CODEWITHSUNDEEP
pandas
Rodrigo Teixeira
Rodrigo Teixeira

Reputation: 157

Grab the Memory Usage Value from Pandas DataFrame.info()

How can I grab the memory usage value (displayed in the output of the funciton DataFrame.info() and assign to a variable?

Upvotes: 9

Views: 5140

Answers (2)

harpan
harpan

Reputation: 8631

As docs says we should have a buffer.

buf : writable buffer, defaults to sys.stdout

For df

import io
impor pandas as pd
df=pd.DataFrame({
    'someCol' : ["foo", "bar"]
}) 
buf = io.StringIO()
df.info(buf=buf)
info = buf.getvalue()
print(info)

Gives me output: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 1 columns):
someCol    2 non-null object
dtypes: object(1)
memory usage: 96.0+ bytes

For specific memory usage value:

info = buf.getvalue().split('\n')[-2]
print(info)

Would give the output: 

memory usage: 96.0+ bytes

Upvotes: 4

Jarad
Jarad

Reputation: 18943

DataFrame.memory_usage().sum()

There's an example on this page:

In [8]: df.memory_usage()
Out[8]: 
Index                 72
bool                5000
complex128         80000
datetime64[ns]     40000
float64            40000
int64              40000
object             40000
timedelta64[ns]    40000
categorical         5800
dtype: int64

# total memory usage of dataframe
In [9]: df.memory_usage().sum()
Out[9]: 290872

Looking at the source code of df.info() shows that using memory_usage() is how they compute the actual memory usage in df.info():

... <last few lines of def info from pandas/frame.py>
    mem_usage = self.memory_usage(index=True, deep=deep).sum()
    lines.append("memory usage: %s\n" %
                 _sizeof_fmt(mem_usage, size_qualifier))
_put_lines(buf, lines)

Upvotes: 11

Related Questions