循环到日期并将功能应用于 pandas 数据框 [英] looping into dates and apply function to pandas dataframe

问题描述

我试图检测事件发生的第一个日期:在产品A的数据框中(请参见数据透视表)，我在2017-04-03首次存储了20个项目.

I'm trying to detect the first dates when an event occur: here in my dataframe for the product A (see pivot table) I have 20 items stored for the first time on 2017-04-03.

所以我想创建一个新变量调用new_var_2017-04-03来存储增量.另一方面，在第二天2017-04-04，我不介意该项目现在是50个而不是20个，我只想存储第一个事件

so I want to create a new variable calle new_var_2017-04-03 that store the increment. On the other hand on the next day 2017-04-04 I don't mind if the item is now 50 instead of 20, I only want to store only the 1st event

它给了我几个错误，我想至少知道它背后的整个逻辑是否有意义，这是"pythonic"，还是我以错误的方式完全搞错了

It gives me several errors, I would like to know at least if the entire logic behind it makes sense, it's "pythonic", or if I'm completeley on the wrong way

raw_data = {'name': ['B','A','A','B'],'date' : pd.to_datetime(pd.Series(['2017-03-30','2017-03-31','2017-04-03','2017-04-04'])),
    'age': [10,20,50,30]}
df1 = pd.DataFrame(raw_data, columns = ['date','name','age'])


table=pd.pivot_table(df1,index=['name'],columns=['date'],values=['age'],aggfunc='sum')
table

我将日期传递到列表中

dates=df1['date'].values.tolist()

我想向后循环进入我的列表日期"，并在发生事件时创建一个变量. 伪代码:对于i-1，我的意思是列表中位于i之前的项目

I want to do a backward loop into my list "dates" and create a variable if an event occurs. pseudo code: with i-1 I mean the item before i in the list

def my_fun(x,list):
    for i in reversed(list):
        if (x[i]-x[i-1])>0 :
            x[new_var+i]=x[i]-x[i-1]
    else:
        x[new_var+i]=0
return x  

print (df.apply(lambda x: my_fun(x,dates), axis=1))

所需的输出:

raw_data2 = {'new_var': ['new_var_2017-03-30','new_var_2017-03-31','new_var_2017-04-03','new_var_2017-04-04'],'result_a': [np.nan,20,np.nan,np.nan],'result_b': [10,np.nan,np.nan,np.nan]}
df2= pd.DataFrame(raw_data2, columns = ['new_var','result_a','result_b'])

df2.T

推荐答案

让我们尝试一下:

df1['age'] = df1.groupby('name')['age'].transform(lambda x: (x==x.min())*x)
df1.pivot_table(index='name', columns='date', values='age').replace(0,np.nan)


date  2017-03-30  2017-03-31  2017-04-03  2017-04-04
name                                                
A            NaN        20.0         NaN         NaN
B           10.0         NaN         NaN         NaN

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