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 during a sort of academic and commercial domains, including Finance, Economics, Statistics, Advertising, Web Analytics, and more. Using Pandas, we will accomplish five typical steps within the processing and analysis of data, no matter the origin of data — load, organize, manipulate, model, and analyse the data.

Below are a number of the important features of Pandas which is employed specifically for processing and Data analysis work.

Key Features of Pandas

Fast and efficient DataFrame object with default and customised 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 huge data sets.
Columns from a data structure are often deleted or inserted.
Group by data for aggregation and transformations.
High performance merging and joining of data .
Time Series functionality.

Pandas deals with the subsequent three data structures −

Series
DataFrame

These data structures are built on top of Numpy array, making them fast and efficient.

Dimension & Description

The best thanks to consider these arrangement s is that the upper dimensional arrangement may be a container of its lower dimensional data structure. for instance , DataFrame may be a container of Series, Panel may be a container of Data Frame.

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

Data Frame is widely used and it’s the foremost important data structures.

Series

Series may be a one-dimensional array like structure with homogeneous data. for instance , the subsequent series may be 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

Data Frame

Data Frame may be a two-dimensional array with heterogeneous data. for instance ,

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 a corporation with their overall performance rating. the data is represented in rows and columns. Each column represents an attribute and every row represents an individual.

Data sort of Columns

The data sorts 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 may be a Python package which stands for ‘Numerical Python’. it’s a library consisting of multidimensional array objects and a set of routines for processing of array.

Operations using NumPy

Using NumPy, a developer can perform the subsequent operations −

Mathematical and logical operations on arrays.
Fourier transforms and routines for shape manipulation.
Operations associated with algebra . NumPy has in-built functions for algebra and random number generation.

NumPy – A Replacement for MatLab

NumPy is usually used alongside packages like SciPy (Scientific Python) and Mat−plotlib (plotting library). this mix is widely used as a replacement for MatLab, a well-liked platform for technical computing. However, Python alternative to MatLab is now seen as a more modern and complete programing language.

It is open source, which is another advantage of NumPy.

ndarray Object

The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the gathering of things of an equivalent type. Items within the collection are often accessed employing a zero-based index. Every item in an ndarray takes an equivalent size of block within 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 1 of array scalar types.

Python – SciPy

The SciPy library of Python is made to figure with NumPy arrays and provides many user-friendly and efficient numerical practices like routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to put in and are freed from charge. NumPy and SciPy are easy to use, but powerful enough to depend upon by a number 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 within 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 arrangement employed by SciPy may be a multidimensional array provided by the NumPy module. NumPy provides some functions for algebra, Fourier Transforms and Random Number Generation, but not with the generality of the equivalent functions in SciPy.

So, this brings us to the end of blog. This Tecklearn ‘Introduction to Pandas , NumPy and SciPy Libraries’ 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 Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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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

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