使用Str中的常数值添加PANDAS DF中的日期列

Question

我在pandas df

中有一个表格

    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

我想添加我在str。

中定义的新列"日期"

dateSelect = "'2016-11-06'"

所以我添加了一个新的常数列

df['dates'] = dateSelect 

但我得到的结果为

   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'

日期中的值以引号为单位。和

type(df['dates']) = str

，但我希望以日期格式，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:[e-mail].x/dbname', echo=False)
df.to_sql(name='tablename', con=engine, if_exists = 'append', index=False)

Answer 1

最直接路由

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

Answer 2

我认为您可以在空空间中使用第一个replace '，然后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

Answer 3

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)

给出输出 -

0.228258825751
0.167258402887

平均。

结论在这种情况下，使用pd_datetime更有效

Answer 4

在其中不要避免将其定义为字符串。

dateSelect = '2016-11-06'  
df['dates'] = dateSelect 

Answer 5

为日期范围pd.date_range函数是最好的。

dataFrame["Date Column"] = pd.date_range("1/08/2020", periods=len(dataFrame))

最好！