matplotlib/seaborn 绘图可视化全面总结

1.概述

使用Matplotlib 绘图实现可视化时，会面临不同的需求有所调整，本文档重点对绘图过程中产生的一些小众需求进行全面总结，其他任务时可以随时即抽即用。

2.绘图

2.1 一般绘图

plt.figure() 参数设置说明

matplotlib.pyplot.figure(

figsize=None, # (float,float)，画布尺寸 ，默认为6.4*4.8

dpi=None, # int 分辨率，默认为100

facecolor=None, #背景色，默认为白色（'w'）

edgecolor=None, # 边界颜色，默认为白色（'w')

frameon=True, # 是否有边界，默认为True

clear=False, #是否对存在的画布进行清楚，默认为False（即自动创建新画布）

**kwargs)
通过命令设置：

plt.xlabel() → ax.set_xlabel()

plt.ylabel() → ax.set_ylabel()

plt.xlim() → ax.set_xlim()

plt.ylim() → ax.set_ylim()

plt.title() → ax.set_title()

plt.xticks→ ax.set_xticks()

plt.yticks→ ax.set_yticks()

plt.xticklabels→ ax.set_xticklabels()

plt.yticklabels→ ax.set_yticklabels()
ex:

a = [1,2,3,4,5]

labels = ['A', 'B', 'C', 'D','E']

plt.xticks(a,labels,rotation = 30)

ax[0].set_yticks([0,5,10,15])

ax[0].set_yticklabels(['low','medium','high','very high'])

#设置刻度尺字体与倾斜角度

ax.tick_params(axis='x', labelsize=8, style='italic')

ax.set_xticklabels(ax.get_xticklabels(), rotation=45)

2.1.1 case 1

import matplotlib.pyplot as plt

# Create a 2x2 grid of subplots
fig, axs = plt.subplots(2, 2)
# fig = plt.figure(figsize=(7, 5))  # 1000 x 900 像素（先宽度 后高度）

# Now axs is a 2D array of Axes objects
axs[0, 0].plot([1, 2, 3], [4, 5, 6])
axs[0, 1].scatter([1, 2, 3], [4, 5, 6])
axs[1, 0].bar([1, 2, 3], [4, 5, 6])
axs[1, 1].hist([1, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 5])

plt.show()

2.1.2 case 2

# importing packages
import numpy as np
import matplotlib.pyplot as plt

# create data
x=np.array([1, 2, 3, 4, 5])

# making subplots
fig, ax = plt.subplots(2, 2)

# set data with subplots and plot
title = ["Linear", "Double", "Square", "Cube"]
y = [x, x*2, x*x, x*x*x]

for i in range(4):
	
	# subplots
	plt.subplot(2, 2, i+1) 
	
	# plotting (x,y)
	plt.plot(x, y[i]) 
	
	# set the title to subplots
	plt.gca().set_title(title[i]) 

# set spacing
fig.tight_layout()
plt.show()

2.1.3 case 3

import matplotlib.pyplot as plt
import numpy as np

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

x1 = [1, 2, 3, 4, 5, 6]
y1 = [45, 34, 30, 45, 50, 38]
y2 = [36, 28, 30, 40, 38, 48]

labels = ["student 1", "student 2"]

# Add title to subplot
fig.suptitle(' Student marks in different subjects ', fontsize=30)

# Creating the sub-plots.
l1 = ax1.plot(x1, y1, 'o-', color='g')
l2 = ax2.plot(x1, y2, 'o-')

fig.legend([l1, l2], labels=labels,
		loc="upper right")
plt.subplots_adjust(right=0.9)

plt.show()

2.1.4 case 4

# importing required libraries
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np

# create a figure
fig = plt.figure()

# to change size of subplot's
# set height of each subplot as 8
fig.set_figheight(8)

# set width of each subplot as 8
fig.set_figwidth(8)

# create grid for different subplots
spec = gridspec.GridSpec(ncols=2, nrows=2,
						width_ratios=[2, 1], wspace=0.5,
						hspace=0.5, height_ratios=[1, 2])

# initializing x,y axis value
x = np.arange(0, 10, 0.1)
y = np.cos(x)

# ax0 will take 0th position in 
# geometry(Grid we created for subplots),
# as we defined the position as "spec[0]"
ax0 = fig.add_subplot(spec[0])
ax0.plot(x, y)

# ax1 will take 0th position in
# geometry(Grid we created for subplots),
# as we defined the position as "spec[1]"
ax1 = fig.add_subplot(spec[1])
ax1.plot(x, y)

# ax2 will take 0th position in
# geometry(Grid we created for subplots),
# as we defined the position as "spec[2]"
ax2 = fig.add_subplot(spec[2])
ax2.plot(x, y)

# ax3 will take 0th position in
# geometry(Grid we created for subplots),
# as we defined the position as "spec[3]"
ax3 = fig.add_subplot(spec[3])
ax3.plot(x, y)

# display the plots
plt.show()

2.1.5 case 5

# importing required library
import matplotlib.pyplot as plt
import numpy as np

# creating grid for subplots
fig = plt.figure()
fig.set_figheight(6)
fig.set_figwidth(6)

ax1 = plt.subplot2grid(shape=(3, 3), loc=(0, 0), colspan=3)
ax2 = plt.subplot2grid(shape=(3, 3), loc=(1, 0), colspan=1)
ax3 = plt.subplot2grid(shape=(3, 3), loc=(1, 2), rowspan=2)
ax4 = plt.subplot2grid((3, 3), (2, 0))
ax5 = plt.subplot2grid((3, 3), (2, 1), colspan=1)


# initializing x,y axis value
x = np.arange(0, 10, 0.1)
y = np.cos(x)

# plotting subplots
ax1.plot(x, y)
ax1.set_title('ax1')
ax2.plot(x, y)
ax2.set_title('ax2')
ax3.plot(x, y)
ax3.set_title('ax3')
ax4.plot(x, y)
ax4.set_title('ax4')
ax5.plot(x, y)
ax5.set_title('ax5')

# automatically adjust padding horizontally 
# as well as vertically.
plt.tight_layout()

# display plot
plt.show()

2.1.6 case 6

# importing packages
import numpy as np
import matplotlib.pyplot as plt

# create data
x=np.array([1, 2, 3, 4, 5])

# making subplots
fig, ax = plt.subplots(2, 2)

# set data with subplots and plot
ax[0, 0].plot(x, x)
ax[0, 1].plot(x, x*2)
ax[1, 0].plot(x, x*x)
ax[1, 1].plot(x, x*x*x)

fig.tight_layout(pad=5.0)
plt.show()

"""
# set the spacing between subplots
plt.subplot_tool()
plt.show()


plt.subplots_adjust(left=0.1,
                    bottom=0.1, 
                    right=0.9, 
                    top=0.9, 
                    wspace=0.4, 
                    hspace=0.4)
plt.show()



# making subplots with constrained_layout=True
fig, ax = plt.subplots(2, 2, 
                       constrained_layout = True)
 
# set data with subplots and plot
ax[0, 0].plot(x, x)
ax[0, 1].plot(x, x*2)
ax[1, 0].plot(x, x*x)
ax[1, 1].plot(x, x*x*x)
plt.show()
"""

2.1.7 case 7

import matplotlib.pyplot as plt 
import numpy as np 


X = np.linspace(-np.pi, np.pi, 15) 
C = np.cos(X) 
S = np.sin(X) 

# [left, bottom, width, height] 
ax = plt.axes([0.1, 0.1, 0.8, 0.8]) 

# 'bs:' mentions blue color, square 
# marker with dotted line. 
ax1 = ax.plot(X, C, 'bs:') 

#'ro-' mentions red color, circle 
# marker with solid line. 
ax2 = ax.plot(X, S, 'ro-') 

ax.legend(labels = ('Cosine Function', 
					'Sine Function'), 
		loc = 'upper left') 

ax.set_title("Trigonometric Functions") 

plt.show() 

2.1.8 case 8

# importing library
import matplotlib.pyplot as plt

# giving values for x and y to plot
student_marks = [50, 60, 70, 80, 90]
student_grade = ['B', 'B', 'B+', 'B+', 'A']
plt.plot(student_marks, student_grade)

# Giving x label using xlabel() method
# with bold setting
plt.xlabel("student_marks", fontweight='bold')
ax = plt.axes()

