DataScienceinPython,Volume2_DataI_O,Jupy.epub - (EPUB全文下载)

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Data Science in Python
Volume 2.
 
Data I/O, GUI, Jupyter notebook, Deployment,
Numeric programming, High performance Python
 
 
Alexander Stepanov
 
Data I/O, GUI, Jupyter notebook, Deployment,
Numeric, High performance Python
Introduction
Reading data in your script
Reading data from file
Dealing with corrupt data
Manipulating data
Sorting data
Filtering data
Writing data to a file
CSV files
XLSX files
Using Jupyter notebook for user interaction
Display tabular data in IPython notebook
Adding user interaction
GUI programming with TkInter.
Tkinter application
tkinter variables
Button
Slider
Entry and Text widgets
Combobox
Menu
File open and file save dialogs
Diet calculator using Tk
Deployment
High performance computing
Numeric computations with Numpy
Numba - Just In Time Python compiler
Troubleshooting numba functions
Process level parallelism
 
 
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 get
volume 3
that describes plotting library Matplotlib and using Python together with SQLite database. I assume that you have a scientific Python bundle installed on your machine and know how to start the Jupyter notebook we are going to use for most examples.
Reading data in your script
Reading data from file
Let’s make our data file using Microsoft Excel, LibreOffice Calc, or some other spreadsheet application and save it in a tab delimited file
ingredients.txt
 
Food
carb
fat
protein
calories
serving size
pasta
39
1
7
210
56
parmesan grated
0
1.5
2
20
5
Sour cream
1
5
1
60
30
Chicken breast
0
3
22
120
112
Potato
28
0
3
110
148
Fire up your IPython notebook server. Using the
New
drop down menu in the top right corner, create a new Python3 notebook and type the following Python program into a c ............

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