dataframe numpy pandas python

# 其他 - 用python pandas 装箱列

``````df['percentage'].head()
46.5
44.2
100.0
42.12
``````

``````bins = [0, 1, 5, 10, 25, 50, 100]
``````

``````[0, 1] bin amount
[1, 5] etc
[5, 10] etc
......
``````

Night Walker

62
jezrael 2017-07-24 14:31

``````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
``````

``````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
``````

`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