大家好，我需要您的幫助才能在 pandas 數據框中獲得預期的輸出/結果。我有一個包含數據的文件，如下所示：Time/Location ValueLocation1 Today 3Next day 0Weekend -6Next week 1Location2 Today 2Next day -1Weekend 3Next week 2Location3 Today 1Next day 3Weekend 1Next week -1Location4 Today 3Next day 2Weekend 5Next week 4Location5 Today 4Next day 2Weekend 3Next week 1Location6 Today -1Next day 3Weekend 3Next week 2并期望輸出如下，它為“位置”創建新列。Location Time ValueLocation1 Today 3Location1 Next day 0Location1 Weekend -6Location1 Next week 1Location2 Today 2Location2 Next day -1Location2 Weekend 3Location2 Next week 2Location3 Today 1Location3 Next day 3Location3 Weekend 1Location3 Next week -1Location4 Today 3Location4 Next day 2Location4 Weekend 5Location4 Next week 4Location5 Today 4Location5 Next day 2Location5 Weekend 3Location5 Next week 1Location6 Today -1Location6 Next day 3Location6 Weekend 3Location6 Next week 2我感謝任何幫助/建議....拜托！
1 回答
MYYA
TA貢獻1868條經驗 獲得超4個贊
如果缺少的值不存在，Value則使用DataFrame.insert替換值以在第一列中丟失并通過 前向填充它們，最后按和列ffill刪除行：DataFrame.dropnarename
df.insert(0, 'Location', df['Time/Location'].mask(df['Value'].notna()).ffill())
df = df.dropna(subset=['Value']).rename(columns={'Time/Location':'Time'})
print (df)
Location Time Value
1 Location1 Today 3.0
2 Location1 Next day 0.0
3 Location1 Weekend -6.0
4 Location1 Next week 1.0
6 Location2 Today 2.0
7 Location2 Next day -1.0
8 Location2 Weekend 3.0
9 Location2 Next week 2.0
11 Location3 Today 1.0
12 Location3 Next day 3.0
13 Location3 Weekend 1.0
14 Location3 Next week -1.0
16 Location4 Today 3.0
17 Location4 Next day 2.0
18 Location4 Weekend 5.0
19 Location4 Next week 4.0
21 Location5 Today 4.0
22 Location5 Next day 2.0
23 Location5 Weekend 3.0
24 Location5 Next week 1.0
26 Location6 Today -1.0
27 Location6 Next day 3.0
28 Location6 Weekend 3.0
29 Location6 Next week 2.0
另一個想法是測試第一列中的值Series.isin并過濾boolean indexing：
L = ['Today','Next day','Weekend','Next week']
m = df['Time/Location'].isin(L)
df.insert(0, 'Location', df['Time/Location'].mask(m).ffill())
df = df[m].rename(columns={'Time/Location':'Time'})
print (df)
Location Time Value
1 Location1 Today 3.0
2 Location1 Next day 0.0
3 Location1 Weekend -6.0
4 Location1 Next week 1.0
6 Location2 Today 2.0
7 Location2 Next day -1.0
8 Location2 Weekend 3.0
9 Location2 Next week 2.0
11 Location3 Today 1.0
12 Location3 Next day 3.0
13 Location3 Weekend 1.0
14 Location3 Next week -1.0
16 Location4 Today 3.0
17 Location4 Next day 2.0
18 Location4 Weekend 5.0
19 Location4 Next week 4.0
21 Location5 Today 4.0
22 Location5 Next day 2.0
23 Location5 Weekend 3.0
24 Location5 Next week 1.0
26 Location6 Today -1.0
27 Location6 Next day 3.0
28 Location6 Weekend 3.0
29 Location6 Next week 2.0
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