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This article mainly explains "how to use Numpy's Anaconda". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use Numpy's Anaconda.

The basic usage of Anaconda

After installing Anaconda under windows, you can see the following components under Anaconda in all programs:

Anaconda Navigator: a graphical interface for managing toolkits and environments.

Anaconda Prompt: a command line interface for managing packages and environments.

Jupyter Note book: an interactive Web-based computing environment that demonstrates the process of data analysis and generates easy-to-read documents.

Spyder:Python integrated development environment, the layout is similar to Matlab.

We are mainly learning to use the third Jupyter Note book.

Here is a simple way to popularize the commonly used Anaconda commands (although I don't use them very often).

Check the software version number

Python-- version # View Python version

Conda-- version # View the version of conda

Add Mirror

Conda config-- add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

Update conda

Conda upgrade-all

View the installed packages

Conda list

Conda install [package name] # install package, installed in the default Python environment

It is recommended for beginners to install only Anaconda, which can save a lot of unnecessary trouble. the above is the basic use of Anaconda. You are welcome to add it in the message area.

Numpy index and slicing

Correct the mistakes in the previous article:

# correct import method

Import numpy as np

The index method of numpy is similar to the list index in Python. Here we mainly introduce the indexes / slices of ordinary array and Boolean array.

Index / slice of one-dimensional array

The index and slice of an one-dimensional array are the same as the list in Python. The index starts at 0, and the slice is left closed and right open.

Import numpy as np

Ar = np.arange (20)

# output the 4th value of ar

Print (ar [3])

# output the first four values of ar

Print (ar [: 4])

> > >

four

[0 1 2 3]

Indexing / slicing of multidimensional arrays

A two-dimensional array can be understood as two one-dimensional arrays stacked horizontally, as long as the corresponding indexes are taken respectively.

Import numpy as np

Ar = np.arange (16) .reshape (4jue 4)

# two-dimensional array indexes follow the first row and then the column (there are two ways to write it)

# Select the value of the second row and the second column

Print (ar [2] [2])

Print (ar [2jue 2])

# 2D array slicing

# take out the values of the first two rows

Print (ar [: 2])

# take out the values of the first two rows and the last two columns

Print (ar [: 2jue 2:])

> > >

[[0 1 2 3]

[4 5 6 7]

[8 9 10 11]

[12 13 14 15]]

ten

ten

[[0 1 2 3]

[4 5 6 7]]

[[2 3]

[6 7]]

The values of indexes and slices of a three-digit array are equivalent to the evolutionary version of a two-dimensional array.

Import numpy as np

Ar = np.arange (12) .reshape (3Jing 2pm 2)

Print (ar)

# 3D array index follows dimension, row and column

Print (ar [2] [0] [1])

Print (ar [2j0jue 1])

# slicing

# get the number of the first column of the first row of the first array

Print (ar [: 1 recuperating 1])

> > >

[0 1]

[2 3]]

[[4 5]

[6 7]]

[[8 9]

[10 11]

[0]

nine

nine

Boolean index and slicing

The use of Boolean arrays is the focus of this article.

# briefly show what a Boolean one-dimensional group looks like

I = np.array ([True,False,True])

J = np.array ([True,True,False,False])

Print (I)

Print (j)

> > >

[True False True]

[True True False False]

And what we often see is this:

Ar = np.arange (12) .reshape (3jue 4)

Print (ar)

Print (ar > 5)

> > >

[[0 1 2 3]

[4 5 6 7]

[8 9 10 11]]

[[False False False False]

[False False True True]

[True]]

When we need to filter out values greater than 3 in ar, we can use Boolean values to filter, as follows:

Ar = np.arange (12) .reshape (3jue 4)

Print (ar > 3)

> > >

[4 5 6 7 8 9 10 11]

Uniform distribution and normal distribution of Numpy random numbers

Generate random numbers by uniform distribution and normal distribution

# numpy.random.rand () generates a 0-1 random floating point number or N-dimensional floating point number-uniform distribution

A = np.random.rand ()

B = np.random.rand (4pd4)

Print (a)

Print (b)

> > >

0.5544023939180306

[[0.46387648 0.97345876 0.12059175 0.7565951]

[0.30192996 0.76633208 0.20107761 0.09315875]

[0.79347118 0.26714404 0.08628158 0.72510313]

[0.06606087 0.93260038 0.90268201 0.90941348]]

Generate random numbers by means of positive etheric distribution

# numpy.random.randn () generates a 0-1 random floating point number or N-dimensional floating point number-normal distribution

A = np.random.randn ()

B = np.random.randn (4pd4)

Print (a)

Print (b)

> > >

0.26901442604096687

[0.40261375-0.23541184 0.96607489-1.11253043]

[- 0.31670703 0.05841136-0.01862511 1.72597729]

[0.17052799 1.03537825-0.94375417 1.32484928]

[0.132761 0.44950533 0.44131534-0.11319535]

According to the above, I believe that everyone's understanding of. Randn () and. Rand () is not clear enough. Here is a visual way to show it:

# average distribution

# numpy.random.rand () generates a 0-1 random floating point number or N-dimensional floating point number-uniform distribution

Data1 = np.random.rand

Data2 = np.random.rand

# normal distribution

# numpy.random.randn () generates a floating point number or N-dimensional floating point number-normal distribution

Data3 = np.random.randn

Data4 = np.random.randn

Import matplotlib.pyplot as plt

% matplotlib inline

Plt.scatter (data1,data2)

Plt.scatter (data3,data4)

This is a randomly distributed pattern:

This is the pattern of normal distribution:

You can see that the imaging of normal distribution and random distribution is still quite different, of course, here is only to deepen people's understanding of .rand () and .rand (), visualization will be further learned later.

Other uses of Numpy random numbers # random integers

Print (np.random.randint (2))

# generate random integers between 2 and 10

Print ((np.random.randint (2) 10)

# generate 10 integers between 0 and 10

Print ((np.random.randint (10 heroin 10)

# generate a two-dimensional array of 10 elements between 0 and 10

Print (np.random.randint (10 refine size = (2) 5))

# generate a two-dimensional array of 10 elements between 10 and 50

Print (np.random.randint (10, 5, 5))

At this point, I believe you have a deeper understanding of "how to use Numpy's Anaconda". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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