我有一個像這樣的數據框，其中包含重復的列名：ID 作為索引加載 JANUARY FEBRUARY MARCH ID Sales Revenue Sales Revenue Sales Revenue 03 10.00 5.00 0.00 0.00 10.00 19.00 05 20.00 20.00 20.00 20.00 20.00 20.00 06 30.00 30.00 30.00 30.00 30.00 30.00 07 30.00 30.00 30.00 30.00 30.00 30.00我想將其顯示如下： ID Sales Revenue 03 10.00 5.00 05 20.00 20.00 06 30.00 30.00 07 30.00 30.00 03 0.00 0.00 05 20.00 20.00 06 30.00 30.00 07 30.00 30.00 03 10.00 19.00 05 20.00 20.00 06 30.00 30.00 07 30.00 30.00目前我正在使用，但期待更好的方法。我嘗試過熔化，但這僅適用于一列：cols = df.columns.to_list()for i in range(1, len(cols), 2): # #Loading each month's data to the data frame sub_cols = cols[i:i + 2] sub_cols .insert(0, cols[0]) sub_df = df.filter(sub_cols , axis=1) sub_df.columns = ['ID', 'Revenue', 'Sales'] if i == 1: final_df = sub_df else: final_df = final_df.append(sub_df)
2 回答
森林海
TA貢獻2011條經驗 獲得超2個贊
Pandas lreshape 幫我解決了這個問題。
df = pd.lreshape(df,
{'Sales': file_df.columns[file_df.columns.str.match(r'^Sales\.?\d?')],
'Revenue': file_df.columns[file_df.columns.str.match(r'^Revenue\.?\d?')]})
大話西游666
TA貢獻1817條經驗 獲得超14個贊
這是堆疊列的另一種方法。不確定它是否更有效，但它需要更少的代碼。
# JANUARY FEBRUARY MARCH
# ID Sales Revenue Sales Revenue Sales Revenue
# 03 10.00 5.00 0.00 0.00 10.00 19.00
# 05 20.00 20.00 20.00 20.00 20.00 20.00
# 06 30.00 30.00 30.00 30.00 30.00 30.00
# 07 30.00 30.00 30.00 30.00 30.00 30.00
import pandas as pd
dd = {
'ID':['03','05','06','07'],
'Sales1':[10,20,30,30],
'Rev1':[5,20,30,30],
'Sales2':[0,20,30,30],
'Rev2':[0,20,30,30],
'Sales3':[10,20,30,30],
'Rev3':[19,20,30,30]
}
df = pd.DataFrame(dd)
print(df.to_string(index=False),'\n') # source dataframe
####################
dfnew = pd.DataFrame(columns = ['ID', 'Sales', 'Revenue']) # new dataframe with all data
for c in range(1,len(df.columns),2):
dftmp = df[['ID',df.columns[c],df.columns[c+1]]] # create df for each month
dftmp.columns = ['ID', 'Sales', 'Revenue'] # must rename columns for append
dfnew = dfnew.append(dftmp) # append to stacked df
print(dfnew.to_string(index=False))
輸出
ID Sales1 Rev1 Sales2 Rev2 Sales3 Rev3
03 10 5 0 0 10 19
05 20 20 20 20 20 20
06 30 30 30 30 30 30
07 30 30 30 30 30 30
ID Sales Revenue
03 10 5
05 20 20
06 30 30
07 30 30
03 0 0
05 20 20
06 30 30
07 30 30
03 10 19
05 20 20
06 30 30
07 30 30
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