在Pandas数据框中转换日期格式 [英] Converting date formats in pandas dataframe
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问题描述
我有一个数据框,日期"列有两种不同类型的日期格式.
I have a dataframe and the Date column has two different types of date formats going on.
eg. 1983-11-10 00:00:00 and 10/11/1983
我希望它们都是同一类型,如何遍历数据框的日期"列并将日期转换为一种格式?
I want them all to be the same type, how can I iterate through the Date column of my dataframe and convert the dates to one format?
推荐答案
我相信您需要to_datetime
:
I believe you need parameter dayfirst=True
in to_datetime
:
df = pd.DataFrame({'Date': {0: '1983-11-10 00:00:00', 1: '10/11/1983'}})
print (df)
Date
0 1983-11-10 00:00:00
1 10/11/1983
df['Date'] = pd.to_datetime(df.Date, dayfirst=True)
print (df)
Date
0 1983-11-10
1 1983-11-10
因为:
df['Date'] = pd.to_datetime(df.Date)
print (df)
Date
0 1983-11-10
1 1983-10-11
或者您可以指定两种格式,然后使用 combine_first
:
Or you can specify both formats and then use combine_first
:
d1 = pd.to_datetime(df.Date, format='%Y-%m-%d %H:%M:%S', errors='coerce')
d2 = pd.to_datetime(df.Date, format='%d/%m/%Y', errors='coerce')
df['Date'] = d1.combine_first(d2)
print (df)
Date
0 1983-11-10
1 1983-11-10
多种格式的通用解决方案:
General solution for multiple formats:
from functools import reduce
def convert_formats_to_datetimes(col, formats):
out = [pd.to_datetime(col, format=x, errors='coerce') for x in formats]
return reduce(lambda l,r: pd.Series.combine_first(l,r), out)
formats = ['%Y-%m-%d %H:%M:%S', '%d/%m/%Y']
df['Date'] = df['Date'].pipe(convert_formats_to_datetimes, formats)
print (df)
Date
0 1983-11-10
1 1983-11-10
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