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
2 回答
www说
TA贡献1775条经验 获得超8个赞
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)
尚方宝剑之说
TA贡献1788条经验 获得超4个赞
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))
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