Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka

Jul 18, 201712 likes1,843 views

This Edureka Python Pandas tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) will help you learn the basics of Pandas. It also includes a use-case, where we will analyse the data containing the percentage of unemployed youth for every country between 2010-2014. Below are the topics covered in this tutorial: 1. What is Data Analysis? 2. What is Pandas? 3. Pandas Operations 4. Use-case

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Data Analysis With Python

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Agenda
Python Applications
Data Life-cycle
Python For Data Analysis
What is Pandas? – Numpy, Scipy
Pandas Operations
Python for Statistics
Python for Hadoop

Most read

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Python Applications

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Python Applications
Web Scraping
Testing
Web
Development
Data Analysis

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Data Life-Cycle

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Data Life-Cycle
Data
Data
Data
Data
Data
Warehousing
Data AnalysisData AnalysisData Analysis Data Visualization

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What is Data Analysis?

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What is Data Analysis?
Percentage increase in unemployed
youth in Afghanistan between 2010-2011
Data of unemployed
youth across the globe
from 2010-2014

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What is Pandas?

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Data Analysis Using Python
Pandas is a software library written for the Python programming language for data manipulation and analysis.
Numpy and Scipy
and Matplotlib
Pandas is well suited for many different kinds of data:
 Tabular data with heterogeneously-typed columns.
 Ordered and unordered time series data.
 Arbitrary matrix data with row and column labels
 Any other form of observational / statistical data sets. The data actually
need not be labeled at all to be placed into a pandas data structure

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Pandas Operations
Changing the Index Concatenation
Slicing the
DataFrame
Data conversion
Changing the
column headers
Joining and Merging

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Slicing

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Slicing
Index Int rate US GDP Thousands
2001 2 50
2002 3 55
2003 2 65
2004 2 55

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Slicing
Slicing the starting 2 rows
Slicing the last 2 rows
Index Int rate US GDP Thousands
2001 2 50
2002 3 55
2003 2 65
2004 2 55
Index Int rate US GDP Thousands
2001 2 50
2002 3 55
Index Int rate US GDP Thousands
2003 2 65
2004 2 55

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Merging

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Merging
Index HPI Int rate US GDP Thousands
2001 80 2 50
2002 85 3 55
2003 88 2 65
2004 85 2 55
Index HPI Int rate US GDP Thousands
2005 80 2 50
2006 85 3 55
2007 88 2 65
2008 85 2 55

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Joining

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Joining
Index Int rate US GDP
Thousands
2001 2 50
2002 3 55
2003 2 65
2004 2 55
Index Low tier
HPI
Unemployment
2001 50 7
2003 52 8
2004 50 9
2005 43 6

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Changing the Index and Column Headers

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Changing the Index and Column Headers
Index Int rate US GDP Thousands
2001 2 50
2002 3 55
2003 2 65
2004 2 55
Index US GDP Thousands
2001 50
2002 55
2003 65
2004 55

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Concatenation

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Concatenation
Student
Name:
Age:
Sex:
Phone number:
Student Data
Concatenate
E-mail
Student
Name:
Age:
Sex:
Phone number:
E-mail:
Concatenation

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Data Munging

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Use-Case

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Example: Youth Unemployment Data

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Example: Youth Unemployment Data
Problem Statement
Find the change in percentage of unemployed youth for every country from 2010-2011
There is approx. 3.1%
increase in unemployed
youth in ‘Arab World’

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Example: Youth Unemployment Data
Column 1 – Country Name
Column 2 – Country Code
Column 3 – 2010
Column 4 – 2011
Column 5 – 2012
Column 6 – 2013
Column 7 – 2014

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Python For Statistics
from statistics import mean
print(mean([1,1,1,1,3,4,4,4,5,2]))
Mean
Median
from statistics import median
print(median([1,1,1,1,3,4,4,4,5,2]))
High Median
Low Median
from statistics import mode
print(mode([1,1,1,1,3,4,4,4,5,2]))
Mode
from statistics import mode
print(mode([1,1,1,1,3,4,4,4,5,2]))
Variance

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Python For Hadoop : Pydoop
Pydoop is a Python interface to Hadoop that allows you to write MapReduce applications and interact with HDFS
in pure Python.

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Python For Hadoop : Pydoop
Python Applications What Is Data Analysis
Pandas Operations Data Analysis Use-Case
What Is Pandas
Python For Statistics And
Python For Hadoop

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Importance of Digital Marketing | EdurekaEdureka!

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EA Algorithm in Machine Learning | EdurekaEdureka!

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Cognitive computing aims to mimic human reasoning and behavior to solve complex problems. It works by simulating human thought processes through adaptive, interactive, iterative and contextual means. Cognitive computing supplements human decision making in sectors like customer service and healthcare, while artificial intelligence focuses more on autonomous decision making with applications in finance, security and more. A use case of cognitive AI is using it to assess skills, find relevant jobs, negotiate pay, suggest career paths and provide salary comparisons and job openings to help humans.

