NumPy 1.8.0用户指南：快速入门与核心功能

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NumPy User Guide Release 1.8.0 是由NumPy社区编写的官方指南，发布于2013年11月10日。该文档详细介绍了NumPy库的基础知识、安装方法、功能特性以及高级用法，旨在帮助用户快速入门并充分利用这个强大的Python科学计算库。 **1. Introduction** - **What is NumPy?**: NumPy（Numerical Python）是Python中的核心库之一，专为处理大型多维数组和矩阵而设计。它提供了高效的数值计算功能，如数组创建、数据类型支持、内存管理和操作，以及与C、Fortran和C++的接口。 **2. Numpy Basics** - **Data Types**: NumPy支持多种基本数据类型，如整型、浮点型和复数，以及用于存储结构化数据的结构数组（record arrays）。理解这些数据类型对于正确地存储和操作数据至关重要。 - **Array Creation**: 用户可以通过多种方式创建数组，如列表推导式、函数`array()`、`zeros()`、`ones()`等。指南详细讲解了如何根据需求选择创建数组的方法。 - **I/O with Numpy**: 提供了读写文件的功能，包括从文本文件、二进制文件或内存缓冲区导入/导出数据，这对于数据处理和分析工作非常重要。 - **Indexing**: 数组索引和切片是高效访问数组元素的关键，文档解释了基本的切片语法以及高级的多维索引。 - ** Broadcasting**: 功能强大，允许不同形状的数组进行元素级别的运算，简化了许多数学和统计操作。 - **Byte-swapping**: 在处理不同架构的数据时，可能需要考虑字节顺序问题，NumPy提供了相应的支持。 - **Structured Arrays (Record Arrays)**: 结构数组允许在单个数组中存储不同类型的数据，类似于数据库记录，方便数据管理。 **3. Performance** - 本部分关注性能优化，包括如何有效地利用NumPy的内存管理，以及避免常见的性能瓶颈，以提高计算效率。 **4. Miscellaneous** - **Floating Point Special Values**: IEEE 754标准下处理浮点数异常情况的规则。 - **Handling Numeric Exceptions**: 学习如何捕获和处理NumPy运行时的数值错误。 - **Interfacing with other languages**: 提供了与C、Fortran和C++的接口，以便与其他语言代码无缝集成。 - **Methods vs Functions**: 解释了NumPy中的函数和方法之间的区别，帮助用户了解何时选择哪种。 **5. Using NumPy C-API** - **Extending NumPy**: 如何通过C API扩展NumPy的功能，增强其灵活性。 - **Using Python as glue**: 利用Python的灵活性，将其他编程语言的库与NumPy集成。 - **Writing your own ufunc**: 用户自定义通用函数（ufunc），实现特定的数学运算或算法。 - **Beyond the Basics**: 进一步探索NumPy的高级特性和潜在应用场景。 NumPy User Guide Release 1.8.0是学习和掌握NumPy的强大工具，无论是初学者还是专业开发者，都能从中找到所需的信息，提升数据分析和科学计算的能力。

NumPy User Guide, Release 1.8.0

and types

>>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])

2.2.3 Intrinsic Numpy Array Creation

Numpy has built-in functions for creating arrays from scratch:

zeros(shape) will create an array ﬁlled with 0 values with the speciﬁed shape. The default dtype is ﬂoat64.

>>> np.zeros((2, 3)) array([[ 0., 0., 0.], [ 0., 0., 0.]])

ones(shape) will create an array ﬁlled with 1 values. It is identical to zeros in all other respects.

arange() will create arrays with regularly incrementing values. Check the docstring for complete information on the

various ways it can be used. A few examples will be given here:

>>> np.arange(10)

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> np.arange(2, 10, dtype=np.float)

array([ 2., 3., 4., 5., 6., 7., 8., 9.])

>>> np.arange(2, 3, 0.1)

array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])

Note that there are some subtleties regarding the last usage that the user should be aware of that are described in the

arange docstring.

linspace() will create arrays with a speciﬁed number of elements, and spaced equally between the speciﬁed beginning

and end values. For example:

>>> np.linspace(1., 4., 6)

array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])

The advantage of this creation function is that one can guarantee the number of elements and the starting and end

point, which arange() generally will not do for arbitrary start, stop, and step values.

indices() will create a set of arrays (stacked as a one-higher dimensioned array), one per dimension with each repre-

senting variation in that dimension. An example illustrates much better than a verbal description:

>>> np.indices((3,3))

array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])

This is particularly useful for evaluating functions of multiple dimensions on a regular grid.

2.2.4 Reading Arrays From Disk

This is presumably the most common case of large array creation. The details, of course, depend greatly on the format

of data on disk and so this section can only give general pointers on how to handle various formats.

