Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning Pandas
  • Table Of Contents Toc
Learning Pandas

Learning Pandas

By : Michael Heydt
3.5 (2)
Buy this Book
close
close
Learning Pandas

Learning Pandas

3.5 (2)
By: Michael Heydt
Buy this Book

Overview of this book

If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory.
Table of Contents (16 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
pandas and Data Analysis
Icon
pandas and Data Analysis
Icon
Introducing pandas
Icon
Data manipulation, analysis, science, and pandas
Icon
The process of data analysis
Icon
Relating the book to the process
Icon
Concepts of data and analysis in our tour of pandas
Icon
Other Python libraries of value with pandas
Icon
Summary
2
Up and Running with pandas
Icon
Up and Running with pandas
Icon
Installation of Anaconda
Icon
IPython and Jupyter Notebook
Icon
Introducing the pandas Series and DataFrame
Icon
Visualization
Icon
Summary
3
Representing Univariate Data with the Series
Icon
Representing Univariate Data with the Series
Icon
Configuring pandas
Icon
Creating a Series
Icon
The .index and .values properties
Icon
The size and shape of a Series
Icon
Specifying an index at creation
Icon
Heads, tails, and takes
Icon
Retrieving values in a Series by label or position
Icon
Slicing a Series into subsets
Icon
Alignment via index labels
Icon
Performing Boolean selection
Icon
Re-indexing a Series
Icon
Modifying a Series in-place
Icon
Summary
4
Representing Tabular and Multivariate Data with the DataFrame
Icon
Representing Tabular and Multivariate Data with the DataFrame
Icon
Configuring pandas
Icon
Creating DataFrame objects
Icon
Accessing data within a DataFrame
Icon
Selecting rows using Boolean selection
Icon
Selecting across both rows and columns
Icon
Summary
5
Manipulating DataFrame Structure
Icon
Manipulating DataFrame Structure
Icon
Configuring pandas
Icon
Renaming columns
Icon
Adding new columns with [] and .insert()
Icon
Adding columns through enlargement
Icon
Adding columns using concatenation
Icon
Reordering columns
Icon
Replacing the contents of a column
Icon
Deleting columns
Icon
Appending new rows
Icon
Concatenating rows
Icon
Adding and replacing rows via enlargement
Icon
Removing rows using .drop()
Icon
Removing rows using Boolean selection
Icon
Removing rows using a slice
Icon
Summary
6
Indexing Data
Icon
Indexing Data
Icon
Configuring pandas
Icon
The importance of indexes
Icon
The pandas index types
Icon
Working with Indexes
Icon
Hierarchical indexing
Icon
Summary
7
Categorical Data
Icon
Categorical Data
Icon
Configuring pandas
Icon
Creating Categoricals
Icon
Renaming categories
Icon
Appending new categories
Icon
Removing categories
Icon
Removing unused categories
Icon
Setting categories
Icon
Descriptive information of a Categorical
Icon
Munging school grades
Icon
Summary
8
Numerical and Statistical Methods
Icon
Numerical and Statistical Methods
Icon
Configuring pandas
Icon
Performing numerical methods on pandas objects
Icon
Performing statistical processes on pandas objects
Icon
Summary
9
Accessing Data
Icon
Accessing Data
Icon
Configuring pandas
Icon
Working with CSV and text/tabular format data
Icon
Reading and writing data in Excel format
Icon
Reading and writing JSON files
Icon
Reading HTML data from the web
Icon
Reading and writing HDF5 format files
Icon
Accessing CSV data on the web
Icon
Reading and writing from/to SQL databases
Icon
Reading data from remote data services
Icon
Summary
10
Tidying Up Your Data
chevron up
Icon
Tidying Up Your Data
Icon
Configuring pandas
Icon
What is tidying your data?
Icon
How to work with missing data
Icon
Handling duplicate data
Icon
Transforming data
Icon
Summary
11
Combining, Relating, and Reshaping Data
Icon
Combining, Relating, and Reshaping Data
Icon
Configuring pandas
Icon
Concatenating data in multiple objects
Icon
Merging and joining data
Icon
Pivoting data to and from value and indexes
Icon
Stacking and unstacking
Icon
Performance benefits of stacked data
Icon
Summary
12
Data Aggregation
Icon
Data Aggregation
Icon
Configuring pandas
Icon
The split, apply, and combine (SAC) pattern
Icon
Data for the examples
Icon
Splitting data
Icon
Applying aggregate functions, transforms, and filters
Icon
Transforming groups of data
Icon
Filtering groups from aggregation
Icon
Summary
13
Time-Series Modelling
Icon
Time-Series Modelling
Icon
Setting up the IPython notebook
Icon
Representation of dates, time, and intervals
Icon
Introducing time-series data
Icon
Calculating new dates using offsets
Icon
Representing durations of time using Period
Icon
Handling holidays using calendars
Icon
Normalizing timestamps using time zones
Icon
Manipulating time-series data
Icon
Time-series moving-window operations
Icon
Summary
14
Visualization
Icon
Visualization
Icon
Configuring pandas
Icon
Plotting basics with pandas
Icon
Creating time-series charts
Icon
Common plots used in statistical analyses
Icon
Manually rendering multiple plots in a single chart
Icon
Summary
15
Historical Stock Price Analysis
Icon
Historical Stock Price Analysis
Icon
Setting up the IPython notebook
Icon
Obtaining and organizing stock data from Google
Icon
Plotting time-series prices
Icon
Plotting volume-series data
Icon
Calculating the simple daily percentage change in closing price
Icon
Calculating simple daily cumulative returns of a stock
Icon
Resampling data from daily to monthly returns
Icon
Analyzing distribution of returns
Icon
Performing a moving-average calculation
Icon
Comparison of average daily returns across stocks
Icon
Correlation of stocks based on the daily percentage change of the closing price
Icon
Calculating the volatility of stocks
Icon
Determining risk relative to expected returns
Icon
Summary

Normalizing timestamps using time zones

Time zone management can be one of the most complicated issues to deal with when working with time-series data. Data is often collected in different systems across the globe using local time, and at some point, it will require coordination with data collected in other time zones.

Fortunately, pandas provides rich support for working with timestamps in different time zones. Under the covers, pandas utilizes the pytz and dateutil libraries to manage the time zone operations. The dateutil support is new as of pandas 0.14.1 and currently only supported for fixed offset and tzfile zones. The default library used by pandas is pytz, with support for dateutil provided for compatibility with other applications.

pandas objects that are time zone-aware support a .tz property. By default, pandas objects that are time zone-aware do not utilize a timezone object for purposes of efficiency. The following gets the current time and demonstrates that there is no time...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Learning Pandas
End of Section 10
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon