Understanding the Exploratory Data Analysis (EDA) in Python

Contributed by: Manorama Yadav

Introduction to EDA in Python

Exploratory data analysis is the analysis of the data and brings out insights. It’s storytelling, a story that data is trying to tell. EDA is an approach to analyzing the data with the help of various tools and graphical techniques like barplot, histogram, etc.

According to Tukey (data analysis in 1961)

“Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data.”

EDA in Python

There are many libraries available in python, like pandas, NumPy, matplotlib, seaborn, etc. with that help, we can analyze the data and bring out helpful insights. I will be using Jupyter Notebook along with these libraries.

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Dataset Introduction

We are using the ‘Cars’ dataset, which has different features of cars like a model, year, engine, and other properties along with their price. It has 28 years of data from 1990 to 2017, and you can download the dataset here.

Data Description:

S.no Variable Description Data Type
1 Make Car Make  String
2 Model  Car Model  String
3 Year  Car Year  Integer
4 Engine Fuel Type Fuel Type String
5 Engine HP  Horse Power(HP) Integer
6 Engine Cylinders  No. of Cylinders Integer
7 Transmission Type  Transmission Type String
8 Driven_Wheels Wheels type String
9 Number of Doors  No. of Doors Integer
10 Market Category  Market Category String
11 Vehicle Size Size of Vehicle String
12 Vehicle Style Type of Vehicle String
13 Highway MPG Highway MPG Integer
14 city mpg miles per gallon Integer
15 Popularity Popularity of the car Integer
16 MSRP  Price of the car in ($) Integer

The objective of this article is to explore the data and make it ready for modeling.

Let’s get started!!!

Exploratory Data Analysis in Python

First, we will import all the libraries required for EDA (Exploratory Data Analysis). This is the first and most important thing to do. Without importing libraries, we will not be able to perform anything.

Import Libraries

Data loading

After importing the libraries, the next step is loading data into the dataframe. We will use the pandas’ library to load the data into the dataframe. It supports various file formats like Comma Separated Values (.csv), excel (.xlsx, .xls) etc. 

To read the dataset, either store the data file in the same directory and read it directly or provide the path of the data file where the dataset is located while reading the data.

Top 5 rows

Now, the data has been loaded. Let’s check the first 5 rows of the dataset.

From the above results, we can see that the index in python starts from 0.

Bottom 5 rows

To check the dimensions of the dataframe, let’s check the number of rows and columns present in the dataset. 

Shape of the Data

There are a total of 11914 rows and 16 columns in the dataset

Concise info of dataset

Now, check the data types along with the concise summary of all the variables in the dataset. It includes the number of non-null values present.

RangeIndex: 11914 entries, 0 to 11913
Data columns (total 16 columns):
Make                 11914 non-null object
Model                11914 non-null object
Year                 11914 non-null int64
Engine Fuel Type     11911 non-null object
Engine HP            11845 non-null float64
Engine Cylinders     11884 non-null float64
Transmission Type    11914 non-null object
Driven_Wheels        11914 non-null object
Number of Doors      11908 non-null float64
Market Category      8172 non-null object
Vehicle Size         11914 non-null object
Vehicle Style        11914 non-null object
highway MPG          11914 non-null int64
city mpg             11914 non-null int64
Popularity           11914 non-null int64
MSRP                 11914 non-null int64
dtypes: float64(3), int64(5), object(8)
memory usage: 1.5+ MB

The data type will be stored as an object if strings are present in the variables. Also, it will be int or float if the data has numerical and decimal values, respectively. MSRP (the price of the car) is stored as an int data type, while Driven_wheels is stored as an object data type. 

The above results show many variables like Engine Fuel Type, Engine HP, Engine Cylinders, No. of Doors, and Market Category have missing values in the data. 

We can check the data types by one more method:

Make                  object
Model                 object
Year                   int64
Engine Fuel Type      object
Engine HP            float64
Engine Cylinders     float64
Transmission Type     object
Driven_Wheels         object
Number of Doors      float64
Market Category       object
Vehicle Size          object
Vehicle Style         object
highway MPG            int64
city mpg               int64
Popularity             int64
MSRP                   int64
dtype: object

To print the columns of the dataset

Index(['Make', 'Model', 'Year', 'Engine Fuel Type', 'Engine HP',
       'Engine Cylinders', 'Transmission Type', 'Driven_Wheels',
       'Number of Doors', 'Market Category', 'Vehicle Size', 
	'Vehicle Style’, ‘highway MPG', 'city mpg', 'Popularity', 'MSRP'],

Since the names of the columns are very lengthy, let’s rename them.

Rename the Columns

Drop Columns

Drop the columns which are not necessary for the dataframe. Not all the columns in the data need to be relevant. In this data, columns like popularity, number of doors, and vehicle_size were not so relevant. So I am dropping these variables from the dataset.

