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Data
Engineering
and Analytics
using Python
PURNA CHANDER RAO. KATHULA
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.
About me..
 Job Title = Architect QA
 Build Tools using Python for QA automation testing .
 Currently Learning
Python modules for Data Science
 Packages used for Data Analysis and Analytics
 Jupyter Notebook
 Pandas
 Numpy
 Scipy
 Matplotlib
 Seaborn
 Scikitlearn
Anaconda
Anaconda Distribution
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.
Pandas
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
Core DataStructures of Pandas
 DataFrames
 Series
Core Operations
Create Select Insert Map
Join Sort Clean ApplyMap
View Update Filter Append
Group Summarize Confirm Rotate
Create ( Creating a DataFrame)
View ( Viewing the rows and columns)
View ( Viewing the rows and columns)
Insert ( Adding a new column to dataframe)
Filter ( Slicing and dicing the datframe)
Map ( Map() and Apply map())
Append (Joining the dataframes based on x-axis=0 )
Concat (Joining the dataframes on Axis = 0 or 1)
Join ( Inner , Left, Right , Outer)
Join ( Inner )
Join ( Outer)
Join ( Left)
Join ( Right)
Group (groupby() )
Sort (by columns ascending True or False)
Clean ( Drop, Fillna, duplicates)
Clean ( Drop)
Clean ( Fillna ( method=‘ffill / bfill’)
Conform ( reindex() / resample, dropping / NAN as needed)
ReSample ()
ReSample (Monthly, Weekly, Yearly)
Rotate ( Transpose)
Rotate ( Pivot_table)
Rotate ( Stack)
Rotate ( unStack)
SeaBorn Analytics
What is Seaborn?
 Seaborn provides a high-level interface to matplotlib. It provides a high level
interface for drawing attractive statistical graphs.
Demo ( Restaurant Dataset visualization)
Machine Learning ( Linear Regression)
DEMO

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Data engineering and analytics using python