将函数应用于数据框；时间戳.dt [英] applying function to dataframe; timestamp.dt

问题描述

最终我想计算从 df['start'] 中的每个日期到本月最后一天的天数并填充 'count' 列结果.

Ultimately I want to calculate the number of days to the last day of the month from every date in df['start'] and populate the 'count' column with the result.

作为实现该目标的第一步，calendar.monthrange方法接受(年，月)参数并返回一个(第一个工作日，天数)元组.

As a first step towards that goal the calendar.monthrange method takes (year, month) arguments and returns a (first weekday, number of days) tuple.

将函数应用于数据框或系列对象似乎存在普遍错误.我想了解，为什么这不起作用.

There seems to be a general mistake regarding applying functions to dataframes or series objects. I would like to understand, why this isn't working.

import numpy as np
import pandas as pd
import calendar

def last_day(row):
    return calendar.monthrange(row['start'].dt.year, row['start'].dt.month)

这一行引发了一个 AttributeError: "Timestamp object has no attribute 'dt'":

This line raises an AttributeError: "Timestamp object has no attribute 'dt'":

df['count'] = df.apply(last_day, axis=1)

这是我的数据框的样子:

this is what my dataframe looks like:

       start  count
0 2016-02-15    NaN
1 2016-02-20    NaN
2 2016-04-23    NaN

df.dtypes

start    datetime64[ns]
count           float64
dtype: object

推荐答案

删除 .dt.这在访问某种向量时通常是需要的.但是当访问单个元素时，它已经是一个 datetime 对象:

Remove the .dt. This is generally needed when accessing a vector of some sort. But when accessing an individual element it will already be a datetime object:

def last_day(row):
    return calendar.monthrange(row['start'].year, row['start'].month)

为什么:

这个 apply 调用 last_day 并传递一个系列.

Why:

This apply calls last_day and passes a Series.

df['count'] = df.apply(last_day, axis=1)

在 last_day 中，然后选择系列的单个元素:

In last_day you then select a single element of the series:

row['start'].year

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