Hands-On Data Analysis with NumPy and pandas

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Hands-On Data Analysis with NumPy and pandas

Hands-On Data Analysis with NumPy and pandas

Introduction  

  Pandas is a high-performance, user-friendly data structure and data analysis package for the Python programming language that is open-source and BSD-licensed. Python and Pandas are utilised in a variety of academic and professional sectors, such as finance, economics, statistics, analytics, etc. The different Python Pandas capabilities and practical applications will be covered in this tutorial.

Numpy

    A general-purpose package for handling arrays is called NumPy. It offers a multidimensional array object with outstanding speed as well as capabilities for interacting with these arrays. It is the cornerstone Python module for scientific computing. The programme is open-source. It has a number of characteristics, including the following crucial ones:

• A powerful N-dimensional array object
• Sophisticated (broadcasting) functions
• Tools for integrating C/C++ and Fortran code
• Useful linear algebra, Fourier transform, and random number capabilities

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Table of contents

1. Setting Up a Python Data Analysis Environment

What is Anaconda?

Installing Anaconda

Exploring Jupyter Notebooks

Exploring alternatives to Jupyter

• Spyder
• Rodeo
• ptpython

Package management with Conda

• What is Conda?
• Conda environment management
• Managing Python
• Package management

Setting up a database

• Installing MySQL
• MySQL connectors
• Creating a database

2. Diving into NumPY

NumPy arrays

Special numeric values

Creating NumPy arrays

• Creating ndarray

3. Operations on NumPy Arrays

Selecting elements explicitly

• Slicing arrays with colons

Advanced indexing

Expanding arrays

Arithmetic and linear algebra with arrays

• Arithmetic with two equal-shaped arrays
• Broadcasting

Linear algebra

Employing array methods and functions

• Array methods
• Vectorization with ufuncs
• Custom ufuncs

4. pandas are Fun! What is pandas?

What does pandas do?

Exploring series and DataFrame objects

• Creating series
• Creating DataFrames
• Adding data
• xSaving DataFrames

Subsetting your data

• Subsetting a series

Indexing methods

• Slicing a DataFrame

5. Arithmetic, Function Application, and Mapping with pandas

Arithmetic

• Arithmetic with DataFrames
• Vectorization with DataFrames
• DataFrame function application

Handling missing data in a pandas DataFrame

• Deleting missing information
• Filling missing information

6. Managing, Indexing, and Plotting

Index sorting

• Sorting by values

Hierarchical indexing

• Slicing a series with a hierarchical index

Plotting with pandas

• Plotting methods

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