我試圖跳過(guò)如下所示的 Pandas 時(shí)間表中的一些站點(diǎn)： departure arrival in out0 a b 1 01 b '#delete' 2 02 '#delete' d 0 33 d e 1 1我嘗試跳過(guò)時(shí)間表中的 #delete 值并加入輸入和輸出值： departure arrival in out0 a b 1 01 b d 2 32 d e 1 1有誰(shuí)知道如何實(shí)現(xiàn)這一目標(biāo)？編輯：對(duì)文的解決方案稍作修改對(duì)我有用：df = df.mask(df=="#delete")df.arrival = df.arrival.fillna(method='ffill')df.departure = df.departure.fillna(method='bfill')df = df.groupby(['arrival', 'departure']).sum()
2 回答
烙印99
TA貢獻(xiàn)1829條經(jīng)驗(yàn) 獲得超13個(gè)贊
更像是自定義fillna問(wèn)題
df=df.mask(df=="'#delete'")
df.departure=df.departure.ffill()
df.arrival=df.arrival.bfill()
df.groupby(['departure','arrival'],as_index=False).sum()
Out[761]:
departure arrival in out
0 a b 1 0
1 b d 2 3
2 d e 1 1
PIPIONE
TA貢獻(xiàn)1829條經(jīng)驗(yàn) 獲得超9個(gè)贊
像這樣的東西（未經(jīng)測(cè)試）：
skipfrom = np.where(df.arrival == '#delete')[0]
skipto = skipfrom + 1
df.arrival[skipfrom] = df.arrival[skipto].values
df.out[skipfrom] = df.out[skipto].values
df = df[df.departure != '#delete']
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