我的函數查看 3 個數據幀，在不同日期之間進行過濾，并創建一個語句。正如您所看到的，該函數一遍又一遍地重復使用相同的步驟，我想減少它們。我相信使用 afor-loop會有所幫助，但我不確定如何return像現在這樣在一小段中做出陳述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說
TA貢獻1775條經驗 獲得超8個贊
我只需將 3 個參數傳遞給您的函數，即 df、date1 和 date2，然后調用您的函數 3 次。
def stat_generator(df,date1,date2):
"..."
return statement
然后將您的數據作為列表列表或類似的內容傳遞。例如：
data = [[df,date1,date2],[df2,date3,date4],[df4,date5,date6]]
for lists in data:
stat_generator(*lists)
尚方寶劍之說
TA貢獻1788條經驗 獲得超4個贊
維持現有形式
df將中的參數更改stat_generator為df1，因此df可以在 中使用for-loop。將每個數據幀的數據分組在一起
創建一個
statements列表，待返回date1anddate2改為d1andd2在循環中更新
statement1為使用更易于閱讀的f-string.我認為這些更新需要對整體代碼進行最少的更改。
可選：
更改
mask為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
完全重寫
該函數最好只做一件事，即提取并返回數據。
負責將多個數據幀傳遞到函數外部的函數，并將結果收集在一個
list或多個數據print幀中。better為和創建新的數據框效率不高worse。使用
.value_counts()withnormalize=True來獲取百分比。
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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