Python数据可视化编程入门指南

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" Beginning Python Visualization 2nd 是一本针对已有Python编程基础的开发人员的英文原版教程，专注于Python数据可视化。这本书旨在引导读者探索数据可视化世界，涵盖了从环境设置、Python编程基础到高级文件处理等多个方面。" 在本书中，作者深入浅出地介绍了Python在数据可视化领域的应用。首先，"Navigating the World of Data Visualization"一章引领读者了解数据可视化的基础知识，包括为何要进行数据可视化以及如何选择合适的工具和技术。第二章"The Environment"讨论了进行数据可视化工作所需的环境设置，可能涉及Python解释器的选择，如Anaconda或Jupyter Notebook，以及相关库的安装，如matplotlib、seaborn和pandas等。第三章"Python for Programmers"是为已经掌握Python基础的开发者准备的，可能涵盖面向对象编程、函数式编程以及Python的高级特性，这些都是进行复杂数据操作和可视化项目所必需的。第四章"Data Organization"探讨了数据的组织结构，如数据框（DataFrame）的概念，以及如何有效地读取、清洗和预处理数据。第五章"Processing Text Files"讲解如何处理文本数据，包括正则表达式和文本分析，这对于从日志文件或大量文本数据中提取信息至关重要。第六章"Graphs and Plots"深入研究各种图表类型，如折线图、散点图、直方图等，以及如何利用Python库创建这些图表。第七章"Math Games"可能涉及到数学在可视化中的应用，如使用Python进行游戏开发或模拟，这有助于提升对数据可视化的理解。第八章"Science and Visualization"专注于科学数据的可视化，可能包括三维图形、时间序列分析以及科学出版物级别的图像制作。第九章"Image Processing"介绍了图像处理技术，包括图像读取、修改和分析，这在医学影像分析、计算机视觉等领域非常有用。第十章"Advanced File Processing"讨论更高级的文件操作技巧，如处理大型数据集、压缩文件和数据库交互。附录提供了额外的源代码清单，方便读者实践和参考。而索引部分则帮助读者快速定位感兴趣的主题。通过本书，读者将能够利用Python进行高效的数据可视化，从而更好地理解和展示数据背后的故事。无论是数据科学家、软件工程师还是对数据感兴趣的爱好者，都能从这本书中学到丰富的知识和实践经验。

CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

len(x) tells us the size of the array of lists. It’s also a crude way for us to ensure that data was actually read into

the array.

The second line in the function is called a docstring, and it is characterized by three quotes (""") surrounding the

text in the following manner: """dosctring""". In this case, a docstring is used to document the function; that is, it

enables us to explain what it does. Issuing the command help(funcname) yields its docstring:

>>> help(read_csv_file)

Help on function read_csv_file in module __main__:

read_csv_file(filename)

Reads a CSV file and return it as a list of rows.

You should use help() whenever you need a reminder of what a function does. help() can be invoked with

functions as well as modules. For example, the following invokes help on module csv:

>>> help(csv)

Help on module csv:

NAME

csv - CSV parsing and writing.

DESCRIPTION

This module provides classes that assist in the reading and writing

of Comma Separated Value (CSV) files, and implements the interface

described by PEP 305. Although many CSV files are simple to parse,

the format is not formally defined by a stable specification and

is subtle enough that parsing lines of a CSV file with something

like line.split(",") is bound to fail. The module supports three

basic APIs: reading, writing, and registration of dialects.

The line data = [] declares a variable named data and initializes it as an empty list. We will use data to store the

values from the CSV file.

The csv module helps us read CSV files by automating a lot of the tasks associated with reading them. I will

discuss CSV files and the csv module in more detail in Chapters 4 and 5; this chapter will only provide an overview.

Here are the steps for reading CSV files with the csv module:

1. Open the file for reading.

2. Create a csv.reader object. The csv.reader object has functions that help us read CSV files.

a. Using the csv.reader object, read the data from the file, a row at a time.

b. Append every row to the variable data.

