Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the DataFrame?

Given the following DataFrame, what will be the output of df.shape?

Data Analysis Python

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
print(df.shape)

A (2, 3)

B (3, 2)

C (3, 3)

D (3, 4)

💡 Hint

Shape shows (rows, columns). Count rows and columns in the data.

The DataFrame has 3 rows and 3 columns, so shape is (3, 3).

❓ Predict Output

intermediate

What are the data types of each column?

What will be the output of df.dtypes for this DataFrame?

Data Analysis Python

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30], 'Height': [5.5, 6.0]}
df = pd.DataFrame(data)
print(df.dtypes)

A

Name     int64
Age      object
Height   float64
dtype: object

B

Name     object
Age       int64
Height  float64
dtype: object

C

Name     float64
Age      int64
Height   object
dtype: object

D

Name     object
Age      float64
Height   int64
dtype: object

💡 Hint

Names are text, ages are whole numbers, heights are decimals.

Name column has text values so type is object, Age is integer, Height is float.

❓ data_output

advanced

What does describe() show for numeric columns?

Given this DataFrame, what will df.describe() output?

Data Analysis Python

import pandas as pd

data = {'Score': [80, 90, 100, 70, 85], 'Attempts': [1, 2, 1, 3, 2]}
df = pd.DataFrame(data)
print(df.describe())

A

       Score  Attempts
count    5.0       5.0
mean    85.0       1.8
std      10.0      0.84
min     70.0       1.0
25%     80.0       1.0
50%     85.0       2.0
75%     90.0       2.0
max    100.0       3.0

B

       Score  Attempts
count    5.0       5.0
mean    85.0       1.8
std      11.18     0.84
min     70.0       1.0
25%     75.0       1.0
50%     85.0       2.0
75%     90.0       3.0
max    100.0       3.0

C

       Score  Attempts
count    5.0       5.0
mean    85.0       2.0
std      11.18     1.0
min     70.0       1.0
25%     80.0       1.0
50%     85.0       2.0
75%     90.0       2.0
max    100.0       3.0

D

       Score  Attempts
count    5.0       5.0
mean    85.0       1.8
std      11.18     0.84
min     70.0       1.0
25%     80.0       1.0
50%     85.0       2.0
75%     90.0       2.0
max    100.0       3.0

💡 Hint

Check the mean and standard deviation carefully.

The mean of Score is 85, Attempts mean is 1.8. Standard deviation and quartiles match option D.

🧠 Conceptual

advanced

Which statement about DataFrame dtypes is true?

Consider a DataFrame with columns of different types. Which statement is correct about df.dtypes?

A It returns a Series showing the data type of each column.

B It returns the data type of the first column only.

C It returns a list of all unique data types in the DataFrame.

D It returns the number of columns for each data type.

💡 Hint

Think about what you want to know about each column's type.

df.dtypes returns a Series indexed by column names with their data types as values.

🔧 Debug

expert

Why does this describe() call miss some columns?

Given this DataFrame, why does df.describe() only show one column?

Data Analysis Python

import pandas as pd
import numpy as np

data = {'A': [1, 2, 3], 'B': ['x', 'y', 'z'], 'C': [np.nan, np.nan, np.nan]}
df = pd.DataFrame(data)
print(df.describe())

A describe() by default only summarizes numeric columns, so only column 'A' appears.

B describe() fails because column 'C' has NaN values and stops processing.

C describe() only shows columns with no missing values, so 'C' is excluded.

D describe() shows all columns but output is truncated to one column by default.

💡 Hint

Check pandas describe default behavior with non-numeric columns.

By default, describe() summarizes only numeric columns. Non-numeric columns are excluded unless specified.