# Setting the background color of the plot 
# using set_facecolor() method
ax.set_facecolor("yellow")

# Giving y label using xlabel() method 
# with bold setting
plt.ylabel("student_grade", fontweight='bold')

# Giving title to the plot
plt.title("Student Marks v/s Student Grade")

# Showing the plot using plt.show()
plt.show()

2.1.9 case 9

# importing libraries
import matplotlib.pyplot as plt
import numpy as np

# giving values for x and y to plot
x = np.arange(0, 10, .1)
y = np.sin(x)

# Set background color of the outer 
# area of the plt
plt.figure(facecolor='yellow')

# Plotting the graph between x and y
plt.plot(x, y)

# Giving x label using xlabel() method
# with bold setting
plt.xlabel("X")
ax = plt.axes()

# Setting the background color of the plot 
# using set_facecolor() method
ax.set_facecolor("violet")

# Giving y label using xlabel() method with
# bold setting
plt.ylabel('sin(x)')

# Showing the plot using plt.show()
plt.show()

2.1.10 case 10

# Code to add text on matplotlib

# Importing library
import matplotlib.pyplot as plt

# Creating x-value and y-value of data
x = [1, 2, 3, 4, 5]
y = [5, 8, 4, 7, 5]

# Creating figure
fig = plt.figure()

# Adding axes on the figure
ax = fig.add_subplot(111)

# Plotting data on the axes
ax.plot(x, y)

# Adding title
ax.set_title('Day v/s No of Questions on GFG', fontsize=15)

# Adding axis title
ax.set_xlabel('Day', fontsize=12)
ax.set_ylabel('No of Questions', fontsize=12)

# Setting axis limits
ax.axis([0, 10, 0, 15])

# Adding text on the plot.
ax.text(1, 13, 'Practice on GFG', style='italic', bbox={
		'facecolor': 'green', 'alpha': 0.5, 'pad': 10})

# Adding text without box on the plot.
ax.text(8, 13, 'December', style='italic')

# Adding annotation on the plot.
ax.annotate('Peak', xy=(2, 8), xytext=(4, 10), fontsize=12,
			arrowprops=dict(facecolor='green', shrink=0.05))

plt.show()

2.1.11 case 11

# importing libraries 
""" How to change Matplotlib color bar size """
import matplotlib.pyplot as plt 
from mpl_toolkits.axes_grid1 import make_axes_locatable 

fig, ax = plt.subplots() 
# Reading image from folder 

img = mpimg.imread(r'img.jpg') 
image = plt.imshow(img) 

# Locating current axes 
divider = make_axes_locatable(ax) 

# creating new axes on the right 
# side of current axes(ax). 
# The width of cax will be 5% of ax 
# and the padding between cax and ax 
# will be fixed at 0.05 inch. 
colorbar_axes = divider.append_axes("right", 
									size="10%", 
									pad=0.1) 
# Using new axes for colorbar 
plt.colorbar(image, cax=colorbar_axes) 
plt.show() 

2.2.12 case 12

from scipy import signal 
import matplotlib.pyplot as plot 
import numpy as np 
# %matplotlib inline 

# Plot the square wave 
t = np.linspace(0, 1, 1000, endpoint=True) 
plot.plot(t, signal.square(2 * np.pi * 5 * t)) 

# Change the x, y axis label to "Brush Script MT" font style. 
plot.xlabel("Time (Seconds)", fontname="Brush Script MT", fontsize=18) 
plot.ylabel("Amplitude", fontname="Brush Script MT", fontsize=18) 

plot.show() 

2.2.13 case 13

import matplotlib.pyplot as plot 

x = [1, 2, 3, 4, 5, 6] 
y = [0, 2, 4, 6, 8, 10] 

# plotting a plot 
plot.plot(x, y) 


# Change the x, y axis label to 'Gabriola' style. 
plot.xlabel("Years", fontname="Gabriola", fontsize=18) 
plot.ylabel("Population (million)", fontname="Gabriola", fontsize=18) 

# Set the title to 'Franklin Gothic Medium' style. 
plot.title("Line Graph - Geeksforgeeks", 
		fontname='Franklin Gothic Medium', fontsize=18) 

plot.show() 

2.1.14 case 14

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

# Create a line chart
plt.figure(figsize=(8, 6))
plt.plot(x, y, marker='o', linestyle='-')

# Add annotations
for i, (xi, yi) in enumerate(zip(x, y)):
	plt.annotate(f'({xi}, {yi})', (xi, yi), textcoords="offset points", xytext=(0, 10), ha='center')

# Add title and labels
plt.title('Line Chart with Annotations')
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')

# Display grid
plt.grid(True)

# Show the plot
plt.show()

2.1.15 case 15

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4])
y = x*2

plt.plot(x, y)

x1 = [2, 4, 6, 8]
y1 = [3, 5, 7, 9]

plt.plot(x, y1, '-.')
plt.xlabel("X-axis data")
plt.ylabel("Y-axis data")
plt.title('multiple plots')

plt.fill_between(x, y, y1, color='green', alpha=0.5)
plt.show()

2.1.16 case 16

# importing required libraries
import numpy as np
import pandas as pd
import chart_studio.plotly as pl
import plotly.offline as po
import cufflinks as cf
po.init_notebook_mode(connected = True)
cf.go_offline()

# define a function for creating
# data set for plotting graph
def createdata(data):

	# creating random data set
	if(data == 1):
		x = np.random.rand(100,5) 
		df1 = pd.DataFrame(x, columns = ['A', 'B',
										'C', 'D',
										'E'])
		
	# creating user data set with input
	elif(data == 2):		 
		x = [0, 0, 0, 0, 0]
		r1 = [0, 0, 0, 0, 0]
		r2 = [0, 0, 0, 0, 0]
		r3 = [0, 0, 0, 0, 0]
		r4 = [0, 0, 0, 0, 0]
		
		print('Enter the values for columns')
		i = 0
		
		for i in [0, 1, 2, 3, 4]:
			x[i] = input()
			i = i + 1
		print('Enter the values for first row')
		i = 0
		
		for i in [0, 1, 2, 3, 4]:
			r1[i] = int(input())
			i = i + 1
			
		print('Enter the values for second row')
		i = 0
		
		for i in [0, 1, 2, 3, 4]:
			r2[i] = int(input())
			i = i + 1
			
		print('Enter the values for third row')
		i = 0
		
		for i in [0, 1, 2, 3, 4]:
			r3[i] = int(input())
			i = i + 1
			
		print('Enter the values for fourth row')
		i = 0
		
		for i in [0, 1, 2, 3, 4]:
			r4[i] = int(input())
			i = i + 1
			
		df1 = pd.DataFrame([r1,r2,r3,r4] , 
						columns = x)
	# creating data set by csv file 
	elif(data == 3):		 
		file = input('Enter the file name')
		x = pd.read_csv(file)
		df1 = pd.DataFrame(x)
		
	else:
		print('DataFrame creation failed please' +
			'enter in between 1 to 3 and try again')
		
	return df1

# define a function for 
# types of plotters
def plotter(plot):

	if(plot == 1):
		finalplot = df1.iplot(kind = 'scatter')
		
	elif(plot == 2):
		finalplot = df1.iplot(kind = 'scatter', mode = 'markers',
							symbol = 'x', colorscale = 'paired')
	elif(plot == 3):
		finalplot = df1.iplot(kind = 'bar')
		
	elif(plot == 4):
		finalplot = df1.iplot(kind = 'hist')
		
	elif(plot == 5):
		finalplot = df1.iplot(kind = 'box')
		
	elif(plot == 6):
		finalplot = df1.iplot(kind = 'surface')
		
	else:
		finalplot = print('Select only between 1 to 7')
		
	return finalplot

# define a function for allowing
# to plot for specific rows and columns
def plotter2(plot):		 

	col = input('Enter the number of columns you' +
				'want to plot by selecting only 1 , 2 or 3')
	
	col = int(col)
	
	if(col==1):
	
		colm = input('Enter the column you want to plot' +
					'by selecting any column from dataframe head')
		if(plot == 1):
			finalplot = df1[colm].iplot(kind = 'scatter')
			
		elif(plot == 2):
			finalplot = df1[colm].iplot(kind = 'scatter', mode = 'markers',
										symbol = 'x', colorscale = 'paired')
		elif(plot == 3):
			finalplot = df1[colm].iplot(kind = 'bar')
			
		elif(plot == 4):
			finalplot = df1[colm].iplot(kind = 'hist')
			
		elif(plot == 5):
			finalplot = df1[colm].iplot(kind = 'box')
			
		elif(plot == 6 or plot == 7):
			finalplot = print('Bubble plot and surface plot require' +
							'more than one column arguments')
		else:
			finalplot = print('Select only between 1 to 7')
			
	elif(col == 2):
	
		print('Enter the columns you want to plot' +
			'by selecting from dataframe head')
		
		x = input('First column')
		y = input('Second column')
		
		if(plot == 1):
			finalplot = df1[[x,y]].iplot(kind = 'scatter')
			
		elif(plot == 2):
			finalplot = df1[[x,y]].iplot(kind = 'scatter', mode = 'markers', 
										symbol = 'x', colorscale = 'paired')
		elif(plot == 3):
			finalplot = df1[[x,y]].iplot(kind = 'bar')
			
		elif(plot == 4):
			finalplot = df1[[x,y]].iplot(kind = 'hist')
			
		elif(plot == 5):
			finalplot = df1[[x,y]].iplot(kind = 'box')
			
		elif(plot == 6):
			finalplot = df1[[x,y]].iplot(kind = 'surface')
			
		elif(plot == 7):
			size = input('Please enter the size column for bubble plot')
			finalplot = df1.iplot(kind = 'bubble', x = x,
								y = y, size = size)
		else:
			finalplot = print('Select only between 1 to 7')
			
	elif(col == 3):
	
		print('Enter the columns you want to plot')
		x = input('First column')
		y = input('Second column')
		z = input('Third column')
		
		if(plot == 1):
			finalplot = df1[[x,y,z]].iplot(kind = 'scatter')
			
		elif(plot == 2):
			finalplot = df1[[x,y,z]].iplot(kind = 'scatter', mode = 'markers',
										symbol = 'x' ,colorscale = 'paired')
		elif(plot == 3):
			finalplot = df1[[x,y,z]].iplot(kind = 'bar')
			
		elif(plot == 4):
			finalplot = df1[[x,y,z]].iplot(kind = 'hist')
			
		elif(plot == 5):
			finalplot = df1[[x,y,z]].iplot(kind = 'box')
			
		elif(plot == 6):
			finalplot = df1[[x,y,z]].iplot(kind = 'surface')
			
		elif(plot == 7):
			size = input('Please enter the size column for bubble plot')
			
			finalplot = df1.iplot(kind = 'bubble', x = x, y = y, 
								z = z, size = size )
		else:
			finalplot = print('Select only between 1 to 7')
	else:
		finalplot = print('Please enter only 1 , 2 or 3')
	return finalplot

