Learning the Pandas Library 1st Edition pdf.pdf

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From the Author

Introduction

Installation

Data Structures

Series

Series CRUD

Series Indexing

Series Methods

Series Plotting

Another Series Example

DataFrames

Data Frame Example

Data Frame Methods

Data Frame Statistics

Grouping, Pivoting, and Reshaping

Dealing With Missing Data

Joining Data Frames

Avalanche Analysis and Plotting

Summary

About the Author

Also Available

One more thing

Treading on Python Series

Python Tools for Data Munging, Data Analysis, and Visualization Learning Pandas Matt Harrison Technical Editor: Copyright © 2016 While every precaution has been taken in the preparation of this book, the publisher and author assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein.

Table of Contents From the Author Introduction Installation Data Structures Series Series CRUD Series Indexing Series Methods Series Plotting Another Series Example DataFrames Data Frame Example Data Frame Methods Data Frame Statistics Grouping, Pivoting, and Reshaping Dealing With Missing Data Joining Data Frames Avalanche Analysis and Plotting Summary About the Author Also Available One more thing

From the Author PYTHON IS EASY TO LEARN. YOU CAN LEARN THE BASICS IN A DAY AND BE PRODUCTIVE with it. With only an understanding of Python, moving to pandas can be difficult or confusing. This book is meant to aid you in mastering pandas. I have taught Python and pandas to many people over the years, in large corporate environments, small startups, and in Python and Data Science conferences. I have seen what hangs people up, and confuses them. With the correct background, an attitude of acceptance, and a deep breath, much of this confusion evaporates. Having said this, pandas is an excellent tool. Many are using it around the world to great success. I hope you do as well. Cheers! Matt

Introduction I HAVE BEEN USING PYTHON IS SOME PROFESSIONAL CAPACITY SINCE THE TURN OF THE century. One of the trends that I have seen in that time is the uptake of Python for various aspects of "data science"- gathering data, cleaning data, analysis, machine learning, and visualization. The pandas library has seen much uptake in this area. pandas 1 is a data analysis library for Python that has exploded in popularity over the past years. The website describes it thusly: “pandas is an open source, BSD-licensed library providing high- performance, easy-to-use data structures and data analysis tools for the Python programming language.” -pandas.pydata.org My description of pandas is: pandas is an in memory nosql database, that has sql-like constructs, basic statistical and analytic support, as well as graphing capability. Because it is built on top of Cython, it has less memory overhead and runs quicker. Many people are using pandas to replace Excel, perform ETL, process tabular data, load CSV or JSON files, and more. Though it grew out of the financial sector (for analysis of time series data), it is now a general purpose data manipulation library. Because pandas has some lineage back to NumPy, it adopts some NumPy'isms that normal Python programmers may not be aware of or familiar with. Certainly, one could go out and use Cython to perform fast typed data analysis with a Python-like dialect, but with pandas, you don't need to. This work is done

for you. If you are using pandas and the vectorized operations, you are getting close to C level speeds, but writing Python.

Who this book is for This guide is intended to introduce pandas to Python programmers. It covers many (but not all) aspects, as well as some gotchas or details that may be counter-intuitive or even non-pythonic to longtime users of Python. This book assumes basic knowledge of Python. The author has written Treading on Python Vol 1 2 that provides all the background necessary.

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