Numpy Basics

NumPy, short for Numerical Python, is one of the most important foundational packages for numerical computing in Python.

NumPy ndarray

ndarrays are N dimensional array object are fast, flexible container of large datasets. Arrays allow mathematical operations on whole blocks of data using similar syntax to the equivalent operations between scalar elements.

import numpy as np

data = np.array([ [1, 1.5, 2], [5, 3, 1] ]) data

data.shape

data.dtype

data.ndim

Create NP Array

np.zeros((3, 6))

np.empty((2, 4))

np.arange(10)

np.arange(10, dtype = np.float64)

np.arange(10, dtype = 'float64').dtype

a1 = np.array([1, 2, 3, 4.5], dtype=np.float64) a1

a1.dtype

# You can convert data type of an array. # Note that the fractional part is truncated. a2 = a1.astype(np.int64) a2

a2.dtype

Array Arithmetic

a1 = np.array([1, 2, 3, 4])

a1 * a1

a1 + a1

1 / a1

a1 ** 4

a2 = np.array([4, 3, 2, 1]) a1 > a2

a3 = np.arange(10, 7, -0.25) a3

Array Indices

Create an array

Access subsets using the methods used for list.

a1 = np.arange(10) a1

a1[2 : 5] = 2000

a1s = a1[2 : 5] a1s

a1s[1] = 10 a1s

a1s[:] = 3076 a1s

3D Array Indices

a3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

a3d

a3d[1]

a3d[0][1][2]

a3d[0, 1, 2]

a3d[0] = 5 a3d

Array Slicing

import numpy as np

a2d = np.array([[1,2,3], [4,5,6], [7,8,9]])

a2d

a2d[:2]

a2d[:2,1:]

Array Transposing

#Create a 3 by 6 matrix from number 0-17 a = np.arange(18).reshape(3,6) a

#Transform matrix so 3 columns and 6 rows instead of other way around a.T

#Multiply matrixes np.dot(a.T,a)

#Mulitply matrixes a.T @ a

Elementwise Functions

#Creat array up tp 5 a = np.arange(5) a

#Find Square root np.sqrt(a)

#Exponent np.exp(a)

#Arange an array starting at 4 and end at -1 in increments of -1 b = np.arange(4, -1, -1) b

#Find the maximum between numbers/objects in 2 arrays np.maximum(a, b)

#Divide every element in the array by 3 c = a/3 c

#Apply more f (separate fractions from numbers) on array c #i takes the fractions out #r takes the whole numbers out i, r = np.modf(c)

Matplotlib

It is a matrix library so it takes matrixes and plots them.

#Makes images %matplotlib inline

#Pyplot is the package we are using from matplotlib import matplotlib.pyplot as plt import numpy as np

#Create an array numbers 1-10 a = np.arange(10) a

#Plot the array a plt.plot(a)

#Create an array numbers 1-10 with the y axis being numbers above 100 a = np.arange(10) + 100 a

#Plot the array with the new y axis plt.plot(a)

Figures and Subplots

#Create figure fig = plt.figure()

#Add subplots to the figure in a 2 by 2 matrix referring to the first plot ax1 = fig.add_subplot(2,2,1) ax1

#Add subplots to the figure in a 2 by 2 matrix referring to the second plot ax2 = fig.add_subplot(2,2,2) ax2

#Have to put all the data in one code block to display information fig = plt.figure() ax1 = fig.add_subplot(2,2,1) ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3)

#Create a plot code ax3.plot(np.random.standard_normal(50).cumsum(),color="black",linestyle="dashed")

#Plot Data fig = plt.figure() ax1 = fig.add_subplot(2,2,1) ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3) ax1.plot(np.random.standard_normal(50).cumsum(),color="blue",linestyle="dashed") ax2.plot(np.random.standard_normal(50).cumsum(),color="green",linestyle="dashed") ax3.plot(np.random.standard_normal(50).cumsum(),color="black",linestyle="dashed")

#Plot Data fig = plt.figure() ax1 = fig.add_subplot(2,2,1) ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3) ax3.plot(np.random.standard_normal(50).cumsum(),color="black",linestyle="dashed") ax2.scatter(np.arange(30),np.arange(30) + 3 * np.random.standard_normal(30))

#Plot Data fig = plt.figure() ax1 = fig.add_subplot(2,2,1) ax2 = fig.add_subplot(2,2,2) ax3 = fig.add_subplot(2,2,3) ax2.scatter(np.arange(30),np.arange(30) + 3 * np.random.standard_normal(30)) ax3.plot(np.random.standard_normal(50).cumsum(),color="black",linestyle="dashed") ax1.hist(np.random.standard_normal(100), bins=20, color= "yellow", alpha=0.3)

#Creat 2 by 3 matrix fig, axes = plt.subplots(2, 3)

axes

fig, axes = plt.subplots(2, 2, sharex= True , sharey= True) for i in range(2): for j in range(2): axes[i, j].hist(np.random.standard_normal(500), bins=50,color= "red", alpha=0.5) fig.subplots_adjust(wspace=0, hspace=0)