嗨,我正在使用pandas将列转换为月份.当我读取我的数据时,它们就是对象:
Date object
dtype: object
所以我首先让它们到达约会时间,然后尝试将它们作为月份:
import pandas as pd
file = '/pathtocsv.csv'
df = pd.read_csv(file, sep = ',', encoding='utf-8-sig', usecols= ['Date', 'ids'])
df['Date'] = pd.to_datetime(df['Date'])
df['Month'] = df['Date'].dt.month
如果这有帮助:
In [10]: df['Date'].dtype
Out[10]: dtype('O')
所以,我得到的错误是这样的:
/Library/Frameworks/Python.framework/Versions/2.7/bin/User/lib/python2.7/site-packages/pandas/core/series.pyc in _make_dt_accessor(self)
2526 return maybe_to_datetimelike(self)
2527 except Exception:
-> 2528 raise AttributeError("Can only use .dt accessor with datetimelike "
2529 "values")
2530
AttributeError: Can only use .dt accessor with datetimelike values
编辑:
日期列如下:
0 2014-01-01
1 2014-01-01
2 2014-01-01
3 2014-01-01
4 2014-01-03 …这是我通过pd.read_csv()读取的输入csv文件
ProductCode,Date,Receipt,Total
x1,07/29/15,101790,17.35
x2,07/29/15,103601,8.89
x3,07/29/15,103601,8.58
x4,07/30/15,101425,11.95
x5,07/29/15,101422,1.09
x6,07/29/15,101422,0.99
x7,07/29/15,101422,3
y7,08/05/15,100358,7.29
x8,08/05/15,100358,2.6
z3,08/05/15,100358,2.99
import pandas as pd
df = pd.read_csv('product.csv')
#I have to add some columns to the data:
df['Receipt_Count'] = df.groupby(['Date','Receipt'])['Receipt'].transform('count')
df['Day_of_Week'] = pd.to_datetime(df['Date']).dt.weekday_name
我的csv文件中有大约800K的行.当我运行将日期转换为weekday_name的代码行时,我需要大约2分钟.我知道我首先将我的'Date'列转换为datetime,因为它被视为来自csv的字符串,然后它被转换为它的工作日等价物.有什么办法可以缩短转换时间吗？
我对Pandas/Python很新,所以我不确定我是否错过了这里的内容.