熊猫切割–连续到分类
原文:https://www.geesforgeks.org/pandas-cut-continuous-to-classic/
数据分析中经常会看到连续的、高度倾斜的数据等数字数据。有时，从连续数据转换到离散数据时，分析变得毫不费力。有许多方法可以进行转换，其中一种方法是使用熊猫的综合切割功能。熊猫切割函数是将数值连续数据转换为分类数据的一种独特方式。它有三个主要的必要部分:
- 首先是输入所需的一维数组/数据帧。
- 另一个主要部分是垃圾箱。表示连续数据的独立箱的边界的箱。第一个数字表示容器的起点，后面的数字表示容器的终点。切割功能允许更清晰的箱柜
- 最后的主要部分是标签。标签的数量无一例外地比箱子的数量少一个。
注意:对于任何 NA 值，结果将存储为 NA。在结果分类箱中，超出范围的值也将是“不适用”。
在使用 pandas cut 函数时，它无法保证值在每个箱中的分布。事实上，我们最终可能会以这样一种方式定义容器，即容器可能不包含任何值。
语法: pandas.cut(x，bin，right=True，labels=None，retbins = False，precision=3，include_low = False，duplicates='raise '，ordered=True)
参数:
- x: 输入数组。需要是一维的。
- 面元:表示分割的面元边界
- 右侧:表示是否应包含箱的最右边。布尔类型的值。默认值为真。
- 标签:定义返回的分段箱的标签。数组或布尔值
返回值:返回分类序列/数值数组/区间索引
示例 1: 假设我们有一个由 15 个从 1 到 100 的随机数组成的数组“Age ”,我们希望将数据分成 4 个类别仓–
'Baby/Toddler' :- 0 to 3 years
'Child' :- 4 to 17 years
'Adult' :- 18 to 63 years
'Elderly' :- 64 to 99 years
蟒蛇 3
# Importing pandas and numpy libraries
import pandas as pd
import numpy as np
# Creating a dummy DataFrame of 15 numbers randomly
# ranging from 1-100 for age
df = pd.DataFrame({'Age': [42, 15, 67, 55, 1, 29, 75, 89, 4,
10, 15, 38, 22, 77]})
# Printing DataFrame Before sorting Continuous
# to Categories
print("Before: ")
print(df)
# A column of name 'Label' is created in DataFrame
# Categorizing Age into 4 Categories
# Baby/Toddler: (0,3], 0 is excluded & 3 is included
# Child: (3,17], 3 is excluded & 17 is included
# Adult: (17,63], 17 is excluded & 63 is included
# Elderly: (63,99], 63 is excluded & 99 is included
df['Label'] = pd.cut(x=df['Age'], bins=[0, 3, 17, 63, 99],
labels=['Baby/Toddler', 'Child', 'Adult',
'Elderly'])
# Printing DataFrame after sorting Continuous to
# Categories
print("After: ")
print(df)
# Check the number of values in each bin
print("Categories: ")
print(df['Label'].value_counts())
输出:
Before:
Age
0 42
1 15
2 67
3 55
4 1
5 29
6 75
7 89
8 4
9 10
10 15
11 38
12 22
13 77
After:
Age Label
0 42 Adult
1 15 Child
2 67 Elderly
3 55 Adult
4 1 Baby/Toddler
5 29 Adult
6 75 Elderly
7 89 Elderly
8 4 Child
9 10 Child
10 15 Child
11 38 Adult
12 22 Adult
13 77 Elderly
Categories:
Adult 5
Elderly 4
Child 4
Baby/Toddler 1
Name: Label, dtype: int64
示例#2: 假设我们有一个 12 人的“高度”数组，从 150 厘米到 180 厘米随机开始，我们希望将数据分成 3 个类别。
'Short' :- greater than 150cm upto 157cm
'Average' :- greater than 157cm upto 170cm
'Tall' :- greater than 170cm upto 180cm
蟒蛇 3
# Importing pandas and numpy libraries
import pandas as pd
import numpy as np
# Creating a dummy DataFrame of 12 numbers randomly
# ranging from 150-180 for height
df = pd.DataFrame({'Height': [150.4, 157.6, 170, 176, 164.2, 155,
159.2, 175, 162.4, 176, 153, 170.9]})
# Printing DataFrame Before Sorting Continuous to Categories
print("Before: ")
print(df)
# A column of name 'Label' is created in DataFrame
# Categorizing Height into 3 Categories
# Short: (150,157], 150 is excluded & 157 is included
# Average: (157,169], 157 is excluded & 169 is included
# Tall: (169,180], 169 is excluded & 180 is included
df['Label'] = pd.cut(x=df['Height'],
bins=[150, 157, 169, 180],
labels=['Short', 'Average', 'Tall'])
# Printing DataFrame After Sorting Continuous to Categories
print("After: ")
print(df)
# Check the number of values in each bin
print("Categories: ")
print(df['Label'].value_counts())
输出:
Before:
Height
0 150.4
1 157.6
2 170.0
3 176.0
4 164.2
5 155.0
6 159.2
7 175.0
8 162.4
9 176.0
10 153.0
11 170.9
After:
Height Label
0 150.4 Short
1 157.6 Average
2 170.0 Tall
3 176.0 Tall
4 164.2 Average
5 155.0 Short
6 159.2 Average
7 175.0 Tall
8 162.4 Average
9 176.0 Tall
10 153.0 Short
11 170.9 Tall
Categories:
Tall 5
Average 4
Short 3
Name: Label, dtype: int64
