python中分组排序

2023-08-14 04:59| 来源: 网络整理| 查看: 265

1.python 中分组统计

1.1按性别统计出年龄最大，最小，平均值

import pandas as pd df = pd.read_excel(r'./data.xlsx') print(df) ages = df.groupby(['gender'])['age'] ages_min = ages.min() ages_max = ages.max() ages_mean = ages.mean() print(ages_min) print(ages_max) print(ages_mean) ''' 输出结果 gender 女 16 男 12 Name: age, dtype: int64 gender 女 32 男 32 Name: age, dtype: int64 gender 女 25.25 男 17.20 Name: age, dtype: float64 '''

1.2生成一列sum_age 对age 进行累加

df['sum_age'] = df['age'].cumsum() print(df)

1.3新生成一列sum_age_new 按照gender和is_good对age进行累加

df['sum_age_new'] = df.groupby(['gender','is_good'])['age'].cumsum() print(df)

2.python中排序问题

2.1 按照年龄进行排序

df['rank'] = df['age'].rank() df['rank_mean'] = df['age'].rank(method='average') df['rank_min'] = df['age'].rank(method='min') df['rank_max'] = df['age'].rank(method='max') df['rank_first'] = df['age'].rank(method='first') print(df)

根据不同的性别对年龄进行排序

df['rank_g'] = df.groupby(['gender'])['age'].rank() print(df)

2.2在排序的过程中遇到两个数值相同，空置的排序情况，在这种条件下rank如何进行参数设置

首先排序过程中存在相同的数值时？

rank（）函数参数设置

1.method : {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’ 主要用来当排序时存在相同值参数设置；

默认为average平均值：年龄为32的数值，排序应该为8,9取平均值则为8.5

min:排序中最小值，年龄排序中取值为8

max：排序中最大值，年龄排序中取值9

first:同样数值按照值出现的前后进行排序 5号性别为男的年龄排序为8,7号性别为女的排序为9

dense: like ‘min’, but rank always increases by 1 between groups 排序时当值相同时，相同的值为同一排名类似min值排序，后续值排名在此排名基础上加一

2.na_option : {‘keep’, ‘top’, ‘bottom’}, default ‘keep’ 当排序数据中存在空值时，默认值设置为keep

How to rank NaN values:

keep: assign NaN rank to NaN values 默认空值不参与排序top: assign smallest rank to NaN values if ascending 默认为升序时从空值为最小值排序bottom: assign highest rank to NaN values if ascending 默认升序时空置为 df['rank'] = df['age'].rank(method='first') df['rank_k'] = df['age'].rank(method='first',na_option='keep') df['rank_t'] = df['age'].rank(method='first',na_option='top') df['rank_b'] = df['age'].rank(method='first',na_option='bottom') print(df)

data['rank'] = data.groupby(['Name_y'])['Salary'].rank(ascending=False,method='dense') print(data)

3.对salary进行降序排序，对于排序中相同salary值按照emp_no的大小进行排序

在使用pandas时先按照emp_no和salary进行值的排序，然后再进行rank（method=‘dense’）排序

df = pd.DataFrame({'emp_no':[10001,10002,10003,10004,10005,10006,10007,10010,10009,10011],'salary':[88958,72527,43311,74057,94692,43311,88070,94409,94409,25828]}) print(df) df['排序-1'] = df.sort_values(by=['emp_no','salary'])['salary'].rank(method='first',ascending=False) dt = df.sort_values(by=['排序-1']) print(dt)

df['排序-1'] = df['salary'].rank(method='dense',ascending=False) dt = df.sort_values(by=['排序-1','emp_no']) print(dt)

【本文地址】

python中分组排序

python中分组排序

今日新闻

推荐新闻