Exploratory data analysis workflow in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of basic data summary

What is the output of the following code snippet that summarizes a DataFrame?

Pandas

import pandas as pd

data = {'Age': [25, 30, 22, 40, 28], 'Salary': [50000, 60000, 45000, 80000, 52000]}
df = pd.DataFrame(data)
summary = df.describe()
print(summary)

A {'Age': {'count': 5.0, 'mean': 29.0, 'std': 7.071, 'min': 22.0, '25%': 25.0, '50%': 28.0, '75%': 30.0, 'max': 40.0}, 'Salary': {'count': 5.0, 'mean': 57400.0, 'std': 13435.028, 'min': 45000.0, '25%': 50000.0, '50%': 52000.0, '75%': 60000.0, 'max': 80000.0}}

B

   Age  Salary
0   25   50000
1   30   60000
2   22   45000
3   40   80000
4   28   52000

C

       Age        Salary
count   5.0      5.000000
mean   29.0  57400.000000
std     7.071 13435.028
min    22.0  45000.000000
25%    25.0  50000.000000
50%    28.0  52000.000000
75%    30.0  60000.000000
max    40.0  80000.000000

D

Age       29.0
Salary  57400.0
dtype: float64

💡 Hint

Look at the output of pandas DataFrame describe() method.

The describe() method returns a DataFrame with count, mean, std, min, quartiles, and max for each numeric column.

❓ data_output

intermediate

Count of missing values per column

Given the DataFrame below, what is the output of counting missing values per column?

Pandas

import pandas as pd
import numpy as np

data = {'Name': ['Alice', 'Bob', None, 'David'], 'Age': [25, None, 22, 40], 'Salary': [50000, 60000, 45000, None]}
df = pd.DataFrame(data)
missing_counts = df.isnull().sum()
print(missing_counts)

A

Name      1
Age       1
Salary    1
dtype: int64

B

Name      1
Age       0
Salary    1
dtype: int64

C

Name      0
Age       1
Salary    1
dtype: int64

D

Name      1
Age       1
Salary    0
dtype: int64

💡 Hint

Check which values are None or np.nan in each column.

The isnull() method marks None as missing. Each column has exactly one missing value.

❓ visualization

advanced

Correct histogram plot code

Which option produces a histogram plot of the 'Age' column with 5 bins using matplotlib?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Age': [25, 30, 22, 40, 28, 35, 33, 31, 29, 27]}
df = pd.DataFrame(data)

# Choose the correct code to plot histogram

A

df.plot.hist('Age', bins=5)
plt.show()

B

df['Age'].plot.hist(bins=5)
plt.show()

C

plt.plot(df['Age'], bins=5)
plt.show()

D

plt.hist(df['Age'], bins=5)
plt.show()

💡 Hint

matplotlib's hist function takes data and bins as arguments.

plt.hist() is the correct matplotlib function to plot a histogram. Option D uses pandas plotting but requires plt.show(). Option D is invalid because plt.plot does not accept bins. Option D is invalid because df.plot.hist expects no argument or column name as parameter.

🔧 Debug

advanced

Identify error in groupby aggregation

What error does the following code raise?

Pandas

import pandas as pd

data = {'Category': ['A', 'B', 'A', 'B'], 'Value': [10, 20, 15, 25]}
df = pd.DataFrame(data)
result = df.groupby('Category').agg({'Value': 'sum', 'NonExistent': 'mean'})
print(result)

A KeyError: 'NonExistent'

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: No numeric types to aggregate

D No error, prints grouped sums and means

💡 Hint

Check if all keys in agg dictionary exist in DataFrame columns.

The aggregation dictionary references a column 'NonExistent' which is not in the DataFrame, causing a KeyError.

🚀 Application

expert

Calculate correlation matrix and interpret output

Given the DataFrame below, what is the correlation coefficient between 'X' and 'Y'?

Pandas

import pandas as pd
import numpy as np

data = {'X': [1, 2, 3, 4, 5], 'Y': [2, 4, 6, 8, 10], 'Z': [5, 4, 3, 2, 1]}
df = pd.DataFrame(data)
corr_matrix = df.corr()
print(round(corr_matrix.loc['X', 'Y'], 2))

A -1.00

B 1.00

C 0.00

D 0.50

💡 Hint

Check if Y is a perfect linear function of X.

Y is exactly 2 times X, so correlation is perfect positive 1.00.