Python 将熊猫数据框中的多列更改为日期时间
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change multiple columns in pandas dataframe to datetime
提问by kwashington122
I have a dataframe of 13 columns and 55,000 rows I am trying to convert 5 of those rows to datetime, right now they are returning the type 'object' and I need to transform this data for machine learning I know that if I do
我有一个包含 13 列和 55,000 行的数据框，我正在尝试将其中的 5 行转换为日期时间，现在它们正在返回“对象”类型，我需要转换这些数据以进行机器学习我知道如果我这样做
data['birth_date'] = pd.to_datetime(data[birth_date], errors ='coerce')
it will return a datetime column but I want to do it for 4 other columns as well, is there one line that I can write to call all of them? I dont think I can index like
它将返回一个日期时间列，但我也想为其他 4 个列执行此操作，是否可以编写一行来调用所有这些列？我不认为我可以索引
data[:,7:12]
thanks!
谢谢！
回答by Ted Petrou
You can use applyto iterate through each column using pd.to_datetime
您可以使用apply迭代每列pd.to_datetime
data.iloc[:, 7:12] = data.iloc[:, 7:12].apply(pd.to_datetime, errors='coerce')
回答by mel el
my_df[['column1','column2']] =
my_df[['column1','column2']].apply(pd.to_datetime, format='%Y-%m-%d %H:%M:%S.%f')
Note: of course the format can be changed as required.
注意：当然可以根据需要更改格式。
回答by SerialDev
If performance is a concern I would advice to use the following function to convert those columns to date_time:
如果性能是一个问题，我建议使用以下函数将这些列转换为 date_time：
def lookup(s):
"""
This is an extremely fast approach to datetime parsing.
For large data, the same dates are often repeated. Rather than
re-parse these, we store all unique dates, parse them, and
use a lookup to convert all dates.
"""
dates = {date:pd.to_datetime(date) for date in s.unique()}
return s.apply(lambda v: dates[v])
to_datetime: 5799 ms
dateutil: 5162 ms
strptime: 1651 ms
manual: 242 ms
lookup: 32 ms
Source: https://github.com/sanand0/benchmarks/tree/master/date-parse
来源：https: //github.com/sanand0/benchmarks/tree/master/date-parse
回答by smishra
If you rather want to convert at load time, you could do something like this
如果你想在加载时转换，你可以做这样的事情
date_columns = ['c1','c2', 'c3', 'c4', 'c5']
data = pd.read_csv('file_to_read.csv', parse_dates=date_columns)
回答by sgDysregulation
First you need to extract all the columns your interested in from datathen you can use pandas applymapto apply to_datetimeto each element in the extracted frame, I assume you know the index of the columns you want to extract, In the code below column names of the third to the sixteenth columns are extracted. you can alternatively define a list and add the names of the columns to it and use that in place, you may also need to pass the date/time format of the the DateTime entries
首先，您需要从中提取您感兴趣的所有列，data然后您可以使用 pandasapplymap将其应用于to_datetime提取的框架中的每个元素，我假设您知道要提取的列的索引，在第三个列名下面的代码中到第十六列被提取。您也可以定义一个列表并将列的名称添加到其中并在适当的位置使用它，您可能还需要传递 DateTime 条目的日期/时间格式
import pandas as pd
cols_2_extract = data.columns[2:15]
data[cols_2_extract] = data[cols_2_extract].applymap(lambda x : pd.to_datetime(x, format = '%d %M %Y'))
