Python | Pandas Timestamp.microsecond

Python | Pandas Series.dt.microsecond

Last Updated : 20 Mar, 2019

Series.dt can be used to access the values of the series as datetimelike and return several properties. Pandas Series.dt.microsecond attribute return a numpy array containing the microsecond of the datetime in the underlying data of the given series object.

Syntax: Series.dt.microsecond

Parameter : None

Returns : numpy array

Example #1: Use Series.dt.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.

# importing pandas as pd 
import pandas as pd 
  
# Creating the Series 
sr = pd.Series(pd.date_range('2012-12-12 12:12', 
                      periods = 5, freq = '5U')) 
  
# Creating the index 
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] 
  
# set the index 
sr.index = idx 
  
# Print the series 
print(sr) 

Output :

Now we will use Series.dt.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.

# return the microsecond 
result = sr.dt.microsecond 
  
# print the result 
print(result) 

Output :

As we can see in the output, the Series.dt.microsecond attribute has successfully accessed and returned the microsecond of the datetime in the underlying data of the given series object.

Example #2 : Use Series.dt.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.

# importing pandas as pd 
import pandas as pd 
  
# Creating the Series 
sr = pd.Series(pd.date_range('2008-2-9 08:20:21', 
                       periods = 5, freq = '9U')) 
  
# Creating the index 
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] 
  
# set the index 
sr.index = idx 
  
# Print the series 
print(sr) 

Output :

Now we will use Series.dt.microsecond attribute to return the microsecond of the datetime in the underlying data of the given Series object.

# return the microsecond 
result = sr.dt.microsecond 
  
# print the result 
print(result) 

Output :

As we can see in the output, the Series.dt.microsecond attribute has successfully accessed and returned the microsecond of the datetime in the underlying data of the given series object.

Python | Pandas Timestamp.microsecond

S

Shubham__Ranjan

Improve

Article Tags :

Practice Tags :

python

Similar Reads

Python | Pandas Timestamp.microsecond

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Timestamp.microsecond attribute return the value of microsecond in the given Ti

Python | Pandas Series.iloc

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be un

Python | Pandas Series.mean()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.mean() function return the me

Python | Pandas Timedelta.microseconds

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Timedelta is a subclass of datetime.timedelta, and behaves in a similar manner. It is

Python | Pandas Series.dtype

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be un

Python | Pandas Series.items()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.items() function iterates ove

Python | Pandas Series.describe()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.describe() function generate

Python | Pandas Series.autocorr()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.autocorr() function compute t

Python | Pandas Series.loc

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be un

Python | Pandas Series.data

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas series is a One-dimensional ndarray with axis labels. The labels need not be un