Pandas：数据分箱

import pandas as pd
df = pd.read_csv("D:\BaiduNetdiskDownload\data\employees.csv")
df.head()

数据链接：

https://download.csdn.net/download/qq_43494013/91334882?spm=1001.2014.3001.5503

df1 = df.head(10)[['employee_id','salary']]
df1

pd.cut(df1['salary'],bins = 2)

其中bins = n，分成n段区间、起始值、结束值是所有数据的最小值、最大值，其中value_counts()是每个区间元素的个数

pd.cut(df1.salary,bins = 2).value_counts()

自定义区间

pd.cut(df1['salary'],bins = [0,10000,20000,30000])
pd.cut(df1['salary'],bins = [0,10000,20000,30000]).value_counts()

添加标签

df1['收入范围'] = pd.cut(df1['salary'],bins = [0,10000,20000,30000],labels = ['低','中','高'])

平均分配
qcut实现

df1 = df[['employee_id','salary']].head(10)
df1
pd.qcut(df1['salary'],3)
pd.qcut(df1['salary'],3).value_counts()

对睡眠数据进行分箱处理

数据链接：

https://download.csdn.net/download/qq_43494013/91336841?spm=1001.2014.3001.5503

df = pd.read_csv("D:\BaiduNetdiskDownload\data\sleep.csv")
df1 = df.head(10)[["person_id",'sleep_quality']]
df1

df1['睡眠质量'] = pd.cut(df1['sleep_quality'],bins = 3)
df1

df1['睡眠质量'] = pd.cut(df1['sleep_quality'],bins = 3,labels = ['差','中','优'])
df1