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

Sunil Ray Last Updated : 14 Oct, 2024
7 min read

Introduction

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):

Code

mport pandas as pd
#Import Library Pandas
df = pd.read_csv("train.csv")  #I am working in Windows environment
#Reading the dataset in a dataframe using Pandas
print(df.head(3))  #Print first three observations

Loading data from excel file(s):

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

Output
print df
Loading_data_Python_3

Loading data from a txt file(s):

Code:

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

Output

Loading_data_Python_4

 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

Code

#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') 
result

Output

Transpose_1


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.

Code

#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?

Histogram:

Code

#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
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)
#Variable
ax.hist(df['Age'],bins = 5)
#Labels and Tit
plt.title('Age distribution')
plt.xlabel('Age')
plt.ylabel('#Employee')
plt.show()

Output

Histo | Data exploration in python

Scatter plot:

Code

#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)
#Variable
ax.scatter(df['Age'],df['Sales'])
#Labels and Tit
plt.title('Sales and Age distribution')
plt.xlabel('Age')
plt.ylabel('Sales')
plt.show()

Output

Scatter

Box-plot:

Code

import seaborn as sns 
sns.boxplot(df['Age']) 
sns.despine()

Output

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.

Code

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

Output

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.

Code

#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

Output

Sample_1


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.

Code

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

Output

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:

Code

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

Output

Group

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.

Code

# Identify missing values of dataframe
df.isnull()

Output

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:

Code:

#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:

Code:

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.

Conclusion

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 is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.

Responses From Readers

Clear

Vikas
Vikas

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

Sandeep Karkhanis
Sandeep Karkhanis

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

Fantastic article with great examples! Thank you.

subbrao
subbrao

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

K CHENDRA TEJA
K CHENDRA TEJA

good article

venugopal
venugopal

super

Animesh
Animesh

Nice article to start with Python.

Sunny
Sunny

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

Ratna Kishore Jupudi
Ratna Kishore Jupudi

Nice summary Thank you.

Ashok Bharat
Ashok Bharat

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

Saksham
Saksham

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

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
anubhaw

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

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