3. Close the file.

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CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

Let’s try this, a step at a time:

>>> import csv

>>> f = open('../data/GPS-2008-06-04-09-03-45.csv')

>>> cr = csv.reader(f)

>>> for row in cr:

... print(row)

['$GPGSA', 'A', '3', '21', '18', '15', '24', '', '22', '', '', '', '', '', '',

'03.5', '02.2', '02.7*09']

['$GPGSV', '3', '1', '12', '21', '86', '267', '39', '18', '66', '286', '44',

'15', '51', '060', '43', '24', '45', '125', '30*7A']

['$GPGSV', '3', '2', '12', '06', '28', '300', '33', '22', '27', '265', '31',

'03', '18', '312', '27', '29', '15', '185', '31*7C']

['$GPGSV', '3', '3', '12', '09', '15', '138', '31', '16', '00', '301', '',

'19', '00', '332', '', '00', '00', '000', '*70']

['$GPRMC', '140706.24', 'A', '4455.6241', 'N', '09328.0519', 'W', '011.4',

'152.7', '040608', '001.2', 'E', 'A*25']

['$GPGGA', '140706.24', '4455.6241', 'N', '09328.0519', 'W', '1', '04', '03.0',

'00295.1', 'M', '-030.7', 'M', '', '*51']

['$GPGSA', 'A', '3', '21', '18', '15', '24', '', '', '', '', '', '', '', '',

'08.9', '03.0', '08.4*04']

>>> f.close()

First, we open the data file and assign it to variable f. The opened file can now be referred to by the variable

f. Next, we create a csv.reader object, cr. We associate the csv.reader object, cr, with the file f. We then iterate

through every row of the csv.reader object and print that row. Lastly, we close the file by calling f.close(). It is

considered good practice to close the file once you’re done with it; but if you neglect to do so, Python will close the file

automatically once the variable f is no longer in use.

Note N You may, after issuing the commands, receive an error similar to this: UnicodeEncodeError: 'charmap'

codec can't encode character '\uABCD'

. If this happens, open the GPS file in a text editor and make sure the file

contains proper alpha-numeric characters. Be sure to delete lines with non-alpha-numeric characters.

Python also lets you implement cascade functions, where you can call new functions based on the results of other

functions. This process can be repeated several times. Cascading (usually) adds clarity and produces more elegant

scripts. In our case, the variable f isn’t really important to us, so we discard it after we attach it to a csv.reader object.

Instead of the preceding code, we can write the following, by cascading the functions:

>>> cr = csv.reader(open('../data/GPS-2008-06-04-09-03-45.csv')):

>>> for row in cr:

... print(row)

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CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

The same holds true for variable cr, for which we can cascade several functions and generate a more compact

line of code:

>>> for row in csv.reader(open('../data/GPS-2008-06-04-09-03-45.csv')):

... print(row)

While the script might be shorter, there’s no performance gain. It is therefore suggested that you cascade

functions only if it adds clarity; there’s a good chance you’ll be editing this code later on, and it’s important to be able

to understand what’s going on. In fact, not cascading functions might be useful at times because you might need

access to intermediate variables (such as f and cr in our case).

The csv.reader object converts each row we read into a row of fields, in the form of a list. That row is then

appended to a list of rows and stored in the variable data.

Note N By now, you’ve seen the dot symbol (

) used several times. Its use might be a bit confusing, so an explanation

is in order. The dot symbol is used to access function members of modules, as well as function members of objects

(classes). You’ve seen it in member functions of modules, such as

csv.reader()

, but also for objects, such as

f.read()

In the latter, it means that the file object has a member function

read()

and that the function is called to operate on

variable f. To access these functions, we use the dot operator. We’ll touch on this again in Chapter 3. Lastly, we use the

ellipsis symbol (

...

) to denote line continuation when interactively entering commands in Python.

Analyzing GPS Data

Also known as NMEA 0183, the GPS format contains many header stamps, some of which hold useful information for

our task. Let’s take a closer look at the GPS data:

Each row seems to start with a text header stamp, beginning with the characters u $GP.

There are several header stamps (e.g., u $GPGSA and $GPRMC).

Additional values follow the header, most of which are numeric.u

Note N NMEA stands for the National Marine Electronics Association; see

http://www.nmea.org

for more information.

The NMEA 0183 data format is described at

http://www.gpsinformation.org/dale/nmea.htm

As mentioned earlier, several $GP header stamps appear in our data files, but we need to determine which ones

exactly are relevant to us. First, it would be nice to know which header stamps from the NMEA standard are even

present in our data files. One option would be to open the files, look for the headers, and jot down every new header

once we see it. Another, of course, would be to use Python to do that for us.

Python is a very high-level programming language. As such, it has built-in support for dictionaries (also known

as associative arrays in Perl), which are data structures that have a one-to-one relationship between a key and a value,

very much like real dictionaries. Traditional dictionaries, however, often have several values for a key; that is, they

have several interpretations (values) for one word (key). You can easily implement this in Python using the dictionary

object, dict, as well by assigning a list value to a key. That way, you can have several entries per one key, because the

key is associated with a list that can hold several values. In reality, it’s still a one-to-one relationship, but enough about

that for now. I’ll cover dictionaries in more detail in future chapters. What we want to do here is use a dictionary object

to hold the number of times a header is encountered. Our key will be the GPS header stamp, and our value will be a

number that indicates occurrence. We’ll increment the value whenever a key is encountered, as shown in Listing 1-5.

