DataScienceinPython.Volume3_PlotsandChar.epub - (EPUB全文下载)

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Data Science in Python
Volume 2.
 
Plots and Charts with Matplotlib
Data Analysis with Python and SQLite
 
Alexander Stepanov
Introduction
Graphs with matplotlib.
Create a figure
Size, resolution, and background
Axes limits
Title
Plotting
Set line properties with named parameters
Set properties of individual lines
Adjusting lines
Legend parameters
Legend position
Number of markers in a legend.
Legend font size, title, columns, transparency
Error bars
Error bar legend label
Customizing pyplot with a resource file
Bar graphs.
Setting your own x ticks
Bar plot error bars
Bar charts showing several series
Stacking bars
Histogram
Kernel density distribution and violin plots
Kernel density estimation
Violin plots
Color schemes
Predefined color schemes
Color schemes from color maps
Black and white plots
Plotting 2D arrays.
Color mapped image
Contour plot
Figures with multiple plots
Sharing an axis among several subplots
Breaking the Y axis
Interaction
Animation
Networks
Databases
SQLite
Single system-wide database with SQLite
GUI tools for SQLite
Connecting to database
Creating a table
Importing data
Querying databases
Filtering
Sorting
Working with multiple tables
HAVING a clause to filter by count(*)
Joining tables
Indexing database
Views
Aliases and joining rows of the same table
Querying database from Python
 
Introduction
Python is the most popular programming language in scientific computing today. It is simple, clear, and powerful. It works on Windows, Mac, Linux, and various other platforms. An excellent introduction to Python can be found in Python’s online help. In the real world data analysis, Python serves as a glue for many mature extension libraries that have become the de-facto standard.
This book is for people who want to start using Python and its popular extension libraries in their work quickly. The best way to start is to install a scientific python distribution, such as
Anaconda
-
available for Windows, Mac, and Linux or
Winpython
-
  available on Windows, that supply many necessary extension libraries. The installation process is described in the
introductory volume 1
of this series. You might also want to look at
volume 2
of this series, that describes how to read tabular data, save it as text or Microsoft Excel file, explore data interactively with Ipython notebook, create GUI application with TkInter, package your program for deployment on other computers, do efficient computation with Numpy, run Python at the speed of compiled program on ............

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