Examples of integration of Python function drawing and advanced algebra (2): Flashpoint function
1: X-axis bar graph drawing
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
'''
Use simple statistical functions to draw simple graphics
function bar()----------used to draw columns Chart
Function: Plot the respective characteristics of qualitative data on the x-axis
Call signature: plt.bar(x, y)
Parameter description:
x: The type of qualitative data marked on the x-axis
y: The number of each type of qualitative data
'''
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
# Use some simple data to draw
x = [1, 2, 3, 4, 5, 6, 7, 8]
y = [3, 1, 4, 5, 8, 9, 7, 2]
# Create a histogram
plt.bar(x, y, align="center", color=" c", tick_label=["q", "a", "c", "e", "r", "j", "b", "p"],hatch="/")
# Set the X|Y axis label text name
plt.xlabel("Box Number")
plt.ylabel("Box Weight (kg)")
plt.show()
2: Bar graph drawing operation effect
Three: Y-axis bar graph drawing
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
'''
Function: Plot the respective characteristics of qualitative data on the y-axis Call
signature: plt.barh(x, y)
parameters Description:
y: The type of qualitative data marked on the x-axis
x: The number of each type of qualitative data
'''
# Set the Chinese display font
mpl.rcParams["font.sans-serif"] = ["SimHei"]
# Set the normal display symbols
mpl.rcParams["axes.unicode_minus"] = False
# Use some simple data to draw
x = [1, 2, 3, 4, 5, 6, 7, 8]
y = [3, 1 , 4, 5, 8, 9, 7, 2]
# Create a bar chart
plt.barh(x, y, align="center", color="c", tick_label=["q", "a", "c ", "e", "r", "j", "b", "p"],hatch="/")
# Set the X|Y axis label text name
plt.xlabel("Box weight (kg)")
plt.ylabel("Box number")
plt.show()
Four: Y-axis bar graph drawing operation effect
Five: Histogram drawing
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
'''
Function: Plot the distribution characteristics of quantitative data on the X axis Call
signature: plt.hist(x )
Parameter description:
x: Draw the quantitative data input axis of the box on the x-axis
'''
# Set the Chinese display font
mpl.rcParams["font.sans-serif"] = ["SimHei"]
# Set the normal display symbol
mpl .rcParams["axes.unicode_minus"] = False
boxWeight = np.random.randint(0, 10, 50)
x = boxWeight
bins = range(0, 10, 1)
plt.hist(x, bins=bins,
color= "g",
histtype="bar",
rwidth=0.9, # This parameter controls the width of each column of the histogram
alpha=0.3,label="Respective characteristics of quantitative data plotted on the X axis")
# Set the X|Y axis label text namelabel="Plot separate characteristics of quantitative data on the X-axis")
plt.xlabel("Box weight (kg)")
plt.ylabel("Sales quantity (units)")
'''
Function function: Add the title of the graphic content
Call signature: plt.title("Sine function graphic")
Parameter description:
string: The title text of the graphic content
'''
plt .title("Drawing histogram")
plt.show()
Six: Histogram drawing operation effect
Seven: Drawing of pie chart
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
'''
Function function: Plot different types of percentages of qualitative data
Call signature: plt.pie(x)
Parameter description:
x: Percentages of different types of qualitative data
'''
# Set the Chinese display font
mpl.rcParams["font.sans-serif"] = ["SimHei"]
# Set the normal display symbol
mpl.rcParams["axes.unicode_minus"] = False
kinds = ['Simple box', 'Insulated box', 'Travel box', 'Sealed box']
colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3'] soldNums
= [0.05 , 0.45, 0.15, 0.35]
plt.pie(soldNums, labels=kinds, autopct="%3.1f%%", startangle=60, colors=colors)
plt.title("Proportion of sales quantity of different types of boxes")
plt.show()
Eight: Pie chart drawing operation effect
Nine: Drawing parallel column charts
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
'''
Side-by-side histogram drawing:
'''
# Set the Chinese display font
mpl.rcParams["font.sans -serif"] = ["SimHei"]
# Set normal display symbols
mpl.rcParams["axes.unicode_minus"] = False
# Provide some simple data
x = np.arange(5)
# Two sets of list data
Y1 = [100, 68, 79, 91, 82]
y2 = [120, 75, 70, 78, 85]
std_error1 = [7, 2, 6, 10, 5]
std_error2 = [5, 1, 4, 8, 9]
error_attr = dict(elinewidth=2, ecolor='black', capsize=3)
bar_width = 0.4
tick_label = ['Park 1', 'Park 2', 'Park 3', 'Park 4','Park Five']
# Create a stacked bar chart
plt.bar(x, y1,
bar_width,
color='#87CbEB',
align='center',
yerr=std_error1,
error_kw=error_attr,
label='2021')
plt.bar(x + bar_width, y2,
bar_width,
color='#CD5C5C',
align=' center',
yerr=std_error2,
error_kw=error_attr,
label='2022')
plt.xlabel("Mango planting area")
plt.ylabel("Harvest volume")
plt.xticks(x + bar_width / 2, tick_label)
plt .title("Single harvest volume in mango planting areas in different years")
plt.grid(True, axis='y', ls=":", color="yellow", alpha=0.8)
plt.legend()
plt.show()
10: Side-by-side histogram drawing operation effect
