.concat(df1, df2, axis=1)

Solution

1. Step 1: Recall the function to stack DataFrames vertically

   pd.concat stacks DataFrames; axis=0 means stacking rows vertically.

2. Step 2: Check the syntax

   The correct syntax is pd.concat([df1, df2], axis=0). Other options misuse pd.merge or wrong axis or argument types.

3. Final Answer:

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

4. Quick Check:

   Stack vertically = concat with axis=0 [OK]

Hint: Use pd.concat with a list and axis=0 to stack rows [OK]

Common Mistakes:

• Using pd.merge instead of pd.concat for stacking
• Passing DataFrames without a list to pd.concat
• Using wrong axis value for vertical stacking

3. What will be the output of this code?

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['B', 'C'], 'val2': [3, 4]})
result = pd.merge(df1, df2, on='key', how='inner')
print(result)

medium

A. key val1
0 A 1
1 B 2

B. key val1 val2
0 A 1 3
1 B 2 4

C. key val1 val2
0 A 1 NaN
1 B 2 3
2 C NaN 4

D. key val1 val2
0 B 2 3

Solution

1. Step 1: Understand pd.merge with how='inner'

   Inner join keeps only rows with keys present in both DataFrames. Here keys are 'A', 'B' in df1 and 'B', 'C' in df2.

2. Step 2: Find common keys and merge rows

   Only 'B' is common. So result has one row with key 'B', val1=2 from df1, val2=3 from df2.

3. Final Answer:

   key val1 val2 0 B 2 3 -> Option D

4. Quick Check:

   Inner join = common keys only [OK]

Hint: Inner merge keeps only matching keys in both DataFrames [OK]

Common Mistakes:

• Expecting all keys from both DataFrames (confusing with outer join)
• Mixing up val2 values for keys
• Ignoring how='inner' effect

4. The following code throws an error. What is the main issue?

import pandas as pd

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

medium

A. Column 'A' does not exist in both DataFrames

B. pd.merge cannot merge DataFrames with different columns

C. pd.merge requires axis parameter

D. DataFrames must have same number of rows to merge

Solution

1. Step 1: Check columns in both DataFrames

   df1 has column 'A', but df2 has only column 'B'. So 'A' is missing in df2.

2. Step 2: Understand pd.merge on parameter

   pd.merge requires the 'on' column to be present in both DataFrames to join on it. Missing column causes error.

3. Final Answer:

   Column 'A' does not exist in both DataFrames -> Option A

4. Quick Check:

   Merge 'on' column must be in both DataFrames [OK]

Hint: Merge 'on' column must exist in both DataFrames [OK]

Common Mistakes:

• Thinking axis parameter is needed for merge
• Believing DataFrames must have same row count
• Assuming merge works with different columns without 'on'

5. You have two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'score': [90, 80, 70]})
df2 = pd.DataFrame({'id': [2, 3, 4], 'grade': ['B', 'C', 'A']})

You want a DataFrame that includes all ids from both, with scores and grades matched where possible, and NaN where missing. Which code achieves this?

hard

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

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

C. pd.merge(df1, df2, on='id', how='outer')

D. pd.merge(df1, df2, on='id', how='inner')

Solution

1. Step 1: Understand the goal

   We want all ids from both DataFrames, matching scores and grades where ids overlap, and NaN where data is missing.

2. Step 2: Choose the correct merge type

   Using how='outer' in pd.merge keeps all keys from both DataFrames, filling missing values with NaN.

3. Step 3: Check other options

   pd.concat stacks data but does not join on keys; how='inner' drops ids not in both.

4. Final Answer:

   pd.merge(df1, df2, on='id', how='outer') -> Option C

5. Quick Check:

   Outer merge = all keys, fill missing with NaN [OK]

Hint: Use outer merge to keep all keys and fill missing data [OK]

Common Mistakes:

• Using inner merge and losing unmatched ids
• Using concat which stacks instead of joining
• Confusing axis parameter with merge how