Exploratory Data Analysis (EDA) template in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of Exploratory Data Analysis (EDA)?

The main goal of EDA is to understand the data by summarizing its main characteristics, often using visual methods, to find patterns, spot anomalies, test hypotheses, and check assumptions.

beginner

Name three common steps in an EDA process.

1. Data cleaning (handling missing values and errors), 2. Data summarization (calculating statistics like mean, median), 3. Data visualization (plots like histograms, scatter plots).

beginner

What Python library is commonly used for data visualization in EDA?

Matplotlib and Seaborn are commonly used Python libraries for data visualization during EDA.

intermediate

Why is checking for missing values important in EDA?

Missing values can affect analysis results and model performance. Identifying them helps decide how to handle them, like filling or removing, to keep data quality.

beginner

What does a box plot show in EDA?

A box plot shows the distribution of data through its quartiles, highlights the median, and identifies outliers.

Which of the following is NOT typically part of EDA?

A Model training

B Data visualization

C Data summarization

D Missing value detection

Model training is part of machine learning, not EDA. EDA focuses on understanding and preparing data.

What Python function shows the first few rows of a DataFrame?

A df.tail()

B df.head()

C df.describe()

D df.info()

df.head() shows the first few rows, useful to quickly see sample data.

Which plot is best to check the distribution of a single numeric variable?

A Scatter plot

B Bar chart

C Histogram

D Box plot

Histograms show how data values are distributed across intervals.

What does df.describe() provide in pandas?

A Summary statistics like mean and quartiles

B Data types of columns

C Number of missing values

D First 5 rows of data

df.describe() gives summary statistics for numeric columns.

Why do we check for outliers in EDA?

A To improve data visualization colors

B To reduce dataset size

C To rename columns

D To identify unusual data points that may affect analysis

Outliers can distort analysis and models, so identifying them is important.

Describe the key steps you would follow to perform Exploratory Data Analysis on a new dataset.

Think about how you get to know your data before using it.

Explain why visualization is important in Exploratory Data Analysis and name two types of plots you might use.

Visuals help tell the story of the data.

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