# define a main function 
# for asking type of plot
# and calling respective function
def main(cat): 

	if(cat == 1):
	
		print('Select the type of plot you need to plot by writing 1 to 6')
		print('1.Line plot')
		print('2.Scatter plot')
		print('3.Bar plot')
		print('4.Histogram')
		print('5.Box plot')
		print('6.Surface plot')
		plot = int(input())
		output = plotter(plot)
		
	elif(cat == 2):
	
		print('Select the type of plot you need to plot by writing 1 to 7')
		print('1.Line plot')
		print('2.Scatter plot')
		print('3.Bar plot')
		print('4.Histogram')
		print('5.Box plot')
		print('6.Surface plot')
		print('7.Bubble plot')
		plot = int(input())
		output = plotter2(plot)
		
	else:
		print('Please enter 1 or 2 and try again') 
		
print('Select the type of data you need to plot(By writing 1,2 or 3)')
print('1.Random data with 100 rows and 5 columns')
print('2.Customize dataframe with 5 columns and. 4 rows')
print('3.Upload csv/json/txt file')

data = int(input())
df1 = createdata(data)
print('Your DataFrame head is given below check the columns to plot using cufflinks')

df1.head()
print('What kind of plot you need , the complete data plot or columns plot')
cat = input('Press 1 for plotting all columns or press 2 for specifying columns to plot')
cat = int(cat)

main(cat)

2.2 line chart

2.2.1 case 1

# importing package 
import matplotlib.pyplot as plt 
import numpy as np 

# create data 
x = [1,2,3,4,5] 
y = [3,3,3,3,3] 

# plot lines 
plt.plot(x, y, label = "line 1", linestyle="-") 
plt.plot(y, x, label = "line 2", linestyle="--") 
plt.plot(x, np.sin(x), label = "curve 1", linestyle="-.") 
plt.plot(x, np.cos(x), label = "curve 2", linestyle=":") 
plt.legend() 
plt.show()

2.2.2 case 2

# importing module 
import matplotlib.pyplot as plt 


# assigning x and y coordinates 
x = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5] 
y = [] 

for i in range(len(x)): 
	y.append(max(0, x[i])) 

# depicting the visualization 
plt.plot(x, y, color='green', alpha=0.75) 
plt.xlabel('x') 
plt.ylabel('y') 

# displaying the title 
plt.title(label="ReLU function graph", 
				fontsize=40, 
				color="green") 

2.2.3 case 3

# importing modules 
from matplotlib import pyplot 
import numpy 

# assigning time values of the signal 
# initial time period, final time period 
# and phase angle 
signalTime = numpy.arange(0, 100, 0.5) 

# getting the amplitude of the signal 
signalAmplitude = numpy.sin(signalTime) 

# depicting the visualization 
pyplot.plot(signalTime, signalAmplitude, 
			color='green', alpha=0.1) 

pyplot.xlabel('Time') 
pyplot.ylabel('Amplitude') 

# displaying the title 
pyplot.title("Signal", 
			loc='right', 
			rotation=45) 

2.2.4 case 4

# importing module 
import matplotlib.pyplot as plt 

# assigning x and y coordinates 
z = [i for i in range(0, 6)] 

for i in range(0, 11, 2): 
	
	# depicting the visualization 
	plt.plot(z, z, color='green', alpha=i/10) 
	plt.xlabel('x') 
	plt.ylabel('y') 

	# displaying the title 
	print('\nIllustration with alpha =', i/10) 

	plt.show() 

2.3 bar chart

2.3.1 case 1

# importing package
import matplotlib.pyplot as plt
import numpy as np

# create data
x = ['A', 'B', 'C', 'D']
y1 = np.array([10, 20, 10, 30])
y2 = np.array([20, 25, 15, 25])
y3 = np.array([12, 15, 19, 6])
y4 = np.array([10, 29, 13, 19])

# plot bars in stack manner
plt.bar(x, y1, color='r')
plt.bar(x, y2, bottom=y1, color='b')
plt.bar(x, y3, bottom=y1+y2, color='y')
plt.bar(x, y4, bottom=y1+y2+y3, color='g')
plt.xlabel("Teams")
plt.ylabel("Score")
plt.legend(["Round 1", "Round 2", "Round 3", "Round 4"])
plt.title("Scores by Teams in 4 Rounds")
plt.show()

2.3.2 case 2

import matplotlib.pyplot as plt
import pandas as pd

# Reading the tips.csv file
data = pd.read_csv('tips.csv')

# initializing the data
x = data['day']
y = data['total_bill']

# plotting the data
plt.bar(x, y, color='green', edgecolor='blue', 
		linewidth=2)

# Adding title to the plot
plt.title("Tips Dataset")

# Adding label on the y-axis
plt.ylabel('Total Bill')

# Adding label on the x-axis
plt.xlabel('Day')

plt.show()

2.3.3 case 3

# importing package
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# create data
df = pd.DataFrame([['A', 10, 20, 10, 26], ['B', 20, 25, 15, 21], ['C', 12, 15, 19, 6],
				['D', 10, 18, 11, 19]],
				columns=['Team', 'Round 1', 'Round 2', 'Round 3', 'Round 4'])
# view data
print(df)

# plot data in stack manner of bar type
df.plot(x='Team', kind='bar', stacked=True,
		title='Stacked Bar Graph by dataframe')
plt.show()

2.3.4 case 4

# importing packages 
import pandas as pd 
import matplotlib.pyplot as plt 

# load dataset 
df = pd.read_excel("Hours.xlsx") 

# view dataset 
print(df) 

# plot a Stacked Bar Chart using matplotlib 
df.plot( 
	x = 'Name', 
	kind = 'barh', 
	stacked = True, 
	title = 'Stacked Bar Graph', 
	mark_right = True) 

2.3.5 case 5

# importing packages 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

# load dataset 
df = pd.read_excel("Hours.xlsx") 

# view dataset 
print(df) 

# plot a Stacked Bar Chart using matplotlib 
df.plot( 
x = 'Name', 
kind = 'barh', 
stacked = True, 
title = 'Percentage Stacked Bar Graph', 
mark_right = True) 

df_total = df["Studied"] + df["Slept"] + df["Other"] 
df_rel = df[df.columns[1:]].div(df_total, 0)*100

for n in df_rel: 
	for i, (cs, ab, pc) in enumerate(zip(df.iloc[:, 1:].cumsum(1)[n], 
										df[n], df_rel[n])): 
		plt.text(cs - ab / 2, i, str(np.round(pc, 1)) + '%', 
				va = 'center', ha = 'center')

2.3.6 cae 6

# importing packages 
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

# load dataset 
df = pd.read_xlsx("Hours.xlsx") 

# view dataset 
print(df) 

# plot a Stacked Bar Chart using matplotlib 
df.plot( 
x = 'Name', 
kind = 'barh', 
stacked = True, 
title = 'Percentage Stacked Bar Graph', 
mark_right = True) 

df_total = df["Studied"] + df["Slept"] + df["Other"] 
df_rel = df[df.columns[1:]].div(df_total, 0) * 100

for n in df_rel: 
	for i, (cs, ab, pc) in enumerate(zip(df.iloc[:, 1:].cumsum(1)[n], 
										df[n], df_rel[n])): 
		plt.text(cs - ab / 2, i, str(np.round(pc, 1)) + '%', 
				va = 'center', ha = 'center', rotation = 20, fontsize = 8)

2.3.7 case 7

# import packages
import numpy as np
import matplotlib.pyplot as plt

# create data
A = np.array([3,6,9,4,2,5])
B = np.array([2,8,1,9,7,3])
X = np.arange(6)

# plot the bars
plt.barh(X, A, align='center',
		alpha=0.9, color = 'y')

plt.barh(X, -B, align='center', 
		alpha=0.6, color = 'c')

plt.grid()
plt.title("Back-to-Back Bar Chart")
plt.ylabel("Indexes")
plt.xlabel("Values")
plt.show()

