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What this article shares with you is about how to deal with the datetime type of Numpy library in Python. The editor thinks it is very practical, so I share it with you to learn. I hope you can get something after reading this article.

With regard to time processing, the processing time modules included in Python are time, datetime, calendar, as well as extended third-party libraries, such as dateutil, and so on. In these ways, you can use Python to deal with time at will. When we use the NumPy library for data analysis, how to convert time?

Since the NumPy 1.7 release, its core array (ndarray) object supports datetime-related functions. Because the data type name 'datetime'' is already used in the datetime module that comes with Python, the type of time data in NumPy is called 'datetime64'.

When a single time format string is converted to a datetime object of numpy, an object can be instantiated using datetime64, as shown below:

# time string to numpy.datetime64datetime_nd=np.datetime64 ('2019-01-01') print (type (datetime_nd)) #

Conversely, the datetime object of numpy is converted to a time format string, using the datetime_as_string () function, as follows:

# numpy.datetime64 to time string datetime_str=np.datetime_as_string (datetime_nd) print (type (datetime_str)) #

When you create an array of datetime objects (array) for numpy from an array of time-formatted strings, you can directly use the numpy.array () function to specify that the elements in the array, such as' datetime64', are of type 'datetime64', as follows:

Datetime_array = np.array ([2019-01-01-02], dtype='datetime64') print (datetime_array) # ['2019-01-05', 2019-01-02 '2019-01-03'] print (type (datetime_array)) # print (datetime_array [0]) #

You can also use the numpy.arange () function to create an array of datetime objects (array) of numpy in a given time starting range. When dtype is specified as' datetime64', the default time interval is day, as shown below:

Datetime_array = np.arange ('2019-01-05) print (datetime_array) # [' 2019-01-05' 2019-01-06' 2019-01-07' 2019-01-08' 2019-01-09']

By setting the dtype parameter in the numpy.arange () function, you can adjust the time interval, such as generating a time array at intervals of year, month, week, or even hours, minutes, and milliseconds, which is the same as Python's datetime module, which is divided into date units and time units. As follows:

# generate year datetime arraydatetime_array = np.arange ('2018-01-01', dtype='datetime64 [Y]') print (datetime_array) # ['2018' '20119] # generate month datetime arraydatetime_array = np.arange (' 2019-01-01') Dtype='datetime64 [M]') print (datetime_array) # ['2019-01', 2019-02', 2019-03, 2019-04, 2019-05, 2019-06, 2019-07, 2019-08, 2019-09] # generate week datetime arraydatetime_array = np.arange (2019-01-05) Dtype='datetime64 [W]') print (datetime_array) # ['2019-01-03' '2019-01-10' '2019-01-17' '2019-01-24' '2019-01-31'] # generate ms datetime arraydatetime_array = np.arange Dtype='datetime64 [ms]') print (datetime_array) # ['2019-01-05T00VOV 00.000'' 2019-01-05T00:00:00.001'# '2019-01-05T00RV 00.002'...' 2019-01-09T23:59:59.997'# '2019-01-09T23VOLNFOR 59.998'' 2019-01-09T23VOT23MORAPHY 59.999']

To convert numpy.datetime64 to datetime format to datetime format, you can convert data types using the astype () method, as follows:

# convert numpy.datetime64 to datetime format datetime_df=datetime_nd.astype (datetime.datetime) print (type (datetime_df)) #

In addition, numpy also provides the function of datetime.timedelta class, which supports the operation of two time objects to get a numerical value in the form of time units. Because the core array (ndarray) object of numpy does not have a physical quantity system (physical quantities system), the timedelta64 data type is created to complement datetime64. The combination of datetime and timedelta provides a simpler datetime calculation method. As follows:

# numpy.datetime64 calculationsdatetime_delta = np.datetime64 ('2009-01-01')-np.datetime64 ('2008-01-01') print (datetime_delta) # 366daysprint (type (datetime_delta)) # datetime_delta = np.datetime64 ('2009') + np.timedelta64 (20,'D') print (datetime_delta) # 2009-01-21datetime_delta = np.datetime64 ('2011-06-15T0000') + np.timedelta64 (12 'h') print (datetime_delta) # 2011-06-15T12:00datetime_delta = np.timedelta64 (1grammatical W') / np.timedelta64 (1recoveryD') print (datetime_delta) # 7.0is how the datetime type of Numpy library in Python is handled. The editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

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