在 Pandas 数据框中将不同的日期时间格式转换为 MM/DD/YYYY 格式
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Converting different date time formats to MM/DD/YYYY format in pandas dataframe
提问by Chris T.
I have a date column in a pandas.DataFramein various date time formats and stored as list object, like the following:
我有一个pandas.DataFrame各种日期时间格式的日期列，并存储为列表对象，如下所示：
date
1 [May 23rd, 2011]
2 [January 1st, 2010]
...
99 [Apr. 15, 2008]
100 [07-11-2013]
...
256 [9/01/1995]
257 [04/15/2000]
258 [11/22/68]
...
360 [12/1997]
361 [08/2002]
...
463 [2014]
464 [2016]
For the sake of convenience, I want to convert them all to MM/DD/YYYYformat. It doesn't seem possible to use regex replace() function to do this, since one cannot execute this operation over list objects. Also, to use strptime() for each cell will be too time-consuming.
为了方便起见，我想将它们全部转换为MM/DD/YYYY格式。似乎不可能使用正则表达式 replace() 函数来执行此操作，因为无法对列表对象执行此操作。此外，为每个单元格使用 strptime() 将太耗时。
What will be the easier way to convert them all to the desired MM/DD/YYYYformat? I found it very hard to do this on list objects within a dataframe.
将它们全部转换为所需MM/DD/YYYY格式的更简单方法是什么？我发现在数据框中的列表对象上很难做到这一点。
Note: for cell values of the form [YYYY](e.g., [2014]and [2016]), I will assume they are the first day of that year (i.e., January 1, 1968) and for cell values such as [08/2002](or [8/2002]), I will assume they the first day of the month of that year (i.e., August 1, 2002).
注意：对于表单的单元格值[YYYY]（例如，[2014]和[2016]），我将假设它们是那一年的第一天（即 1968 年 1 月 1 日），对于诸如[08/2002](或[8/2002]) 的单元格值，我将假设它们是第一天当年的月份（即 2002 年 8 月 1 日）。
回答by Stephen Rauch
Given your sample data, with the addition of a NaT, this works:
给定您的示例数据，加上 a NaT，这有效：
Code:
代码：
df.date.apply(lambda x: pd.to_datetime(x).strftime('%m/%d/%Y')[0])
Test Code:
测试代码：
import pandas as pd
df = pd.DataFrame([
[['']],
[['May 23rd, 2011']],
[['January 1st, 2010']],
[['Apr. 15, 2008']],
[['07-11-2013']],
[['9/01/1995']],
[['04/15/2000']],
[['11/22/68']],
[['12/1997']],
[['08/2002']],
[['2014']],
[['2016']],
], columns=['date'])
df['clean_date'] = df.date.apply(
lambda x: pd.to_datetime(x).strftime('%m/%d/%Y')[0])
print(df)
Results:
结果：
date clean_date
0 [] NaT
1 [May 23rd, 2011] 05/23/2011
2 [January 1st, 2010] 01/01/2010
3 [Apr. 15, 2008] 04/15/2008
4 [07-11-2013] 07/11/2013
5 [9/01/1995] 09/01/1995
6 [04/15/2000] 04/15/2000
7 [11/22/68] 11/22/1968
8 [12/1997] 12/01/1997
9 [08/2002] 08/01/2002
10 [2014] 01/01/2014
11 [2016] 01/01/2016
回答by Ashu007
It would be better if you use this it'll give you the date format in MM-DD-YYYY the you can apply strftime:
如果你使用它会更好，它会给你 MM-DD-YYYY 的日期格式，你可以应用 strftime：
df['Date_ColumnName'] = pd.to_datetime(df['Date_ColumnName'], dayfirst = False, yearfirst = False)
回答by Sarender Reddy
Provided code will work for following scenarios.
提供的代码适用于以下场景。
- Change date format from M/D/YY to MM/DD/YY (5/2/2009 to 05/02/2009)
- change form ANY FORMAT to MM/DD/YY
- 将日期格式从 M/D/YY 更改为 MM/DD/YY (5/2/2009 到 05/02/2009)
- 将表格任何格式更改为 MM/DD/YY
import pandas as pd
将Pandas导入为 pd
'''
* checking provided input file date format correct or not
* if format is correct change date format from M/D/YY to MM/DD/YY
* else date format is not correct in input file
Date format change form ANY FORMAT to MM/DD/YY
'''
input_file_name = 'C:/Users/Admin/Desktop/SarenderReddy/predictions.csv'
dest_file_name = 'C:/Users/Admin/Desktop/SarenderReddy/Enrich.csv'
#input_file_name = 'C:/Users/Admin/Desktop/SarenderReddy/enrichment.csv'
read_data = pd.read_csv(input_file_name)
print(pd.to_datetime(read_data['Date'], format='%m/%d/%Y', errors='coerce').notnull().all())
if pd.to_datetime(read_data['Date'], format='%m/%d/%Y', errors='coerce').notnull().all():
print("Provided correct input date format in input file....!")
read_data['Date'] = pd.to_datetime(read_data['Date'],format='%m/%d/%Y')
read_data['Date'] = read_data['Date'].dt.strftime('%m/%d/%Y')
read_data.to_csv(dest_file_name,index=False)
print(read_data['Date'])
else:
print("NOT... Provided correct input date format in input file....!")
data_format = pd.read_csv(input_file_name,parse_dates=['Date'], dayfirst=True)
#print(df['Date'])
data_format['Date'] = pd.to_datetime(data_format['Date'],format='%m/%d/%Y')
data_format['Date'] = data_format['Date'].dt.strftime('%m/%d/%Y')
data_format.to_csv(dest_file_name,index=False)
print(data_format['Date'])
