# Numpy core syntax and code sorting summary!

2022-05-15 07:44:28

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## Numpy Summary

Numpy It's a use python Implementation of scientific computing extension library , Include

• A powerful N Dimensional array object Array;

• For consolidation C/C++ and Fortran Code toolkit ;

• Practical linear algebra 、 Fourier transform and random number generation function .numpy And sparse matrix operation package scipy With the use of more convenient .

NumPy（Numeric Python） Many advanced numerical programming tools are provided , Such as ： Matrix data type 、 Vector processing , And a sophisticated computing library . Produced for rigorous digital processing . Used by many large financial companies , And the core scientific computing organization ：Lawrence Livermore,NASA Use it to deal with some of the original use C++,Fortran or Matlab And so on .

This paper sorts out a Numpy A little meter reading , Sum up Numpy Common operations of , You can collect it. Take your time .

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1、 install Numpy

Can pass Pip perhaps Anaconda install Numpy:

``\$ pip install numpy``

or

``\$ conda install numpy``

2、 Basics

NumPy One of the most common features is NumPy Array ： List and NumPy The main difference between arrays is functionality and speed .

Lists provide basic operations , but NumPy Added FTTs、 Convolution 、 Quick search 、 Basic statistics 、 linear algebra 、 Histogram, etc .

The most important difference between the two data science is Able to use NumPy Array for element level computation .

• axis 0： Usually refers to the line

• axis 1： Usually refers to the column

1. Place holder

give an example ：

``````import numpy as np

# 1 dimensional
x = np.array([1,2,3])
# 2 dimensional
y = np.array([(1,2,3),(4,5,6)])

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

y = np.arange(3.0)
>>> array([ 0., 1., 2.])

x = np.arange(3,7)
>>> array([3, 4, 5, 6])

y = np.arange(3,7,2)
>>> array([3, 5])``````

2. Array attribute

3. Copy / Sort

give an example ：

``````import numpy as np
# Sort sorts in ascending order
y = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
y.sort()
print(y)
>>> [ 1  2  3  4  5  6  7  8  9  10]``````

4. Array manipulation routines

give an example ：

``````import numpy as np
# Append items to array
a = np.array([(1, 2, 3),(4, 5, 6)])
b = np.append(a, [(7, 8, 9)])
print(b)
>>> [1 2 3 4 5 6 7 8 9]

# Remove index 2 from previous array
print(np.delete(b, 2))
>>> [1 2 4 5 6 7 8 9]``````

Combining arrays

give an example ：

``````import numpy as np
a = np.array([1, 3, 5])
b = np.array([2, 4, 6])

# Stack two arrays row-wise
print(np.vstack((a,b)))
>>> [[1 3 5]
[2 4 6]]

# Stack two arrays column-wise
print(np.hstack((a,b)))
>>> [1 3 5 2 4 6]``````

Split array

give an example ：

``````# Split array into groups of ~3
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(np.array_split(a, 3))
>>> [array([1, 2, 3]), array([4, 5, 6]), array([7, 8])]``````

Array shape changes

• operation

• other

give an example ：

``````# Find inverse of a given matrix
>>> np.linalg.inv([[3,1],[2,4]])
array([[ 0.4, -0.1],
[-0.2, 0.3]])``````

5. Mathematical calculation

operation

give an example :

``````# If a 1d array is added to a 2d array (or the other way), NumPy
# chooses the array with smaller dimension and adds it to the one
# with bigger dimension
a = np.array([1, 2, 3])
b = np.array([(1, 2, 3), (4, 5, 6)])
>>> [[2 4 6]
[5 7 9]]

# Example of np.roots
# Consider a polynomial function (x-1)^2 = x^2 - 2*x + 1
# Whose roots are 1,1
>>> np.roots([1,-2,1])
array([1., 1.])
# Similarly x^2 - 4 = 0 has roots as x=±2
>>> np.roots([1,0,-4])
array([-2., 2.])``````

Compare

give an example ：

``````# Using comparison operators will create boolean NumPy arrays
z = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
c = z < 6
print(c)
>>> [ True  True  True  True  True False False False False False]``````

Basic statistics

give an example :

``````# Statistics of an array
a = np.array([1, 1, 2, 5, 8, 10, 11, 12])

# Standard deviation
print(np.std(a))
>>> 4.2938910093294167

# Median
print(np.median(a))
>>> 6.5``````

more

6. Slices and subsets

give an example :

``````b = np.array([(1, 2, 3), (4, 5, 6)])

# The index *before* the comma refers to *rows*,
# the index *after* the comma refers to *columns*
print(b[0:1, 2])
>>> [3]

print(b[:len(b), 2])
>>> [3 6]

print(b[0, :])
>>> [1 2 3]

print(b[0, 2:])
>>> [3]

print(b[:, 0])
>>> [1 4]

c = np.array([(1, 2, 3), (4, 5, 6)])
d = c[1:2, 0:2]
print(d)
>>> [[4 5]]``````

Slice for example :

``````import numpy as np
a1 = np.arange(0, 6)
a2 = np.arange(10, 16)
a3 = np.arange(20, 26)
a4 = np.arange(30, 36)
a5 = np.arange(40, 46)
a6 = np.arange(50, 56)
a = np.vstack((a1, a2, a3, a4, a5, a6))``````

Generate matrix and slice diagram

7. Tips

Boolean index

``````# Index trick when working with two np-arrays
a = np.array([1,2,3,6,1,4,1])
b = np.array([5,6,7,8,3,1,2])

# Only saves a at index where b == 1
other_a = a[b == 1]
#Saves every spot in a except at index where b != 1
other_other_a = a[b != 1]``````
``````import numpy as np
x = np.array([4,6,8,1,2,6,9])
y = x > 5
print(x[y])
>>> [6 8 6 9]

# Even shorter
x = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])
print(x[x < 5])
>>> [1 2 3 4 4]``````
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