对每个客户每年的交易趋势进行排名

Question

在 jupyter 上工作，我的数据框有每个客户每年的交易数量和字段，表明“趋势 - 交易量比去年多，交易量比去年少，第一年为空。

我想创建一个分子，每个客户每“上升”一次，就会增加 1，每“下降”一次，就会“减少”1。

我知道我需要首先对 df 进行排序，然后构建一个根据客户数量运行的循环和一个每年都会运行的内部循环，但我需要帮助。

DF 样本：

df = pd.DataFrame({
    'group number': [1,1,1,1,3,3,3],
    'year': ['2012','2013','2014','2015','2011','2012','2013'],
    'trend': [NaN,'down','up','up',NaN,'down','up']
}) 

这是我到目前为止所做的：

df =pd.read_excel('totals_new.xlsx',sheet_name='Sheet1').sort_values(['group number', 'year'])

noofgroups = len(df['group number'].unique())
yearspergroup = df.groupby('group number')['year'].nunique()

vtrend =0

for i in noofgroups:
    for j in yearspergroup:
        if df["trend"] == "up":
            vtrend = vtrend+1
        if df["trend"] == "down":
            vtrend = vtrend-1

Answer 1

这个案子现在可能已经结案了，但是，这是一个可能的解决方案，因为它之前没有得出结论。

import pandas as pd

"""
In this case, the original dataframe is already properly sorted by group number and year.
If it isn't, the 2 columns should be sorted first
"""
df = pd.DataFrame({
    'group number': [1,1,1,1,3,3,3],
    'year': ['2012','2013','2014','2015','2011','2012','2013'],
    'trend': [np.nan,'down','up','up', np.nan,'down','up']
}) 

df['trend_val'] = df.loc[df['trend'].isna() == False, 'trend'].map(lambda x: -1 if x == 'down' else 1)
df.join(df.groupby('group number')['trend_val'].cumsum(), rsuffix='_cumulative')

>>>df
   group number  year trend  trend_val  trend_val_cumulative
0             1  2012   NaN        NaN                   NaN
1             1  2013  down       -1.0                  -1.0
2             1  2014    up        1.0                   0.0
3             1  2015    up        1.0                   1.0
4             3  2011   NaN        NaN                   NaN
5             3  2012  down       -1.0                  -1.0
6             3  2013    up        1.0                   0.0

Answer 2

IIUC，您可以使用嵌套的np.where() 转换您的trend 列，然后执行groupby() 和agg()。拿这个示例数据框：

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'group number': [1,1,1,1,1,1,1,2,2,2,2,2,2,1,1,1,2,2,1,2,1,2],
    'year': ['2017','2016','2018','2017','2016','2018','2017','2016','2018','2017','2016','2018',
        '2017','2016','2018','2017','2016','2018','2017','2016','2018','2017'],
    'trend': ['up','down','up',np.nan,'up','down',np.nan,'up','up','up','down',
        'up',np.nan,'up','up','up','down','up','up','up',np.nan,'down']
    })

产量：

    group number  year trend
0              1  2017    up
1              1  2016  down
2              1  2018    up
3              1  2017   NaN
4              1  2016    up
5              1  2018  down
6              1  2017   NaN
7              2  2016    up
8              2  2018    up
9              2  2017    up
10             2  2016  down
11             2  2018    up
12             2  2017   NaN
13             1  2016    up
14             1  2018    up
15             1  2017    up
16             2  2016  down
17             2  2018    up
18             1  2017    up
19             2  2016    up
20             1  2018   NaN
21             2  2017  down

然后：

df['trend'] = np.where(df['trend']=='up', 1, np.where(df['trend']=='down', -1, 0))

df.groupby(['group number','year']).agg({'trend': 'sum'})

返回：

                   trend
group number year       
1            2016      1
             2017      3
             2018      1
2            2016      0
             2017      0
             2018      3