如何将 MultiIndex 转换为 dataframe，其中一个索引作为行，第二个索引作为列，另一列作为值

Question

如何转换以下多索引 dataframe：

		打开	高的	低的	关	体积
象征	日期
4005	2017-12-03 07:00:00	38.75	38.75	38.75	38.75	2518
4333	2018-01-04 09:58:00	12.06	12.06	12.06	12.06	2000
4338	2018-01-22 10:06:00	9.22	9.22	9.22	9.22	10
	2018-01-22 11:23:00	9.22	9.22	9.22	9.22	1000
4334	2017-12-14 08:41:00	9.28	9.28	9.28	9.28	2
4332	2018-01-02 10:19:00	10.30	10.30	10.30	10.30	100
	2018-01-09 08:56:00	10.28	10.30	10.28	10.30	1500

到下表中，以符号为列，“关闭”列为值

	4005	4333	4338	4334	4332
日期
2017-12-03 07:00:00	38.75	楠	楠	楠	楠
2018-01-04 09:58:00	楠	12.06	楠	楠	楠
2018-01-22 10:06:00	楠	楠	9.22	楠	楠
2018-01-22 11:23:00	楠	楠	9.22	楠	楠
2017-12-14 08:41:00	楠	楠	楠	9.28	楠
2018-01-02 10:19:00	楠	楠	楠	楠	10.30
2018-01-09 08:56:00	楠	楠	楠	楠	10.30

Answer 1

使用pivot

设置

import pandas as pd

df = pd.DataFrame({
    'symbol': {0: 4005, 1: 4333, 2: 4338, 3: 4338, 4: 4334, 5: 4332, 6: 4332},
    'date': {0: '2017-12-03 07:00:00', 1: '2018-01-04 09:58:00',
             2: '2018-01-22 10:06:00', 3: '2018-01-22 11:23:00',
             4: '2017-12-14 08:41:00', 5: '2018-01-02 10:19:00',
             6: '2018-01-09 08:56:00'},
    'open': {0: 38.75, 1: 12.06, 2: 9.22, 3: 9.22, 4: 9.28, 5: 10.3, 6: 10.28},
    'high': {0: 38.75, 1: 12.06, 2: 9.22, 3: 9.22, 4: 9.28, 5: 10.3, 6: 10.3},
    'low': {0: 38.75, 1: 12.06, 2: 9.22, 3: 9.22, 4: 9.28, 5: 10.3, 6: 10.28},
    'close': {0: 38.75, 1: 12.06, 2: 9.22, 3: 9.22, 4: 9.28, 5: 10.3, 6: 10.3},
    'volume': {0: 2518, 1: 2000, 2: 10, 3: 1000, 4: 2, 5: 100, 6: 1500}
})
# Recreate Multi-Index Data
df = df.set_index(['symbol', 'date'])

使用透视数据创建new_df 。

# Reset Index and Pivot
new_df = df \
    .reset_index() \
    .pivot(index='date', columns='symbol', values='close') \
    .rename_axis(None, axis=1)

print(new_df)

                      4005  4332   4333  4334  4338
date                                               
2017-12-03 07:00:00  38.75   NaN    NaN   NaN   NaN
2017-12-14 08:41:00    NaN   NaN    NaN  9.28   NaN
2018-01-02 10:19:00    NaN  10.3    NaN   NaN   NaN
2018-01-04 09:58:00    NaN   NaN  12.06   NaN   NaN
2018-01-09 08:56:00    NaN  10.3    NaN   NaN   NaN
2018-01-22 10:06:00    NaN   NaN    NaN   NaN  9.22
2018-01-22 11:23:00    NaN   NaN    NaN   NaN  9.22