2.3.8 case 8

# import packages
import numpy as np
import matplotlib.pyplot as plt

# create data
A = np.array([3,6,9,4,2,5])
X = np.arange(6)

# plot the bars
plt.bar(X, A, color = 'r')
plt.bar(X, -A, color = 'b')
plt.title("Back-to-Back Bar Chart")
plt.show()

2.3.9 case 9

import pandas as pd
from matplotlib import pyplot as plt

# Read CSV into pandas
data = pd.read_csv(r"cars.csv")
data.head()
df = pd.DataFrame(data)

name = df['car'].head(12)
price = df['price'].head(12)

# Figure Size
fig, ax = plt.subplots(figsize =(16, 9))

# Horizontal Bar Plot
ax.barh(name, price)

# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
	ax.spines[s].set_visible(False)

# Remove x, y Ticks
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')

# Add padding between axes and labels
ax.xaxis.set_tick_params(pad = 5)
ax.yaxis.set_tick_params(pad = 10)

# Add x, y gridlines
ax.grid(b = True, color ='grey',
		linestyle ='-.', linewidth = 0.5,
		alpha = 0.2)

# Show top values 
ax.invert_yaxis()

# Add annotation to bars
for i in ax.patches:
	plt.text(i.get_width()+0.2, i.get_y()+0.5, 
			str(round((i.get_width()), 2)),
			fontsize = 10, fontweight ='bold',
			color ='grey')

# Add Plot Title
ax.set_title('Sports car and their price in crore',
			loc ='left', )

# Add Text watermark
fig.text(0.9, 0.15, 'Jeeteshgavande30', fontsize = 12,
		color ='grey', ha ='right', va ='bottom',
		alpha = 0.7)

# Show Plot
plt.show()

2.3.10 case 10

import numpy as np 
import matplotlib.pyplot as plt 

# set width of bar 
barWidth = 0.25
fig = plt.subplots(figsize =(12, 8)) 

# set height of bar 
IT = [12, 30, 1, 8, 22] 
ECE = [28, 6, 16, 5, 10] 
CSE = [29, 3, 24, 25, 17] 

# Set position of bar on X axis 
br1 = np.arange(len(IT)) 
br2 = [x + barWidth for x in br1] 
br3 = [x + barWidth for x in br2] 

# Make the plot
plt.bar(br1, IT, color ='r', width = barWidth, 
		edgecolor ='grey', label ='IT') 
plt.bar(br2, ECE, color ='g', width = barWidth, 
		edgecolor ='grey', label ='ECE') 
plt.bar(br3, CSE, color ='b', width = barWidth, 
		edgecolor ='grey', label ='CSE') 

# Adding Xticks 
plt.xlabel('Branch', fontweight ='bold', fontsize = 15) 
plt.ylabel('Students passed', fontweight ='bold', fontsize = 15) 
plt.xticks([r + barWidth for r in range(len(IT))], 
		['2015', '2016', '2017', '2018', '2019'])

plt.legend()
plt.show() 

2.3.11 case 11

import numpy as np
import matplotlib.pyplot as plt

N = 5

boys = (20, 35, 30, 35, 27)
girls = (25, 32, 34, 20, 25)
boyStd = (2, 3, 4, 1, 2)
girlStd = (3, 5, 2, 3, 3)
ind = np.arange(N) 
width = 0.35

fig = plt.subplots(figsize =(10, 7))
p1 = plt.bar(ind, boys, width, yerr = boyStd)
p2 = plt.bar(ind, girls, width,
			bottom = boys, yerr = girlStd)

plt.ylabel('Contribution')
plt.title('Contribution by the teams')
plt.xticks(ind, ('T1', 'T2', 'T3', 'T4', 'T5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('boys', 'girls'))

plt.show()

2.3.12 case 12

import os
import numpy as np
import matplotlib.pyplot as plt

x = [0, 1, 2, 3, 4, 5, 6, 7]
y = [160, 167, 17, 130, 120, 40, 105, 70]
fig, ax = plt.subplots()
width = 0.75
ind = np.arange(len(y))

ax.barh(ind, y, width, color = "green")

for i, v in enumerate(y):
	ax.text(v + 3, i + .25, str(v), 
			color = 'blue', fontweight = 'bold')
plt.show()

2.4 hist chart

2.4.1 case 1

# importing pyplot for getting graph
import matplotlib.pyplot as plt

# importing numpy for getting array
import numpy as np

# importing scientific python
from scipy import stats

# list of values
x = [10, 40, 20, 10, 30, 10, 56, 45]

res = stats.cumfreq(x, numbins=4,
					defaultreallimits=(1.5, 5))

# generating random values
rng = np.random.RandomState(seed=12345)

# normalizing
samples = stats.norm.rvs(size=1000,
						random_state=rng)

res = stats.cumfreq(samples,
					numbins=25)

x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size,
								res.cumcount.size)

# specifying figure size
fig = plt.figure(figsize=(10, 4))

# adding sub plots
ax1 = fig.add_subplot(1, 2, 1)

# adding sub plots
ax2 = fig.add_subplot(1, 2, 2)

# getting histogram using hist function
ax1.hist(samples, bins=25,
		color="green")

# setting up the title
ax1.set_title('Histogram')

# cumulative graph
ax2.bar(x, res.cumcount, width=4, color="blue")

# setting up the title
ax2.set_title('Cumulative histogram')

ax2.set_xlim([x.min(), x.max()])

# display the figure(histogram)
plt.show()

2.4.2 case 2

import matplotlib.pyplot as plt
import pandas as pd

# Reading the tips.csv file
data = pd.read_csv('tips.csv')

# initializing the data
x = data['total_bill']

# plotting the data
plt.hist(x, bins=25, color='green', edgecolor='blue',
		linestyle='--', alpha=0.5)

# Adding title to the plot
plt.title("Tips Dataset")

# Adding label on the y-axis
plt.ylabel('Frequency')

# Adding label on the x-axis
plt.xlabel('Total Bill')

plt.show()

2.4.3 case 3

# importing libraries
import matplotlib.pyplot as plt

# giving two age groups data
age_g1 = [1, 3, 5, 10, 15, 17, 18, 16, 19,
		21, 23, 28, 30, 31, 33, 38, 32, 
		40, 45, 43, 49, 55, 53, 63, 66, 
		85, 80, 57, 75, 93, 95]

age_g2 = [6, 4, 15, 17, 19, 21, 28, 23, 31, 
		36, 39, 32, 50, 56, 59, 74, 79, 34, 
		98, 97, 95, 67, 69, 92, 45, 55, 77,
		76, 85]

# plotting first histogram
plt.hist(age_g1, label='Age group1', bins=14, alpha=.7, edgecolor='red')

# plotting second histogram
plt.hist(age_g2, label="Age group2", bins=14, alpha=.7, edgecolor='yellow')
plt.legend()

# Showing the plot using plt.show()
plt.show()

2.4.4 case 4

# importing libraries
import matplotlib.pyplot as plt

# giving two age groups data
age_g1 = [1, 3, 5, 10, 15, 17, 18, 16, 19, 21,
		23, 28, 30, 31, 33, 38, 32, 40, 45, 
		43, 49, 55, 53, 63, 66, 85, 80, 57, 
		75, 93, 95]

age_g2 = [6, 4, 15, 17, 19, 21, 28, 23, 31, 36,
		39, 32, 50, 56, 59, 74, 79, 34, 98, 97,
		95, 67, 69, 92, 45, 55, 77, 76, 85]

# plotting first histogram
plt.hist(age_g1, label='Age group1', alpha=.7, color='red')

# plotting second histogram
plt.hist(age_g2, label="Age group2", alpha=.5,
		edgecolor='black', color='yellow')
plt.legend()

# Showing the plot using plt.show()
plt.show()

2.4.5 case 5

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Generate random data for the histogram
data = np.random.randn(1000)

# Creating a customized histogram with a density plot
sns.histplot(data, bins=30, kde=True, color='lightgreen', edgecolor='red')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Density')
plt.title('Customized Histogram with Density Plot')

# Display the plot
plt.show()

2.4.6 case 6

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter


# Creating dataset
np.random.seed(23685752)
N_points = 10000
n_bins = 20

# Creating distribution
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
legend = ['distribution']

# Creating histogram
fig, axs = plt.subplots(1, 1,
						figsize =(10, 7), 
						tight_layout = True)


# Remove axes splines 
for s in ['top', 'bottom', 'left', 'right']: 
	axs.spines[s].set_visible(False) 

# Remove x, y ticks
axs.xaxis.set_ticks_position('none') 
axs.yaxis.set_ticks_position('none') 

# Add padding between axes and labels 
axs.xaxis.set_tick_params(pad = 5) 
axs.yaxis.set_tick_params(pad = 10) 

# Add x, y gridlines 
axs.grid(b = True, color ='grey', 
		linestyle ='-.', linewidth = 0.5, 
		alpha = 0.6) 

# Add Text watermark 
fig.text(0.9, 0.15, 'Jeeteshgavande30', 
		fontsize = 12, 
		color ='red',
		ha ='right',
		va ='bottom', 
		alpha = 0.7) 

