如何使pandas数据框由长变宽

Question

这是输入数据框。

input = pd.DataFrame({'order': {0: '1',
  1: '1',
  2: '2',
  3: '2',
  4: '3',
  5: '3'},
 'start': {0: pd.Timestamp('2023-04-01 04:00:00+0000', tz='UTC'),
  1: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC'),
  2: pd.Timestamp('2023-04-01 04:00:00+0000', tz='UTC'),
  3: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC'),
  4: pd.Timestamp('2023-04-01 04:00:00+0000', tz='UTC'),
  5: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC')},
 'end': {0: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC'),
  1: pd.Timestamp('2023-06-01 04:00:00+0000', tz='UTC'),
  2: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC'),
  3: pd.Timestamp('2023-06-01 04:00:00+0000', tz='UTC'),
  4: pd.Timestamp('2023-05-01 04:00:00+0000', tz='UTC'),
  5: pd.Timestamp('2023-06-01 04:00:00+0000', tz='UTC')},
 'quant': {0: 10, 1: 10, 2: 20, 3: 30, 4: 40, 5: 50},
 'price': {0: 44, 1: 44, 2: 5, 3: 6, 4: 8, 5: 8}})

输入df图像->

我的要求是根据 order 扩大这个数据框，所以我的预期输出是

有人可以帮我吗？

Answer 1

首先，尽量不要使用

input

作为变量。这是Python中的预定义函数。

要扩大此数据框，请将开始和结束设置为索引。然后根据

'order'

列取消堆叠。

# Convert 'start' and 'end' to datetime
df['start'] = pd.to_datetime(df['start'])
df['end'] = pd.to_datetime(df['end'])

# Setting 'start' and 'end' as the index and 'order' as a column level
df.set_index(['start', 'end', 'order'], inplace=True)

# Unstack 'order' to make it a top-level column
df_unstacked = df.unstack(level='order')


# Creating multi-level columns for 'quant' and 'price' per 'order'
df_wide = df_unstacked.swaplevel(axis=1).sort_index(axis=1)


df_flat_columns = df_wide.copy()
df_flat_columns.columns = [f'{var}.{order}' if var else f'{order}' for order, var in df_flat_columns.columns]

df_flat_columns.reset_index(inplace=True)

输出：

print(df_flat_columns.to_string())
                      start                       end  price.1  quant.1  price.2  quant.2  price.3  quant.3
0 2023-04-01 04:00:00+00:00 2023-05-01 04:00:00+00:00       44       10        5       20        8       40
1 2023-05-01 04:00:00+00:00 2023-06-01 04:00:00+00:00       44       10        6       30        8       50