007.python科学计算库matplotlib(下)

我就是我 2021-10-06 15:18 367阅读 0赞

### 测试数据 [fandango\_scores.csv][fandango_scores.csv] ###

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### bar ###

import matplotlib.pyplot as plt import pandas as pd from numpy import arange 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] # bar()方法有两个必需的参数，左边和高度。 # 我们使用左参数来指定条形图左侧的x坐标。 # 我们使用高度参数来指定每个栏的高度 num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars'] # ix[i, num_cols] 获取第i行的num_cols列中的数据，i从0开始 # 获取的列中数据即分别对应条形图的高度 bar_heights = norm_reviews.ix[0, num_cols].values print(bar_heights) # [4.3 3.55 3.9 4.5 5.0] # 条形图的位置 bar_positions = arange(5) + 0.75 print(bar_positions) # [0.75 1.75 2.75 3.75 4.75] fig, ax = plt.subplots() # 0.5 条形图的宽度 ax.bar(bar_positions, bar_heights, 0.5) plt.show()

![pycharm64_2018-09-16_15-31-36.png][]

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import matplotlib.pyplot as plt import pandas as pd from numpy import arange 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] # 默认情况下，matplotlib将x轴标记标签设置为条上的整数值 # 在x轴上(从0到6)，我们只需要在横轴上的横轴上标记条就可以了。 # 我们可以使用ax .set_xticks()将ticks的位置改变为[1,2,3,4,5]: num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars'] bar_heights = norm_reviews.ix[0, num_cols].values bar_positions = arange(5) + 0.75 tick_positions = range(1, 6) # 创建一个图和一组子图 fig, ax = plt.subplots() ax.bar(bar_positions, bar_heights, 0.5) ax.set_xticks(tick_positions) # 用字符串标签列表设置x-tick标签 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()

![pycharm64_2018-09-16_15-39-49.png][]

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### barh ###

import matplotlib.pyplot as plt
    import pandas as pd
    from numpy import arange
    
    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]
    # 默认情况下，matplotlib将x轴标记标签设置为条上的整数值
    # 在x轴上(从0到6)，我们只需要在横轴上的横轴上标记条就可以了。
    # 我们可以使用ax .set_xticks()将ticks的位置改变为[1,2,3,4,5]:
    num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
    bar_heights = norm_reviews.ix[0, num_cols].values
    bar_positions = arange(5) + 0.75
    tick_positions = range(1, 6)
    # 创建一个图和一组子图
    fig, ax = plt.subplots()
    # barh 做一个水平条形图
    ax.barh(bar_positions, bar_heights, 0.5)
    
    ax.set_yticks(tick_positions)
    # 用字符串标签列表设置y-tick标签
    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()

![pycharm64_2018-09-16_15-45-31.png][]

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### scatter ###

import matplotlib.pyplot as plt import pandas as pd 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] # 让我们来看一个可以帮助我们形象化许多点的图 fig, ax = plt.subplots() # 不同标记大小和/或颜色的 y vs x 散点图 ax.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm']) ax.set_xlabel('Fandango') ax.set_ylabel('Rotten Tomatoes') plt.show()

![pycharm64_2018-09-16_15-49-35.png][]

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import matplotlib.pyplot as plt import pandas as pd 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] fig = plt.figure(figsize=(5, 10)) # 添加两个子图，均为散点图 ax1 = fig.add_subplot(2, 1, 1) ax2 = fig.add_subplot(2, 1, 2) ax1.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm']) ax1.set_xlabel('Fandango') ax1.set_ylabel('Rotten Tomatoes') ax2.scatter(norm_reviews['RT_user_norm'], norm_reviews['Fandango_Ratingvalue']) ax2.set_xlabel('Rotten Tomatoes') ax2.set_ylabel('Fandango') plt.show()

![pycharm64_2018-09-16_15-53-26.png][]

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### hist ###

import pandas as pd 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] # value_counts 返回包含唯一值计数的对象。结果对象将按降序排列，因此第一个元素是最频繁出现的元素。默认情况下排除NA值 fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts() # 按标签(沿着轴)对对象排序 此处的标签是 Fandango_Ratingvalue 的值 fandango_distribution = fandango_distribution.sort_index() imdb_distribution = norm_reviews['IMDB_norm'].value_counts() # 按标签(沿着轴)对对象排序 此处的标签是 IMDB_norm 的值 imdb_distribution = imdb_distribution.sort_index() print(fandango_distribution) print("-------------------------------") print(imdb_distribution)

![mspaint_2018-09-16_16-06-59.png][]

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import matplotlib.pyplot as plt import pandas as pd 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] fig, ax = plt.subplots() # 将Fandango_Ratingvalue的范围平均拆分为bins个宽度， # 并只显示(4, 5)范围内的数据 # range不指定默认展示所有,bins不指定默认展示10个 ax.hist(norm_reviews['RT_user_norm'], range=(4, 5), bins=20) plt.show()

![pycharm64_2018-09-16_16-18-53.png][]

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import matplotlib.pyplot as plt import pandas as pd 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] fig = plt.figure(figsize=(5, 10)) ax1 = fig.add_subplot(2, 1, 1) ax2 = fig.add_subplot(2, 1, 2) ax1.hist(norm_reviews['Fandango_Ratingvalue'], bins=20, range=(0, 5)) ax1.set_title('Distribution of Fandango Ratings') # 设置y轴的数据限制 ax1.set_ylim(0, 50) ax2.hist(norm_reviews['RT_user_norm'], 20, range=(0, 5)) ax2.set_title('Distribution of Rotten Tomatoes Ratings') ax2.set_ylim(0, 50) plt.show()

![pycharm64_2018-09-16_16-24-59.png][]

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### boxplot ###

import matplotlib.pyplot as plt import pandas as pd 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] fig, ax = plt.subplots() # 做一个盒须图 # 为“x”的每一列或“x”序列中的每个向量做一个盒状和须状图。 # 该框从数据的下四分位数扩展到上四分位数，中间有一条线。 # 晶须从盒中伸出来显示数据的范围。传单点是那些超过末尾的胡子 ax.boxplot(norm_reviews['RT_user_norm']) ax.set_xticklabels(['Rotten Tomatoes']) ax.set_ylim(0, 5) plt.show()

![pycharm64_2018-09-16_16-29-33.png][]

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import matplotlib.pyplot as plt import pandas as pd 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] 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()

![pycharm64_2018-09-16_16-35-43.png][]

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[fandango_scores.csv]: http://upyun.qubianzhong.cn/file/python_matplotlib/fandango_scores.csv
[pycharm64_2018-09-16_15-31-36.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_15-31-36.png
[pycharm64_2018-09-16_15-39-49.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_15-39-49.png
[pycharm64_2018-09-16_15-45-31.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_15-45-31.png
[pycharm64_2018-09-16_15-49-35.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_15-49-35.png
[pycharm64_2018-09-16_15-53-26.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_15-53-26.png
[mspaint_2018-09-16_16-06-59.png]: http://upyun.qubianzhong.cn/picture/mspaint_2018-09-16_16-06-59.png
[pycharm64_2018-09-16_16-18-53.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_16-18-53.png
[pycharm64_2018-09-16_16-24-59.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_16-24-59.png
[pycharm64_2018-09-16_16-29-33.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_16-29-33.png
[pycharm64_2018-09-16_16-35-43.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_16-35-43.png