Python | Pandas Timestamp.floor

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.floor() function return a new Timestamp floored to this resolution. The function takes the desired time series frequency as an input.

Syntax : Timestamp.floor() Parameters : freq : a freq string indicating the flooring resolution Return : Timestamp

Example #1: Use Timestamp.floor() function to floor the given Timestamp object to Daily time series frequency. Python3

# importing pandas as pd
import pandas as pd

# Create the Timestamp object
ts = pd.Timestamp(year = 2011,  month = 11, day = 21,
           hour = 10, second = 49, tz = 'US/Central')

# Print the Timestamp object
print(ts)

Output : Now we will use the Timestamp.floor() function to floor the ts object to Daily frequency. Python3

# floor the given object to daily frequency
ts.floor(freq ='D')

Output : As we can see in the output, the Timestamp.floor() function has floored the time series frequency of the given Timestamp object to the input frequency. Example #2: Use Timestamp.floor() function to floor the given Timestamp object to minutely time series frequency. Python3

# importing pandas as pd
import pandas as pd

# Create the Timestamp object
ts = pd.Timestamp(year = 2009,  month = 5, day = 31, 
       hour = 4, second = 49, tz = 'Europe/Berlin')

# Print the Timestamp object
print(ts)

Output : Now we will use the Timestamp.floor() function to floor the ts object to minutely frequency. Python3

# floor the given object to minutely frequency
ts.floor(freq ='T')

Output : As we can see in the output, the Timestamp.floor() function has floored the time series frequency of the given Timestamp object to the input frequency.