Python的Matplotlib库简述

小灰灰 2021-03-18 14:30 535阅读 0赞

Matplotlib 库是 python 的数据可视化库  
import matplotlib.pyplot as plt

1、字符串转化为日期

unrate = pd.read_csv("unrate.csv")
    unrate["DATE"] = pd.to_datetime(unrate["DATE"])

2、拆线图

data1 = unrate[0: 12]
    plt.plot(data1["DATE"], data1["VALUE"])    # x轴数据和y轴数据
    plt.xticks(rotation = 45)   # 将x轴的属性旋转一个角度
    plt.xlabel("Date Month")    # x轴描述
    plt.ylabel("Rate Value")    # y轴描述
    plt.title("my first plt")   # 标题
    plt.show()

3、多图拼切

fig = plt.figure()
    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()

4、一图多线

fig = plt.figure(figsize=(6, 3)) # 设定图尺寸
    
    data1 = unrate[0: 12]
    data1["MONTH"] = data1["DATE"].dt.month
    plt.plot(data1["MONTH"], data1["VALUE"], c="red")
    
    data2 = unrate[12: 24]
    data2["MONTH"] = data2["DATE"].dt.month
    plt.plot(data2["MONTH"], data2["VALUE"], c="blue")
    
    plt.xticks(rotation = 45)       #将x轴的属性旋转一个角度
    plt.xlabel("Date Month")
    plt.ylabel("Rate Value")
    plt.title("my first plt")
    plt.show()

5、一图多线 - 自动跑代码（带图例）

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')   # 位置
    plt.show()

6、条形图

fand_col = ["Fandango_Stars", "Fandango_Ratingvalue", "Metacritic_norm", "RT_user_norm_round", "IMDB_norm_round"]
    
    bar_heights = fand_new.ix[0, fand_col].values # 条形图高度
    bar_positions = np.arange(5) + 0.75           # 条形图起始位置
    tick_positions = range(1, 6)
    fig, ax = plt.subplots()
    
    ax.bar(bar_positions, bar_heights, 0.5)       # 0.5表示条形图的宽度
    ax.set_xticks(tick_positions)
    ax.set_xticklabels(fand_col, rotation = 90)
    
    ax.set_xlabel('Rating Source')
    ax.set_ylabel('Average Rating')
    ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
    plt.show()

7、条形图 - 横向

fand_col = ["Fandango_Stars", "Fandango_Ratingvalue", "Metacritic_norm", "RT_user_norm_round", "IMDB_norm_round"]
    bar_heights = fand_new.ix[0, fand_col].values
    bar_positions = np.arange(5) + 0.75
    tick_positions = range(1, 6)
    fig, ax = plt.subplots()
    
    ax.barh(bar_positions, bar_heights, 0.5)    # 横向
    ax.set_yticks(tick_positions)
    ax.set_yticklabels(fand_col, rotation = 0)
    
    ax.set_xlabel('Rating Source')
    ax.set_ylabel('Average Rating')
    ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
    plt.show()

8、散点图

fig, ax = plt.subplots()
    ax.scatter(fand_new['Fandango_Stars'], fand_new['Metacritic_norm'])    # 散点图
    ax.set_xlabel('Fandango')
    ax.set_ylabel('Rotten Tomatoes')
    plt.show()

9、直方图

fandango_distribution = fand_new['Fandango_Stars'].value_counts()
    fandango_distribution = fandango_distribution.sort_index()
    imdb_distribution = fand_new['IMDB_norm_round'].value_counts()
    imdb_distribution = imdb_distribution.sort_index()
    
    # bins 是什么？通俗一点就是分组，将N多数据分成X组。默认：bins=10
    fig, ax = plt.subplots()
    ax.hist(fand_new['Fandango_Stars'], range=(4, 5), bins=5)    # range 需要查看x轴的范围
    plt.show()