# Creating histogram
N, bins, patches = axs.hist(x, bins = n_bins)

# Setting color
fracs = ((N**(1 / 5)) / N.max())
norm = colors.Normalize(fracs.min(), fracs.max())

for thisfrac, thispatch in zip(fracs, patches):
	color = plt.cm.viridis(norm(thisfrac))
	thispatch.set_facecolor(color)

# Adding extra features 
plt.xlabel("X-axis")
plt.ylabel("y-axis")
plt.legend(legend)
plt.title('Customized histogram')

# Show plot
plt.show()

2.4.7 case 7

import matplotlib.pyplot as plt
import numpy as np

# Generate random data for stacked histograms
data1 = np.random.randn(1000)
data2 = np.random.normal(loc=3, scale=1, size=1000)

# Creating a stacked histogram
plt.hist([data1, data2], bins=30, stacked=True, color=['cyan', 'Purple'], edgecolor='black')

# Adding labels and title
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Stacked Histogram')

# Adding legend
plt.legend(['Dataset 1', 'Dataset 2'])

# Display the plot
plt.show()

2.4.8 case 8

import matplotlib.pyplot as plt
import numpy as np

# Generate random 2D data for hexbin plot
x = np.random.randn(1000)
y = 2 * x + np.random.normal(size=1000)

# Creating a 2D histogram (hexbin plot)
plt.hexbin(x, y, gridsize=30, cmap='Blues')

# Adding labels and title
plt.xlabel('X values')
plt.ylabel('Y values')
plt.title('2D Histogram (Hexbin Plot)')

# Adding colorbar
plt.colorbar()

# Display the plot
plt.show()

2.4.9 case 9

# importing matplotlib
import matplotlib.pyplot as plt

# Storing set of values in
# x, height, error and colors for plotting the graph
x= range(4)
height=[ 3, 6, 5, 4]
error=[ 1, 5, 3, 2]
colors = ['red', 'green', 'blue', 'black']

# using tuple unpacking
# to grab fig and axes
fig, ax = plt.subplots()

# plotting the bar plot
ax.bar( x, height, alpha = 0.1)

# Zip function acts as an
# iterator for tuples so that
# we are iterating through 
# each set of values in a loop
for pos, y, err, colors in zip(x, height, 
							error, colors):

	ax.errorbar(pos, y, err, lw = 2,
				capsize = 4, capthick = 4, 
				color = colors)
	
# Showing the plotted error bar
# plot with different color 
plt.show()

2.4.10 case 10

# importing matplotlib package
import matplotlib.pyplot as plt

# importing the numpy package
import numpy as np

# Storing set of values in
# names, x, height, 
# error and colors for plotting the graph
names= ['Bijon', 'Sujit', 'Sayan', 'Saikat']
x=np.arange(4)
marks=[ 60, 90, 55, 46]
error=[ 11, 15, 5, 9]
colors = ['red', 'green', 'blue', 'black']

# using tuple unpacking
# to grab fig and axes
fig, ax = plt.subplots()

# plotting the bar plot
ax.bar(x, marks, alpha = 0.5,
	color = colors)

# Zip function acts as an
# iterator for tuples so that
# we are iterating through 
# each set of values in a loop
for pos, y, err, colors in zip(x, marks,
							error, colors):

	ax.errorbar(pos, y, err, lw = 2,
				capsize = 4, capthick = 4,
				color = colors)
	
# Showing the plotted error bar
# plot with different color 
ax.set_ylabel('Marks of the Students')

# Using x_ticks and x_labels
# to set the name of the
# students at each point
ax.set_xticks(x)
ax.set_xticklabels(names)
ax.set_xlabel('Name of the students')

# Showing the plot
plt.show()

2.4.11 case 11

# importing matplotlib
import matplotlib.pyplot as plt

# importing the numpy package
import numpy as np

# Storing set of values in
# names, x, height, error, 
# error1 and colors for plotting the graph
names= ['USA', 'India', 'England', 'China']
x=np.arange(4)
economy=[21.43, 2.87, 2.83, 14.34]
error=[1.4, 1.5, 0.5, 1.9]
error1=[0.5, 0.2, 0.6, 1]
colors = ['red', 'grey', 'blue', 'magenta']

# using tuple unpacking
# to grab fig and axes
fig, ax = plt.subplots()

# plotting the bar plot
ax.bar(x, economy, alpha = 0.5,
	color = colors)

# Zip function acts as an
# iterator for tuples so that
# we are iterating through 
# each set of values in a loop
for pos, y, err,err1, colors in zip(x, economy,
									error, error1, 
									colors):

	ax.errorbar(pos, y, err, err1, fmt = 'o',
				lw = 2, capsize = 4, capthick = 4,
				color = colors)
	
# Showing the plotted error bar
# plot with different color 
ax.set_ylabel('Economy(in trillions)')

# Using x_ticks and x_labels
# to set the name of the
# countries at each point
ax.set_xticks(x)
ax.set_xticklabels(names)
ax.set_xlabel('Name of the countries')

# Showing the plot
plt.show()

2.5 scatter chart

2.5.1 case 1

import matplotlib.pyplot as plt
import pandas as pd

# Reading the tips.csv file
data = pd.read_csv('tips.csv')

# initializing the data
x = data['day']
y = data['total_bill']

# plotting the data
plt.scatter(x, y, c=data['size'], s=data['total_bill'],
			marker='D', alpha=0.5)

# Adding title to the plot
plt.title("Tips Dataset")

# Adding label on the y-axis
plt.ylabel('Total Bill')

# Adding label on the x-axis
plt.xlabel('Day')

plt.show()

2.5.2 case 2

import matplotlib.pyplot as plt 

plt.style.use('seaborn') 
plt.figure(figsize=(10, 10)) 

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 
y = [3*i+2 for i in x] 
size = [n*100 for n in range(1, len(x)+1)] 
# print(size) 

plt.scatter(x, y, s=size, c='g') 
plt.title("Scatter Plot with increase in size of scatter points ", fontsize=22) 

plt.xlabel('X-axis', fontsize=20) 
plt.ylabel('Y-axis', fontsize=20) 

plt.xticks(x, fontsize=12) 
plt.yticks(y, fontsize=12) 

plt.show() 

2.6 pie chart

2.6.1 case 1

import matplotlib.pyplot as plt 

years = [2016, 2017, 2018, 2019, 2020] 
profit = [15, 19, 35, 14, 17] 

# Plotting the pie chart 
plt.pie(profit, labels = years, autopct = '%1.1f%%', 
		startangle = 90, 
		wedgeprops = {"edgecolor" : "black", 
					'linewidth': 2, 
					'antialiased': True}) 

# Equal aspect ratio ensures 
# that pie is drawn as a circle. 
plt.axis('equal') 

# Display the graph onto the screen 
plt.show() 

2.6.2 case 2

import matplotlib.pyplot as plt 

# the slices are ordered and 
# plotted counter-clockwise: 
product = 'Product A', 'Product B', 
			'Product C', 'Product D'
	
stock = [15, 30, 35, 20] 
explode = (0.1, 0, 0.1, 0) 

plt.pie(stock, explode = explode, 
		labels = product, autopct = '%1.1f%%', 
		shadow = True, startangle = 90, 
		wedgeprops= {"edgecolor":"black", 
					'linewidth': 3, 
					'antialiased': True}) 

# Equal aspect ratio ensures that 
# pie is drawn as a circle. 
plt.axis('equal') 

plt.show() 

2.6.3 case 3

import matplotlib.pyplot as plt 

# the slices are ordered and 
# plotted counter-clockwise: 
continents = ['Asia', 'Europe', 'North America', 
			'South America','Australia', 
			'Africa','Antarctica'] 

area = [25, 20, 15, 10,15,10,5] 
explode = (0.1, 0, 0.1, 0,0.1,0.1,0.1) 

plt.pie(area, explode = explode, labels = continents, 
		autopct = '%1.1f%%',startangle = 0, 
		wedgeprops = {"edgecolor" : "black", 
					'linewidth' : 2, 
					'antialiased': True}) 

# Equal aspect ratio ensures 
# that pie is drawn as a circle. 
plt.axis('equal') 

plt.show() 

2.7 3D graph

2.7.1 case 1

# Import libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# Creating dataset
z = np.random.randint(100, size =(50))
x = np.random.randint(80, size =(50))
y = np.random.randint(60, size =(50))

# Creating figure
fig = plt.figure(figsize = (10, 7))
ax = plt.axes(projection ="3d")

# Creating plot
ax.scatter3D(x, y, z, color = "green")
plt.title("simple 3D scatter plot")

# show plot
plt.show()

2.7.2 case 2

# Import libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# Creating dataset
z = 4 * np.tan(np.random.randint(10, size =(500))) + np.random.randint(100, size =(500))
x = 4 * np.cos(z) + np.random.normal(size = 500)
y = 4 * np.sin(z) + 4 * np.random.normal(size = 500)

