Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search 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 Python Data Analysis
  • Table Of Contents Toc
Python Data Analysis

Python Data Analysis

By : Ivan Idris
3.9 (16)
Buy this Book
close
close
Python Data Analysis

Python Data Analysis

3.9 (16)
By: Ivan Idris
Buy this Book

Overview of this book

This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.
Table of Contents (17 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
1. Getting Started with Python Libraries
Icon
1. Getting Started with Python Libraries
Icon
Software used in this book
Icon
Building NumPy, SciPy, matplotlib, and IPython from source
Icon
Installing with setuptools
Icon
NumPy arrays
Icon
A simple application
Icon
Using IPython as a shell
Icon
Reading manual pages
Icon
IPython notebooks
Icon
Where to find help and references
Icon
Summary
2
2. NumPy Arrays
Icon
2. NumPy Arrays
Icon
The NumPy array object
Icon
Creating a multidimensional array
Icon
Selecting NumPy array elements
Icon
NumPy numerical types
Icon
One-dimensional slicing and indexing
Icon
Manipulating array shapes
Icon
Creating array views and copies
Icon
Fancy indexing
Icon
Indexing with a list of locations
Icon
Indexing NumPy arrays with Booleans
Icon
Broadcasting NumPy arrays
Icon
Summary
3
3. Statistics and Linear Algebra
chevron up
Icon
3. Statistics and Linear Algebra
Icon
NumPy and SciPy modules
Icon
Basic descriptive statistics with NumPy
Icon
Linear algebra with NumPy
Icon
Finding eigenvalues and eigenvectors with NumPy
Icon
NumPy random numbers
Icon
Creating a NumPy-masked array
Icon
Summary
4
4. pandas Primer
Icon
4. pandas Primer
Icon
Installing and exploring pandas
Icon
pandas DataFrames
Icon
pandas Series
Icon
Querying data in pandas
Icon
Statistics with pandas DataFrames
Icon
Data aggregation with pandas DataFrames
Icon
Concatenating and appending DataFrames
Icon
Joining DataFrames
Icon
Handling missing values
Icon
Dealing with dates
Icon
Pivot tables
Icon
Remote data access
Icon
Summary
5
5. Retrieving, Processing, and Storing Data
Icon
5. Retrieving, Processing, and Storing Data
Icon
Writing CSV files with NumPy and pandas
Icon
Comparing the NumPy .npy binary format and pickling pandas DataFrames
Icon
Storing data with PyTables
Icon
Reading and writing pandas DataFrames to HDF5 stores
Icon
Reading and writing to Excel with pandas
Icon
Using REST web services and JSON
Icon
Reading and writing JSON with pandas
Icon
Parsing RSS and Atom feeds
Icon
Parsing HTML with Beautiful Soup
Icon
Summary
6
6. Data Visualization
Icon
6. Data Visualization
Icon
matplotlib subpackages
Icon
Basic matplotlib plots
Icon
Logarithmic plots
Icon
Scatter plots
Icon
Legends and annotations
Icon
Three-dimensional plots
Icon
Plotting in pandas
Icon
Lag plots
Icon
Autocorrelation plots
Icon
Plot.ly
Icon
Summary
7
7. Signal Processing and Time Series
Icon
7. Signal Processing and Time Series
Icon
statsmodels subpackages
Icon
Moving averages
Icon
Window functions
Icon
Defining cointegration
Icon
Autocorrelation
Icon
Autoregressive models
Icon
ARMA models
Icon
Generating periodic signals
Icon
Fourier analysis
Icon
Spectral analysis
Icon
Filtering
Icon
Summary
8
8. Working with Databases
Icon
8. Working with Databases
Icon
Lightweight access with sqlite3
Icon
Accessing databases from pandas
Icon
SQLAlchemy
Icon
Pony ORM
Icon
Dataset – databases for lazy people
Icon
PyMongo and MongoDB
Icon
Storing data in Redis
Icon
Apache Cassandra
Icon
Summary
9
9. Analyzing Textual Data and Social Media
Icon
9. Analyzing Textual Data and Social Media
Icon
Installing NLTK
Icon
Filtering out stopwords, names, and numbers
Icon
The bag-of-words model
Icon
Analyzing word frequencies
Icon
Naive Bayes classification
Icon
Sentiment analysis
Icon
Creating word clouds
Icon
Social network analysis
Icon
Summary
10
10. Predictive Analytics and Machine Learning
Icon
10. Predictive Analytics and Machine Learning
Icon
A tour of scikit-learn
Icon
Preprocessing
Icon
Classification with logistic regression
Icon
Classification with support vector machines
Icon
Regression with ElasticNetCV
Icon
Support vector regression
Icon
Clustering with affinity propagation
Icon
Mean Shift
Icon
Genetic algorithms
Icon
Neural networks
Icon
Decision trees
Icon
Summary
11
11. Environments Outside the Python Ecosystem and Cloud Computing
Icon
11. Environments Outside the Python Ecosystem and Cloud Computing
Icon
Exchanging information with MATLAB/Octave
Icon
Installing rpy2
Icon
Interfacing with R
Icon
Sending NumPy arrays to Java
Icon
Integrating SWIG and NumPy
Icon
Integrating Boost and Python
Icon
Using Fortran code through f2py
Icon
Setting up Google App Engine
Icon
Running programs on PythonAnywhere
Icon
Working with Wakari
Icon
Summary
12
12. Performance Tuning, Profiling, and Concurrency
Icon
12. Performance Tuning, Profiling, and Concurrency
Icon
Profiling the code
Icon
Installing Cython
Icon
Calling C code
Icon
Creating a process pool with multiprocessing
Icon
Speeding up embarrassingly parallel for loops with Joblib
Icon
Comparing Bottleneck to NumPy functions
Icon
Performing MapReduce with Jug
Icon
Installing MPI for Python
Icon
IPython Parallel
Icon
Summary
13
A. Key Concepts
Icon
A. Key Concepts
14
B. Useful Functions
Icon
B. Useful Functions
Icon
matplotlib
Icon
NumPy
Icon
pandas
Icon
Scikit-learn
Icon
SciPy
15
C. Online Resources
Icon
C. Online Resources
16
Index
Icon
Index

Chapter 3. Statistics and Linear Algebra

Statistics and linear algebra are branches of mathematics that are especially useful for data analysis. That's why we will focus on them in this chapter. Statistics is needed to make inferences from raw data. For instance, we can compute that the data for a variable has a certain arithmetic mean and standard deviation. From these numbers, we can then infer a range and the expected value for this variable. Then, we can run statistical tests to check how likely it is that we made the right conclusion.

Linear algebra concerns itself with systems of linear equations. These are easy to solve with NumPy and SciPy using the linalg package. Linear algebra is useful, for instance, to fit data to a model. We shall introduce other NumPy and SciPy packages in this chapter for random number generation and masked arrays.

In this chapter, we will cover the following topics:

  • Descriptive statistics
  • The linalg package
  • Polynomials
  • Matrices as specialized ndarray...
Visually different images
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.
Python Data Analysis
End of Section 3
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