Use Matplotlib to draw a preliminary chart

Required libraries

• matplotlib In bag pyplot modular , For drawing .
• numpy modular , Used to complete numerical operations 、 Matrix operations, etc .
• pandas modular , For data manipulation and analysis 、 Import data files, etc .

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

notes ： All project examples in this article are in Jupyter Notebook Operating in the environment .

Draw a curve plot

General steps

• plt.figure Specify the image display scale and pixels

#  Specify the image display scale and pixels  5*3  Every unit 200 Pixels , namely 1000*600
plt.figure(figsize=(5,3), dpi=200)

• plt.style.use Customize canvas style

#  Customize canvas style 
plt.style.use('seaborn-whitegrid')

• plt.plot Draw curve line

#  Draw curve line , You can set the legend 、 Color 、 Line width 、 Marker points 、 Mark point size 、 Mark point edge color 、 Line type, etc 
plt.plot(x, y, label='blue', color='#6699ff', linewidth=4, marker='.', markersize=20, markeredgecolor='#3333ff', linestyle='-.')
#  Can pass fmt = '[marker][line][color]' Quickly set the marking point 、 Linetype and color , But be careful before keyword parameters 
plt.plot(x, z, 'g^-.', label='green')

• plt.titleplt.xlabelplt.ylabel Set chart title and axis title

#  Set chart title , You can set the title font 、 Font size （ The dictionary form ）
plt.title("First graph!",fontdict={
    'fontname':'Comic Sans MS','fontsize':20})
#  Set the axis title , You can set the axis font 、 Font size （ The dictionary form ）
plt.xlabel("X Axis",fontdict={
    'fontname':'Times New Roman','fontsize':10})
plt.ylabel("Y Axis",fontdict={
    'fontname':'Times New Roman','fontsize':10})

• plt.xticksplt.yticks Set up x,y Axis scale point

#  Set up x Axis scale point 
plt.xticks([1,2,3,4,5])
#  Set up y Axis scale point 
plt.yticks([2,3,4,5,6,7])

• plt.legend() Show Legend （ namely plt.plot Medium label）

#  Show Legend 
plt.legend()

• plt.savefig Save the picture

#  Save the picture 
plt.savefig('firstgraph.png',dpi=200)

• plt.show Display images

#  Display images 
plt.show()

Other commonly used numpy Method

• np.linspace(start, stop, num=50) Generate an array at equal intervals according to the start and end data , from start Start to stop end , common num A little bit （ Default 50 individual ）.
• np.sin(array) Calculate the sine of the array , Returns an array .

x = np.linspace(0, 10, 1000)
plt.plot(x,np.sin(x),'b--')
plt.show()

• np.arange(start, stop, step) And np.linspace() similar , Generate an array at equal intervals according to the start and end data , But it is often used for step value step When known .

x2 = np.arange(0, 4.5, 0.5)

#  Piecewise functions 
plt.plot(x2[:6], x2[:6]**2, 'b.-', label='x1')
plt.plot(x2[5:], x2[5:]**2, 'b.:', label='x2')

plt.legend()

plt.show()

Draw a column chart bar chart

General steps

• plt.figure Specify the image display scale and pixels

#  Specify the image display scale and pixels 
plt.figure(figsize=(6,4),dpi=200)

• plt.style.use Customize canvas style

#  Customize canvas style （ Default ）
plt.style.use('default')

• Set column name list and corresponding value list

labels = ['A','B','C']
values = [1, 4, 2]

• plt.bar Draw the column

#  Draw a column diagram and pass it to bars,bars[0] It means the first column  
bars = plt.bar(labels, values)

• bar.set_hatch Set the column pattern

#  Set the histogram pattern 
patterns = ['*','/','O']
for bar in bars:
    bar.set_hatch(patterns.pop(0))

• plt.title Set the histogram Title

#  Set the histogram Title 
plt.title("My Bars")

• plt.savefig Save the picture

#  Save the picture 
plt.savefig('bar.png',dpi=200)

• plt.show Display images

#  Display images 
plt.show()

Draw the pie chart pie chart

General steps

• pd.read_csv() Reading data （ It's used here pandas Method of library ） Return to table

fifa = pd.read_csv('fifa_data.csv')
fifa.head(5) #  View the data of the first five rows 

• plt.figure Specify the image display scale and pixels

#  Specify image pixels 
plt.figure(dpi=200)

• plt.style.use Customize canvas style

#  Customize canvas style 
plt.style.use('ggplot')

• fifa.loc[] Set conditions , Filter data , Return to table （ Remove the lines that do not meet the conditions ）
• fifa.loc[...].count Get the number of rows that meet the condition （ At this time, we only get the number of rows in each column , Need index 0 Subscripts get int Type line number ）

#  Set conditions , Filter data 
left = fifa.loc[fifa['Preferred Foot'] == 'Left'].count()[0]
right = fifa.loc[fifa['Preferred Foot'] == 'Right'].count()[0]

• Enter label parameters 、 Color parameters 、 Explosion parameters （0~1, The bigger it is, the farther it is from the center ） etc.

#  Tag parameters 
labels=['Left','Right']
#  Color parameters 
colors=['#0099ff','#ff6699']
#  Explosion parameters 
explode=(0.2, 0)

• plt.pie Draw the pie chart , Include label 、 Color and explosion parameters , Include the distance from the center point in the number （0~1, The bigger it is, the farther it is from the center ）, Contains the number of decimal places displayed by default

#  Draw the pie chart , The default display is two decimal places plus %( Escape characters are used here %%)
plt.pie([left, right],labels=labels, colors=colors, explode=explode, pctdistance=0.7, autopct='%.2f%%')

• plt.title Set pie chart title

#  Set pie chart title 
plt.title("Foot Preference of FIFA Players")

• plt.legend() Show Legend （ namely label）

#  Show Legend 
plt.legend()

• plt.savefig Save the picture

#  Save the picture 
plt.savefig('bar.png',dpi=200)

• plt.show Display images

#  Display images 
plt.show()

Draw a histogram hist

General steps

• pd.read_csv() Reading data （ It's used here pandas Method of library ） Return to table
• plt.figure Specify the image display scale and pixels
• Enter the range of boxes

plt.figure(figsize=(8,5), dpi=200)

• plt.hist Draw a histogram , Given the detected columns and boxes , The histogram will be automatically generated

plt.hist(fifa.Overall, bins=bins, color='#33ccff')

• plt.titleplt.xlabelplt.ylabel Set chart title and axis title

plt.title("Distribution of Player Skills in FIFA")
plt.xlabel("Number of Players")
plt.ylabel("Skill Level")

• plt.xticks Set up x Axis scale point

plt.xticks(bins)

• plt.savefig Save the picture

#  Save the picture 
plt.savefig('bar.png',dpi=200)

• plt.show Display images

#  Display images 
plt.show()