Python | Pandas TimedeltaIndex.memory_usage
Last Updated :
30 Jan, 2023
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 TimedeltaIndex.memory_usage() function return the memory usage of the given TimedeltaIndex object. It returns the number of bytes required to store the object.
Syntax : TimedeltaIndex.memory_usage(deep=False) Parameters : deep : Introspect the data deeply, interrogate object dtypes for system-level memory consumption Return : bytes used
Example #1: Use TimedeltaIndex.memory_usage() function to find the memory usage of the given TimedeltaIndex object.
Python3
import pandas as pd
tidx = pd.TimedeltaIndex(data = [ '3 days 06:05:01.000030' , '1 days 06:05:01.000030' ,
'3 days 06:05:01.000030' , '1 days 02:00:00' ,
'21 days 06:15:01.000030' ])
print (tidx)
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Output : Now we will use the TimedeltaIndex.memory_usage() function to find the memory needed to store the object.
Python3
tidx.memory_usage(deep = True )
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Output : As we can see in the output, the TimedeltaIndex.memory_usage() function has returned 40 indicating that 40 Bytes are required to store the given TimedeltaIndex object. Example #2: Use TimedeltaIndex.memory_usage() function to find the memory usage of the given TimedeltaIndex object.
Python3
import pandas as pd
tidx = pd.TimedeltaIndex(data = [ '06:05:01.000030' , '3 days 06:05:01.000030' ,
'22 day 2 min 3us 10ns' , '+23:59:59.999999' ,
'13 days 06:05:01.000030' , '+12:19:59.999999' ])
print (tidx)
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Output : Now we will use the TimedeltaIndex.memory_usage() function to find the memory needed to store the object.
Python3
tidx.memory_usage(deep = True )
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Output : As we can see in the output, the TimedeltaIndex.memory_usage() function has returned 48 indicating that 48 Bytes are required to store the given TimedeltaIndex object.
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