[introduction to Python project] create bar chart animation in Python

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Animation is a good way to make visualization more attractive and user attractive . It helps us show data visualization in a meaningful way .Python Help us use existing powerful Python Library to create animation visualization .Matplotlib Is a very popular data visualization Library , It is usually used for graphical representation of data and animation using built-in functions .

Use Matplotlib There are two ways to create animation ：

• Use pause() function
• Use FuncAnimation() function

Method 1 ： Use pause() function

On hold （） Of matplotlib Library pyplot The module is functionally used to pause the interval seconds mentioned for the parameter . Consider the following example , We will use matplotlib Create a simple linear graph and display animation in it ：

establish 2 An array X and Y, And store from 1 To 100 Value . Use plot() Function to draw X and Y. Add at appropriate intervals pause() function Run the program , You will see the animation .

Python

from matplotlib import pyplot as plt
  
x = []
y = []
  
for i in range(100):
    x.append(i)
    y.append(i)
  
    #  mention  x  and  y  Limit to define its scope 
    plt.xlim(0, 100)
    plt.ylim(0, 100)
      
    #  Drawing graphics 
    plt.plot(x, y, color = 'green')
    plt.pause(0.01)
  
plt.show()
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Output ：

Again , You can also use pause() Function to create animation in various drawings .

Method 2 ： Use FuncAnimation() function

This FuncAnimation() Functions do not create their own animation , Instead, we create animation from a series of graphics we pass .

grammar : FuncAnimation(figure, animation_function, frames=None, init_func=None, fargs=None, save_count=None, *, cache_frame_data=True, **kwargs)

Now you can use FuncAnimation Function to animate multiple types ：

Linear graph animation ：

In this case , We will create a simple linear graph , It displays an animation of a line . Again , Use FuncAnimation, We can create many types of animated visual representations . We just need to define our animation in a function , Then pass it to with the appropriate parameters FuncAnimation.

Python

from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
import numpy as np
  
x = []
y = []
  
figure, ax = plt.subplots()
  
#  Set up  x  and  y  Axis limitation 
ax.set_xlim(0, 100)
ax.set_ylim(0, 12)
  
#  Draw a single graphic 
line,  = ax.plot(0, 0) 
  
def animation_function(i):
    x.append(i * 15)
    y.append(i)
  
    line.set_xdata(x)
    line.set_ydata(y)
    return line,
  
animation = FuncAnimation(figure,
                          func = animation_function,
                          frames = np.arange(0, 10, 0.1), 
                          interval = 10)
plt.show()
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Output ：

Python Bar chart in catch-up animation

In this example , We will create a simple bar graph animation , It displays the animation for each bar .

Python

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

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  
fig = plt.figure(figsize = (7,5))
axes = fig.add_subplot(1,1,1)
axes.set_ylim(0, 300)
palette = ['blue', 'red', 'green',
		'darkorange', 'maroon', 'black']

y1, y2, y3, y4, y5, y6 = [], [], [], [], [], []

def animation_function(i):
	y1 = i
	y2 = 6 * i
	y3 = 3 * i
	y4 = 2 * i
	y5 = 5 * i
	y6 = 3 * i

	plt.xlabel(" Country ")
	plt.ylabel(" Country GDP")
	
	plt.bar([" India ", " China ", " Germany ",
			" The United States ", " Canada ", " The British "],
			[y1, y2, y3, y4, y5, y6],
			color = palette)

plt.title(" Bar graph animation ")

animation = FuncAnimation(fig, animation_function,
						interval = 50)
plt.show()
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Output ：

Python Scatter animation in ：

In this case , We will use random functions in python Animated scatter in . We're going to traverse animation_func And draw during iteration x and y Random value of axis .

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

x = []
y = []
colors = []
fig = plt.figure(figsize=(7,5))

def animation_func(i):
	x.append(random.randint(0,100))
	y.append(random.randint(0,100))
	colors.append(np.random.rand(1))
	area = random.randint(0,30) * random.randint(0,30)
	plt.xlim(0,100)
	plt.ylim(0,100)
	plt.scatter(x, y, c = colors, s = area, alpha = 0.5)

animation = FuncAnimation(fig, animation_func,
						interval = 100)
plt.show()
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Output ：

* The bar chart catches up with the horizontal movement ：

ad locum , We will use the highest population in the city dataset to draw a bar graph contest . Different cities have different bar charts , The bar graph will start from 1990 Year to 2018 Annual iteration . I chose the country with the highest city from the most populous data set . The required data sets can be downloaded here ：city_populations

Python

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.animation import FuncAnimation
  
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']  
df = pd.read_csv('city_populations.csv',
                 usecols=['name', 'group', 'year', 'value'])
  
colors = dict(zip(['India','Europe','Asia',
                   'Latin America','Middle East',
                   'North America','Africa'],
                    ['#adb0ff', '#ffb3ff', '#90d595',
                     '#e48381', '#aafbff', '#f7bb5f', 
                     '#eafb50']))
  
group_lk = df.set_index('name')['group'].to_dict()
  
def draw_barchart(year):
    dff = df[df['year'].eq(year)].sort_values(by='value',
                                              ascending=True).tail(10)
    ax.clear()
    ax.barh(dff['name'], dff['value'],
            color=[colors[group_lk[x]] for x in dff['name']])
    dx = dff['value'].max() / 200
      
    for i, (value, name) in enumerate(zip(dff['value'],
                                          dff['name'])):
        ax.text(value-dx, i,     name,           
                size=14, weight=600,
                ha='right', va='bottom')
        ax.text(value-dx, i-.25, group_lk[name],
                size=10, color='#444444', 
                ha='right', va='baseline')
        ax.text(value+dx, i,     f'{value:,.0f}', 
                size=14, ha='left',  va='center')
         
    ax.text(1, 0.4, year, transform=ax.transAxes, 
            color='#777777', size=46, ha='right',
            weight=800)
    ax.text(0, 1.06, 'Population (thousands)',
            transform=ax.transAxes, size=12,
            color='#777777')
      
    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
    ax.xaxis.set_ticks_position('top')
    ax.tick_params(axis='x', colors='#777777', labelsize=12)
    ax.set_yticks([])
    ax.margins(0, 0.01)
    ax.grid(which='major', axis='x', linestyle='-')
    ax.set_axisbelow(True)
    ax.text(0, 1.12, ' from  1500  Year to  2018  The most populous city in the world in ',
            transform=ax.transAxes, size=24, weight=600, ha='left')
      
    ax.text(1, 0, 'by haiyong.site |  Hai Yong ', 
            transform=ax.transAxes, ha='right', color='#777777', 
            bbox=dict(facecolor='white', alpha=0.8, edgecolor='white'))
    plt.box(False)
    plt.show()
  
fig, ax = plt.subplots(figsize=(15, 8))
animator = FuncAnimation(fig, draw_barchart, 
                         frames = range(1990, 2019))
plt.show()
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Output ：

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