This topic Covers Data Science concepts and the modules using Python

1. Data
Engineering
and Analytics
using Python
PURNA CHANDER RAO. KATHULA

2. Talking Topics
 Jupyter notebook
 About me
 Python modules for Data Science
 Anaconda
 Pandas
 About pandas
 Data Munging / Data Preparation.
 Demo
 Seaborn
 About seaborn
 Machine Learning
 Linear Regression.

3. About me..
 Job Title = Architect QA
 Build Tools using Python for QA automation testing .
 Currently Learning

4. Python modules for Data Science
 Packages used for Data Analysis and Analytics
 Jupyter Notebook
 Pandas
 Numpy
 Scipy
 Matplotlib
 Seaborn
 Scikitlearn

5. Anaconda

6. Anaconda Distribution

7. What is Anaconda ?
 Essentially a Large ( ~ 400 MB ) Python Installation.
 But Contains Everything you need for Data Analysis
 Unless you have a special reason not to , you should just install and use this.

8. Pandas

9. About Pandas
 What is Pandas ?
Pandas is a Python library for data analysis and data manipulation. A python version of the R
data.frame library.
 Key Features of Pandas
 It has API’s for loading data from different file formats into memory.
 ( exel, tsv, csv, db and etc).
 Data is structured in the form of Rows and Columns.
 Retrieval of data is similar as SQL, can perform all the operations such as Groupby, Joins, Views and etc..
 Merging of data from multiple datasets.
 Does support much of DataTime series functionality, Timezone, Business Days, Holidays and etc..
 Boolean Indexing
 Fancy Indexing

10. Core DataStructures of Pandas
 DataFrames
 Series
Core Operations
Create Select Insert Map
Join Sort Clean ApplyMap
View Update Filter Append
Group Summarize Confirm Rotate

11. Create ( Creating a DataFrame)
View ( Viewing the rows and columns)

12. View ( Viewing the rows and columns)

13. Insert ( Adding a new column to dataframe)

14. Filter ( Slicing and dicing the datframe)

15. Map ( Map() and Apply map())

16. Append (Joining the dataframes based on x-axis=0 )

17. Concat (Joining the dataframes on Axis = 0 or 1)

18. Join ( Inner , Left, Right , Outer)

19. Join ( Inner )

20. Join ( Outer)

21. Join ( Left)

22. Join ( Right)

23. Group (groupby() )

24. Sort (by columns ascending True or False)

25. Clean ( Drop, Fillna, duplicates)

26. Clean ( Drop)

27. Clean ( Fillna ( method=‘ffill / bfill’)

28. Conform ( reindex() / resample, dropping / NAN as needed)

29. ReSample ()

30. ReSample (Monthly, Weekly, Yearly)

31. Rotate ( Transpose)

32. Rotate ( Pivot_table)

33. Rotate ( Stack)

34. Rotate ( unStack)

35. SeaBorn Analytics

36. What is Seaborn?
 Seaborn provides a high-level interface to matplotlib. It provides a high level
interface for drawing attractive statistical graphs.

37. Demo ( Restaurant Dataset visualization)

38. Machine Learning ( Linear Regression)
DEMO