pandas 使用str中的常量值在pandas df中添加日期列
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/40858880/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Add a date column in pandas df using constant value in str
提问by Shubham R
i have a table in pandas df
我在Pandas df 中有一张桌子
product_id_x product_id_y count
0 2727846 7872456 1
1 29234 2932348 2
2 29346 9137500 1
3 29453 91365738 1
4 2933666 91323494 1
i want to add a new column 'dates' which i have defined in a str.
我想添加一个我在 str 中定义的新列“日期”。
dateSelect = "'2016-11-06'"
so i added a new constant column
所以我添加了一个新的常量列
df['dates'] = dateSelect
but i am getting result as
但我得到的结果是
product_id_x product_id_y count dates
0 2727846 7872456 1 '2016-11-06'
1 29234 2932348 2 '2016-11-06'
2 29346 9137500 1 '2016-11-06'
3 29453 91365738 1 '2016-11-06'
4 2933666 91323494 1 '2016-11-06'
the values in the dates are coming in quotes. and
日期中的值用引号引起来。和
type(df['dates']) = str
but i want it in date format, because further i am going to store this table in my mysql database. and i want the type to be date.
但我希望它采用日期格式，因为我将进一步将此表存储在我的 mysql 数据库中。我希望类型是日期。
from sqlalchemy import create_engine
engine = create_engine('mysql+mysqldb://name:[email protected]/dbname', echo=False)
df.to_sql(name='tablename', con=engine, if_exists = 'append', index=False)
回答by jezrael
I think you can use first replace'by empty space and then to_datetime:
我认为您可以先使用replace'空格，然后使用to_datetime：
dateSelect = pd.to_datetime("'2016-11-06'".replace("'",""))
print (dateSelect)
2016-11-06 00:00:00
print (type(dateSelect))
<class 'pandas.tslib.Timestamp'>
df['dates'] = pd.to_datetime("'2016-11-06'".replace("'",""))
print (df)
product_id_x product_id_y count dates
0 2727846 7872456 1 2016-11-06
1 29234 2932348 2 2016-11-06
2 29346 9137500 1 2016-11-06
3 29453 91365738 1 2016-11-06
4 2933666 91323494 1 2016-11-06
print (df.dtypes)
product_id_x int64
product_id_y int64
count int64
dates datetime64[ns]
dtype: object
回答by piRSquared
most direct route
最直接的路线
df['dates'] = pd.Timestamp('2016-11-06')
df
product_id_x product_id_y count dates
0 2727846 7872456 1 2016-11-06
1 29234 2932348 2 2016-11-06
2 29346 9137500 1 2016-11-06
3 29453 91365738 1 2016-11-06
4 2933666 91323494 1 2016-11-06
回答by Vivek Kalyanarangan
Ahh! @jezrael got there first...
啊！@jezrael 首先到达那里...
print timeit.timeit("""
import pandas as pd
import datetime as dt
import timeit
df = pd.read_csv('date_time_pandas.csv')
dateSelect_str = "2016-11-06"
# using standard datetime
dateSelect = dt.datetime.strptime(dateSelect_str,"%Y-%m-%d")
df['dates'] = dateSelect
#print(df['dates'])
""",number=100)
# Alternate method using pandas datetime
print timeit.timeit("""
import pandas as pd
import datetime as dt
import timeit
df = pd.read_csv('date_time_pandas.csv')
dateSelect_str = "2016-11-06"
dateSelect = pd.to_datetime(dateSelect_str, format='%Y-%m-%d', errors='ignore')
df['dates'] = dateSelect
#print df['dates']
""",number=100)
gives output -
给出输出 -
0.228258825751
0.167258402887
on an average.
平均而言。
ConclusionUsing pd_datetime in this case is more efficient
结论在这种情况下使用 pd_datetime 更有效
回答by Chandan
In it don't put double quote avoiding to define it as string.
在其中不要放双引号，避免将其定义为字符串。
dateSelect = '2016-11-06'
df['dates'] = dateSelect
