对多个数据帧和返回语句进行计算的更好方法？

Question

我的函数查看 3 个数据帧，在不同日期之间进行过滤，并创建一个语句。正如您所看到的，该函数一遍又一遍地重复使用相同的步骤，我想减少它们。我相信使用 afor-loop会有所帮助，但我不确定如何return像现在这样在一小段中做出陈述def stat_generator(df,date1,date2,df2,date3,date4,df4,date5,date6):     ##First Date Filter for First Dataframe, and calculations for first dataframe        df['Announcement Date'] = pd.to_datetime(df['Announcement Date'])    mask = ((df['Announcement Date'] >= date1) & (df['Announcement Date'] <= date2))    df_new = df.loc[mask]    total = len(df_new)    better = df_new[(df_new['performance'] == 'better')]    better_perc = round(((len(better)/total)*100),2)    worse = df_new[(df_new['performance'] == 'worse')]    worse_perc = round(((len(worse)/total)*100),2)    statement1 = "During the time period between {} and {}, {} % of the students performed better. {} %     of the students performed worse" .format(date1,date2,better_perc,worse_perc)        ##Second Date Filter for Second Dataframe, and calculations for second dataframe        df2['Announcement Date'] = pd.to_datetime(df2['Announcement Date'])    mask2 = ((df2['Announcement Date'] >= date3) & (df2['Announcement Date'] <= date4))    df_new2 = df2.loc[mask2]    total2 = len(df_new2)    better2 = df_new2[(df_new2['performance'] == 'better')]    better_perc2 = round(((len(better2)/total2)*100),2)    worse2 = df_new2[(df_new2['performance'] == 'worse')]    worse_perc2 = round(((len(worse2)/total2)*100),2)    statement2 = "During the time period between {} and {}, {} % of the students performed better. {} %     of the students performed worse" .format(date3,date4,better_perc2,worse_perc2)        ##Third Date Filter for Third Dataframe, and calculations for third dataframe   

Answer 1

我只需将 3 个参数传递给您的函数，即 df、date1 和 date2，然后调用您的函数 3 次。

def stat_generator(df,date1,date2):

    "..."

    return statement

然后将您的数据作为列表列表或类似的内容传递。例如：

data = [[df,date1,date2],[df2,date3,date4],[df4,date5,date6]]

for lists in data:

    stat_generator(*lists)

Answer 2

维持现有形式

• df将中的参数更改stat_generator为df1，因此df可以在 中使用for-loop。

• 将每个数据帧的数据分组在一起

• 创建一个statements列表，待返回

• date1anddate2改为d1andd2在循环中

• 更新statement1为使用更易于阅读的f-string.

• 我认为这些更新需要对整体代码进行最少的更改。

• 可选：

  • 更改mask为mask = df['Announcement Date'].between(d1, d2, inclusive=True)

def stat_generator(df1, date1 ,date2 ,df2 ,date3 ,date4 ,df4 ,date5 ,date6): 

    ##First Date Filter for First Dataframe, and calculations for first dataframe

    

    # create groups

    groups = [(df1, date1, date2), (df2, date3, date4), (df3, date5, date6)]

    

    # create a statements list for each statement

    statements = list()

    

    # iterate through each group

    for (df, d1, d2) in groups:

    

        df['Announcement Date'] = pd.to_datetime(df['Announcement Date'])

        mask = ((df['Announcement Date'] >= d1) & (df['Announcement Date'] <= d2))

        df_new = df.loc[mask]

        total = len(df_new)

        better = df_new[(df_new['performance'] == 'better')]

        better_perc = round(((len(better)/total)*100),2)

        worse = df_new[(df_new['performance'] == 'worse')]

        worse_perc = round(((len(worse)/total)*100),2)

        statement1 = f"During the time period between {d1} and {d2}, {better_perc}% of the students performed better. {worse_perc}%  of the students performed worse"

        

        # append the statement of the dataframe

        statements.append(statement1)

    # return a list of all the statements    

    return statements

完全重写

• 该函数最好只做一件事，即提取并返回数据。

• 负责将多个数据帧传递到函数外部的函数，并将结果收集在一个list或多个数据print帧中。

• better为和创建新的数据框效率不高worse。

  • 使用.value_counts()withnormalize=True来获取百分比。

def stat_generator(df: pd.DataFrame, d1: str, d2: str) -> str: 

           

    df['Announcement Date'] = pd.to_datetime(df['Announcement Date'])

    # create the mask

    mask = df['Announcement Date'].between(d1, d2, inclusive=True)

    # apply the mask

    df_new = df.loc[mask]

    # calculate the percentage

    per = (df_new.performance.value_counts(normalize=True) * 100).round(2)

    return f"During the time period between {d1} and {d2}, {per['better']}% of the students performed better. {per['worse']}%  of the students performed worse"

groups = [(df1, date1, date2), (df2, date3, date4), (df3, date5, date6)]

statements = list()

for group in groups:

    statements.append(stat_generator(*group))