Question

我有一个看起来像这样的Pandas DF：

我想使用本地定义的int参数'days'来过滤DF。例如当days = 10时，我过滤的DF只有最后10个日期的数据。

到现在为止，我尝试过以下方法：

days=10    
cutoff_date = df["SeriesDate"][-1:] - datetime.timedelta(days=days)

但是，然后尝试使用以下方法输出已过滤的DF：

df[df['SeriesDate'] > cutoff_date]

我得到了以下错误：

ValueError: Can only compare identically-labeled Series objects

我还在学习Python，所以我将非常感谢能够获得的任何帮助。

Answer 1

我认为您需要按iloc选择列SeriesDate的最后一个值：

start = pd.to_datetime('2015-02-24')
rng = pd.date_range(start, periods=15, freq='20H')
df = pd.DataFrame({'SeriesDate': rng, 'Value_1': np.random.random(15)})  
print (df)
            SeriesDate   Value_1
0  2015-02-24 00:00:00  0.849160
1  2015-02-24 20:00:00  0.332487
2  2015-02-25 16:00:00  0.687638
3  2015-02-26 12:00:00  0.310326
4  2015-02-27 08:00:00  0.660795
5  2015-02-28 04:00:00  0.354475
6  2015-03-01 00:00:00  0.061312
7  2015-03-01 20:00:00  0.443908
8  2015-03-02 16:00:00  0.708326
9  2015-03-03 12:00:00  0.257419
10 2015-03-04 08:00:00  0.618363
11 2015-03-05 04:00:00  0.121625
12 2015-03-06 00:00:00  0.637324
13 2015-03-06 20:00:00  0.058292
14 2015-03-07 16:00:00  0.047624

days=10    
cutoff_date = df["SeriesDate"].iloc[-1] - pd.Timedelta(days=days)
print (cutoff_date)
2015-02-25 16:00:00

df1 = df[df['SeriesDate'] > cutoff_date] 
print (df1)
            SeriesDate   Value_1
3  2015-02-26 12:00:00  0.310326
4  2015-02-27 08:00:00  0.660795
5  2015-02-28 04:00:00  0.354475
6  2015-03-01 00:00:00  0.061312
7  2015-03-01 20:00:00  0.443908
8  2015-03-02 16:00:00  0.708326
9  2015-03-03 12:00:00  0.257419
10 2015-03-04 08:00:00  0.618363
11 2015-03-05 04:00:00  0.121625
12 2015-03-06 00:00:00  0.637324
13 2015-03-06 20:00:00  0.058292
14 2015-03-07 16:00:00  0.047624

另一种选择是使用max，感谢Pocin：

cutoff_date = df["SeriesDate"].max() - pd.Timedelta(days=days)
print (cutoff_date)
2015-02-25 16:00:00

如果您只希望按dates进行过滤：

days=10    
cutoff_date = df["SeriesDate"].dt.date.iloc[-1] - pd.Timedelta(days=days)
print (cutoff_date)
2015-02-25

编辑：

您可以使用dayofweek过滤掉周末的日期，然后使用isin

start = pd.to_datetime('2015-02-24')
rng = pd.date_range(start, periods=15)
df = pd.DataFrame({'SeriesDate': rng, 'Value_1': np.random.random(15)})  
print (df)
   SeriesDate   Value_1
0  2015-02-24  0.498387
1  2015-02-25  0.435767
2  2015-02-26  0.299233
3  2015-02-27  0.489286
4  2015-02-28  0.892167
5  2015-03-01  0.507436
6  2015-03-02  0.360427
7  2015-03-03  0.903886
8  2015-03-04  0.718148
9  2015-03-05  0.645489
10 2015-03-06  0.251285
11 2015-03-07  0.139275
12 2015-03-08  0.756845
13 2015-03-09  0.565863
14 2015-03-10  0.148077

days=10    
last_day = df["SeriesDate"].dt.date.iloc[-1]
cutoff_date = last_day - pd.Timedelta(days=days)
rng = pd.date_range(cutoff_date, last_day)

rng = rng[(rng.dayofweek != 0) & (rng.dayofweek != 6)]
print (rng)
DatetimeIndex(['2015-02-28', '2015-03-03', '2015-03-04', '2015-03-05',
               '2015-03-06', '2015-03-07', '2015-03-10'],
              dtype='datetime64[ns]', freq=None)

df1 = df[df['SeriesDate'].isin(rng)]
print (df1)
   SeriesDate   Value_1
4  2015-02-28  0.892167
7  2015-03-03  0.903886
8  2015-03-04  0.718148
9  2015-03-05  0.645489
10 2015-03-06  0.251285
11 2015-03-07  0.139275
14 2015-03-10  0.148077

在过去的n个日期过滤Pandas DataFrame

1 个答案: