Ultra-detailed matplotlib study notes (including line charts, histograms, histograms, and pie chart cases)

Language 2022-04-13 10:53:19 views: null

  • matplotlib:

    • Data can be visualized and presented more intuitively

    • Make data more objective and convincing

  • matplotlib architecture:

    • The upper layer calls the lower layer
    • Backend: implement drawing area (allocate drawing resources)
    • Art: figure, axes, axis
    • Script: pyplot

  • Basic drawing:

    • Line chart: coordinates of points (horizontal, vertical)
    • figure (draw the drawing area), plot, show (display the axes)
    • xticks, yticks (set ticks, Chinese)
    • xlable, ylable (set axis interpretation)
    • plot(parameter) (plot coordinates)
    • lengthend (set display legend)
    • plt.subplots(nrows=Num,ncols=Num,figsize=(20,8)) (realize the drawing of multiple coordinate systems)

  • Histogram:

    • Number of groups: The data is divided into several groups according to different ranges

    • Group distance: the difference between the two endpoints of each group

    • API：

      plt.hist(x,bins=None,normed=None,**kwargs)

      plt.grid(True,linestyle='-',alpha=0.5): Display grid

  • Compared:

    • Histogram: suitable for data where the x-coordinate is connected, the amount of data is large
    • Histogram: suitable for few categories and small amount of data

  • Pie chart:

    • Application scenario: Indicates the proportion of different categories
    • API：plt.pie(x,explode=List,labels=None,autopct=’%1.2f%%’,colors=List,shadow=True,startangle=Num)
    • Drawing: pay attention to display the whole circle plt.axis('equal')

  • K line chart

    • API：

      candlestick_ochl(axes,day,width=0.2,colorup=‘r’,colordown=‘g’)

  • Line chart, column chart, histogram, pie chart case

    • Line chart first experience
    # 实现温度变化（折线图）的绘制
#1、先生成fig
plt.figure(figsize=(20,8))

# 2、准备数据、折现图点，x，y坐标个数一一对应
x = range(10)
y = range(10,20)
plt.plot(x,y)

# 3、显示调用show()
plt.show()

    insert image description here

    • Line chart first experience
    # 画出温度变化图
# 创建一个figure
plt.figure(figsize=(20,8))

# 准备x，y坐标的数据
x = range(60)
y_shanghai = [random.uniform(15,18) for i in x]

# 画折现图
plt.plot(x,y_shanghai)

plt.show()

    insert image description here

    • The temperatures of two cities are displayed in one coordinate system
    # 显示某城市的温度变化图，11-12
# 调价刻度调节
plt.figure(figsize=(20,8))

# 准备x，y坐标的数据
x = range(60)

# 准备上海y轴坐标
y_shanghai = [random.uniform(15,18) for i in x]

# 准备北京的y轴坐标
y_beijing = [random.uniform(1,3) for i in x]

# 设置中文刻度值
x_ch = ["11点{}分".format(i) for i in x]

y_ticks = range(40)

# 画折现图
plt.plot(x,y_shanghai,label='上海')
plt.plot(x,y_beijing,color='r',linestyle='--',label='北京')

# 修改刻度值
# 指定显示的x刻度的列表
# 第一个参数：必须是值
# 第二个参数：指定跟第一个参数对应的中文
# y 0-40
plt.xticks(x[::5],x_ch[::5],fontsize=20)
plt.yticks(y_ticks[::5],fontsize=20)

# 增加标题，坐标轴描述
plt.xlabel('时间',fontsize=20)
plt.ylabel('温度',fontsize=20)
plt.title('一些城市从11点到12点之间的温度',fontsize=20)

# 增加图例的显示
plt.legend(loc='best',fontsize=20)
plt.show()

    insert image description here

    • Temperatures in two cities, displayed in multiple coordinate systems
    # 两个城市的温度，在多个坐标系中显示
# 调价刻度调节
# plt.figure(figsize=(20,8))
fig,ax = plt.subplots(nrows=1,ncols=2,figsize=(20,8))

# 准备数据
x = range(60)

# 准备上海y轴坐标
y_shanghai = [random.uniform(15,18) for i in x]

# 准备北京的y轴坐标
y_beijing = [random.uniform(1,3) for i in x]

# 设置中文刻度值
x_ch = ["11点{}分".format(i) for i in x]

y_ticks = range(40)