Missing Values:

Make              0
Model             0
Year              0
Fuel_Type         3
HP               69
Cylinders        30
Transmission      0
Driven_Wheels     0
Vehicle_Style     0
h_mpg             0
c_mpg             0
price             0
dtype: int64

The above results show that out of 12 variables, 3 variables, Fuel_type, HP, and cylinders, have missing values. 

Let’s check the percentage of the data are missing column wise

Make             0.000000
Model            0.000000
Year             0.000000
Fuel_Type        0.025180
HP               0.579151
Cylinders        0.251805
Transmission     0.000000
Driven_Wheels    0.000000
Vehicle_Style    0.000000
h_mpg            0.000000
c_mpg            0.000000
price            0.000000
dtype: float64

There are 0.025%, 0.58% and 0.25% data are missing in the variables Fuel_type, HP and cylinders respectively. 

There are many ways to treat these missing values. 

  1. Drop
  2. Impute

We can either drop the rows where missing values are present or replace the missing values with some values like mean, median, or mode.

Since the % of the data missing is very less, we can remove those rows from the dataset.

Make             0
Model            0
Year             0
Fuel_Type        0
HP               0
Cylinders        0
Transmission     0
Driven_Wheels    0
Vehicle_Style    0
h_mpg            0
c_mpg            0
price            0
dtype: int64

The drop function will default drop the complete row if any of the variables have missing values. 

After dropping the missing values, now the count of missing values is 0. That means there are no missing values present in the dataset.

Check the number of rows present after removing the missing values.

Make             11813
Model            11813
Year             11813
Fuel_Type        11813
HP               11813
Cylinders        11813
Transmission     11813
Driven_Wheels    11813
Vehicle_Style    11813
h_mpg            11813
c_mpg            11813
price            11813
dtype: int64

The original number of rows was 11914, and now the number of rows left is 11813.

Statistical Summary

Now, let’s find out the dataset’s statistical or 5-point summary. The 5-point summary tells the descriptive summary, which includes the meaning, median, mode, no. of rows, maximum value, and minimum value for each variable.

Mean, standard deviation, max, and percentile values will be NaN for variables that have object datatype. 

The unique, top, frequency will be NaN for variables with the int data type. 

From the descriptive summary, we got to know that there is 47 unique make of cars and 904 models. Data has maximum Chevrolet make cars with 1115 counts. The average price of the car is 40581.5 dollars, and the 50th percentile or median of the price is 29970. There is a huge difference between the mean and median of the price. This depicts that the price variable is highly skewed, which we can check visually using a histogram.

Data Visualisation

As its name suggests, data visualization is observing the data using various plots, graphs, etc. Various plots include histogram, scatterplot, boxplot, heatmap, etc. We will use matplotlib and seaborn together to visualize a few variables.

Histogram (Distribution Plot)

A histogram shows the shape and distribution of the numerical variable. For categorical variables, it shows the count of the categories present in the variable.

From both histograms, it is shown that the HP variable is quite distributed. It is a little bit tilted on the right, and that means it is slightly right-skewed but normally distributed. However, the price variable is highly skewed. 

Histogram for Categorical Variable

This is the countplot for Make Variable. Every bar shows the count of the category present in the dataset.

Outliers Check

Outliers are the values that are significantly different from other values/observations. An outlier can create major issues in modeling. So it is necessary to find outliers and treat them.

Outliers can be detected by using a boxplot. Boxplot depicts the variable distribution using quartile, also known as a box and whiskers plot.

All the above boxplots show that there are many outliers present in the price and c_mpg variables. In the Cylinder variable, only 4 observations are outliers.

According to the box plot, any observation which is out of the range of Q1 (25 percentile) and Q3 (75 percentile) or IQR (Inter quartile range) is observed as an outlier.

If many outliers are present in the dataset, then the treatment of outliers is necessary. There are methods like flooring and capping which can be used to impute outliers. 

Correlation Plot

Correlation is calculated to find out the intensity of the relationship between 2 variables. Correlation ranges from -1 to 1. -1 correlation value suggests a strong negative relationship, and 1 shows a strong positive relationship. 0 means there is no relation between the 2 variables.

From the above correlation plot, it can be inferred that there are many variables that are strongly related to each other. For Example, the correlation value between c_mpg and h_mpg is 0.85, which is near 1. That means there is a strong positive relationship between them. Likewise, Cylinders and c_mpg have a negative relationship.


Pairplot is used to find out the relationship between variables, and it plots the scatter plot between each variable. Scatter plots can also be used independently. But pairplot will give the relationship plot among all the numerical variables in one line.


All the above steps are part of EDA, and this is not the end of EDA. All the steps above performed are the basics that should be performed to analyze the data before doing feature engineering or modeling.

EDA is one of the important steps during the whole process of data science. It is said that most of the time, the model building goes into EDA and feature engineering. If you want to create a big setup of information from the data, you need to do an extensive EDA.

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Source : https://www.mygreatlearning.com/blog/artificial-intelligence/