# Creating figure
fig = plt.figure(figsize = (16, 9))
ax = plt.axes(projection ="3d")

# Add x, y gridlines 
ax.grid(b = True, color ='grey', 
		linestyle ='-.', linewidth = 0.3, 
		alpha = 0.2) 


# Creating color map
my_cmap = plt.get_cmap('hsv')

# Creating plot
sctt = ax.scatter3D(x, y, z,
					alpha = 0.8,
					c = (x + y + z), 
					cmap = my_cmap, 
					marker ='^')

plt.title("simple 3D scatter plot")
ax.set_xlabel('X-axis', fontweight ='bold') 
ax.set_ylabel('Y-axis', fontweight ='bold') 
ax.set_zlabel('Z-axis', fontweight ='bold')
fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)

# show plot
plt.show()

2.7.3 case 3

# Import libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# Creating dataset
x = np.outer(np.linspace(-3, 3, 32), np.ones(32))
y = x.copy().T # transpose
z = (np.sin(x **2) + np.cos(y **2) )

# Creating figure
fig = plt.figure(figsize =(14, 9))
ax = plt.axes(projection ='3d')

# Creating plot
ax.plot_surface(x, y, z)

# show plot
plt.show()

2.7.4 case 4

# Import libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

# Creating dataset
x = np.outer(np.linspace(-3, 3, 32), np.ones(32))
y = x.copy().T # transpose
z = (np.sin(x **2) + np.cos(y **2) )

# Creating figure
fig = plt.figure(figsize =(14, 9))
ax = plt.axes(projection ='3d')

# Creating color map
my_cmap = plt.get_cmap('hot')

# Creating plot
surf = ax.plot_surface(x, y, z,
					cmap = my_cmap,
					edgecolor ='none')

fig.colorbar(surf, ax = ax,
			shrink = 0.5, aspect = 5)

ax.set_title('Surface plot')

# show plot
plt.show()

2.7.5 case 5

# Import libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

# Creating dataset
x = np.outer(np.linspace(-3, 3, 32), np.ones(32))
y = x.copy().T # transpose
z = (np.sin(x **2) + np.cos(y **2) )

# Creating figure
fig = plt.figure(figsize =(14, 9))
ax = plt.axes(projection ='3d')

# Creating color map
my_cmap = plt.get_cmap('hot')

# Creating plot
surf = ax.plot_surface(x, y, z, 
					rstride = 8,
					cstride = 8,
					alpha = 0.8,
					cmap = my_cmap)
cset = ax.contourf(x, y, z,
				zdir ='z',
				offset = np.min(z),
				cmap = my_cmap)
cset = ax.contourf(x, y, z,
				zdir ='x',
				offset =-5,
				cmap = my_cmap)
cset = ax.contourf(x, y, z, 
				zdir ='y',
				offset = 5,
				cmap = my_cmap)
fig.colorbar(surf, ax = ax, 
			shrink = 0.5,
			aspect = 5)

# Adding labels
ax.set_xlabel('X-axis')
ax.set_xlim(-5, 5)
ax.set_ylabel('Y-axis')
ax.set_ylim(-5, 5)
ax.set_zlabel('Z-axis')
ax.set_zlim(np.min(z), np.max(z))
ax.set_title('3D surface having 2D contour plot projections')

# show plot
plt.show()

2.7.6 case 6

# importing modules
from mpl_toolkits.mplot3d import axes3d
from matplotlib import pyplot 

# creating the visualization
fig = pyplot.figure()
wf = fig.add_subplot(111, projection='3d')
x, y, z = axes3d.get_test_data(0.05)
wf.plot_wireframe(x,y,z, rstride=2, 
				cstride=2,color='green')

# displaying the visualization
wf.set_title('Example 1')
pyplot.show()

2.7.7 case 7

from mpl_toolkits import mplot3d 
import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import cm 
import math 

x = [i for i in range(0, 200, 100)] 
y = [i for i in range(0, 200, 100)] 

X, Y = np.meshgrid(x, y) 
Z = [] 
for i in x: 
	t = [] 
	for j in y: 
		t.append(math.tan(math.sqrt(i*2+j*2))) 
	Z.append(t) 

fig = plt.figure() 
ax = plt.axes(projection='3d') 
ax.contour3D(X, Y, Z, 50, cmap=cm.cool) 
ax.set_xlabel('a') 
ax.set_ylabel('b') 
ax.set_zlabel('c') 
ax.set_title('3D contour for tan') 
plt.show() 

2.7.8 case 8

# Import libraries
from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 
import numpy as np 


# Creating radii and angles
r = np.linspace(0.125, 1.0, 100) 
a = np.linspace(0, 2 * np.pi, 
				100,
				endpoint = False) 

# Repeating all angles for every radius 
a = np.repeat(a[..., np.newaxis], 100, axis = 1) 

# Creating dataset
x = np.append(0, (r * np.cos(a))) 
y = np.append(0, (r * np.sin(a))) 
z = (np.sin(x ** 4) + np.cos(y ** 4)) 

# Creating figure
fig = plt.figure(figsize =(16, 9)) 
ax = plt.axes(projection ='3d') 

# Creating color map
my_cmap = plt.get_cmap('hot')

# Creating plot
trisurf = ax.plot_trisurf(x, y, z,
						cmap = my_cmap,
						linewidth = 0.2, 
						antialiased = True,
						edgecolor = 'grey') 
fig.colorbar(trisurf, ax = ax, shrink = 0.5, aspect = 5)
ax.set_title('Tri-Surface plot')

# Adding labels
ax.set_xlabel('X-axis', fontweight ='bold') 
ax.set_ylabel('Y-axis', fontweight ='bold') 
ax.set_zlabel('Z-axis', fontweight ='bold')
	
# show plot
plt.show()

2.7.9 case 9

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 

a = np.array([1, 2, 3]) 
b = np.array([4, 5, 6, 7]) 

a, b = np.meshgrid(a, b) 

# surface plot for a**2 + b**2 
a = np.arange(-1, 1, 0.02) 
b = a 
a, b = np.meshgrid(a, b) 

fig = plt.figure() 
axes = fig.gca(projection ='3d') 
axes.plot_surface(a, b, a**2 + b**2) 

plt.show() 

2.7.10 case 10

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 

a = np.array([1, 2, 3]) 
b = np.array([4, 5, 6, 7]) 

a, b = np.meshgrid(a, b) 

# surface plot for a**2 + b**2 
a = np.arange(-1, 1, 0.02) 
b = a 
a, b = np.meshgrid(a, b) 

fig = plt.figure() 
axes = fig.gca(projection ='3d') 
axes.contour(a, b, a**2 + b**2) 

plt.show() 

2.7.11 case 11

""" change view angle"""
from mpl_toolkits import mplot3d 
import numpy as np 
import matplotlib.pyplot as plt 

fig = plt.figure(figsize = (8,8)) 
ax = plt.axes(projection = '3d') 

# Data for a three-dimensional line 
z = np.linspace(0, 15, 1000) 
x = np.sin(z) 
y = np.cos(z) 
ax.plot3D(x, y, z, 'green') 

ax.view_init(-140, 60) 

plt.show()

2.7.12 case 12

""" change view angle"""
import numpy as np 
from matplotlib import pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D 
from math import sin, cos 

fig = plt.figure(figsize = (8,8)) 
ax = fig.add_subplot(111, projection = '3d') 

#creating Datasheet 
y = np.linspace(-1, 1, 200) 
x = np.linspace(-1, 1, 200) 
x,y = np.meshgrid(x, y) 

#set z values 
z = x + y 

# rotate the samples by changing the value of 'a' 
a = 50

t = np.transpose(np.array([x, y, z]), ( 1, 2, 0)) 

m = [[cos(a), 0, sin(a)],[0, 1, 0], 
	[-sin(a), 0, cos(a)]] 

X,Y,Z = np.transpose(np.dot(t, m), (2, 0, 1)) 

#label axes 
ax.set_xlabel('X') 
ax.set_ylabel('Y') 
ax.set_zlabel('Z') 

#plot figure 
ax.plot_surface(X,Y,Z, alpha = 0.5, 
				color = 'red') 

plt.show()

2.7.13 case 13

from mpl_toolkits import mplot3d 
import numpy as np 
import matplotlib.pyplot as plt 

fig = plt.figure(figsize = (8, 8)) 
ax = plt.axes(projection = '3d') 

# Data for a three-dimensional line 
z = np.linspace(0, 15, 1000) 
x = np.sin(z) 
y = np.cos(z) 
ax.plot3D(x, y, z, 'green') 

ax.view_init(120, 30) 

plt.show()

2.7 box graph

2.7.1 case 1

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10, 5), 
	columns =['A', 'B', 'C', 'D', 'E'])

df.plot.box()
plt.show()

2.7.2 case 2

import matplotlib.pyplot as plt
import numpy as np

from matplotlib.patches import Polygon

# Fixing random state for reproducibility
np.random.seed(19680801)

# fake up some data
spread = np.random.rand(50) * 100
center = np.ones(25) * 50
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
data = np.concatenate((spread, center, flier_high, flier_low))

fig, axs = plt.subplots(2, 3)

