数据框某列分段方法

[slsongge]

问题

在python中，对数据框的某列使用if-else语法进行分段？

代码

import pandas as pd
import numpy as np

np.random.seed(12)
age = np.random.randint(low = 0, high = 100, size = 1000)
df_raw = pd.DataFrame({'age': age})

## 方法一：pd.cut()
df_raw['age_cut_1'] = pd.cut(df_raw['age'], bins=[0,18,30,60,1000], right=False, labels=['A','B','C','D'])

## 方法二：np.select()
conditions = [(df_raw['age'] < 18), 
              (df_raw['age'] < 30), 
              (df_raw['age'] < 60), 
              (df_raw['age'] >= 60)]
choices = ['A','B','C','D']
df_raw['age_cut_2'] = np.select(conditions, choices)

## 方法三：if-else

[JiaxiangBU]

#30

import pandas as pd
import numpy as np

np.random.seed(12)
age = np.random.randint(low = 0, high = 100, size = 1000)
df_raw = pd.DataFrame({'age': age})

## 方法一：pd.cut()
df_raw['age_cut_1'] = pd.cut(df_raw['age'], bins=[0,18,30,60,1000], right=False, labels=['A','B','C','D'])

df_raw.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	age	age_cut_1
0	75	D
1	27	B
2	6	A
3	2	A
4	3	A

## 方法二：np.select()
conditions = [(df_raw['age'] < 18), 
              (df_raw['age'] < 30), 
              (df_raw['age'] < 60), 
              (df_raw['age'] >= 60)]
choices = ['A','B','C','D']
df_raw['age_cut_2'] = np.select(conditions, choices)

df_raw.head()

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	age	age_cut_1	age_cut_2
0	75	D	D
1	27	B	B
2	6	A	A
3	2	A	A
4	3	A	A

# https://mp.weixin.qq.com/s/6Kda5qGzX6IZyYi8McDruA
# https://blog.csdn.net/weixin_39750084/article/details/103437665
# 不要忘记 return
def int2cat(x):
    if x < 18:
        return 'A'
    elif x < 30:
        return 'B'
    elif x < 60:
        return 'C'
    elif x >= 60:
        return 'D'
    else:
        return 'E'

df_raw['age'].apply(int2cat).head()

0    D
1    B
2    A
3    A
4    A
Name: age, dtype: object

# https://stackoverflow.com/questions/9987483/elif-in-list-comprehension-conditionals
df_raw['age'].apply(lambda age: 'A' if age < 18 else 'B' if age < 30 else 'C' if age < 60 else 'D').head()

0    D
1    B
2    A
3    A
4    A
Name: age, dtype: object

[JiaxiangBU]

@slsongge 晓松，见 commit cffbe1d 见 notebook