Ultimate Guide for Data Exploration in Python using NumPy, Matplotlib and Pandas

Sunil Ray 02 Jul, 2024
7 min read


Data exploration sets and developing a deep understanding of the data is one of the most important skills every data scientist should possess. People sometimes estimate that the time spent on these activities can go as high as 80% of the project time. Python has been gaining much ground as a preferred tool for data scientists lately, and for the right reasons. Ease of learning, powerful libraries with integration of C/C++, production readiness, and integration with the web stack are some of the main reasons for this move lately.

I will use NumPy, Matplotlib, Seaborn, and Pandas to explore data in this guide. These are powerful libraries that can perform data exploration in Python. The idea is to create a ready reference for some of the regular operations that are required frequently. I am using an iPython Notebook to perform data exploration and would recommend the same for its natural fit for exploratory analysis.

In case you missed it, I suggest you refer to the Python baby steps series to understand the basics of programming.

Data Exploration in Python using NumPy, Matplotlib and Pandas

How do I load data file(s) using Pandas?

Input data sets can be in various formats (XLS, TXT, CSV, JSON ). Python makes it easy to load data from any source due to its simple syntax and availability of predefined libraries, such as Pandas. Here, I will use Pandas itself.

Pandas features many functions for reading tabular data as a Pandas DataFrame object. Below are the common functions that can be used to read data (including read_csv in Pandas):

Python, read_table, read_csv, read_excel, pandas

Loading data from a CSV file(s):


Loading data from excel file(s):

df=pd.read_excel("E:/EMP.xlsx", "Data") # Load Data sheet of excel file EMP
print df

Loading data from a txt file(s):


df=pd.read_csv("E:/Test.txt",sep='\t') # Load Data from text file having tab '\t' delimeter print df



 How to convert a variable to a different data type?

Converting a variable data type to others is an important and common procedure after loading data. Let’s look at some of the commands to perform these conversions:

Convert numeric variables to string variables and vice versa

srting_outcome = str(numeric_input) #Converts numeric_input to string_outcome
integer_outcome = int(string_input) #Converts string_input to integer_outcome
float_outcome = float(string_input) #Converts string_input to integer_outcome

The later operations are especially useful when you input value from user using raw_input(). By default, the values are read at string.

Convert character date to Date:

There are multiple ways to do this. The simplest would be to use the datetime library and strptime function. Here is the code:

from datetime import datetime
char_date = 'Apr 1 2015 1:20 PM' #creating example character date
date_obj = datetime.strptime(char_date, '%b %d %Y %I:%M%p')
print date_obj

How to transpose a Data set or dataframe using Pandas?

I want to transpose Table A into Table B on the variable Product. This task can be accomplished by using Pandas dataframe.pivot:

Transpose, SAS | Data exploration in python


#Transposing Pandas dataframe by a variable

df=pd.read_excel("E:/transpose.xlsx", "Sheet1") # Load Data sheet of excel file EMP 
print df 
result= df.pivot(index= 'ID', columns='Product', values='Sales') 



How to sort a Pandas DataFrame?

Data can be sorted using dataframe.sort() for data exploration in Python. It can be based on multiple variables and can be ascending or descending in both orders.


#Sorting Pandas Dataframe 
df=pd.read_excel("E:/transpose.xlsx", "Sheet1") #Add by variable name(s) to sort
print df.sort(['Product','Sales'], ascending=[True, False])

Sort, SAS

Above is a table with variables ID, Product, and Sales. Now, we want to sort it by Product and Sales (in descending order), as shown in Table 2.

How to create plots (Histogram, Scatter, Box Plot)?

Plot, Histogram, Box Plot, Scatter Plot, Proc SGPLOT, Proc GPLOT, Proc Univariate

Data visualization helps us understand the data easily. Python has libraries like matplotlib and Seaborn to create multiple graphs effectively. Let’s look at some of the visualizations below to understand the behavior of the variable(s).

  • The distribution of age
  • Relation between age and sales
  • If sales are normally distributed or not?



#Plot Histogram
import matplotlib.pyplot as plt
import pandas as pd
df=pd.read_excel("E:/First.xlsx", "Sheet1")
#Plots in matplotlib reside within a figure object, use plt.figure to create new figure
#Create one or more subplots using add_subplot, because you can't create blank figure
ax = fig.add_subplot(1,1,1)
ax.hist(df['Age'],bins = 5)
#Labels and Tit
plt.title('Age distribution')


Histo | Data exploration in python

Scatter plot:


#Plots in matplotlib reside within a figure object, use plt.figure to create new figure fig=plt.figure()
#Create one or more subplots using add_subplot, because you can't create blank figure
ax = fig.add_subplot(1,1,1)
#Labels and Tit
plt.title('Sales and Age distribution')





import seaborn as sns 


Box | Data exploration in python

How to generate frequency tables with Pandas?

Frequency tables can be used to understand the distribution of categorical or n categorical variables using frequency tables.


import pandas as pd
df=pd.read_excel("E:/First.xlsx", "Sheet1") 
print df
test= df.groupby(['Gender','BMI'])


Frequency | Data exploration in python

How to do sample Data set in Python?

To select a sample from a data set, we will use the libraries numpy and random for data exploration in Python. Sampling data sets always helps us understand them quickly.

Let’s say, from the EMP table, I want to select a random sample of 5 employees.