# basic plot
axs[0, 0].boxplot(data)
axs[0, 0].set_title('basic plot')

# notched plot
axs[0, 1].boxplot(data, 1)
axs[0, 1].set_title('notched plot')

# change outlier point symbols
axs[0, 2].boxplot(data, 0, 'gD')
axs[0, 2].set_title('change outlier\npoint symbols')

# don't show outlier points
axs[1, 0].boxplot(data, 0, '')
axs[1, 0].set_title("don't show\noutlier points")

# horizontal boxes
axs[1, 1].boxplot(data, 0, 'rs', 0)
axs[1, 1].set_title('horizontal boxes')

# change whisker length
axs[1, 2].boxplot(data, 0, 'rs', 0, 0.75)
axs[1, 2].set_title('change whisker length')

fig.subplots_adjust(left=0.08, right=0.98, bottom=0.05, top=0.9,
                    hspace=0.4, wspace=0.3)

# fake up some more data
spread = np.random.rand(50) * 100
center = np.ones(25) * 40
flier_high = np.random.rand(10) * 100 + 100
flier_low = np.random.rand(10) * -100
d2 = np.concatenate((spread, center, flier_high, flier_low))
# Making a 2-D array only works if all the columns are the
# same length.  If they are not, then use a list instead.
# This is actually more efficient because boxplot converts
# a 2-D array into a list of vectors internally anyway.
data = [data, d2, d2[::2]]

# Multiple box plots on one Axes
fig, ax = plt.subplots()
ax.boxplot(data)

plt.show()

2.7.3 case 3

random_dists = ['Normal(1, 1)', 'Lognormal(1, 1)', 'Exp(1)', 'Gumbel(6, 4)',
                'Triangular(2, 9, 11)']
N = 500

norm = np.random.normal(1, 1, N)
logn = np.random.lognormal(1, 1, N)
expo = np.random.exponential(1, N)
gumb = np.random.gumbel(6, 4, N)
tria = np.random.triangular(2, 9, 11, N)

# Generate some random indices that we'll use to resample the original data
# arrays. For code brevity, just use the same random indices for each array
bootstrap_indices = np.random.randint(0, N, N)
data = [
    norm, norm[bootstrap_indices],
    logn, logn[bootstrap_indices],
    expo, expo[bootstrap_indices],
    gumb, gumb[bootstrap_indices],
    tria, tria[bootstrap_indices],
]

fig, ax1 = plt.subplots(figsize=(10, 6))
fig.canvas.manager.set_window_title('A Boxplot Example')
fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)

bp = ax1.boxplot(data, notch=False, sym='+', vert=True, whis=1.5)
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='red', marker='+')

# Add a horizontal grid to the plot, but make it very light in color
# so we can use it for reading data values but not be distracting
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
               alpha=0.5)

ax1.set(
    axisbelow=True,  # Hide the grid behind plot objects
    title='Comparison of IID Bootstrap Resampling Across Five Distributions',
    xlabel='Distribution',
    ylabel='Value',
)

# Now fill the boxes with desired colors
box_colors = ['darkkhaki', 'royalblue']
num_boxes = len(data)
medians = np.empty(num_boxes)
for i in range(num_boxes):
    box = bp['boxes'][i]
    box_x = []
    box_y = []
    for j in range(5):
        box_x.append(box.get_xdata()[j])
        box_y.append(box.get_ydata()[j])
    box_coords = np.column_stack([box_x, box_y])
    # Alternate between Dark Khaki and Royal Blue
    ax1.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2]))
    # Now draw the median lines back over what we just filled in
    med = bp['medians'][i]
    median_x = []
    median_y = []
    for j in range(2):
        median_x.append(med.get_xdata()[j])
        median_y.append(med.get_ydata()[j])
        ax1.plot(median_x, median_y, 'k')
    medians[i] = median_y[0]
    # Finally, overplot the sample averages, with horizontal alignment
    # in the center of each box
    ax1.plot(np.average(med.get_xdata()), np.average(data[i]),
             color='w', marker='*', markeredgecolor='k')

# Set the axes ranges and axes labels
ax1.set_xlim(0.5, num_boxes + 0.5)
top = 40
bottom = -5
ax1.set_ylim(bottom, top)
ax1.set_xticklabels(np.repeat(random_dists, 2),
                    rotation=45, fontsize=8)

# Due to the Y-axis scale being different across samples, it can be
# hard to compare differences in medians across the samples. Add upper
# X-axis tick labels with the sample medians to aid in comparison
# (just use two decimal places of precision)
pos = np.arange(num_boxes) + 1
upper_labels = [str(round(s, 2)) for s in medians]
weights = ['bold', 'semibold']
for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):
    k = tick % 2
    ax1.text(pos[tick], .95, upper_labels[tick],
             transform=ax1.get_xaxis_transform(),
             horizontalalignment='center', size='x-small',
             weight=weights[k], color=box_colors[k])

# Finally, add a basic legend
fig.text(0.80, 0.08, f'{N} Random Numbers',
         backgroundcolor=box_colors[0], color='black', weight='roman',
         size='x-small')
fig.text(0.80, 0.045, 'IID Bootstrap Resample',
         backgroundcolor=box_colors[1],
         color='white', weight='roman', size='x-small')
fig.text(0.80, 0.015, '*', color='white', backgroundcolor='silver',
         weight='roman', size='medium')
fig.text(0.815, 0.013, ' Average Value', color='black', weight='roman',
         size='x-small')

plt.show()

2.7.4 case 4

def fake_bootstrapper(n):
    """
    This is just a placeholder for the user's method of
    bootstrapping the median and its confidence intervals.

    Returns an arbitrary median and confidence interval packed into a tuple.
    """
    if n == 1:
        med = 0.1
        ci = (-0.25, 0.25)
    else:
        med = 0.2
        ci = (-0.35, 0.50)
    return med, ci

inc = 0.1
e1 = np.random.normal(0, 1, size=500)
e2 = np.random.normal(0, 1, size=500)
e3 = np.random.normal(0, 1 + inc, size=500)
e4 = np.random.normal(0, 1 + 2*inc, size=500)

treatments = [e1, e2, e3, e4]
med1, ci1 = fake_bootstrapper(1)
med2, ci2 = fake_bootstrapper(2)
medians = [None, None, med1, med2]
conf_intervals = [None, None, ci1, ci2]

fig, ax = plt.subplots()
pos = np.arange(len(treatments)) + 1
bp = ax.boxplot(treatments, sym='k+', positions=pos,
                notch=True, bootstrap=5000,
                usermedians=medians,
                conf_intervals=conf_intervals)

ax.set_xlabel('treatment')
ax.set_ylabel('response')
plt.setp(bp['whiskers'], color='k', linestyle='-')
plt.setp(bp['fliers'], markersize=3.0)
plt.show()

2.8 heatmap

2.8.1 case 1

# Program to plot 2-D Heat map 
# using matplotlib.pyplot.imshow() method 
import numpy as np 
import matplotlib.pyplot as plt 

data = np.random.random((12, 12)) 
plt.imshow(data, cmap='autumn') 

plt.title("Heatmap with different color") 
plt.show() 

2.8.2 case 2

# Program to plot 2-D Heat map 
# using matplotlib.pyplot.imshow() method 
import numpy as np 
import matplotlib.pyplot as plt 

data = np.random.random(( 12 , 12 )) 
plt.imshow( data ) 

plt.title( "2-D Heat Map" ) 
plt.show() 

2.8.3 case 3

data = np.random.random((12, 12)) 
plt.imshow(data, cmap='autumn', interpolation='nearest') 

# Add colorbar 
plt.colorbar() 

plt.title("Heatmap with color bar") 
plt.show() 

2.8.4 case 4

import matplotlib.colors as colors 

# Generate random data 
data = np.random.randint(0, 100, size=(8, 8)) 

# Create a custom color map 
# with blue and green colors 
colors_list = ['#0099ff', '#33cc33'] 
cmap = colors.ListedColormap(colors_list) 

# Plot the heatmap with custom colors and annotations 
plt.imshow(data, cmap=cmap, vmin=0,\ 
		vmax=100, extent=[0, 8, 0, 8]) 
for i in range(8): 
	for j in range(8): 
		plt.annotate(str(data[i][j]), xy=(j+0.5, i+0.5), 
					ha='center', va='center', color='white') 

# Add colorbar 
cbar = plt.colorbar(ticks=[0, 50, 100]) 
cbar.ax.set_yticklabels(['Low', 'Medium', 'High']) 

# Set plot title and axis labels 
plt.title("Customized heatmap with annotations") 
plt.xlabel("X-axis") 
plt.ylabel("Y-axis") 

# Display the plot 
plt.show() 

2.8.5 case 5

import pandas as pd 
import matplotlib.pyplot as plt 
from matplotlib import colors 

df = pd.read_csv("gold_price_data.csv") 

# Calculate correlation between columns 
corr_matrix = df.corr() 

# Create a custom color 
# map with blue and green colors 
colors_list = ['#FF5733', '#FFC300'] 
cmap = colors.ListedColormap(colors_list) 

# Plot the heatmap with custom colors and annotations 
plt.imshow(corr_matrix, cmap=cmap, vmin=0\ 
		, vmax=1, extent=[0, 5, 0, 5]) 
for i in range(5): 
	for j in range(5): 
		plt.annotate(str(round(corr_matrix.values[i][j], 2)),\ 
					xy=(j+0.25, i+0.7), 
					ha='center', va='center', color='white') 

