Python Pandas——用前一列的值向前填充整行

Question

pandas 开发新手。如何使用先前看到的列中包含的值向前填充 DataFrame？

独立的示例：

import pandas as pd
import numpy as np
O = [1, np.nan, 5, np.nan]
H = [5, np.nan, 5, np.nan]
L = [1, np.nan, 2, np.nan]
C = [5, np.nan, 2, np.nan]
timestamps = ["2017-07-23 03:13:00", "2017-07-23 03:14:00", "2017-07-23 03:15:00", "2017-07-23 03:16:00"]
dict = {'Open': O, 'High': H, 'Low': L, 'Close': C}
df = pd.DataFrame(index=timestamps, data=dict)
ohlc = df[['Open', 'High', 'Low', 'Close']]

这会产生以下数据帧：

print(ohlc)
                     Open  High  Low  Close
2017-07-23 03:13:00   1.0   5.0  1.0    5.0
2017-07-23 03:14:00   NaN   NaN  NaN    NaN
2017-07-23 03:15:00   5.0   5.0  2.0    2.0
2017-07-23 03:16:00   NaN   NaN  NaN    NaN

我想从最后一个 DataFrame 变成这样的：

                     Open  High  Low  Close
2017-07-23 03:13:00   1.0   5.0  1.0    5.0
2017-07-23 03:14:00   5.0   5.0  5.0    5.0
2017-07-23 03:15:00   5.0   5.0  2.0    2.0
2017-07-23 03:16:00   2.0   2.0  2.0    2.0

这样，“关闭”中先前看到的值就会填充整行，直到看到新的填充行。填写“关闭”列非常简单，如下所示：

column2fill = 'Close'
ohlc[column2fill] = ohlc[column2fill].ffill()
print(ohlc)
                     Open  High  Low  Close
2017-07-23 03:13:00   1.0   5.0  1.0    5.0
2017-07-23 03:14:00   NaN   NaN  NaN    5.0
2017-07-23 03:15:00   5.0   5.0  2.0    2.0
2017-07-23 03:16:00   NaN   NaN  NaN    2.0

但是有没有办法用 03:14:00 和 03:16:00 行的“收盘”值填充这些行？有没有一种方法可以使用一次向前填充而不是先填充“关闭”列来一步完成此操作？

Answer 1

看来您需要assignwith ffill，然后bfill每行 by axis=1，但需要完整的NaNs 行：

df = ohlc.assign(Close=ohlc['Close'].ffill()).bfill(axis=1)
print (df)
                     Open  High  Low  Close
2017-07-23 03:13:00   1.0   5.0  1.0    5.0
2017-07-23 03:14:00   5.0   5.0  5.0    5.0
2017-07-23 03:15:00   5.0   5.0  2.0    2.0
2017-07-23 03:16:00   2.0   2.0  2.0    2.0

与以下内容相同：

ohlc['Close'] = ohlc['Close'].ffill()
df = ohlc.bfill(axis=1)
print (df)
                     Open  High  Low  Close
2017-07-23 03:13:00   1.0   5.0  1.0    5.0
2017-07-23 03:14:00   5.0   5.0  5.0    5.0
2017-07-23 03:15:00   5.0   5.0  2.0    2.0
2017-07-23 03:16:00   2.0   2.0  2.0    2.0