Exploratory data analysis workflow in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the first step in an Exploratory Data Analysis (EDA) workflow?

The first step is to load and inspect the data. This means reading the data into a program and looking at its structure, like rows, columns, and data types.

beginner

Why do we check for missing values during EDA?

Checking for missing values helps us understand if some data points are incomplete. This is important because missing data can affect analysis and may need to be handled specially.

beginner

What does 'summary statistics' mean in EDA?

Summary statistics are simple numbers that describe data, like mean (average), median (middle value), minimum, maximum, and standard deviation (spread). They give a quick idea about the data.

beginner

How can visualizations help in EDA?

Visualizations like histograms, scatter plots, and box plots help us see patterns, trends, and outliers in data. They make it easier to understand data than just looking at numbers.

beginner

What is the purpose of checking data types in EDA?

Checking data types ensures that each column has the correct kind of data (numbers, text, dates). This helps avoid errors and guides how to analyze or transform the data.

What is the main goal of Exploratory Data Analysis?

A To build a final machine learning model

B To understand the main features and patterns in the data

C To clean the data by removing all rows

D To write documentation for the dataset

EDA helps us understand the data's main features and patterns before further analysis.

Which pandas function shows the first few rows of a DataFrame?

A df.head()

B df.tail()

C df.describe()

D df.info()

df.head() shows the first 5 rows by default, helping to inspect data quickly.

Which method helps find missing values in a pandas DataFrame?

A df.sum()

B df.mean()

C df.isnull()

D df.dropna()

df.isnull() returns True for missing values, helping identify them.

What does df.describe() provide?

A Summary statistics for numeric columns

B List of column names

C Data types of columns

D Number of missing values

df.describe() gives count, mean, std, min, max, and quartiles for numeric data.

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

A Scatter plot

B Bar chart

C Line plot

D Histogram

Histograms show how data values are spread across ranges.

Describe the main steps you would follow in an Exploratory Data Analysis workflow using pandas.

Think about how you get to know a new dataset step by step.

Explain why visualizations are important in exploratory data analysis.

Consider how pictures help us understand complex information.