Exploratory Data Analysis (EDA) template in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a CSV file into a DataFrame using pandas.

Data Analysis Python

import pandas as pd

df = pd.[1]('data.csv')

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

Aload_csv

Bread_excel

CDataFrame

Dread_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel instead of read_csv

Trying to call DataFrame directly without reading the file

The read_csv function loads CSV files into a pandas DataFrame.

2fill in blank

medium

Complete the code to display the first 5 rows of the DataFrame.

Data Analysis Python

print(df.[1]())

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

Ahead

Btail

Csample

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using tail() which shows the last rows

Using info() which shows summary info, not rows

The head() method shows the first 5 rows by default.

3fill in blank

hard

Fix the error in the code to get summary statistics of the DataFrame.

Data Analysis Python

summary = df.[1]()

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

Adescribe

Bsummary

Cstats

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using info() which shows data types, not statistics

Using summary() or stats() which do not exist

The describe() method returns summary statistics like mean and std.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the condition

Using word instead of len(word) for the value

We use len(word) to get length and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper()

Using < instead of > in the condition

Swapping keys and values

Keys are converted to uppercase with k.upper(), values are v, and filtered where v > 0.

Practice

1. What is the main purpose of Exploratory Data Analysis (EDA) in data science?

easy

A. To build machine learning models directly

B. To clean data without checking it

C. To understand the data quickly and find patterns

D. To create final reports without analysis

Solution

1. Step 1: Understand EDA's role

   EDA is used to explore and understand data before any modeling or cleaning.

2. Step 2: Compare options

   Only To understand the data quickly and find patterns correctly states EDA's purpose as understanding data quickly and finding patterns.

3. Final Answer:

   To understand the data quickly and find patterns -> Option C

4. Quick Check:

   EDA = Understand data fast [OK]

Hint: EDA means exploring data first, not modeling [OK]

Common Mistakes:

• Confusing EDA with model building
• Thinking EDA skips data checking
• Assuming EDA creates final reports

2. Which Python command is commonly used to get a quick summary of a DataFrame's structure during EDA?

easy

A. df.describe()

B. df.info()

C. df.plot()

D. df.head()

Solution

1. Step 1: Recall DataFrame methods

   df.info() shows data types, non-null counts, and memory usage.

2. Step 2: Compare with other methods

   df.describe() gives stats, df.plot() makes graphs, df.head() shows rows. Only df.info() summarizes structure.

3. Final Answer:

   df.info() -> Option B

4. Quick Check:

   DataFrame structure = df.info() [OK]

Hint: Use df.info() for quick data structure summary [OK]

Common Mistakes:

• Using df.describe() for structure instead of stats
• Confusing df.head() with info summary
• Trying to plot before understanding data

3. Given the code:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 2, 3], 'B': [4, 5, 6, 7]})
print(df['A'].value_counts())

What is the output?

medium

A. 2 2
1 1
3 1
Name: A, dtype: int64

B. 1 2
2 1
3 1
Name: A, dtype: int64

C. 4 1
5 1
6 1
7 1
Name: B, dtype: int64

D. Error: value_counts() not found

Solution

1. Step 1: Understand value_counts()

   It counts how many times each unique value appears in the column.

2. Step 2: Count values in column 'A'

   Values are 1 (once), 2 (twice), 3 (once). value_counts() sorts by frequency descending, so: 2 2 1 1 3 1 Name: A, dtype: int64

3. Final Answer:

   2 2 1 1 3 1 Name: A, dtype: int64 -> Option A

4. Quick Check:

   Count unique values = 2 2 1 1 3 1 Name: A, dtype: int64 [OK]

Hint: value_counts() counts unique values in a column [OK]

Common Mistakes:

• Mixing counts of column B instead of A
• Misreading counts order
• Expecting error from value_counts()

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2, None, 4], 'Y': [5, None, 7, 8]})
print(df.describe())

But the output misses some rows. What is the likely problem?

medium

A. describe() excludes rows with missing values by default

B. describe() ignores non-numeric columns

C. DataFrame has no numeric columns

D. Syntax error in code

Solution

1. Step 1: Understand describe() behavior

   describe() calculates stats on numeric columns but skips missing values in calculations.

2. Step 2: Check missing data effect

   Rows with None are ignored in count and stats, so output shows fewer rows counted.

3. Final Answer:

   describe() excludes rows with missing values by default -> Option A

4. Quick Check:

   Missing data skipped in describe() = describe() excludes rows with missing values by default [OK]

Hint: describe() skips missing values in stats by default [OK]

Common Mistakes:

• Thinking describe() drops entire rows
• Assuming syntax error causes missing rows
• Believing describe() works only on non-numeric data

5. You have a DataFrame df with columns age, income, and gender. You want to quickly check if income differs by gender. Which EDA step is best?

hard

A. Plot df['age'] histogram

B. Use df.describe() only

C. Drop missing values without checking

D. Use df.groupby('gender')['income'].mean() to compare averages

Solution

1. Step 1: Identify goal

   You want to compare income by gender groups.

2. Step 2: Choose correct EDA method

   Grouping by gender and calculating mean income shows differences clearly.

3. Final Answer:

   Use df.groupby('gender')['income'].mean() to compare averages -> Option D

4. Quick Check:

   Group and average to compare = Use df.groupby('gender')['income'].mean() to compare averages [OK]

Hint: Group by category and calculate mean to compare groups [OK]

Common Mistakes:

• Using describe() without grouping
• Plotting unrelated columns
• Dropping data before checking