Standard Binary Formats

Various ﬁelds have standard formats for array data. The following lists the ones with known python libraries to read

them and return numpy arrays (there may be others for which it is possible to read and convert to numpy arrays so

check the last section as well)

HDF5: PyTables

FITS: PyFITS

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Examples of formats that cannot be read directly but for which it is not hard to convert are those formats supported by

libraries like PIL (able to read and write many image formats such as jpg, png, etc).

Common ASCII Formats

Comma Separated Value ﬁles (CSV) are widely used (and an export and import option for programs like Excel). There

are a number of ways of reading these ﬁles in Python. There are CSV functions in Python and functions in pylab (part

of matplotlib).

More generic ascii ﬁles can be read using the io package in scipy.

Custom Binary Formats

There are a variety of approaches one can use. If the ﬁle has a relatively simple format then one can write a simple

I/O library and use the numpy fromﬁle() function and .toﬁle() method to read and write numpy arrays directly (mind

your byteorder though!) If a good C or C++ library exists that read the data, one can wrap that library with a variety of

techniques though that certainly is much more work and requires signiﬁcantly more advanced knowledge to interface

with C or C++.

Use of Special Libraries

There are libraries that can be used to generate arrays for special purposes and it isn’t possible to enumerate all of

them. The most common uses are use of the many array generation functions in random that can generate arrays of

random values, and some utility functions to generate special matrices (e.g. diagonal).

2.3 I/O with Numpy

2.3.1 Importing data with genfromtxt

Numpy provides several functions to create arrays from tabular data. We focus here on the genfromtxt function.

In a nutshell, genfromtxt runs two main loops. The ﬁrst loop converts each line of the ﬁle in a sequence of strings.

The second loop converts each string to the appropriate data type. This mechanism is slower than a single loop, but

gives more ﬂexibility. In particular, genfromtxt is able to take missing data into account, when other faster and

simpler functions like loadtxt cannot.

Note: When giving examples, we will use the following conventions:

>>> import numpy as np

>>> from StringIO import StringIO

Deﬁning the input

The only mandatory argument of genfromtxt is the source of the data. It can be a string corresponding to the name

of a local or remote ﬁle, or a ﬁle-like object with a read method (such as an actual ﬁle or a StringIO.StringIO

object). If the argument is the URL of a remote ﬁle, this latter is automatically downloaded in the current directory.

The input ﬁle can be a text ﬁle or an archive. Currently, the function recognizes gzip and bz2 (bzip2) archives. The

type of the archive is determined by examining the extension of the ﬁle: if the ﬁlename ends with ’.gz’, a gzip

archive is expected; if it ends with ’bz2’, a bzip2 archive is assumed.

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Splitting the lines into columns

The delimiter argument

Once the ﬁle is deﬁned and open for reading, genfromtxt splits each non-empty line into a sequence of strings.

Empty or commented lines are just skipped. The delimiter keyword is used to deﬁne how the splitting should take

place.

Quite often, a single character marks the separation between columns. For example, comma-separated ﬁles (CSV) use

a comma (,) or a semicolon (;) as delimiter:

>>> data = "1, 2, 3\n4, 5, 6"

>>> np.genfromtxt(StringIO(data), delimiter=",")

array([[ 1., 2., 3.],

[ 4., 5., 6.]])

Another common separator is "\t", the tabulation character. However, we are not limited to a single character, any

string will do. By default, genfromtxt assumes delimiter=None, meaning that the line is split along white

spaces (including tabs) and that consecutive white spaces are considered as a single white space.

Alternatively, we may be dealing with a ﬁxed-width ﬁle, where columns are deﬁned as a given number of characters.

In that case, we need to set delimiter to a single integer (if all the columns have the same size) or to a sequence of

integers (if columns can have different sizes):

>>> data = " 1 2 3\n 4 5 67\n890123 4"

>>> np.genfromtxt(StringIO(data), delimiter=3)

array([[ 1., 2., 3.],

[ 4., 5., 67.],

[ 890., 123., 4.]])

>>> data = "123456789\n 4 7 9\n 4567 9"

>>> np.genfromtxt(StringIO(data), delimiter=(4, 3, 2))

array([[ 1234., 567., 89.],

[ 4., 7., 9.],

[ 4., 567., 9.]])

The autostrip argument

By default, when a line is decomposed into a series of strings, the individual entries are not stripped of leading nor

trailing white spaces. This behavior can be overwritten by setting the optional argument autostrip to a value of

True:

>>> data = "1, abc , 2\n 3, xxx, 4"

>>> # Without autostrip

>>> np.genfromtxt(StringIO(data), dtype="|S5")

array([[’1’, ’ abc ’, ’ 2’],

[’3’, ’ xxx’, ’ 4’]],

dtype=’|S5’)

>>> # With autostrip

>>> np.genfromtxt(StringIO(data), dtype="|S5", autostrip=True)

array([[’1’, ’abc’, ’2’],

[’3’, ’xxx’, ’4’]],

dtype=’|S5’)

The comments argument

The optional argument comments is used to deﬁne a character string that marks the beginning of a comment. By

default, genfromtxt assumes comments=’#’. The comment marker may occur anywhere on the line. Any

character present after the comment marker(s) is simply ignored:

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NumPy User Guide, Release 1.8.0

>>> data = """#

... # Skip me !