#Create Sample dataframe
import numpy as np
import pandas as pd
from random import sample
# create random index
rindex = np.array(sample(xrange(len(df)), 5))
# get 5 random rows from the dataframe df
dfr = df.ix[rindex]
print dfr



How do duplicate values of a variable in a Pandas Dataframe be removed?

We often encounter duplicate observations. We can use the dataframe.drop_duplicates() to tackle this in Python.


#Remove Duplicate Values based on values of variables "Gender" and "BMI"
rem_dup=df.drop_duplicates(['Gender', 'BMI'])
print rem_dup


Duplicate_values | Data exploration in python

How to group variables in Pandas to calculate count, average, sum?

I suggest you use a dataframe to understand the count, average, and sum of variables. Describe () with Pandas groupby() in data exploration in Python.

Let’s look at the code:


test= df.groupby(['Gender'])



How to recognize and Treat missing values and outliers in Pandas?

To identify missing values , we can use dataframe.isnull(). You can also refer article “Data Munging in Python (using Pandas)“, here we have done a case study to recognize and treat missing and outlier values.


# Identify missing values of dataframe


Missing | Data exploration in python

Various imputation methods are available to treat missing values. You can refer these articles for methods to detect Outlier and Missing values. Imputation methods for both missing and outlier values are almost similar. Here, we will discuss general case imputation methods to replace missing values. Let’s do it using an example:


#Example to impute missing values in Age by the mean
import numpy as np
meanAge = np.mean(df.Age)     #Using numpy mean function to calculate the mean value
df.Age = df.Age.fillna(meanAge) #replacing missing values in the DataFrame

How to merge / join data sets and Pandas dataframes?

Joining/merging is a common operation to integrate datasets from different sources for data exploration in Python. They can be handled effectively in Pandas using the merge function:


df_new = pd.merge(df1, df2, how = 'inner', left_index = True, right_index = True) # merges df1 and df2 on index
# By changing how = 'outer', you can do outer join.
# Similarly how = 'left' will do a left join
# You can also specify the columns to join instead of indexes, which are used by default.


This comprehensive guide examined the Python codes for various data exploration in Python and munging steps. We also examined the Python libraries Pandas, Numpy, Matplotlib, and Seaborn to perform these steps. In the next article, I will reveal the codes to perform these steps in R.

Frequently Asked Questions

Q1. How to Perform Data Exploration in Python?

A. Data exploration in Python involves using libraries like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and NumPy for numerical operations. It includes loading data, examining data types, summary statistics, missing values, correlations, and distributions to understand data structure and detect patterns or anomalies.

Q2. What is the Data Exploration Process?

A. The data exploration process involves collecting and analyzing data to understand its structure, quality, and patterns. Steps include data collection, cleaning (handling missing values and outliers), summarizing with descriptive statistics, visualizing distributions, relationships, and trends, and deriving initial insights for further analysis.

Q3. What is Exploratory Data Analysis in Python?

A. Exploratory Data Analysis (EDA) in Python entails using tools like Pandas, Matplotlib, and Seaborn to summarize data sets visually and statistically. It involves examining data distributions, spotting outliers, detecting patterns, and checking assumptions to form hypotheses and guide data preprocessing and modeling.

Q4. What is the EDA Process?

A. The EDA process includes several steps: data collection, Data cleaning (handling missing values and outliers), Data transformation, Generating summary statistics, Visualizing data distributions and relationships, Drawing initial conclusions, and Identifying the next steps for modeling or further analysis.

Sunil Ray 02 Jul, 2024

I am a Business Analytics and Intelligence professional with deep experience in the Indian Insurance industry. I have worked for various multi-national Insurance companies in last 7 years.

Frequently Asked Questions

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Responses From Readers


Vikas 10 Apr, 2015

Need the same thing/article in R. Please do need full asap. Thanks.

Sandeep Karkhanis
Sandeep Karkhanis 10 Apr, 2015

About data imputation... I'm struggling to find methods (read packages / libraries) to carry out imputation in Python beyond the usual mean/ median approach...coming from an R background , there are tonnes of good packages to carry out very sophisticated imputation...this is something I miss in Python (it may be my lack of knowledge). i tried r2py to call imputation packages from R in python but it doesnt run as smoothly do you have suggestions in this regards?

Ken Stulce
Ken Stulce 16 Apr, 2015

Fantastic article with great examples! Thank you.

subbrao 27 Aug, 2015

Great article. I learned something today. Any books which helps me to do these kind of data analysis?

K CHENDRA TEJA 27 Aug, 2015

good article

venugopal 30 Nov, 2015


Animesh 22 Dec, 2016

Nice article to start with Python.

Sunny 27 Feb, 2017

Thank you so much. This was very helpful. :)

Ratna Kishore Jupudi
Ratna Kishore Jupudi 14 Jul, 2017

Nice summary Thank you.

Ashok Bharat
Ashok Bharat 19 Sep, 2017

Very Useful, covered all important data manipulation/exploration libraries, very handy.. !

Saksham 09 Nov, 2017

Could you please provide the data sets that can be loaded directly to the code ? It will be really very helpful.

Ball Gum
Ball Gum 29 Nov, 2017

Where can we download the datasets referred to in the examples above? I'd like to replicate the steps exactly first before getting some other sample data. Thanks

anubhaw 27 May, 2018

AttributeError: 'DataFrame' object has no attribute 'sort'