Answers:
您可以使用pandas.cut
:
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
percentage binned
0 46.50 (25, 50]
1 44.20 (25, 50]
2 100.00 (50, 100]
3 42.12 (25, 50]
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
percentage binned
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5
...然后value_counts
or groupby
和合计size
:
s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50] 3
(50, 100] 1
(10, 25] 0
(5, 10] 0
(1, 5] 0
(0, 1] 0
Name: percentage, dtype: int64
s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1] 0
(1, 5] 0
(5, 10] 0
(10, 25] 0
(25, 50] 3
(50, 100] 1
dtype: int64
默认cut
返回categorical
。
Series
像这样的方法Series.value_counts()
将使用所有类别,即使数据中不存在某些类别,也可以使用categorical 操作。
df.groupby(pd.cut(df['percentage'], bins=bins)).mean()
吗?
numba
模块加速。在大型数据集(500k >
)pd.cut
上,对数据进行合并可能会非常慢。
我编写了自己的函数,numba
并进行了及时编译,这大约16x
要快一些:
from numba import njit
@njit
def cut(arr):
bins = np.empty(arr.shape[0])
for idx, x in enumerate(arr):
if (x >= 0) & (x < 1):
bins[idx] = 1
elif (x >= 1) & (x < 5):
bins[idx] = 2
elif (x >= 5) & (x < 10):
bins[idx] = 3
elif (x >= 10) & (x < 25):
bins[idx] = 4
elif (x >= 25) & (x < 50):
bins[idx] = 5
elif (x >= 50) & (x < 100):
bins[idx] = 6
else:
bins[idx] = 7
return bins
cut(df['percentage'].to_numpy())
# array([5., 5., 7., 5.])
可选:您还可以将其作为字符串映射到垃圾箱:
a = cut(df['percentage'].to_numpy())
conversion_dict = {1: 'bin1',
2: 'bin2',
3: 'bin3',
4: 'bin4',
5: 'bin5',
6: 'bin6',
7: 'bin7'}
bins = list(map(conversion_dict.get, a))
# ['bin5', 'bin5', 'bin7', 'bin5']
速度比较:
# create dataframe of 8 million rows for testing
dfbig = pd.concat([df]*2000000, ignore_index=True)
dfbig.shape
# (8000000, 1)
%%timeit
cut(dfbig['percentage'].to_numpy())
# 38 ms ± 616 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%%timeit
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
pd.cut(dfbig['percentage'], bins=bins, labels=labels)
# 215 ms ± 9.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
bins = [0, 1, 5, 10, 25, 50, 100]
,我可以说创建5个垃圾箱,它将按平均削减量进行削减吗?例如,我有110条记录,我想将它们切成5个槽,每个槽中有22条记录。