时间戳

首页 > 解决方案 > 将多个函数应用于 pandas 数据框

python - 将多个函数应用于 pandas 数据框

问题描述

我正在寻找一种方法来从我的原始数据中集成多个应用功能。这是一些简化的代码。

import pandas as pd 

df = pd.DataFrame({'name':["alice","bob","charlene","alice","bob","charlene","alice","bob","charlene","edna" ],
                   'date':["2020-01-01","2020-01-01","2020-01-01","2020-01-01","2020-01-01","2020-01-01","2020-01-02","2020-01-01","2020-01-02","2020-01-01"],
                   'contribution': [5,5,10,20,30,1,5,5,10,100],
                   'payment-type': ["cash","transfer","cash","transfer","cash","transfer","cash","transfer","cash","transfer",]})
df['date'] = pd.to_datetime(df['date'])

def myfunction(input):
    output = input["name"].value_counts()
    output.index.set_names(['name_x'], inplace=True)
    return output

daily_count = df.groupby(pd.Grouper(key='date', freq='1D')).apply(myfunction)

print(daily_count.reset_index())

输出：

        date    name_x  name
0 2020-01-01       bob     3
1 2020-01-01  charlene     2
2 2020-01-01     alice     2
3 2020-01-01      edna     1
4 2020-01-02  charlene     1
5 2020-01-02     alice     1

我想将此代码的输出集成到先前的结果中。

def myfunction(input):
    output = input["contribution"].sum()
    # output.index.set_names(['name_x'], inplace=True)
    return output
    
daily_count = df.groupby([pd.Grouper(key='date', freq='1D'), "name"]).apply(myfunction)

这会给我类似的东西：

        date      name   num_contrubutions  total_pp
0 2020-01-01       bob                   3        25
1 2020-01-01  charlene                   2        40
2 2020-01-01     alice                   2        11
3 2020-01-01      edna                   1       100
4 2020-01-02  charlene                   1         5
5 2020-01-02     alice                   1        10

使用 apply() 对我来说很重要，因为我计划在函数中进行一些 API 调用和数据库查找。

ta，安德鲁

标签： pythonpandas

解决方案

df.groupby(["date","name"])["contribution"].agg(["count","sum"]).reset_index().sort_values(by="count",ascending=False)

#output

     date       name    count   sum
1   2020-01-01  bob        3    40
0   2020-01-01  alice      2    25
2   2020-01-01  charlene   2    11
3   2020-01-01  edna       1    100
4   2020-01-02  alice      1    5
5   2020-01-02  charlene   1    10

因此，首先，我们按日期和名称分组，然后选择要应用聚合/计算的列，首先是count每个人的贡献。然后我们sum他们。之后为了保持正常的形状dataframe，我们reset_index和我们sort_values by="count"在descending某种程度上。

推荐阅读