如何在pandas数据帧中找到行的iloc？

Question

我有一个索引的pandas数据帧.通过搜索其索引,我发现了一排感兴趣.我如何找到这一行的iloc？

例:

dates = pd.date_range('1/1/2000', periods=8)
df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
df
                   A         B         C         D
2000-01-01 -0.077564  0.310565  1.112333  1.023472
2000-01-02 -0.377221 -0.303613 -1.593735  1.354357
2000-01-03  1.023574 -0.139773  0.736999  1.417595
2000-01-04 -0.191934  0.319612  0.606402  0.392500
2000-01-05 -0.281087 -0.273864  0.154266  0.374022
2000-01-06 -1.953963  1.429507  1.730493  0.109981
2000-01-07  0.894756 -0.315175 -0.028260 -1.232693
2000-01-08 -0.032872 -0.237807  0.705088  0.978011

window_stop_row = df[df.index < '2000-01-04'].iloc[-1]
window_stop_row
Timestamp('2000-01-08 00:00:00', offset='D')
#which is the iloc of window_stop_row?

Answer 1

您需要该.name属性并将其传递给get_loc:

In [131]:
dates = pd.date_range('1/1/2000', periods=8)
df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
df

Out[131]:
                   A         B         C         D
2000-01-01  0.095234 -1.000863  0.899732 -1.742152
2000-01-02 -0.517544 -1.274137  1.734024 -1.369487
2000-01-03  0.134112  1.964386 -0.120282  0.573676
2000-01-04 -0.737499 -0.581444  0.528500 -0.737697
2000-01-05 -1.777800  0.795093  0.120681  0.524045
2000-01-06 -0.048432 -0.751365 -0.760417 -0.181658
2000-01-07 -0.570800  0.248608 -1.428998 -0.662014
2000-01-08 -0.147326  0.717392  3.138620  1.208639

In [133]:    
window_stop_row = df[df.index < '2000-01-04'].iloc[-1]
window_stop_row.name

Out[133]:
Timestamp('2000-01-03 00:00:00', offset='D')

In [134]:
df.index.get_loc(window_stop_row.name)

Out[134]:
2

get_loc 返回索引中标签的序号位置,这是您想要的:

In [135]:    
df.iloc[df.index.get_loc(window_stop_row.name)]

Out[135]:
A    0.134112
B    1.964386
C   -0.120282
D    0.573676
Name: 2000-01-03 00:00:00, dtype: float64

如果你只想搜索索引,那么只要它被排序,那么你可以使用searchsorted:

In [142]:
df.index.searchsorted('2000-01-04') - 1

Out[142]:
2

Answer 2

虽然pandas.Index.get_loc()仅当您只有一个键时才有效，但以下范例也适用于获取iloc多个元素：

np.argwhere(condition).flatten()   # array of all iloc where condition is True

在您的情况下，选择最新的元素，其中df.index < '2000-01-04'：

np.argwhere(df.index < '2000-01-04').flatten()[-1]  # returns 2