What is Pandas?

Introduction to Pandas

The Python library to do the mathematical operations in a flexible manner is called Pandas library. This is an open source library used in data analysis and also in data manipulation so that data scientists can retrieve information from the data. It has BSD license and the number tables are manipulated easily. It is written in Python, Cython and C language. It is an open source software and has high performance when compared with other libraries. The library provides tables and not arrays for the memory retrieval. The data structures are easy to use.

Why do people consider python?

1) programmer friendliness and easy to understand

2) Extensive support libraries

3) Good flexibility and component Integration (Can be combined easily with applications and tools)

4) platform portability

5) Opensource availability Etc……..

Work areas of python?

1) System programming (Scripting face of python)

2) Build GUI’s (Ex: thinner)

3) Web Design

4) Database programming

5) Scientific programming (Ex: For Analytics)

6) Gaming, Image processing, Robotics Etc.

Role of Pandas in Python

Pandas being an open source setup for a python programming language and also a python library which has been licensed by which offers high-performance, data analysis tools and easy-to-use data structures for the Python programming language.

For achieving profound performance in data manipulation functions and analysis, segment Pandas was introduced by developer Mckinney as a part of python. Being an open source library. here the abbreviation of pandas is as below

Pandas ==> Pan (Panel) + Das (Data)

Preparing the data and munging the same was the initial outcomes of python before the introduction of Panda libraries. after the introduction of panda libraries python began to flourish a lot in the analytics sector. The major outcomes of panda are:

1) analysis of data

2) preparation of data

3) data manipulation

4) data modeling

5) data analysis

The major fields in which Python with Pandas is used are as below,

1) Finance

2) economics

3) analytics etc

Pandas package installation

1) Open Installed anaconda prompt

2) Use the below command for package installation

pip install <packagename>

Ex: pip install pandas

3) Now we can import the installed package in your program

Understanding Pandas

The key data structures in pandas are as below:

1. Series: One-dimensional data structure is immutable by size.

Ex :

10

23

56

17

52

61

73

90

26

72

Parameters :

Parameter	Description
data	Constants, lists, and ndarrays
Index	Unique values which act as index representation
dtype	Represents the data type
copy	Copy data. false by default

Sample Code Snippet :

import pandas as PD

import numpy as np

Test_data = np.array([‘a’,’b’,’c’,’d’])

Sample = PD.Series(Test_data)

print sample

2. Dataframe: An array which is heterogeneous and two dimensional in format.

Ex :

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

Parameters :

Parameter	Description
Data	Ndarrays, series, maps, list
Index	Unique values which act as index representation
Columns	Labels for columns
dtype	Data type values
copy	Used to copy data

Sample Code snippet :

import pandas as PD

data = [[ ‘ Alex ‘ , 10 ],[ ‘ Bob ‘, 12 ],[ ‘ Clarke ‘ , 13 ]]

df = PD.DataFrame(data,columns=[‘ Name ‘ , ‘ Age ‘])

print df

3. Panel: It is a heterogeneous data structure which is three dimensional in format. which handles data in panels.

Parameters :

Parameter	Description
data	Data takes various forms like ndarray, series, map, lists, dict, constants and also another DataFrame
items	axis=0
major_axis	axis=1
minor_axis	axis=2
dtype	A data type of each column
copy	Copy data. Default, false

Sample Code snippet :

import pandas as PD

import numpy as np

data = {‘Item1’ : PD.DataFrame(np.random.randn(4, 3)),

‘Item2’ : PD.DataFrame(np.random.randn(4, 2))}

p = PD.Panel(data)

print

Pandas Advantages

1) Customizable indexed data frame objects.

2) Various tools to support data load into data objects irrespective of their file formats.

3) Data alignment in an efficient manner.

4) Pivot dataset.

5) Reshape datasets.

6) Label oriented slicing.

7) Data Indexing and subsetting higher volume dataset.

8) Merging high-performance datasets in an efficient manner

9) Time series-functionality

Required Python Pandas Skills

1. Knowledge in python web

2. ORM and related libraries familiarity

3. Database integration

4. Problem-solving ability

5. Capability to effectively organize code

Audience for Python pandas

• Audience with interest to learn Python.
• Individual who aspire to become Python Architect, developer, Analyst, Tester also relative professional roles.
• Helps to move forward the professional aspects and technical skillset of professionals who are intended to do the same.
• Python application development interested candidates.
• People who are interested to learn analytics and get expertise in this field.

Conclusion

Definitely, Python is one among the most versatile and stable languages over a decade. In this extremely stable programmatic setup, panda library programs hold a great role in boosting the data related aspects of this widely spread language. All the major data handling related needs of this flexible language is nicely addressed in pandas setup.

Recommended Articles

This has been a guide to What is Pandas?. Here we discussed the Working, Understanding, Role, Skills, and Advantages of pandas. You can also go through our other suggested articles to learn more –

1. Introduction To Python
2. Python Operators
3. Python Pandas DataFrame
4. Python Pandas Join