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Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka

1. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Analysis With Python

2. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Agenda Python Applications Data Life-cycle Python For Data Analysis What is Pandas? – Numpy, Scipy Pandas Operations Python for Statistics Python for Hadoop

3. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python Applications

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5. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Life-Cycle

6. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Life-Cycle Data Data Data Data Data Warehousing Data AnalysisData AnalysisData Analysis Data Visualization

7. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Data Analysis?

8. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Data Analysis? Percentage increase in unemployed youth in Afghanistan between 2010-2011 Data of unemployed youth across the globe from 2010-2014

9. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING What is Pandas?

10. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Analysis Using Python Pandas is a software library written for the Python programming language for data manipulation and analysis. Numpy and Scipy and Matplotlib Pandas is well suited for many different kinds of data:  Tabular data with heterogeneously-typed columns.  Ordered and unordered time series data.  Arbitrary matrix data with row and column labels  Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure

11. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Pandas Operations Changing the Index Concatenation Slicing the DataFrame Data conversion Changing the column headers Joining and Merging

12. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Slicing

13. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Slicing Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55

14. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Slicing Slicing the starting 2 rows Slicing the last 2 rows Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55 Index Int rate US GDP Thousands 2001 2 50 2002 3 55 Index Int rate US GDP Thousands 2003 2 65 2004 2 55

15. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Merging

16. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Merging Index HPI Int rate US GDP Thousands 2001 80 2 50 2002 85 3 55 2003 88 2 65 2004 85 2 55 Index HPI Int rate US GDP Thousands 2005 80 2 50 2006 85 3 55 2007 88 2 65 2008 85 2 55

17. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Merging Index HPI Int rate US GDP Thousands 2001 80 2 50 2002 85 3 55 2003 88 2 65 2004 85 2 55 Index HPI Int rate US GDP Thousands 2005 80 2 50 2006 85 3 55 2007 88 2 65 2008 85 2 55 Merging Index HPI Int rate US GDP Thousands x US GDP Thousands y 0 80 2 50 50 1 85 3 55 55 2 88 2 65 65 3 85 2 55 55

18. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Joining

19. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Joining Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55 Index Low tier HPI Unemployment 2001 50 7 2003 52 8 2004 50 9 2005 43 6

20. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Joining Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55 Index Low tier HPI Unemployment 2001 50 7 2003 52 8 2004 50 9 2005 43 6 Index Int rate US GDP Thousands Low tier HPI Unemployment 2001 2.0 50.0 50.0 7.0 2002 3.0 55.0 NaN NaN 2003 2.0 65.0 52.0 8.0 2004 2.0 55.0 50.0 9.0 2005 NaN NaN 53.0 6.0 Joining

21. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Changing the Index and Column Headers

22. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Changing the Index and Column Headers Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55 Index US GDP Thousands 2001 50 2002 55 2003 65 2004 55

23. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Changing the Index and Column Headers Index Int rate US GDP Thousands 2001 2 50 2002 3 55 2003 2 65 2004 2 55 Index US GDP Thousands 2001 50 2002 55 2003 65 2004 55 Index US GDP Thousands 2 50 3 55 2 65 2 55 Index GDP 2001 50 2002 55 2003 65 2004 55 Changing the Index Changing the column headers

24. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Concatenation

25. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Concatenation Student Name: Age: Sex: Phone number: Student Data Concatenate E-mail Student Name: Age: Sex: Phone number: E-mail: Concatenation

26. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Munging

27. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Data Munging

28. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Use-Case

29. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Example: Youth Unemployment Data

30. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Example: Youth Unemployment Data Problem Statement Find the change in percentage of unemployed youth for every country from 2010-2011 There is approx. 3.1% increase in unemployed youth in ‘Arab World’

31. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Example: Youth Unemployment Data Column 1 – Country Name Column 2 – Country Code Column 3 – 2010 Column 4 – 2011 Column 5 – 2012 Column 6 – 2013 Column 7 – 2014

32. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python For Statistics from statistics import mean print(mean([1,1,1,1,3,4,4,4,5,2])) Mean Median from statistics import median print(median([1,1,1,1,3,4,4,4,5,2])) High Median Low Median from statistics import mode print(mode([1,1,1,1,3,4,4,4,5,2])) Mode from statistics import mode print(mode([1,1,1,1,3,4,4,4,5,2])) Variance

33. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python For Hadoop : Pydoop Pydoop is a Python interface to Hadoop that allows you to write MapReduce applications and interact with HDFS in pure Python.

34. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING Python For Hadoop : Pydoop Python Applications What Is Data Analysis Pandas Operations Data Analysis Use-Case What Is Pandas Python For Statistics And Python For Hadoop

35. www.edureka.co/pythonEDUREKA PYTHON CERTIFICATION TRAINING

Python For Data Analysis | Python Pandas Tutorial | Learn Python | Python Training | Edureka

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