Python 在数据帧中给定一个pd.Interval列，根据区间范围内的值进行筛选
我想给数据帧中的每一行分配一个间隔，这样所有行就不会重叠，并且覆盖整个可能的范围。因此，我可以根据给定间隔内的值筛选行 我使用了Python 在数据帧中给定一个pd.Interval列，根据区间范围内的值进行筛选,python,pandas,dataframe,Python,Pandas,Dataframe,我想给数据帧中的每一行分配一个间隔，这样所有行就不会重叠，并且覆盖整个可能的范围。因此，我可以根据给定间隔内的值筛选行 我使用了pd.Interval，但当我尝试“正常”筛选时，它不起作用： df=pd.DataFrame（{“评级”：[“差”、“平均”、“好”]， “星号”：[pd.间隔（左=0，右=2，闭合=“左”）， pd.间隔（左=2，右=4，闭合=“左”）， pd.Interval（左=4，右=5，closed=“both”）]} 星值=2.5 过滤的_df=df[星_值在df.sta
pd.Intervaldf=pd.DataFrame（{“评级”：[“差”、“平均”、“好”]，
“星号”：[pd.间隔（左=0，右=2，闭合=“左”），
pd.间隔（左=2，右=4，闭合=“左”），
pd.Interval（左=4，右=5，closed=“both”）]}
星值=2.5
过滤的_df=df[星_值在df.stars中]
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2656 try:
-> 2657 return self._engine.get_loc(key)
2658 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: False
评级明星
1平均值[2,4]
filtered=[]
for i in df.index:
filtered.append(stars_val in df.stars[i])
filtered_df=df[filtered]
rating stars
1 average [2, 4)
False
[False, True, False]
说明：
stars_val in df.stars
rating stars
1 average [2, 4)
False
[False, True, False]
filtered
rating stars
1 average [2, 4)
False
[False, True, False]
因此，
df[filtered]stars\u valfiltered_df = df[[stars_val in x for x in df.stars]]
rating stars
1 average [2, 4)
如果您的所有时间间隔都是相同的
关闭的In [2]: np.random.seed(123)
In [3]: start = np.random.randint(100, size=1000)
In [4]: ia = pd.arrays.IntervalArray.from_arrays(start, start + 5)
In [5]: df = pd.DataFrame({'A': list('abcde') * 200, 'B': ia})
In [6]: df.head()
Out[6]:
A B
0 a (66, 71]
1 b (92, 97]
2 c (98, 103]
3 d (17, 22]
4 e (83, 88]
In [7]: df[df['B'].array.contains(70)]
Out[7]:
A B
0 a (66, 71]
20 a (68, 73]
23 d (67, 72]
27 c (66, 71]
45 a (69, 74]
87 c (67, 72]
111 b (65, 70]
128 d (68, 73]
133 d (65, 70]
135 a (67, 72]
155 a (65, 70]
177 c (69, 74]
193 d (67, 72]
217 c (69, 74]
221 b (66, 71]
223 d (69, 74]
227 c (66, 71]
243 d (66, 71]
250 a (67, 72]
251 b (65, 70]
263 d (68, 73]
407 c (65, 70]
419 e (69, 74]
425 a (65, 70]
446 b (69, 74]
449 e (69, 74]
451 b (66, 71]
523 d (66, 71]
552 c (68, 73]
589 e (66, 71]
609 e (69, 74]
613 d (68, 73]
627 c (69, 74]
637 c (68, 73]
650 a (67, 72]
674 e (69, 74]
711 b (69, 74]
769 e (67, 72]
777 c (69, 74]
800 a (66, 71]
803 d (68, 73]
818 d (69, 74]
822 c (67, 72]
883 d (66, 71]
889 e (68, 73]
944 e (67, 72]
953 d (69, 74]
966 b (65, 70]
混合
closed对象