Python数据可视化（趋势图、条形图、动态图）

雨点打透心脏的1/2处 2022-09-09 06:10 816阅读 0赞

# Python数据可视化 #

代码已放到[GitHub][]

# 1.使用matplotlib绘制最简单的趋势图 #

先查看一下数据

import pandas as pd
    unrate = pd.read_csv('unrate.csv')  # 使用pandas读取数据
    unrate['DATE'] = pd.to_datetime(unrate['DATE'])
    print(unrate.head(12)) # 查看前12条数据

DATE  VALUE
    0  1948-01-01    3.4
    1  1948-02-01    3.8
    2  1948-03-01    4.0
    3  1948-04-01    3.9
    4  1948-05-01    3.5
    5  1948-06-01    3.6
    6  1948-07-01    3.6
    7  1948-08-01    3.9
    8  1948-09-01    3.8
    9  1948-10-01    3.7
    10 1948-11-01    3.8
    11 1948-12-01    4.0

绘图

import matplotlib.pyplot as plt
    #%matplotlib inline
    #Using the different pyplot functions, we can create, customize, and display a plot. For example, we can use 2 functions to :
    first_twelve = unrate[0:12]
    plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16]

# 我们可以设置x轴的标签旋转
    plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
    plt.xticks(rotation=45)
    #print help(plt.xticks)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 1]

#xlabel(): 给x轴标签命名
    #ylabel(): 给y轴标签命名
    #title(): 绘图标题
    
    plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
    plt.xticks(rotation=90)
    plt.xlabel('Month')
    plt.ylabel('Unemployment Rate')
    plt.title('Monthly Unemployment Trends, 1948')
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_10_color_FFFFFF_t_70_g_se_x_16]

# 2.绘制多张图 #

## 2.1绘制子图 ##

### 例子1 ###

#add_subplot(first,second,index) first:第几行,second：第几列，index：第几个图.
    import pandas as pd
    import matplotlib.pyplot as plt
    import matplotlib.pyplot as plt
    fig = plt.figure()
    ax1 = fig.add_subplot(3,2,1)
    ax2 = fig.add_subplot(3,2,2)
    ax3 = fig.add_subplot(3,2,3)
    ax4 = fig.add_subplot(3,2,6)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_10_color_FFFFFF_t_70_g_se_x_16 1]

### 例子2.例如绘制两个子图 ###

import numpy as np
    fig = plt.figure()
    #fig = plt.figure(figsize=(3, 3))
    ax1 = fig.add_subplot(2,1,1)
    ax2 = fig.add_subplot(2,1,2)
    
    ax1.plot(np.random.randint(1,5,5), np.arange(5))
    ax2.plot(np.arange(10)*3, np.arange(10))
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 2]

## 2.2把多条曲线绘制到一个图里 ##

### 例子1.用不同颜色绘制多条曲线 ###

unrate = pd.read_csv('unrate.csv')
    unrate['DATE'] = pd.to_datetime(unrate['DATE'])
    
    fig = plt.figure(figsize=(10,6))
    unrate['MONTH'] = unrate['DATE'].dt.month
    unrate['MONTH'] = unrate['DATE'].dt.month
    colors = ['red', 'blue', 'green', 'orange', 'black']  #颜色
    for i in range(5):
        start_index = i*12
        end_index = (i+1)*12
        subset = unrate[start_index:end_index]
        plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i])
        
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_15_color_FFFFFF_t_70_g_se_x_16]

### 例子2.把标签显示出来 ###

fig = plt.figure(figsize=(10,6))
    colors = ['red', 'blue', 'green', 'orange', 'black']
    for i in range(5):
        start_index = i*12
        end_index = (i+1)*12
        subset = unrate[start_index:end_index]
        label = str(1948 + i)
        plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)
    plt.legend(loc='best')
    # plt.legend(loc='upper left')
    #print help(plt.legend)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_15_color_FFFFFF_t_70_g_se_x_16 1]

# 绘制条形图 #

## 例子1. ##

import pandas as pd
    # fandango_scores.csv为电影评分的数据，第一列是电影名称，其余列是评分公司对应的评分
    reviews = pd.read_csv('fandango_scores.csv')
    cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    norm_reviews = reviews[cols]
    print(norm_reviews[:1])

FILM  RT_user_norm  Metacritic_user_nom  \
    0  Avengers: Age of Ultron (2015)           4.3                 3.55   
    
       IMDB_norm  Fandango_Ratingvalue  Fandango_Stars  
    0        3.9                   4.5             5.0

import matplotlib.pyplot as plt
    from numpy import arange
    
    num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    
    # 取第一行的电影评分数据进行绘制条形图，loc[行索引，列索引]取行列对应的数据
    # 柱状图高度，即y轴
    bar_heights = norm_reviews.loc[0,num_cols].values
    print(bar_heights)
    # 柱状图的水平位置，即x轴
    bar_positions = arange(5) + 0.75
    print(bar_positions)
    fig, ax = plt.subplots()
    ax.bar(bar_positions, bar_heights, 0.5)
    plt.show()

[4.3 3.55 3.9 4.5 5.0]
    [0.75 1.75 2.75 3.75 4.75]

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 3]

## 例子2.竖状条形图 ##

# 默认x轴是整数值1,2,3,4,...
    # 我们可以使用 Axes.set_xticks() 来改变x轴的标签值:
    
    num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    bar_heights = norm_reviews.loc[0, num_cols].values
    bar_positions = arange(5) + 0.75
    tick_positions = range(1,6)
    fig, ax = plt.subplots()
    
    ax.bar(bar_positions, bar_heights, 0.5)
    ax.set_xticks(tick_positions)
    ax.set_xticklabels(num_cols, rotation=45)
    
    ax.set_xlabel('Rating Source')
    ax.set_ylabel('Average Rating')
    ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_10_color_FFFFFF_t_70_g_se_x_16 2]

## 例子3.横状条形图 ##

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    
    bar_widths = norm_reviews.loc[0, num_cols].values
    bar_positions = arange(5) + 0.75
    tick_positions = range(1,6)
    fig, ax = plt.subplots()
    ax.barh(bar_positions, bar_widths, 0.5)
    
    ax.set_yticks(tick_positions)
    ax.set_yticklabels(num_cols)
    ax.set_ylabel('Rating Source')
    ax.set_xlabel('Average Rating')
    ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_13_color_FFFFFF_t_70_g_se_x_16]

# 散点图 #

#Let's look at a plot that can help us visualize many points.
    fig, ax = plt.subplots()
    ax.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])
    ax.set_xlabel('Fandango')
    ax.set_ylabel('Rotten Tomatoes')
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_10_color_FFFFFF_t_70_g_se_x_16 3]

# 绘制直方图（频数图） #

## 例子1 ##

import pandas as pd
    import matplotlib.pyplot as plt
    reviews = pd.read_csv('fandango_scores.csv')
    cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
    norm_reviews = reviews[cols]
    
    fig, ax = plt.subplots()
    # 频数图，水平方向表示范围，高度表示在某个范围的个数
    ax.hist(norm_reviews['Fandango_Ratingvalue'])
    # bins=，可以指定多少个箱子，默认是10个
    ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)
    #ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 4]

# 绘制箱型图 #

不太常用，从下到上依次是最小值，1/4分位数,1/2分位数,3/4分位数,最大值

## 例子1 ##

fig, ax = plt.subplots()
    ax.boxplot(norm_reviews['RT_user_norm'])
    ax.set_xticklabels(['Rotten Tomatoes'])
    ax.set_ylim(0, 5)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 5]

## 例子2 ##

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
    fig, ax = plt.subplots()
    ax.boxplot(norm_reviews[num_cols].values)
    ax.set_xticklabels(num_cols, rotation=90)
    ax.set_ylim(0,5)
    plt.show()

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA5aSn54Gw54u85a2m57yW56iL_size_9_color_FFFFFF_t_70_g_se_x_16 6]

# 绘制动态图 #

## 例子1. ##

import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.animation as animation
    # jupyter运行，需要输入下面这个命令
    %matplotlib qt5
    
    N = 20
    plt.close() # 关闭打开的图形窗口
    def anni():
        fig = plt.figure()
        plt.ion() # 打开交互式绘图interactive
        for i in range(N):
            plt.cla()           # 清除原有图像
            plt.xlim(-0.2,20.4) # 设置x轴坐标范围
            plt.ylim(-1.2,1.2)  # 设置y轴坐标范围
            # 每当i增加的时候，增加自变量x的区间长度，可以理解为不断叠加绘图，所以每次循环之前都使用plt.cla()命令清除原有图像
            x = np.linspace(0,i+1,1000) 
            y = np.sin(x)
            plt.plot(x,y)
            plt.pause(0.1)
        # plt.ioff() #关闭交互式绘图
        
        plt.show()
    
    anni()

## 例子2. ##

fig, ax = plt.subplots()
    xdata, ydata = [], []
    ln, = plt.plot([], [], 'ro',animated=True)
    
    def init():
        ax.set_xlim(-np.pi,np.pi)
        ax.set_ylim(-1, 1)
        return ln,
    
    def update(frame):
        xdata.append(frame)
        ydata.append(np.sin(frame))
        ln.set_data(xdata, ydata)
        return ln,
    
    anim = animation.FuncAnimation(fig, update, frames=np.linspace(-np.pi,np.pi, 90),interval=10,
                        init_func=init,blit=True)
    anim.save('test2.gif',writer='pillow')
    plt.show()

![在这里插入图片描述][9d7d3a48db2044e48045aa03fd910016.gif]

## 例子3 ##

import numpy as np
    import matplotlib.pyplot as pl
    import matplotlib.animation as animation
    
    x = np.linspace(0, 10, 100)
    y = np.sin(x)
    
    fig, ax = plt.subplots()
    line, = ax.plot(x, y, color='k')
    
    def update(num, x, y, line):
        line.set_data(x[:num], y[:num])
        line.axes.axis([0, 10, -1, 1])
        return line,
    
    ani = animation.FuncAnimation(fig, update, len(x), fargs=[x, y, line],
                                  interval=25, blit=False)
    ani.save('test3.gif',writer='pillow')
    plt.show()

![在这里插入图片描述][43b07b84284b428db958e4cc082a933e.gif]

代码已放到[GitHub][]

![assets_2F-MhDzDd_4O-YwQrXjtUl_2Fsync_2F519c64844907fe7be1e26f3f7bd0d4ec1c3532ce.png_alt_media][]

[GitHub]: https://github.com/LoyalWilliams/data-visualization
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