... # Skip me too !

... 1, 2

... 3, 4

... 5, 6 #This is the third line of the data

... 7, 8

... # And here comes the last line

... 9, 0

... """

>>> np.genfromtxt(StringIO(data), comments="#", delimiter=",")

[[ 1. 2.]

[ 3. 4.]

[ 5. 6.]

[ 7. 8.]

[ 9. 0.]]

Note: There is one notable exception to this behavior: if the optional argument names=True, the ﬁrst commented

line will be examined for names.

Skipping lines and choosing columns

The skip_header and skip_footer arguments

The presence of a header in the ﬁle can hinder data processing. In that case, we need to use the skip_header

optional argument. The values of this argument must be an integer which corresponds to the number of lines to skip

at the beginning of the ﬁle, before any other action is performed. Similarly, we can skip the last n lines of the ﬁle by

using the skip_footer attribute and giving it a value of n:

>>> data = "\n".join(str(i) for i in range(10))

>>> np.genfromtxt(StringIO(data),)

array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])

>>> np.genfromtxt(StringIO(data),

... skip_header=3, skip_footer=5)

array([ 3., 4.])

By default, skip_header=0 and skip_footer=0, meaning that no lines are skipped.

The usecols argument

In some cases, we are not interested in all the columns of the data but only a few of them. We can select which

columns to import with the usecols argument. This argument accepts a single integer or a sequence of integers

corresponding to the indices of the columns to import. Remember that by convention, the ﬁrst column has an index of

0. Negative integers behave the same as regular Python negative indexes.

For example, if we want to import only the ﬁrst and the last columns, we can use usecols=(0, -1):

>>> data = "1 2 3\n4 5 6"

>>> np.genfromtxt(StringIO(data), usecols=(0, -1))

array([[ 1., 3.],

[ 4., 6.]])

If the columns have names, we can also select which columns to import by giving their name to the usecols

argument, either as a sequence of strings or a comma-separated string:

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>>> data = "1 2 3\n4 5 6"

>>> np.genfromtxt(StringIO(data),

... names="a, b, c", usecols=("a", "c"))

array([(1.0, 3.0), (4.0, 6.0)],

dtype=[(’a’, ’<f8’), (’c’, ’<f8’)])

>>> np.genfromtxt(StringIO(data),

... names="a, b, c", usecols=("a, c"))

array([(1.0, 3.0), (4.0, 6.0)],

dtype=[(’a’, ’<f8’), (’c’, ’<f8’)])

Choosing the data type

The main way to control how the sequences of strings we have read from the ﬁle are converted to other types is to set

the dtype argument. Acceptable values for this argument are:

• a single type, such as dtype=float. The output will be 2D with the given dtype, unless a name has been

associated with each column with the use of the names argument (see below). Note that dtype=float is the

default for genfromtxt.

• a sequence of types, such as dtype=(int, float, float).

• a comma-separated string, such as dtype="i4,f8,|S3".

• a dictionary with two keys ’names’ and ’formats’.

• a sequence of tuples (name, type), such as dtype=[(’A’, int), (’B’, float)].

• an existing numpy.dtype object.

• the special value None. In that case, the type of the columns will be determined from the data itself (see below).

In all the cases but the ﬁrst one, the output will be a 1D array with a structured dtype. This dtype has as many ﬁelds as

items in the sequence. The ﬁeld names are deﬁned with the names keyword.

When dtype=None, the type of each column is determined iteratively from its data. We start by checking whether a

string can be converted to a boolean (that is, if the string matches true or false in lower cases); then whether it can

be converted to an integer, then to a ﬂoat, then to a complex and eventually to a string. This behavior may be changed

by modifying the default mapper of the StringConverter class.

The option dtype=None is provided for convenience. However, it is signiﬁcantly slower than setting the dtype

explicitly.

Setting the names

The names argument

A natural approach when dealing with tabular data is to allocate a name to each column. A ﬁrst possibility is to use an

explicit structured dtype, as mentioned previously:

>>> data = StringIO("1 2 3\n 4 5 6")

>>> np.genfromtxt(data, dtype=[(_, int) for _ in "abc"])

array([(1, 2, 3), (4, 5, 6)],

dtype=[(’a’, ’<i8’), (’b’, ’<i8’), (’c’, ’<i8’)])

Another simpler possibility is to use the names keyword with a sequence of strings or a comma-separated string:

>>> data = StringIO("1 2 3\n 4 5 6")

>>> np.genfromtxt(data, names="A, B, C")

16 Chapter 2. Numpy basics

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