常用数据可视化方式参考

折线图 Line Chart

用于观察数据和比较走势，比如横坐标时月份，纵坐标是销售额

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import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

if __name__ == '__main__':
    A = [0, 10, 20, 10, 0, 10, 20, 30, 5, 0, 10, 20, 10, 0, 20]
    B = [0, 1.0, 2.9, 3.61, 3.249, 3.9241, 5.5317, 7.9785, 7.6807, 6.9126, 7.2213, 8.4992, 8.6493, 7.7844, 8.0059]
    C = [0, 10.0, 15.2632, 13.321, 9.4475, 9.5824, 11.8057, 15.2932, 13.4859, 11.2844, 11.0872, 12.3861, 12.0536,
         10.4374, 10.38077]
    data = pd.DataFrame({'A': A, 'B': B, 'C': C})
    # data.plot.line()
    sns.lineplot(data=data)
    plt.show()

注释部分是使用seaborn绘图得到

柱状图(条形图) Bar Chart

柱状图(条形图)表示数据的大小.和Line Chart其实可以是一样的数据,不过Bar Chart重在观察和比较数据之间的差距

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import pandas as pd
import matplotlib.pyplot as plt

if __name__ == '__main__':
    A = [0, 10, 20, 10, 0, 10, 20, 30, 5, 0, 10, 20, 10, 0, 20]
    B = [0, 1.0, 2.9, 3.61, 3.249, 3.9241, 5.5317, 7.9785, 7.6807, 6.9126, 7.2213, 8.4992, 8.6493, 7.7844, 8.0059]
    C = [0, 10.0, 15.2632, 13.321, 9.4475, 9.5824, 11.8057, 15.2932, 13.4859, 11.2844, 11.0872, 12.3861, 12.0536,
         10.4374, 10.3807]
    data = pd.DataFrame({'A': A, 'B': B, 'C': C})
    data.plot.bar()
    plt.show()

data.plot.bar()横向柱状图,bar(stacked=True)堆叠柱状图

直方图 Histgram

直方图表示数据的多少.和Bar Chart的区别是Histgram统计的是数据数量的多少,它的纵坐标和数据的大小无关,只和数据的多少有关.通常横坐标表示某个数据,纵坐标表示该数据数据数量。

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import pandas as pd
import matplotlib.pyplot as plt

if __name__ == '__main__':
    A = [0, 10, 20, 10, 0, 10, 20, 30, 5, 0, 10, 20, 10, 0, 20]
    B = [0, 1.0, 2.9, 3.61, 3.249, 3.9241, 5.5317, 7.9785, 7.6807, 6.9126, 7.2213, 8.4992, 8.6493, 7.7844, 8.0059]
    C = [0, 10.0, 15.2632, 13.321, 9.4475, 9.5824, 11.8057, 15.2932, 13.4859, 11.2844, 11.0872, 12.3861, 12.0536,
         10.4374, 10.38077]
    data = pd.DataFrame({'A': A, 'B': B, 'C': C})
    data.hist()
    plt.show()

其他图

未完待续… …