蟒蛇 pandas .日期对象按单独的列拆分.

Python Pandas. Date object split by separate columns.(蟒蛇 pandas .日期对象按单独的列拆分.)
本文介绍了蟒蛇 pandas .日期对象按单独的列拆分.的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

问题描述

我在 Python (pandas) 中有日期写为2010 年 1 月 31 日".要应用线性回归,我希望有 3 个单独的变量:天数、月数、年数.

I have dates in Python (pandas) written as "1/31/2010". To apply linear regression I want to have 3 separate variables: number of day, number of month, number of year.

将 pandas 中的日期列拆分为 3 列的方法是什么?另一个问题是将相同但分组的日子分成 3 组:1-10、11-20、21-31.

What will be the way to split a column with date in pandas into 3 columns? Another question is to have the same but group days into 3 groups: 1-10, 11-20, 21-31.

推荐答案

df['date'] = pd.to_datetime(df['date'])

#Create 3 additional columns
df['day'] = df['date'].dt.day
df['month'] = df['date'].dt.month
df['year'] = df['date'].dt.year

理想情况下,您无需创建 3 个额外的列即可执行此操作,您只需将 Series 传递给您的函数.

Ideally, you can do this without having to create 3 additional columns, you can just pass the Series to your function.

In [2]: pd.to_datetime('01/31/2010').day
Out[2]: 31

In [3]: pd.to_datetime('01/31/2010').month
Out[3]: 1

In [4]: pd.to_datetime('01/31/2010').year
Out[4]: 2010

这篇关于蟒蛇 pandas .日期对象按单独的列拆分.的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

本站部分内容来源互联网,如果有图片或者内容侵犯了您的权益,请联系我们,我们会在确认后第一时间进行删除!

相关文档推荐

Seasonal Decomposition of Time Series by Loess with Python(Loess 用 Python 对时间序列进行季节性分解)
Resample a time series with the index of another time series(使用另一个时间序列的索引重新采样一个时间序列)
How can I simply calculate the rolling/moving variance of a time series in python?(如何在 python 中简单地计算时间序列的滚动/移动方差?)
How to use Dynamic Time warping with kNN in python(如何在python中使用动态时间扭曲和kNN)
Keras LSTM: a time-series multi-step multi-features forecasting - poor results(Keras LSTM:时间序列多步多特征预测 - 结果不佳)
Python pandas time series interpolation and regularization(Python pandas 时间序列插值和正则化)