Why combining DataFrames matters in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this DataFrame merge?

Consider two DataFrames merged on a common column. What is the resulting DataFrame?

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'val1': [1, 2, 3]})
df2 = pd.DataFrame({'key': ['B', 'C', 'D'], 'val2': [4, 5, 6]})
result = pd.merge(df1, df2, on='key', how='inner')
print(result)

A

  key  val1  val2
0   B     2     4
1   C     3     5

B

  key  val1  val2
0   A     1     4
1   B     2     5
2   C     3     6

C

  key  val1  val2
0   B     2     5
1   C     3     6
2   D   NaN     6

D

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

💡 Hint

Inner merge keeps only keys present in both DataFrames.

The inner merge keeps only rows with keys 'B' and 'C' present in both DataFrames. Values from both are combined accordingly.

❓ data_output

intermediate

How many rows after concatenation?

Two DataFrames are concatenated vertically. How many rows does the result have?

Pandas

import pandas as pd

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6, 7], 'B': [8, 9, 10]})
result = pd.concat([df1, df2], ignore_index=True)
print(len(result))

A 5

B 6

C 4

D 3

💡 Hint

Count rows in both DataFrames and add them.

df1 has 2 rows, df2 has 3 rows, so concatenation results in 5 rows total.

🔧 Debug

advanced

Why does this merge produce NaN values?

Examine the code and identify why the merged DataFrame has NaN values in some columns.

Pandas

import pandas as pd

df1 = pd.DataFrame({'id': [1, 2, 3], 'score': [90, 80, 70]})
df2 = pd.DataFrame({'id': [2, 3, 4], 'grade': ['B', 'C', 'D']})
result = pd.merge(df1, df2, on='id', how='left')
print(result)

A Because the 'id' columns have different data types causing merge failure.

B Because 'how="left"' keeps all rows from df2, missing matches in df1 cause NaN.

C Because 'how="left"' keeps all rows from df1, missing matches in df2 cause NaN.

D Because the merge key 'id' is missing in one DataFrame.

💡 Hint

Left merge keeps all rows from the left DataFrame.

Left merge keeps all rows from df1. Rows with id=1 have no match in df2, so 'grade' is NaN for that row.

❓ visualization

advanced

Which plot shows the combined data correctly?

Given two DataFrames combined by concatenation, which plot correctly shows the combined data distribution?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df1 = pd.DataFrame({'value': [1, 2, 3]})
df2 = pd.DataFrame({'value': [4, 5, 6]})
combined = pd.concat([df1, df2], ignore_index=True)
plt.hist(combined['value'])
plt.show()

A A scatter plot with points only from df1

B A histogram with bars at 1, 2, 3, 4, 5, 6 each with height 1

C A bar chart showing counts of df2 values only

D A line plot connecting points 1 to 6

💡 Hint

Histogram shows frequency of all combined values.

The histogram shows all values from both DataFrames combined, each value appears once, so bars have height 1.

🚀 Application

expert

How to combine DataFrames to keep all unique keys?

You have two DataFrames with overlapping and unique keys. You want to combine them so that all keys appear, with matching data where possible. Which merge option achieves this?

Pandas

import pandas as pd

df1 = pd.DataFrame({'key': ['X', 'Y', 'Z'], 'val1': [10, 20, 30]})
df2 = pd.DataFrame({'key': ['Y', 'Z', 'W'], 'val2': [40, 50, 60]})
result = pd.merge(df1, df2, on='key', how=?)
print(result)

A "inner"

B "right"

C "left"

D "outer"

💡 Hint

Think about keeping all keys from both DataFrames.

Outer merge keeps all keys from both DataFrames, filling missing values with NaN where no match exists.