• Anyone having basic logical and analytical skills in any programming language.

• Basic python programming knowledge.

Course Outline:

Environment setup

• Anaconda 3
• Ipython Notebook

1. Python Classes and Objects

• Creating Classes
• Creating instance objects
• Destroying Objects (Garbage Collection)
• Custom Classes
• Attributes and Methods
• Inheritance and Polymorphism
• Using Properties to Control Attribute Access

2. Functional Programming

• Lambda
• Filter
• Map
• Functools

3. Iterators and Generators

• Itertools
• Generators
• Decorators

4. Collections

• Deque
• Counter
• OrderedDict
• ChainMap

5. Database Access

• Sqlite databases
• MySQL databases

6. Introduction to NumPy

• NumPy Standard Data Types
• The Basics of NumPy Arrays
• NumPy Array Attributes
• Array Indexing: Accessing Single Elements
• Array Slicing: Accessing Subarrays
• Reshaping of Arrays
• Array Concatenation and Splitting
• Computation on NumPy Arrays: Universal Functions
• The Slowness of Loops
• Introducing UFuncs
• Exploring NumPy’s UFuncs
• Advanced Ufunc Features
• Ufuncs: Learning More
• Aggregations: Min, Max, and Everything in Between
• Summing the Values in an Array
• Working with Boolean Arrays

7. Data Manipulation with Pandas

• Installing and Using Pandas
• Introducing Pandas Objects
• The Pandas Series Object
• The Pandas DataFrame Object
• The Pandas Index Object
• Data Indexing and Selection
• Data Selection in Series
• Data Selection in DataFrame
• Operating on Data in Pandas
• Ufuncs: Index Preservation
• UFuncs: Index Alignment
• Ufuncs: Operations Between DataFrame and Series
• Handling Missing Data
• Trade-Offs in Missing Data Conventions
• Missing Data in Pandas
• Operating on Null Values
• Hierarchical Indexing
• A Multiply Indexed Series
• Methods of MultiIndex Creation
• Indexing and Slicing a MultiIndex
• Rearranging Multi-Indices
• Data Aggregations on Multi-Indices
• Combining Datasets: Concat and Append
• Recall: Concatenation of NumPy Arrays
• Simple Concatenation with pd.concat
• Combining Datasets: Merge and Join
• Categories of Joins
• Specification of the Merge Key
• Specifying Set Arithmetic for Joins
• Overlapping Column Names: The suffixes Keyword
• Aggregation and Grouping
• Simple Aggregation in Pandas
• GroupBy: Split, Apply, Combine
• Introducing Pandas String Operations
• Tables of Pandas String Methods

8. Visualization with Matplotlib

• General Matplotlib Tips
• Importing matplotlib
• Setting Styles
• show() or No show()? How to Display Your Plots
• Saving Figures to File
• Two Interfaces for the Price of One
• Simple Line Plots
• Adjusting the Plot: Line Colors and Styles
• Adjusting the Plot: Axes Limits
• Labeling Plots
• Simple Scatter Plots
• Scatter Plots with plt.plot
• Scatter Plots with plt.scatter
• plot Versus scatter: A Note on Efficiency
• Visualizing Errors
• Basic Errorbars
• Continuous Errors
• Density and Contour Plots
• Visualizing a Three-Dimensional Function
• Histograms, Binnings, and Density
• Two-Dimensional Histograms and Binnings
• Customizing Plot Legends
• Choosing Elements for the Legend
• Legend for Size of Points
• Multiple Legends
• Customizing Colorbars
• Multiple Subplots
• axes: Subplots by Hand
• subplot: Simple Grids of Subplots
• subplots: The Whole Grid in One Go
• GridSpec: More Complicated Arrangements
• Transforms and Text Position
• Customizing Ticks
• Major and Minor Ticks
• Hiding Ticks or Labels
• Reducing or Increasing the Number of Ticks

 Doubt Clearing / Queries session

Training material provided:

Yes (Digital format)