使用Python Pandas绑定列
使用Python Pandas绑定列
提问于 2017-07-24 14:13:09
我有一个包含数值的数据框列：
df['percentage'].head()
46.5
44.2
100.0
42.12我希望将该列显示为bin counts
bins = [0, 1, 5, 10, 25, 50, 100]我怎样才能得到带有值计数的结果？
[0, 1] bin amount
[1, 5] etc
[5, 10] etc
...回答 2
Stack Overflow用户
回答已采纳
发布于 2017-07-24 14:14:28
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 5bins = [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...and，然后是value_counts或groupby，然后聚合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: int64s = 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默认情况下，cut返回categorical。
像Series.value_counts()这样的Series方法将使用所有类别，即使数据中没有某些类别，operations in categorical。
Stack Overflow用户
发布于 2022-02-25 00:29:35
我们也可以使用np.select
bins = [0, 1, 5, 10, 25, 50, 100]
df['groups'] = (np.select([df['percentage'].between(i, j, inclusive='right')
for i,j in zip(bins, bins[1:])],
[1, 2, 3, 4, 5, 6]))输出：
percentage groups
0 46.50 5
1 44.20 5
2 100.00 6
3 42.12 5页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/45273731
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