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社区首页 >问答首页 >条件交通灯系统

问条件交通灯系统
EN

Code Review用户
提问于 2020-10-28 19:07:25
回答 1查看 143关注 0票数 2

我编写了Python代码，它使用了多个if条件和一个for循环。守则的主要目的是在一定条件下产生一个交通灯系统。

代码语言：javascript
复制
Red = -1
Yellow = 0
Green = 1

它需要4个月(m0、m1、m2、m3)和dataframe作为输入，在每一行中运行条件，然后返回-1、0或1。

代码将月份1与月份0，月2与月1，月3与月2进行比较。

供投入：

代码语言：javascript
复制
if month+1 < Month for any value, then red else green.

例如，如果2020年7月的收入低于2020年6月，则输入为红色，否则为绿色。在三个比较的基础上，计算了计算结果。结果可能是1-，0或1。

我编写的代码运行良好，但没有以任何方式进行优化。有更好的方法吗？

这将是一个O(n)操作，但至少应该有一种用Python简明地编写它的方法。或者，如果代码也可以在操作上得到改进。

代码语言：javascript
复制
def getTrafficLightData(df, dimension, m1, m2, m3, m4):
'''
Inputs - 
    Dataframe
    dimension = on which we want to calculate traffic light system
    m1, m2, m3, m4 - Could be any for months but we have taken consecutive months for Traffic Light System.
    Example Call - getTrafficLightData(report6_TLS_data, "Revenue_","2020-6","2020-7","2020-8","2020-9")

'''

TFS_df = pd.DataFrame(columns=[dimension + "_TLS"])

if dimension == "Overstrike_":
    suffix = "%"
    for i in range(len(df)):
        if (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [0]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (  #
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (  #
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [0]
        else:
            TFS_df.loc[i] = [0]
    return TFS_df
else:
    if dimension == "Margin_":
        suffix = "%"
    else:
        suffix = ""
    for i in range(len(df)):
        if (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                < df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [0]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                > df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                > df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [-1]
        elif (
            (
                df[dimension + m1 + suffix].iloc[i]
                < df[dimension + m2 + suffix].iloc[i]
            )
            and (
                df[dimension + m2 + suffix].iloc[i]
                < df[dimension + m3 + suffix].iloc[i]
            )
            and (
                df[dimension + m3 + suffix].iloc[i]
                > df[dimension + m4 + suffix].iloc[i]
            )
        ):
            TFS_df.loc[i] = [0]
        else:
            TFS_df.loc[i] = [0]
    return TFS_df

这个函数的调用方式如下-

代码语言：javascript
复制
report6_TLS_data['Revenue_TLS']=getTrafficLightData(report6_TLS_data, "Revenue_","2020-6","2020-7","2020-8","2020-9")
report6_TLS_data["Margin_TLS"]=getTrafficLightData(report6_TLS_data, "Margin_","2020-6","2020-7","2020-8","2020-9")
report6_TLS_data["Overstrike_TLS"]=getTrafficLightData(report6_TLS_data, "Overstrike_","2020-6","2020-7","2020-8","2020-9")

