我有以下資料，我想在其中添加一個新列，即當前的月環比百分比變化。日期是我的資料框中的索引
date close
1/26/1990 421.2999878
1/29/1990 418.1000061
1/30/1990 410.7000122
1/31/1990 415.7999878
2/23/1990 419.5
2/26/1990 421
2/27/1990 422.6000061
2/28/1990 425.7999878
3/26/1990 438.7999878
3/27/1990 439.5
3/28/1990 436.7000122
3/29/1990 435.3999939
3/30/1990 435.5
我能想到的最簡單的方法是添加一個包含上個月結束日期的列，并且為了方便起見，上個月結束的“關閉”值 - 然后我可以計算當前的月度改變。所以最后，我會有一個看起來像這樣的表：
我能夠很好地添加上月末，但是我現在在嘗試根據上月結束日期查找上月末收盤時遇到問題。在下面的代碼中，第一行可以很好地將上個月的結束日期添加為新列。但第二個沒有 - 想法是使用 prev_month_end 日期查找月末收盤值并將其添加為列。
df['prev_month_end'] = df.index pd.offsets.BMonthEnd(-1)
df['prev_month_close'] = df[df.index == df['prev_month_end']]
任何有關如何實作這一點的幫助或建議將不勝感激。
uj5u.com熱心網友回復：
你可以有prev_month_close如下：
df.reset_index(inplace=True)
df = df[['date', 'close', 'prev_month_end']].merge(df[['date', 'close']].rename(columns={'close': 'prev_month_close',
'date': 'prev_month_end'}),
how='left', on='prev_month_end')
OUTPUT
date close prev_month_end prev_month_close
0 1990-01-26 421.299988 1989-12-29 NaN
1 1990-01-29 418.100006 1989-12-29 NaN
2 1990-01-30 410.700012 1989-12-29 NaN
3 1990-01-31 415.799988 1989-12-29 NaN
4 1990-02-23 419.500000 1990-01-31 415.799988
5 1990-02-26 421.000000 1990-01-31 415.799988
6 1990-02-27 422.600006 1990-01-31 415.799988
7 1990-02-28 425.799988 1990-01-31 415.799988
8 1990-03-26 438.799988 1990-02-28 425.799988
9 1990-03-27 439.500000 1990-02-28 425.799988
10 1990-03-28 436.700012 1990-02-28 425.799988
11 1990-03-29 435.399994 1990-02-28 425.799988
12 1990-03-30 435.500000 1990-02-28 425.799988
或不使用 reset_index
df = df[['close', 'prev_month_end']].merge(df[['close']].rename(columns={'close': 'prev_month_close'}),
how='left', left_on='prev_month_end', right_index=True)
OUTPUT
close prev_month_end prev_month_close
date
1990-01-26 421.299988 1989-12-29 NaN
1990-01-29 418.100006 1989-12-29 NaN
1990-01-30 410.700012 1989-12-29 NaN
1990-01-31 415.799988 1989-12-29 NaN
1990-02-23 419.500000 1990-01-31 415.799988
1990-02-26 421.000000 1990-01-31 415.799988
1990-02-27 422.600006 1990-01-31 415.799988
1990-02-28 425.799988 1990-01-31 415.799988
1990-03-26 438.799988 1990-02-28 425.799988
1990-03-27 439.500000 1990-02-28 425.799988
1990-03-28 436.700012 1990-02-28 425.799988
1990-03-29 435.399994 1990-02-28 425.799988
1990-03-30 435.500000 1990-02-28 425.799988
uj5u.com熱心網友回復：
我們可以將索引轉換為period index，然后group按周期和聚合close使用資料幀last，然后shift是一個月前的周期索引和map收盤值，最后計算百分比變化
i = pd.to_datetime(df.index).to_period('M')
s = i.shift(-1).map(df.groupby(i)['close'].last())
df['mom_pct_change'] = df['close'].sub(s).div(s).mul(100)
close mom_pct_change
date
1/26/1990 421.299988 NaN
1/29/1990 418.100006 NaN
1/30/1990 410.700012 NaN
1/31/1990 415.799988 NaN
2/23/1990 419.500000 0.889854
2/26/1990 421.000000 1.250604
2/27/1990 422.600006 1.635406
2/28/1990 425.799988 2.405002
3/26/1990 438.799988 3.053077
3/27/1990 439.500000 3.217476
3/28/1990 436.700012 2.559893
3/29/1990 435.399994 2.254581
3/30/1990 435.500000 2.278068
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