在 For 循环中使用 Pandas Dataframe Append

Question

我继续使用pandas.dataframe.append，但是在for循环中追加继续被覆盖并且信息被复制。 for 循环的输出应该与原始数据帧完全一样（我不包括消除任何复杂性的函数）。 任何帮助，将不胜感激。

import pandas as pd
df = pd.DataFrame({
    'date': ['2019-01-01','2019-01-01','2019-01-01',
             '2019-02-01','2019-02-01','2019-02-01',
             '2019-03-01','2019-03-01','2019-03-01',],
    'Asset': ['Asset A', 'Asset A', 'Asset A', 'Asset B', 'Asset B', 'Asset B',
             'Asset C', 'Asset C', 'Asset C'],
    'Monthly Value': [2100, 8100, 1400, 1400, 3100, 1600, 2400, 2100, 2100]
})
print(df.sort_values(by=['Asset']))

         date    Asset  Monthly Value
0  2019-01-01  Asset A           2100
1  2019-01-01  Asset A           8100
2  2019-01-01  Asset A           1400
3  2019-02-01  Asset B           1400
4  2019-02-01  Asset B           3100
5  2019-02-01  Asset B           1600
6  2019-03-01  Asset C           2400
7  2019-03-01  Asset C           2100
8  2019-03-01  Asset C           2100

此 for 循环为 df 创建多个附属物并复制行

assetlist = list(df['Asset'].unique())

for asset in assetlist:
    df_subset = df[df['Asset'] == asset]
    dfcopy = df_subset.copy()
    newdf = newdf.append(dfcopy)
print(newdf)

此输出不正确，它应该与原始数据帧完全一样。

date    Asset   Monthly Value
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100
0   2019-01-01  Asset A 2100
1   2019-01-01  Asset A 8100
2   2019-01-01  Asset A 1400
3   2019-02-01  Asset B 1400
4   2019-02-01  Asset B 3100
5   2019-02-01  Asset B 1600
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100
0   2019-01-01  Asset A 2100
1   2019-01-01  Asset A 8100
2   2019-01-01  Asset A 1400
3   2019-02-01  Asset B 1400
4   2019-02-01  Asset B 3100
5   2019-02-01  Asset B 1600
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100
0   2019-01-01  Asset A 2100
1   2019-01-01  Asset A 8100
2   2019-01-01  Asset A 1400
3   2019-02-01  Asset B 1400
4   2019-02-01  Asset B 3100
5   2019-02-01  Asset B 1600
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100
0   2019-01-01  Asset A 2100
1   2019-01-01  Asset A 8100
2   2019-01-01  Asset A 1400
3   2019-02-01  Asset B 1400
4   2019-02-01  Asset B 3100
5   2019-02-01  Asset B 1600
6   2019-03-01  Asset C 2400
7   2019-03-01  Asset C 2100
8   2019-03-01  Asset C 2100

Answer 1

我认为您缺少一行：

assetlist = list(df['Asset'].unique())
newdf = pd.DataFrame()   # <-- define it as a data frame
for asset in assetlist:
    df_subset = df[df['Asset'] == asset]
    dfcopy = df_subset.copy()
    newdf = newdf.append(dfcopy)
print(newdf)

         date    Asset  Monthly Value
0  2019-01-01  Asset A           2100
1  2019-01-01  Asset A           8100
2  2019-01-01  Asset A           1400
3  2019-02-01  Asset B           1400
4  2019-02-01  Asset B           3100
5  2019-02-01  Asset B           1600
6  2019-03-01  Asset C           2400
7  2019-03-01  Asset C           2100
8  2019-03-01  Asset C           2100

但是，更简单的方法是：

newdf = pd.concat([df.query("Asset == @asset") for asset in assetlist])