# NumPy

The NumPy library is the core library for scientific computing in Python. This Python NumPy cheat sheet is a quick reference for NumPy beginners looking to get started with data analysis.

import convention
import numpy as np

How to create arrays
a = np.array([1,2,3])
b = np.array([(1.5,2,3), (4,5,6)], dtype = float)
c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]], dtype = float)

Create an array of zeros :  np.zeros((3,4))
Create an array of ones :  np.ones((2,3,4),dtype=np.int16)
Create an array of evenly spaced values (step value) :  d = np.arange(10,25,5)
Create an array of evenly  spaced values (number of samples) :  np.linspace(0,2,9)
Create a constant array :  e = np.full((2,2),7)
Create a 2X2 identity matrix :  f = np.eye(2)
Create an array with random values :  np.random.random((2,2))
Create an empty array :  np.empty((3,2))

np.save(‘my_array’, a)
np.savez(‘array.npz’, a, b)

np.genfromtxt(“my_file.csv”, delimiter=’,’)
np.savetxt(“myarray.txt”, a, delimiter=” “)

Data Types
Signed 64-bit integer types:  np.int64
Standard double-precision floating point :  np.float32
Complex numbers represented by 128 floats :  np.complex
Boolean type storing TRUE and FALSE values :  np.bool
Python object type :  np.object
Fixed-length string type :  np.string_
Fixed-length unicode type :  np.unicode_

Array dimensions :  a.shape
Length of array :  len(a)
Number of array dimensions :  b.ndim
Number of array elements :  e.size
Data type of array elements :  b.dtype
Name of data type :  b.dtype.name
Convert an array to a different type :  b.astype(int)

Help
np.info(np.ndarray.dtype)

Subtraction : g = a – b
array([[-0.5, 0. , 0. ],  [-3. , -3. , -3. ]])
np.subtract(a,b)

Addition : b + a
array([[ 2.5, 4. , 6. ],  [ 5. , 7. , 9. ]])

Division : a / b
array([[ 0.66666667, 1. , 1. ], [ 0.25 , 0.4 , 0.5 ]])
np.divide(a,b)

Multiplication :  a * b

1. Multiplication :  np.multiply(a,b)
2. Exponentiation :  np.exp(b)
3. Square root :  np.sqrt(b)
4. Print sines of an array :  np.sin(a)
5. Element-wise cosine :  np.cos(b)
6. Element-wise natural logarithm :  np.log(a)
7. Dot product :  e.dot(f)

Element-wise comparison :  a == b
array([[False, True, True], [False, False, False]], dtype=bool)

Element-wise comparison : a < 2
array([True, False, False], dtype=bool)

Array-wise comparison : np.array_equal(a, b)

Aggregate Functions
Array-wise sum :  a.sum()
Array-wise minimum value :  a.min()
Maximum value of an array row :  b.max(axis=0)
Cumulative sum of the elements :  b.cumsum(axis=1)
Mean :  a.mean()
Median :  b.median()
Correlation coefficient :  a.corrcoef()
Standard deviation :  np.std(b)

Copying Arrays
Create a view of the array with the same data :  h = a.view()
Create a copy of the array :  np.copy(a)
Create a deep copy of the array :   h = a.copy()

Sort an array :  a.sort()
Sort the elements of an array’s axis :  c.sort(axis=0)

Transposing Array
i = np.transpose(b)
i.T

Changing Array Shape
Flatten the array
b.ravel()
g.reshape(3,-2)

Return a new array with shape (2,6) :  h.resize((2,6))
Append items to an array :  np.append(h,g)
Insert items in an array :  np.insert(a, 1, 5)
Delete items from an array :  np.delete(a,[1])

Combining Arrays
Concatenate arrays :
np.concatenate((a,d),axis=0)
array([ 1, 2, 3, 10, 15, 20])

Stack arrays vertically (row-wise)
np.vstack((a,b))
array([[ 1. , 2. , 3. ],[ 1.5, 2. , 3. ],[ 4. , 5. , 6. ]])

Stack arrays vertically (row-wise) : np.r_[e,f]

np.hstack((e,f)) Stack arrays horizontally (column-wise) : array([[ 7., 7., 1., 0.], [ 7., 7., 0., 1.]])

Create stacked column-wise arrays :
np.column_stack((a,d)) Create stacked column-wise arrays
array([[ 1, 10],[ 2, 15],[ 3, 20]])

Create stacked column-wise arrays : np.c_[a,d]

Split the array horizontally at the 3rd index
np.hsplit(a,3)
[array([1]),array([2]),array([3])]

Split the array vertically at the 2nd index
np.vsplit(c,2)
[array([[[ 1.5, 2. , 1. ], [ 4. , 5. , 6. ]]]), array([[[ 3., 2., 3.], [ 4., 5., 6.]]])]

Author: user