Datetime data type ---now() gets the current time, datetime() creation date, performs mathematical operations, and to_ Datetime() converts to date type and extracts various parts of date

Catalog

Get the current date and time

Date of creation  

Yes datetime Do math

Convert to datetime data type

Parts of the extraction date

---

Get the current date and time

>>> from datetime import datetime as dt
>>> print(dt.now())
2022-01-11 11:22:23.612976

Date of creation  

>>> t1=dt(1999,5,23)
>>> print(t1)
1999-05-23 00:00:00

Yes datetime Do math

>>> t1=dt(1999,5,23)
>>> t2=dt.now()
>>> diff=t2-t1
>>> print(diff)
8269 days, 11:26:14.647381
>>> print(type(diff))
<class 'datetime.timedelta'>

Convert to datetime data type

>>> import pandas as pd
>>> ebola=pd.read_csv(r'D:\pandas Flexible use \pandas_for_everyone-master\data/country_timeseries.csv')
>>> print(ebola.iloc[:5,:5])
         Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
0    1/5/2015  289        2776.0            NaN            10030.0
1    1/4/2015  288        2775.0            NaN             9780.0
2    1/3/2015  287        2769.0         8166.0             9722.0
3    1/2/2015  286           NaN         8157.0                NaN
4  12/31/2014  284        2730.0         8115.0             9633.0
>>> print(ebola.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 122 entries, 0 to 121
Data columns (total 18 columns):
 #   Column               Non-Null Count  Dtype
---  ------               --------------  -----
 0   Date                 122 non-null    object
 1   Day                  122 non-null    int64
 2   Cases_Guinea         93 non-null     float64
 3   Cases_Liberia        83 non-null     float64
 4   Cases_SierraLeone    87 non-null     float64
 5   Cases_Nigeria        38 non-null     float64
 6   Cases_Senegal        25 non-null     float64
 7   Cases_UnitedStates   18 non-null     float64
 8   Cases_Spain          16 non-null     float64
 9   Cases_Mali           12 non-null     float64
 10  Deaths_Guinea        92 non-null     float64
 11  Deaths_Liberia       81 non-null     float64
 12  Deaths_SierraLeone   87 non-null     float64
 13  Deaths_Nigeria       38 non-null     float64
 14  Deaths_Senegal       22 non-null     float64
 15  Deaths_UnitedStates  18 non-null     float64
 16  Deaths_Spain         16 non-null     float64
 17  Deaths_Mali          12 non-null     float64
dtypes: float64(16), int64(1), object(1)
memory usage: 17.3+ KB
None

>>> ebola['date_dt']=pd.to_datetime(ebola['Date'])
>>> print(ebola.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 122 entries, 0 to 121
Data columns (total 19 columns):
 #   Column               Non-Null Count  Dtype
---  ------               --------------  -----
 0   Date                 122 non-null    object
 1   Day                  122 non-null    int64
 2   Cases_Guinea         93 non-null     float64
 3   Cases_Liberia        83 non-null     float64
 4   Cases_SierraLeone    87 non-null     float64
 5   Cases_Nigeria        38 non-null     float64
 6   Cases_Senegal        25 non-null     float64
 7   Cases_UnitedStates   18 non-null     float64
 8   Cases_Spain          16 non-null     float64
 9   Cases_Mali           12 non-null     float64
 10  Deaths_Guinea        92 non-null     float64
 11  Deaths_Liberia       81 non-null     float64
 12  Deaths_SierraLeone   87 non-null     float64
 13  Deaths_Nigeria       38 non-null     float64
 14  Deaths_Senegal       22 non-null     float64
 15  Deaths_UnitedStates  18 non-null     float64
 16  Deaths_Spain         16 non-null     float64
 17  Deaths_Mali          12 non-null     float64
 18  date_dt              122 non-null    datetime64[ns]
dtypes: datetime64[ns](1), float64(16), int64(1), object(1)
memory usage: 18.2+ KB
None

# Convert to date format or specify date format , Be careful format The specified date format is the acquired date format 
>>> ebola['date_dt']=pd.to_datetime(ebola['Date'],format='%m/%d/%Y')

>>> print(ebola.iloc[:5,:5])
         Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
0    1/5/2015  289        2776.0            NaN            10030.0
1    1/4/2015  288        2775.0            NaN             9780.0
2    1/3/2015  287        2769.0         8166.0             9722.0
3    1/2/2015  286           NaN         8157.0                NaN
4  12/31/2014  284        2730.0         8115.0             9633.0
>>> ebola['date_dt']=pd.to_datetime(ebola['Date'],format='%d/%m/%Y')
# Due to the output above ebola The data shows that , The fifth row of data has 31, It cannot be used as a month , Therefore, the following error occurs 
ValueError: time data '12/31/2014' does not match format '%d/%m/%Y' (match)

For the format of how to represent the date, please refer to  Python strftime Behavior _ I am a little monster blog -CSDN Blog

Load data containing dates

When reading the file, we can go through read_csv Parameters in parse_dates To specify the column that you want to resolve to a date

>>> import pandas as pd
>>> ebola=pd.read_csv(r'D:\pandas Flexible use \pandas_for_everyone-master\data/country_timeseries.csv',parse_dates=[0])
>>> print(ebola.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 122 entries, 0 to 121
Data columns (total 18 columns):
 #   Column               Non-Null Count  Dtype
---  ------               --------------  -----
 0   Date                 122 non-null    datetime64[ns]
 1   Day                  122 non-null    int64
 2   Cases_Guinea         93 non-null     float64
 3   Cases_Liberia        83 non-null     float64
 4   Cases_SierraLeone    87 non-null     float64
 5   Cases_Nigeria        38 non-null     float64
 6   Cases_Senegal        25 non-null     float64
 7   Cases_UnitedStates   18 non-null     float64
 8   Cases_Spain          16 non-null     float64
 9   Cases_Mali           12 non-null     float64
 10  Deaths_Guinea        92 non-null     float64
 11  Deaths_Liberia       81 non-null     float64
 12  Deaths_SierraLeone   87 non-null     float64
 13  Deaths_Nigeria       38 non-null     float64
 14  Deaths_Senegal       22 non-null     float64
 15  Deaths_UnitedStates  18 non-null     float64
 16  Deaths_Spain         16 non-null     float64
 17  Deaths_Mali          12 non-null     float64
dtypes: datetime64[ns](1), float64(16), int64(1)
memory usage: 17.3 KB
None

Parts of the extraction date

year\month\day\quarter

>>> t1=dt(1999,5,23)
>>> print(t1)
1999-05-23 00:00:00
>>> print(type(t1))
<class 'datetime.datetime'>
>>> print(t1.year)
1999
>>> print(t1.month)
5
>>> print(t1.day)
23
>>> print(ebola.loc[0:3,'Date'].dt.quarter)
0    1
1    1
2    1
3    1