# 画折现图
ax[0].plot(x,y_shanghai,label='上海')
ax[1].plot(x,y_beijing,color='r',linestyle='--',label='北京')

# 修改刻度值
# 指定显示的x刻度的列表
# 第一个参数：必须是值
# 第二个参数：指定跟第一个参数对应的中文
# y 0-40
ax[0].set_xticks(x[::5],x_ch[::5])
ax[1].set_xticks(x[::5],x_ch[::5])

ax[0].set_yticks(y_ticks[::5])
ax[1].set_yticks(y_ticks[::5])

# 增加标题，坐标轴描述
ax[0].set_xlabel('时间',fontsize=20)
ax[0].set_ylabel('温度',fontsize=20)
ax[0].set_title('一些城市从11点到12点之间的温度',fontsize=20)

ax[1].set_xlabel('时间',fontsize=20)
ax[1].set_ylabel('温度',fontsize=20)
ax[1].set_title('一些城市从11点到12点之间的温度',fontsize=20)

# 增加图例的显示
ax[0].legend(loc='best',fontsize=20)
ax[1].legend(loc='best',fontsize=20)
plt.show()

    insert image description here

    • Comparison of movie box office data (bar chart)
    # 电影票房数据的对比（柱状图）
plt.figure(figsize=(20,8))

# 电影名字，每部电影对应的票房
movie_name = ['雷神3:诸神黄昏','正义联盟','东方快车谋杀案','寻梦环游记','全球风暴','降魔传','追捕','七十七天','密战','狂兽','其他']
y = [73853,57767,22354,15969,14839,8725,8716,8318,7916,6764,52222]
# 放进横坐标的数字列表
x = range(len(movie_name))
color = ['b','r','g','y','c','m','y','k','c','g','g']


# 使用plt.bar去显示
# plot.bar:填入的x坐标必须全是数字
plt.bar(x,y,width=0.2,color=color)

# 去修改刻度，以及电影名字显示
plt.xticks(x,movie_name,fontsize=16)
plt.show()

    insert image description here

    • Compare the box office of the first day and first week of different movies
    # 对比不同电影的首日、首周的电影票房
# 电影票房数据的对比（柱状图）
plt.figure(figsize=(20,8))


# 电影名字，每部电影对应的票房
movie_name = ['雷声3:诸神黄昏','正义联盟','寻梦环游记']
first_day = [10587.6,10062.5,1275.7]
first_weekend = [36224.9,34479.6,11830]

x = range(len(movie_name))

# 使用plt.bar去显示
# plot.bar:填入的x坐标必须全是数字
plt.bar(x,first_day,width=0.2,label='首日票房')
plt.bar([i+0.2 for i in x],first_weekend,width=0.2,label='首周票房')

# 去修改刻度，以及电影名字显示
plt.xticks([i+0.1 for i in x],movie_name,fontsize=16)

# 显示图例
plt.legend(loc='best',fontsize=20)

plt.show()

    insert image description here

    • Histogram of movie duration distribution
    # 电影时长分布直方图 
# 组数 组距
# 组距：2
# 创建
plt.figure(figsize=(20,8))

time = [131,98,125,131,139,131,117,128,108,135,138,131,102,107,114,119,128,121,142,127,130,124,124,127,138,117,121,102,130,138,137,126,128,132,131,119,114,126,115]
bins = 2

# 组数 = 极差/组距 
group = int((max(time)-min(time))/bins)

# 画直方图
# normed参数：指定纵坐标显示频率
# plt.hist(time,group,normed=1)
plt.hist(time,group)

# 指定刻度的范围，以及步长
plt.xticks(list(range(min(time),max(time)))[::2])

plt.xlabel('电影市场大小',fontsize=20)
plt.ylabel('电影的数据量',fontsize=20)

# 添加网格
plt.grid(True,linestyle='--',alpha=0.5)

plt.show()

    insert image description here

    • The percentage of films made
    # 电影的拍片占比显示
plt.figure(figsize=(20,8))


movie_name = ['雷神3:诸神黄昏','正义联盟','东方快车谋杀案','寻梦环游记','全球风暴','降魔传','追捕','七十七天','密战','狂兽','其他']
place_count = [60605,54546,45819,28243,13270,9945,7679,6799,6101,4621,20105]
color = ['b','r','g','y','c','m','y','k','c','g','g']
# 显示饼图
plt.pie(place_count,labels=movie_name,autopct='%1.2f%%',colors=color)
plt.axis('equal')
plt.legend(loc='best')
plt.show()

    insert image description here

    • Pie chart showing pet preference ratio
    labels = 'Frogs','Hogs','Dogs','Logs'
sizes = [15,30,45,10]
explode = (0,0.1,0,0)
fi1,ax1 = plt.subplots()
ax1.pie(sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=True,startangle=90)

ax1.axis('equal')

plt.show()

    insert image description here

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