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CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

Listing 1-5. Function list_gps_commands( )

def list_gps_commands(data):

"""Counts the number of times a GPS command is observed.

Returns a dictionary object."""

gps_cmds = dict()

for row in data:

try:

gps_cmds[row[0]] += 1

except KeyError:

gps_cmds[row[0]] = 1

return gps_cmds

There are few things to keep in mind about this function. First, the docstring spans multiple lines, which is one

of the key benefits of docstrings. Docstrings will display all the spaces and line breaks as shown in the function itself.

Second, we initialize a variable, gps_cmds, to be our dictionary. We then process every list in the GPS data: we only

care about the first element of every row, as that’s the value that holds the GPS header stamps. We then increment the

value associated with the key: gps_cmds[row[0]] += 1. We use the += operation to increment the value by 1, similar

to how it’s done in C (Python, however, does not use the ++ operator). If the key does not exist, which will happen

whenever we encounter a new header stamp, an exception will be raised. We catch the exception with our except

KeyError statement. In the case of an exception, we set the dictionary value associated with the key to 1.

We can write the function list_gps_commands() even more compactly using the dictionary method get();

see Chapter 3 for details.

Let’s analyze some GPS data:

>>> x = read_csv_file('../data/GPS-2008-05-30-09-00-50.csv')

>>> list_gps_commands(x)

{'$GPGSA': 282, '$GPGSV': 846, '$GPGGA': 282, '$GPRMC': 283}

It turns out there are four distinct GPS headers being generated by my GPS. Of those, only two interest us: $GPGSV,

which holds the number of satellites in view (Hey! It’s really important!); and $GPRMC, which holds location and

velocity information.

What we’d like to do is code a function that takes the GPS data and, whenever the header field is $GPGSV or

$GPRMC, extracts the information and stores it in numerical arrays that will be easier to manipulate later on. Numerical

arrays are introduced with the NumPy module, so we have to import numpy. Since we’ll be using a lot of the

functionality of NumPy, SciPy, and matplotlib, an easier approach would be to import pylab, which imports all these

modules, as follows:

>>> from pylab import *

Note N The name PyLab comes from Python and MATLAB. The PyLab module provides MATLAB-like functionality

in Python.

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CHAPTER 1 N NAVIGATING THE WORLD OF DATA VISUALIZATION

Extracting GPS Data

In the case of a $GPGSV header, the number of satellites is the fourth entry. In the case of a $GPRMC header, we have a

bit more interesting information. The second field is the timestamp, the fourth field is the latitude, the sixth field is

the longitude, and the eighth field is the velocity. Again, refer to the NMEA 0183 format for more details. Table 1-1

summarizes the fields and their values in a $GPRMC line.

Table 1-1. $GPRMC Information (Excerpt)

Field Name Index Format

Header 0 $GPRMC (fixed)

Timestamp 1 hhmmss.ss

Latitude 3 DDMM.MMM

Longitude 5 DDDMM.MMM

Velocity 7 VVV.V

We need to keep in mind some caveats regarding the information in $GPRMC. For example, let’s look at the

timestamp of an arbitrary line:

>>> x[12]

['$GPRMC', '140055.00', 'A', '4454.1740', 'N', '09325.0143', 'W', '000.0',

'128.7', '300508', '001.1', 'E', 'A*28']

In this output, the timestamp appears as '140055.00'. This follows the format hhmmss.ss where hh are two digits

representing the hour (it will always consist of two digits—if the hour is one digit, say 7 in the morning, a 0 will be

added before it), mm are two digits representing the minute (again, always two digits), and ss.ss are five characters

(four digits plus the dot) representing seconds and fractions of seconds. There’s also a North/South field, as well as

an East/West field. Here, for simplicity, we assume northern hemisphere, but you can easily change these values by

reading the entire $GPRMC structure.

Note N In the ISO time format, we’ve used HHMMSS to denote hours minutes and seconds. In this case, we follow

the convention in NMEA, which uses hhmmss.ss for hours, minutes, and seconds, and then sets DD and MM to angular

degrees and minutes.

The timestamp string is a bit hard to work with, especially when plotting data. The first reason is that it’s a string,

not a number. But even if you translate it to a number, the system does not lend itself nicely to plotting because there

are 60 seconds in a minute, not 100. So what we want to do is “linearize” the timestamp. To achieve this, we translate

the timestamp as seconds elapsed since midnight, as follows: T = hh * 3600 + mm * 60 + ss.ss.

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