# Add colorbar 
cbar = plt.colorbar(ticks=[0, 0.5, 1]) 
cbar.ax.set_yticklabels(['Low', 'Medium', 'High']) 

# Set plot title and axis labels 
plt.title("Correlation Matrix Of The Dataset") 
plt.xlabel("Features") 
plt.ylabel("Features") 

# Set tick labels 
plt.xticks(range(len(corr_matrix.columns)),\ 
		corr_matrix.columns, rotation=90) 
plt.yticks(range(len(corr_matrix.columns)), 
		corr_matrix.columns) 

# Display the plot 
plt.show()

2.8.6 case 6

# importing the modules 
import numpy as np 
import seaborn as sns 
import matplotlib.pyplot as plt 
	
# generating 2-D 10x10 matrix of random numbers 
# from 1 to 100 
data = np.random.randint(low=1, 
						high=100, 
						size=(10, 10)) 
	
# plotting the heatmap 
hm = sns.heatmap(data=data, 
				annot=True) 
	
# displaying the plotted heatmap 
plt.show()

2.9 save fig

2.9.1 case 1

import matplotlib.pyplot as plt

# Creating data
year = ['2010', '2002', '2004', '2006', '2008']
production = [25, 15, 35, 30, 10]

# Plotting barchart
plt.bar(year, production)

# Saving the figure.
plt.savefig("output.jpg")

# Saving figure by changing parameter values
plt.savefig("output1", facecolor='y', bbox_inches="tight",
			pad_inches=0.3, transparent=True)

2.9.2 case 2

import matplotlib.pyplot as plt

# Creating data
year = ['2010', '2002', '2004', '2006', '2008']
production = [25, 15, 35, 30, 10]

# Plotting barchart
plt.bar(year, production)

# Saving the figure.
plt.savefig("output.jpg")

# Saving figure by changing parameter values
plt.savefig("output1", facecolor='y', bbox_inches="tight",
			pad_inches=0.3, transparent=True)

2.10 pandas绘图

2.10.1 case 1

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
								'1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), 
index = ts.index, columns = list('ABCD'))

df = df.cumsum()
plt.figure()
df.plot()
plt.show()

2.10.2 case 2

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
								'1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), 
index = ts.index, columns = list('ABCD'))

df = df.cumsum()
plt.figure()
df.plot()
plt.show()

2.10.3 case 3

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
								'1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), index = ts.index,
									columns = list('ABCD'))

df3 = pd.DataFrame(np.random.randn(1000, 2),
			columns =['B', 'C']).cumsum()

df3['A'] = pd.Series(list(range(len(df))))
df3.plot(x ='A', y ='B')
plt.show()

2.10.4 case 4

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

ts = pd.Series(np.random.randn(1000), index = pd.date_range(
								'1/1/2000', periods = 1000))

df = pd.DataFrame(np.random.randn(1000, 4), index = ts.index,
									columns = list('ABCD'))

df3 = pd.DataFrame(np.random.randn(1000, 2),
			columns =['B', 'C']).cumsum()

df3['A'] = pd.Series(list(range(len(df))))
df3.iloc[5].plot.bar()
plt.axhline(0, color ='k')

plt.show()

2.10.5 case 5

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 
					'b': np.random.randn(1000), 
					'c': np.random.randn(1000) - 1},
						columns =['a', 'b', 'c'])
plt.figure()

df4.plot.hist(alpha = 0.5)
plt.show()

2.10.6 case 6

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10, 5), 
	columns =['A', 'B', 'C', 'D', 'E'])

df.plot.area()
plt.show()

2.10.7 case 7

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(500, 4),
		columns =['a', 'b', 'c', 'd'])

df.plot.scatter(x ='a', y ='b')
plt.show()

2.10.8 case 8

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000, 2), columns =['a', 'b'])

df['a'] = df['a'] + np.arrange(1000)
df.plot.hexbin(x ='a', y ='b', gridsize = 25)
plt.show()

2.10.9 case 9

# importing libraries
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

series = pd.Series(3 * np.random.rand(4),
index =['a', 'b', 'c', 'd'], name ='series')

series.plot.pie(figsize =(4, 4))
plt.show()

2.10.10 case 10

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com / plotly / datasets / 718417069ead87650b90472464c7565dc8c2cb1c / sunburst-coffee-flavors-complete.csv')

fig = go.Figure()

fig.add_trace(go.Sunburst(
	ids = df.ids,
	labels = df.labels,
	parents = df.parents,
	domain = dict(column = 0)
))

fig.show()

2.11 error plot

2.11.1 case 1

# importing matplotlib
import matplotlib.pyplot as plt 


# making a simple plot
x =[1, 2, 3, 4, 5, 6, 7]
y =[1, 2, 1, 2, 1, 2, 1]

# creating error
y_error = 0.2

# plotting graph
plt.plot(x, y)

plt.errorbar(x, y,
			yerr = y_error,
			fmt ='o')

2.11.2 case 2

# importing matplotlib
import matplotlib.pyplot as plt 

# making a simple plot
x =[1, 2, 3, 4, 5, 6, 7]
y =[1, 2, 1, 2, 1, 2, 1]

# creating error
x_error = 0.5

# plotting graph
plt.plot(x, y)
plt.errorbar(x, y,
			xerr = x_error,
			fmt ='o')

2.11.3 case 3

# importing matplotlib
import matplotlib.pyplot as plt 


# making a simple plot
x =[1, 2, 3, 4, 5, 6, 7]
y =[1, 2, 1, 2, 1, 2, 1]

# creating error
x_error = 0.5
y_error = 0.3

# plotting graph
plt.plot(x, y)
plt.errorbar(x, y, 
			yerr = y_error, 
			xerr = x_error, 
			fmt ='o')

2.11.4 case 4

# importing matplotlib 
import matplotlib.pyplot as plt


# making a simple plot
x =[1, 2, 3, 4, 5]
y =[1, 2, 1, 2, 1]

# creating error
y_errormin =[0.1, 0.5, 0.9,
			0.1, 0.9]
y_errormax =[0.2, 0.4, 0.6, 
			0.4, 0.2]

x_error = 0.5
y_error =[y_errormin, y_errormax]

# plotting graph
# plt.plot(x, y)
plt.errorbar(x, y,
			yerr = y_error,
			xerr = x_error, 
			fmt ='o')

2.11.5 case 5

# import require modules 
import numpy as np
import matplotlib.pyplot as plt


# defining our function 
x = np.arange(10)/10
y = (x + 0.1)**2

# defining our error 
y_error = np.linspace(0.05, 0.2, 10)

# plotting our function and 
# error bar
plt.plot(x, y)

plt.errorbar(x, y, yerr = y_error, fmt ='o')

3.动画

3.1 case 1

from matplotlib import pyplot as plt 
import numpy as np 
from matplotlib.animation import FuncAnimation 

# initializing a figure in 
# which the graph will be plotted 
fig = plt.figure() 

# marking the x-axis and y-axis 
axis = plt.axes(xlim =(0, 4), 
				ylim =(-2, 2)) 

# initializing a line variable 
line, = axis.plot([], [], lw = 3) 

# data which the line will 
# contain (x, y) 
def init(): 
	line.set_data([], []) 
	return line, 

def animate(i): 
	x = np.linspace(0, 4, 1000) 

	# plots a sine graph 
	y = np.sin(2 * np.pi * (x - 0.01 * i)) 
	line.set_data(x, y) 
	
	return line, 

anim = FuncAnimation(fig, animate, init_func = init, 
					frames = 200, interval = 20, blit = True) 


anim.save('continuousSineWave.mp4', 
		writer = 'ffmpeg', fps = 30) 

3.2 case 2

import matplotlib.animation as animation 
import matplotlib.pyplot as plt 
import numpy as np 


# creating a blank window 
# for the animation 
fig = plt.figure() 
axis = plt.axes(xlim =(-50, 50), 
				ylim =(-50, 50)) 

line, = axis.plot([], [], lw = 2) 

# what will our line dataset 
# contain? 
def init(): 
	line.set_data([], []) 
	return line, 

# initializing empty values 
# for x and y co-ordinates 
xdata, ydata = [], [] 

# animation function 
def animate(i): 
	# t is a parameter which varies 
	# with the frame number 
	t = 0.1 * i 
	
	# x, y values to be plotted 
	x = t * np.sin(t) 
	y = t * np.cos(t) 
	
	# appending values to the previously 
	# empty x and y data holders 
	xdata.append(x) 
	ydata.append(y) 
	line.set_data(xdata, ydata) 
	
	return line, 

# calling the animation function	 
anim = animation.FuncAnimation(fig, animate, init_func = init, 
							frames = 500, interval = 20, blit = True) 

# saves the animation in our desktop 
anim.save('growingCoil.mp4', writer = 'ffmpeg', fps = 30) 

"""
    anim = animation.FuncAnimation(fig, update_line, frames=len_frames,
        fargs=(data_args, plot_list),interval=100,repeat=False)
    anim.save("growingCoil.gif", writer='pillow')
"""