Pandas is an open-source Python Library used for high-performance data manipulation and data analysis using its powerful data structures. Python with pandas is in use in a variety of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. Using Pandas, we can accomplish five typical steps in the processing and analysis of data, regardless of the origin of data — load, organize, manipulate, model, and analyse the data.
Below are the some of the important features of Pandas which is used specifically for Data processing and Data analysis work.

Key Features of Pandas

• Fast and efficient DataFrame object with default and customized indexing.
• Tools for loading data into in-memory data objects from different file formats.
• Data alignment and integrated handling of missing data.
• Reshaping and pivoting of date sets.
• Label-based slicing, indexing and subsetting of large data sets.
• Columns from a data structure can be deleted or inserted.
• Group by data for aggregation and transformations.
• High performance merging and joining of data.
• Time Series functionality.
Pandas deals with the following three data structures −
• Series
• DataFrame
These data structures are built on top of Numpy array, making them fast and efficient.

Dimension & Description

The best way to think of these data structures is that the higher dimensional data structure is a container of its lower dimensional data structure. For example, DataFrame is a container of Series, Panel is a container of DataFrame.

Data Structure	Dimensions	Description
Series	1	1D labeled homogeneous array, size-immutable.
Data Frames	2	General 2D labeled, size-mutable tabular structure with potentially heterogeneously typed columns.

DataFrame is widely used and it is the most important data structures.

Series

Series is a one-dimensional array like structure with homogeneous data. For example, the following series is a collection of integers 10, 23, 56, …
10 23 56 17 52 61 73 90 26 72

Key Points of Series

• Homogeneous data
• Size Immutable
• Values of Data Mutable

DataFrame

DataFrame is a two-dimensional array with heterogeneous data. For example,

Name	Age	Gender	Rating
Steve	32	Male	3.45
Lia	28	Female	4.6
Vin	45	Male	3.9
Katie	38	Female	2.78

The table represents the data of a sales team of an organization with their overall performance rating. The data is represented in rows and columns. Each column represents an attribute and each row represents a person.

Data Type of Columns

The data types of the four columns are as follows −

Column	Type
Name	String
Age	Integer
Gender	String
Rating	Float

Key Points of Data Frame
• Heterogeneous data
• Size Mutable
• Data Mutable

Python – Numpy

NumPy is a Python package which stands for ‘Numerical Python’. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.

Operations using NumPy

Using NumPy, a developer can perform the following operations −
• Mathematical and logical operations on arrays.
• Fourier transforms and routines for shape manipulation.
• Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.

NumPy – A Replacement for MatLab

NumPy is often used along with packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). This combination is widely used as a replacement for MatLab, a popular platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programming language.
It is open source, which is an added advantage of NumPy.

ndarray Object

The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index. Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype). Any item extracted from ndarray object (by slicing) is represented by a Python object of one of array scalar types.

Python – SciPy

The SciPy library of Python is built to work with NumPy arrays and provides many user-friendly and efficient numerical practices such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install and are free of charge. NumPy and SciPy are easy to use, but powerful enough to depend on by some of the world’s leading scientists and engineers.

SciPy Sub-packages

SciPy is organized into sub-packages covering different scientific computing domains. These are summarized in the following table −

scipy.constants	Physical and mathematical constants
scipy.fftpack	Fourier transform
scipy.integrate	Integration routines
scipy.interpolate	Interpolation
scipy.io	Data input and output
scipy.linalg	Linear algebra routines
scipy.optimize	Optimization
scipy.signal	Signal processing
scipy.sparse	Sparse matrices
scipy.spatial	Spatial data structures and algorithms
scipy.special	Any special mathematical functions
scipy.stats	Statistics

Data Structure

The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for Linear Algebra, Fourier Transforms and Random Number Generation, but not with the generality of the equivalent functions in SciPy.

Python – Matplotlib

Matplotlib is a python library used to create 2D graphs and plots by using python scripts. It has a module named pyplot which makes things easy for plotting by providing feature to control line styles, font properties, formatting axes etc. It supports a very wide variety of graphs and plots namely – histogram, bar charts, power spectra, error charts etc. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython.
Conventionally, the package is imported into the Python script by adding the following statement −
from matplotlib import pyplot as plt

Matplotlib Example

The following script produces the sine wave plot using matplotlib.
Example
import numpy as np
import matplotlib.pyplot as plt

# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
plt.title(“sine wave form”)

# Plot the points using matplotlib
plt.plot(x, y)
plt.show()
Its output is as follows −

p 12

So, this brings us to the end of blog. This Tecklearn ‘Data Science Libraries in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Python Programming domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period.

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Python with Data Science training lets you master the concepts of the widely used and powerful programming language, Python. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, NumPy, Matplotlib which are essential for Data Science. You will work on real-world projects in the domain of Python and apply it for various domains of Big Data, Data Science and Machine Learning.

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What you will Learn in this Course?

Introduction to Python

• Define Python
• Understand the need for Programming
• Know why to choose Python over other languages
• Setup Python environment
• Understand Various Python concepts – Variables, Data Types Operators, Conditional Statements and Loops
• Illustrate String formatting
• Understand Command Line Parameters and Flow control

Python Environment Setup and Essentials

• Python installation
• Windows, Mac & Linux distribution for Anaconda Python
• Deploying Python IDE
• Basic Python commands, data types, variables, keywords and more

Python language Basic Constructs

• Looping in Python
• Data Structures: List, Tuple, Dictionary, Set
• First Python program
• Write a Python Function (with and without parameters)
• Create a member function and a variable
• Tuple
• Dictionary
• Set and Frozen Set
• Lambda function

OOP (Object Oriented Programming) in Python

• Object-Oriented Concepts
Working with Modules, Handling Exceptions and File Handling
• Standard Libraries
• Modules Used in Python (OS, Sys, Date and Time etc.)
• The Import statements
• Module search path
• Package installation ways
• Errors and Exception Handling
• Handling multiple exceptions

Introduction to NumPy

• Introduction to arrays and matrices
• Indexing of array, datatypes, broadcasting of array math
• Standard deviation, Conditional probability
• Correlation and covariance
• NumPy Exercise Solution

Introduction to Pandas

• Pandas for data analysis and machine learning
• Pandas for data analysis and machine learning Continued
• Time series analysis
• Linear regression
• Logistic Regression
• ROC Curve
• Neural Network Implementation
• K Means Clustering Method

Data Visualisation

• Matplotlib library
• Grids, axes, plots
• Markers, colours, fonts and styling
• Types of plots – bar graphs, pie charts, histograms
• Contour plots

Data Manipulation

• Perform function manipulations on Data objects
• Perform Concatenation, Merging and Joining on DataFrames
• Iterate through DataFrames
• Explore Datasets and extract insights from it

Scikit-Learn for Natural Language Processing

• What is natural language processing, working with NLP on text data
• Scikit-Learn for Natural Language Processing
• The Scikit-Learn machine learning algorithms
• Sentimental Analysis – Twitter

Introduction to Python for Hadoop

• Deploying Python coding for MapReduce jobs on Hadoop framework.
• Python for Apache Spark coding
• Deploying Spark code with Python
• Machine learning library of Spark MLlib
• Deploying Spark MLlib for Classification, Clustering and Regression

Got a question for us? Please mention it in the comments section and we will get back to you.

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