任何指示都会有帮助。

输入数据的形式是-

代码语言：javascript
复制
ym  PART NUMBER BranchCode  Revenue_2019-1  Revenue_2019-10 Revenue_2019-11 Revenue_2019-12 Revenue_2019-2  Revenue_2019-3  Revenue_2019-4  Revenue_2019-5  Revenue_2019-6  Revenue_2019-7  Revenue_2019-8  Revenue_2019-9  Revenue_2020-1  Revenue_2020-2  Revenue_2020-3  Revenue_2020-4  Revenue_2020-5  Revenue_2020-6  Revenue_2020-7  Revenue_2020-8  Revenue_2020-9  Margin_2019-1   Margin_2019-10  Margin_2019-11  Margin_2019-12  Margin_2019-2   Margin_2019-3   Margin_2019-4   Margin_2019-5   Margin_2019-6   Margin_2019-7   Margin_2019-8   Margin_2019-9   Margin_2020-1   Margin_2020-2   Margin_2020-3   Margin_2020-4   Margin_2020-5   Margin_2020-6   Margin_2020-7   Margin_2020-8   Margin_2020-9   Overstrike_2019-1   Overstrike_2019-10  Overstrike_2019-11  Overstrike_2019-12  Overstrike_2019-2   Overstrike_2019-3   Overstrike_2019-4   Overstrike_2019-5   Overstrike_2019-6   Overstrike_2019-7   Overstrike_2019-8   Overstrike_2019-9   Overstrike_2020-1   Overstrike_2020-2   Overstrike_2020-3   Overstrike_2020-4   Overstrike_2020-5   Overstrike_2020-6   Overstrike_2020-7   Overstrike_2020-8   Overstrike_2020-9   Transactions_2019-1 Transactions_2019-10    Transactions_2019-11    Transactions_2019-12    Transactions_2019-2 Transactions_2019-3 Transactions_2019-4 Transactions_2019-5 Transactions_2019-6 Transactions_2019-7 Transactions_2019-8 Transactions_2019-9 Transactions_2020-1 Transactions_2020-2 Transactions_2020-3 Transactions_2020-4 Transactions_2020-5 Transactions_2020-6 Transactions_2020-7 Transactions_2020-8 Transactions_2020-9 Margin_2019-1%  Margin_2019-10% Margin_2019-11% Margin_2019-12% Margin_2019-2%  Margin_2019-3%  Margin_2019-4%  Margin_2019-5%  Margin_2019-6%  Margin_2019-7%  Margin_2019-8%  Margin_2019-9%  Margin_2020-1%  Margin_2020-2%  Margin_2020-3%  Margin_2020-4%  Margin_2020-5%  Margin_2020-6%  Margin_2020-7%  Margin_2020-8%  Margin_2020-9%  Overstrike_2019-1%  Overstrike_2019-10% Overstrike_2019-11% Overstrike_2019-12% Overstrike_2019-2%  Overstrike_2019-3%  Overstrike_2019-4%  Overstrike_2019-5%  Overstrike_2019-6%  Overstrike_2019-7%  Overstrike_2019-8%  Overstrike_2019-9%  Overstrike_2020-1%  Overstrike_2020-2%  Overstrike_2020-3%  Overstrike_2020-4%  Overstrike_2020-5%  Overstrike_2020-6%  Overstrike_2020-7%  Overstrike_2020-8%  Overstrike_2020-9%
0   BAGG001 BC  71.75   90.00   20.25   43.50   42.50   30.00   70.00   44.25   45.00   46.75   129.50  58.00   81.00   36.00   33.25   0.75    15.00   24.75   0.00    0.00    2.50    32.97   39.15   8.95    14.31   18.95   7.86    30.68   19.27   19.74   18.12   59.38   22.30   34.95   17.59   14.10   0.32    6.35    5.30    0.00    0.00    1.06    0.00    0.00    0.00    1.00    0.00    1.00    0.00    0.00    3.00    3.00    1.00    1.00    2.00    0.00    0.00    0.00    0.00    2.00    0.00    0.00    0.00    8   16  5   9   5   6   12  7   10  7   13  10  13  5   11  1   2   4   0   0   1   1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    0.00    0.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    1.00    0.00    0.00    1.00
1   BAGG001 PK  0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    25.50   0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    9.90    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    1.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0   0   0   0   0   0   0   0   0   2   0   0   0   0   0   0   0   0   0   0   0   0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    1.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    1.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00
python
performance
EN

回答 1

Code Review用户

发布于 2020-10-28 23:10:46

重要的是要理解Pandas是Numpy的包装器，两者都面向向量化。尽量避免循环和重复，这种方法确实可以简化。值得说的是

如果任何值都是month+1 < Month，则红色else绿色。

绝对不是发生了什么。目前，我已经提出了一个解决方案，它使用查找表处理固定的月数。你可以在必要的时候调整它。它基于您展示的表，将比较解释为字段中的位。

否则:不要在dimension字符串中使用下划线；不要在输出数据中生成双下划线；添加类型提示；并接受月份作为变量参数或元组。

代码语言：javascript
复制
import numpy as np
import pandas as pd


lookup = np.array((-1, 0, -1, 1, -1, 1, 0, 1))

to_binary = np.array((4, 2, 1)).T


def get_traffic_light_data_new(
    df: pd.DataFrame,
    dimension: str,
    *months: str,
) -> np.array:
    assert len(months) == 4
    by_month = df[[f'{dimension}_{month}' for month in months]].to_numpy()
    signs = by_month[:, 1:] - by_month[:, :-1] > 0
    index = np.matmul(signs, to_binary)
    return lookup[index]


def test():
    scenarios = pd.DataFrame(
        [
            [3, 2, 1, 0],
            [1, 2, 3, 4],
            [1, 2, 1.5, 1.7],
            [2, 1, 3, 4],
            [3, 2, 1, 4],
            [3, 2, 2.5, 2.4],
            [5, 6, 1, 0],
            [3, 4, 5, 4],

        ],
        columns=(
            'Revenue_2020-6',
            'Revenue_2020-7',
            'Revenue_2020-8',
            'Revenue_2020-9',
        ),
    )
    args = (
        scenarios,
        'Revenue',
        '2020-6',
        '2020-7',
        '2020-8',
        '2020-9',
    )
    new_result = get_traffic_light_data_new(*args)
    print(new_result)


if __name__ == '__main__':
    test()
票数 1
EN
页面原文内容由Code Review提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：

https://codereview.stackexchange.com/questions/251266

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