Book Image

Hands-On Data Analysis with NumPy and Pandas

By : Curtis Miller
5 (1)
Book Image

Hands-On Data Analysis with NumPy and Pandas

5 (1)
By: Curtis Miller

Overview of this book

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.
Table of Contents (12 chapters)

Index

A

  • advanced indexing / Advanced indexing
  • Anaconda
    • about / What is Anaconda?
    • installing / Installing Anaconda
    • free installation, reference link / Installing Anaconda
  • arithmetic
    • with two-equal shaped arrays / Arithmetic with two equal-shaped arrays
    • about / Arithmetic
    • with DataFrames / Arithmetic with DataFrames
  • array methods / Array methods
  • arrays
    • slicing, with colors / Slicing arrays with colons
    • expanding / Expanding arrays
    • arithmetic algebra / Arithmetic and linear algebra with arrays
    • linear algebra / Arithmetic and linear algebra with arrays
    • two-equal shaped, arithmetic with / Arithmetic with two equal-shaped arrays
  • axes / NumPy arrays

B

  • broadcasting / Broadcasting

C

  • colons
    • used, for slicing arrays / Slicing arrays with colons
  • Conda
    • package management / Package management with Conda, Package management
    • about / What is Conda?
    • environment management / Conda environment management
    • Python, managing / Managing Python

D

  • data
    • creating / Adding data
    • subsetting / Subsetting your data
    • series, subsetting / Subsetting a series
  • database
    • setting up / Setting up a database
    • MySQL, installing / Installing MySQL
    • MySQL connectors / MySQL connectors
    • creating / Creating a database
  • DataFrame objects
    • exploring / Exploring series and DataFrame objects
  • DataFrames
    • creating / Creating DataFrames
    • saving / Saving DataFrames
    • slicing / Slicing a DataFrame
    • arithmetic, using / Arithmetic with DataFrames
    • vectorization, using / Vectorization with DataFrames
    • function application / DataFrame function application

E

  • elements
    • selecting / Selecting elements explicitly

H

  • hierarchical indexing
    • about / Hierarchical indexing
    • series, slicing with / Slicing a series with a hierarchical index

I

  • index sorting
    • about / Index sorting
    • by values / Sorting by values

J

  • Jupyter, alternatives
    • about / Exploring alternatives to Jupyter
    • Spyder / Spyder
    • Rodeo / Rodeo
    • ptpython / ptpython
  • Jupyter Notebooks
    • exploring / Exploring Jupyter Notebooks

L

  • linear algebra / Linear algebra

M

  • Markdown / Exploring Jupyter Notebooks
  • methods
    • indexing / Indexing methods
  • missing data, Pandas DataFrame
    • handling / Handling missing data in a pandas DataFrame
  • missing information, Pandas DataFrame
    • deleting / Deleting missing information
    • filling / Filling missing information
  • MySQL
    • installing / Installing MySQL
    • connectors / MySQL connectors
  • MySQL Community Edition
    • reference link / Setting up a database

N

  • ndarray
    • creating / Creating ndarray
  • Not A Number (nan) / Special numeric values
  • numeric values
    • special / Special numeric values
  • NumPy arrays
    • about / NumPy arrays
    • creating / Creating NumPy arrays
    • ndarray, creating / Creating ndarray

P

  • package
    • managing, with Conda / Package management with Conda
  • pandas
    • about / What does pandas do?
    • plotting with / Plotting with pandas
    • plotting, methods / Plotting methods
  • Pandas DataFrame
    • missing data, handling / Handling missing data in a pandas DataFrame
    • missing information, deleting / Deleting missing information
    • missing information, filling / Filling missing information
  • ptpython / ptpython
  • Python
    • managing / Managing Python
  • Python connector
    • reference link / MySQL connectors

R

  • Rodeo / Rodeo

S

  • scatter plot matrix / Plotting methods
  • series
    • exploring / Exploring series and DataFrame objects
    • creating / Creating series
    • subsetting / Subsetting a series
    • slicing, with hierarchical index / Slicing a series with a hierarchical index
  • Spyder / Spyder

U

  • ufuncs
    • using, with vectorization / Conventions used, Vectorization with ufuncs
    • custom ufuncs / Custom ufuncs