切分資料¶

• .cut(): 將資料切分到 bins 裡面去。
• .qcut(): 將資料盡量平均分配到指定數量的bin裡面去。

In [1]:

import pandas as pd
import numpy as np

import pandas as pd import numpy as np

In [16]:

df1 = pd.DataFrame(np.linspace(0.5, 9.5, 10))

df1 = pd.DataFrame(np.linspace(0.5, 9.5, 10))

In [17]:

df1

df1

Out[17]:

	0
0	0.5
1	1.5
2	2.5
3	3.5
4	4.5
5	5.5
6	6.5
7	7.5
8	8.5
9	9.5

In [18]:

np.random.seed(987)
df2 = pd.DataFrame(np.random.normal(5, 2, 10))

np.random.seed(987) df2 = pd.DataFrame(np.random.normal(5, 2, 10))

In [19]:

df2

df2

Out[19]:

	0
0	1.628146
1	2.057760
2	4.776400
3	7.072740
4	6.322080
5	6.765825
6	2.833345
7	3.305737
8	5.316556
9	5.198195

.cut()¶

https://pandas.pydata.org/docs/reference/api/pandas.cut.html

In [20]:

pd.cut(df1[0], 3)

pd.cut(df1[0], 3)

Out[20]:

0    (0.491, 3.5]
1    (0.491, 3.5]
2    (0.491, 3.5]
3    (0.491, 3.5]
4      (3.5, 6.5]
5      (3.5, 6.5]
6      (3.5, 6.5]
7      (6.5, 9.5]
8      (6.5, 9.5]
9      (6.5, 9.5]
Name: 0, dtype: category
Categories (3, interval[float64, right]): [(0.491, 3.5] < (3.5, 6.5] < (6.5, 9.5]]

In [21]:

pd.cut(df1[0], range(0, 11, 2))

pd.cut(df1[0], range(0, 11, 2))

Out[21]:

0     (0, 2]
1     (0, 2]
2     (2, 4]
3     (2, 4]
4     (4, 6]
5     (4, 6]
6     (6, 8]
7     (6, 8]
8    (8, 10]
9    (8, 10]
Name: 0, dtype: category
Categories (5, interval[int64, right]): [(0, 2] < (2, 4] < (4, 6] < (6, 8] < (8, 10]]

In [22]:

pd.cut(df1[0], range(0, 11, 2), labels=[1, 2, 3, 4, 5])

pd.cut(df1[0], range(0, 11, 2), labels=[1, 2, 3, 4, 5])

Out[22]:

0    1
1    1
2    2
3    2
4    3
5    3
6    4
7    4
8    5
9    5
Name: 0, dtype: category
Categories (5, int64): [1 < 2 < 3 < 4 < 5]

.qcut()¶

In [23]:

df1

df1

Out[23]:

	0
0	0.5
1	1.5
2	2.5
3	3.5
4	4.5
5	5.5
6	6.5
7	7.5
8	8.5
9	9.5

In [24]:

pd.qcut(df1[0], 5)

pd.qcut(df1[0], 5)

Out[24]:

0    (0.499, 2.3]
1    (0.499, 2.3]
2      (2.3, 4.1]
3      (2.3, 4.1]
4      (4.1, 5.9]
5      (4.1, 5.9]
6      (5.9, 7.7]
7      (5.9, 7.7]
8      (7.7, 9.5]
9      (7.7, 9.5]
Name: 0, dtype: category
Categories (5, interval[float64, right]): [(0.499, 2.3] < (2.3, 4.1] < (4.1, 5.9] < (5.9, 7.7] <
                                           (7.7, 9.5]]

In [25]:

pd.qcut(df2[0], 5)

pd.qcut(df2[0], 5)

Out[25]:

0    (1.627, 2.678]
1    (1.627, 2.678]
2    (4.188, 5.246]
3    (6.411, 7.073]
4    (5.246, 6.411]
5    (6.411, 7.073]
6    (2.678, 4.188]
7    (2.678, 4.188]
8    (5.246, 6.411]
9    (4.188, 5.246]
Name: 0, dtype: category
Categories (5, interval[float64, right]): [(1.627, 2.678] < (2.678, 4.188] < (4.188, 5.246] <
                                           (5.246, 6.411] < (6.411, 7.073]]

In [26]:

pd.qcut(df2[0], 5, labels=[1, 2, 3, 4, 5])

pd.qcut(df2[0], 5, labels=[1, 2, 3, 4, 5])

Out[26]:

0    1
1    1
2    3
3    5
4    4
5    5
6    2
7    2
8    4
9    3
Name: 0, dtype: category
Categories (5, int64): [1 < 2 < 3 < 4 < 5]