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

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.

• Learning Python for data analysis – with instructions on installation and creating the environment
• Libraries and data structures
• Exploratory analysis in Python (using Pandas)
• Data Munging in Python (using Pandas)

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):

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 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

 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:

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

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])

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)?

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

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

Box-plot:

Code

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

Output

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

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

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

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

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

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

Sunil Ray

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.