10、多图

fig = plt.figure(figsize=(12, 12))
    ax1 = fig.add_subplot(2,2,1)
    ax2 = fig.add_subplot(2,2,2)
    ax3 = fig.add_subplot(2,2,3)
    ax4 = fig.add_subplot(2,2,4)
    ax1.hist(fand_new['Fandango_Stars'], bins=20, range=(0, 5))
    ax1.set_title('Distribution of Fandango Ratings')
    ax1.set_ylim(0, 50)
    
    ax2.hist(fand_new['IMDB_norm_round'], 20, range=(0, 5))
    ax2.set_title('Distribution of Rotten Tomatoes Ratings')
    ax2.set_ylim(0, 50)
    
    ax3.hist(fand_new['Metacritic_norm'], 20, range=(0, 5))
    ax3.set_title('Distribution of Metacritic Ratings')
    ax3.set_ylim(0, 50)
    
    ax4.hist(fand_new['RT_user_norm_round'], 20, range=(0, 5))
    ax4.set_title('Distribution of IMDB Ratings')
    ax4.set_ylim(0, 50)
    
    plt.show()

11、四分图

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

12、多图 - 通过数组

num_cols = ['Fandango_Stars', 'IMDB_norm_round', 'Metacritic_norm', 'RT_user_norm_round']
    fig, ax = plt.subplots()
    
    ax.boxplot(fand_new[num_cols].values)
    ax.set_xticklabels(num_cols, rotation=90)
    ax.set_ylim(0, 5)
    
    plt.show()

13、数据可视化 - 简洁一些

fig, ax = plt.subplots()
    
    ax.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')
    ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')
    ax.tick_params(bottom="off", top="off", left="off", right="off")    # 可配置参数
    
    for key,spine in ax.spines.items():
    spine.set_visible(False)
    
    ax.legend(loc='upper right')
    
    plt.show()

14、数据可视化 - 多图 - 通过程序

15、数据可视化 - 多图 - 通过程序跑 - 多图 简洁

major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
    
    fig = plt.figure(figsize=(12, 12))
    
    for sp in range(0, 4):
    ax = fig.add_subplot(2, 2, sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')
    
    for key,spine in ax.spines.items():
    spine.set_visible(False)
    
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")
    
    plt.legend(loc='upper right')
    plt.show()

16、如何使图表更好看？

cb_dark_blue = (0/255, 107/255, 164/255)    # 自定义颜色
    cb_orange = (255/255, 128/255, 14/255)
    
    fig = plt.figure(figsize=(12, 12))
    
    for sp in range(0, 4):
    ax = fig.add_subplot(2, 2, sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women')
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men')
    
    for key,spine in ax.spines.items():
    spine.set_visible(False)
    
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")
    
    plt.legend(loc='upper right')
    plt.show()

17、加粗线

cb_dark_blue = (0/255, 107/255, 164/255)
    cb_orange = (255/255, 128/255, 14/255)
    
    fig = plt.figure(figsize=(18, 3))
    
    for sp in range(0, 4):
    ax = fig.add_subplot(1, 4, sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)    # 线条粗细
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=3)
    
    for key,spine in ax.spines.items():
    spine.set_visible(False)
    
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")
    
    plt.legend(loc='upper right')
    plt.show()

18、加注释

fig = plt.figure(figsize=(18, 3))
    
    for sp in range(0, 4):
    ax = fig.add_subplot(1, 4, sp+1)
    ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)
    ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=3)
    for key,spine in ax.spines.items():
    spine.set_visible(False)
    ax.set_xlim(1968, 2011)
    ax.set_ylim(0,100)
    ax.set_title(major_cats[sp])
    ax.tick_params(bottom="off", top="off", left="off", right="off")
    
    if sp == 0:
    ax.text(2005, 87, 'Men')    # 注释
    ax.text(2002, 8, 'Women')
    elif sp == 3:
    ax.text(2005, 62, 'Men')
    ax.text(2001, 35, 'Women')
    
    plt.show()