World's Best AI Learning Platform with profoundly Demanding Certification Programs
Designed by IITians, only for AI Learners.
Download our e-book of Introduction To Python
Related Blog
Top Discussion
USA(+256704300651)Top Powerful Love Spells That Really Work By Powerful Revenge Spell Caster in# Canada, #Australia #,UK,//USA Black magic IN >@>USA,/ UK,/ Kuwait,/ Germany, +256704300651 == WASHINGTON DC// TOP DEATH SPELLS @POWERFUL REVENGE DEATH SPELL CASTER IN USA,CANADA, UK, CALIFONIA, MEXICO, IRELAND, NY, CANADA, LONDON, SWEDEN, BOTSWANA How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? +256704300651- Psychic lost love Spell Caster // Divorce Spell // Marriage Proposal ((+256704300651}} INSTANT DEATH SPELL CASTER / REVENGE SPELL/ VOODOO A SPELL CASTER/╬ ₩௹]+256704300651₩௹]╬MAMA ZAMIE A TRADITIONAL HEALER /SANGOMA AND A SPIRITUAL HEALER in BELGIUM, Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) +256704300651 NY ( ITALY /UK/ SWEDEN/USA )) Black Magic Instant Death Spell Caster Lubbock Join Discussion
Top Courses
Master's In Artificial Intelligence Job Guarantee Program
4.5 (1,292 Ratings)
559 Learners
Webinars
Why You Should Learn Data Science in 2023?
Jun 8th (7:00 PM) 289 Registered
Data Wrangling in Python
Shashank Shanu
a year ago
Table of Contents
- Introduction
- How to Merge different dataset?
- How to Group Data in a Dataframe?
- How to Concatenate different Dataframe?
Introduction
Data wrangling is a process to manipulate the data in
various formats. It involves processes such as merging, grouping,
concatenating etc. Data wrangling is required for analyzing or getting them
ready to be used with other data. It is an important part of any data science
projects and many data scientists spent much of their time in it.
Python provides us some good built-in features to apply these
wrangling methods to various data sets to achieve the analytical goal as per
requirements.
How to Merge different dataset?
Python, panda’s library provides us a
function, merge, which help us to join different datasets according to
our requirements. Its works similar to the join operations between the
DataFrame as standard databases.
pd.merge(left,
right, how='inner', on=none, left_on=none, right_on=none,
left_index=false,
right_index=false, sort=true)
Let’s take an example to get better
understanding
# import the pandas library
import pandas as pd
left_df = pd.DataFrame({
'S.No.':[1,2,3,4,5],
'Name': ['Alex', 'Amy', 'Allen',
'Alice', 'Ayoung'],
'subjects':['Maths','Physics','Chemistry','Biology','Civis']})
right_df = pd.DataFrame(
{'S.No.':[1,2,3,4,5],
'Name': ['Billy', 'Brian', 'Bran',
'Bryce', 'Betty'],
'subjects':['Physics','Chemistry','History','Biology','Civis']})
print(left_df)
print(right_df)Output:
S.No. Name subjects
0 1 Alex Maths
1 2 Amy Physics
2 3 Allen Chemistry
3 4 Alice Biology
4 5 Ayoung Civics
S.No. Name subjects
0 1 Billy Physics
1 2 Brian Chemistry
2 3 Bran History
3 4 Bryce Biology
4 5 Betty CivicsHow to Group Data in a Dataframe?
While perform Exploratory data analysis many
times we need to group our dataframe where we need
the result in terms of various groups present in the data set. Panadas provides us with in-built methods which
can roll the data into various groups.
Let’s take an example
Below I have shown you how we can group the data
by year and then get the result for a specific year.
# import the pandas library
import pandas as pd
ipl = {'Team name': ['Riders', 'Riders', 'Devils',
'Devils', 'Kings',
'kings',
'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
'Rank':
[1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
'Year':
[2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
'Total
Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df1 = pd.DataFrame(ipl)
grouped = df.groupby('Year')
print(grouped.get_group(2014))Output:
Team Rank Year Points
0 Riders 1 2014 876
2 Devils 2 2014 863
4 Kings 3 2014 741
9 Royals 4 2014 701How to Concatenate different Dataframe?
Many times, we also need to concatenate
different dataframe for our task to accomplished. Python Pandas package also provides various facilities for easily
combining together Series, DataFrame, and Panel objects.
Let’s take an example
import
pandas as pd
first_df
= pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen',
'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
second_df
= pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran',
'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(pd.concat([first_df,second_df]))Output:
Name subject_id Marks_scored
1 Alex sub1 98
2 Amy sub2 90
3 Allen sub4 87
4 Alice sub6 69
5 Ayoung sub5 78
1 Billy sub2 89
2 Brian sub4
80
3 Bran sub3 79
4 Bryce sub6 97
5 Betty sub5 88I hope you enjoyed reading this article and finally, you came
to know about Data Wrangling in Python.
For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…