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
+256704300651- Psychic lost love Spell Caster // Divorce Spell // Marriage Proposal +256704300651 == WASHINGTON DC// TOP DEATH SPELLS @POWERFUL REVENGE DEATH SPELL CASTER IN USA,CANADA, UK, CALIFONIA, MEXICO, IRELAND, NY, CANADA, LONDON, SWEDEN, BOTSWANA Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) 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}} INSTANT DEATH SPELL CASTER / REVENGE SPELL/ VOODOO A SPELL CASTER/╬ ₩௹]+256704300651₩௹]╬MAMA ZAMIE A TRADITIONAL HEALER /SANGOMA AND A SPIRITUAL HEALER in BELGIUM, +256704300651.. === Revenge Death spells caster In Denmark / Sweden / Germany / spell caster, revenge spell, spell caster review, witchcraft, psychic, magic forum. ↩⁜↪ +91-9352799565↩⁜↪ Divorce Problem Solution IN mumbai 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
Python - Data Wrangling
Neha Kumawat
a year ago
Table of Content
- Merging Data
- Grouping Data
- Concatenating Data
Data wrangling involves processing the data in various formats like - merging, grouping, concatenating, etc. for the purpose of analyzing or getting them ready to be used with another set of data.
Python has built-in features to apply these wrangling methods to various data sets to achieve the analytical goal. In this chapter, we will look at a few examples describing these methods.
Merging Data
The Pandas library in python provides a single function, merge, as the entry point for all standard database join operations between DataFrame objects −
pd.merge(left, right, how='inner', on=none, left_on=none, right_on=none,
left_index=false, right_index=false, sort=true)
Let us now create two different DataFrames and perform the merging operations on it.
# import the pandas library
import pandas as pd
left = pd.DataFrame({
'id':[1,2,3,4,5],
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame(
{'id':[1,2,3,4,5],
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print left
print right
Its output is as follows −
Name id subject_id
0 Alex 1 sub1
1 Amy 2 sub2
2 Allen 3 sub4
3 Alice 4 sub6
4 Ayoung 5 sub5
Name id subject_id
0 Billy 1 sub2
1 Brian 2 sub4
2 Bran 3 sub3
3 Bryce 4 sub6
4 Betty 5 sub5
Grouping Data
Grouping data sets is a frequent need in data analysis where we need the result in terms of various groups present in the data set. Panadas has in-built methods
which can roll the data into various groups.
In the below example we group the data by year and then get the result for a specific year.
# import the pandas library
import pandas as pd
ipl_data = {'Team': ['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],
'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
grouped = df.groupby('Year')
print grouped.get_group(2014)
Its output is as follows −
Points Rank Team Year
0 876 1 Riders 2014
2 863 2 Devils 2014
4 741 3 Kings 2014
9 701 4 Royals 2014
Concatenating Data
Pandas provide various facilities for easily combining together Series, DataFrame, and Panel objects. In the below example
the Concat function performs concatenation operations along an axis. Let us create different objects and do concatenation.
import pandas as pd
one = 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])
two = 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([one,two])
Its output is as follows −
Marks_scored Name subject_id
1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
I hope you enjoyed reading this article and finally, you came
to know about Python - Data Wrangling.
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…