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DOC: update the Series.memory_usage() docstring #20086

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Merged
merged 6 commits into from
Mar 10, 2018
Merged

DOC: update the Series.memory_usage() docstring #20086

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ad7f06f
Docstring for memory_usage now follows Pandas convention.
lebigot Mar 9, 2018
601ffd7
Clearer wording.
lebigot Mar 9, 2018
da08897
Removed useless concrete address.
lebigot Mar 9, 2018
6c0205d
Slightly more realistic example.
lebigot Mar 9, 2018
6626d67
Added related function.
lebigot Mar 10, 2018
cea218f
More common example of a Series of objects. Also an interesting Panda…
lebigot Mar 10, 2018
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53 changes: 40 additions & 13 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2696,28 +2696,55 @@ def reindex_axis(self, labels, axis=0, **kwargs):
return self.reindex(index=labels, **kwargs)

def memory_usage(self, index=True, deep=False):
"""Memory usage of the Series
"""
Return the memory usage of the Series.

The memory usage can optionally include the contribution of
the index and of elements of `object` dtype.

Parameters
----------
index : bool
Specifies whether to include memory usage of Series index
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
index : bool, default True
Specifies whether to include the memory usage of the Series index.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned value.

Returns
-------
scalar bytes of memory consumed

Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False
int
Bytes of memory consumed.

See Also
--------
numpy.ndarray.nbytes
numpy.ndarray.nbytes : Total bytes consumed by the elements of the
array.

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Maybe also DataFrame.memory_usage ?

Examples
--------

>>> s = pd.Series(range(3))
>>> s.memory_usage()
104

Not including the index gives the size of the rest of the data, which
is necessarily smaller:

>>> s.memory_usage(index=False)
24

The memory footprint of `object` values is ignored by default:

>>> class MyClass: pass
>>> s = pd.Series(MyClass())
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I would maybe use an existing python object (something from the standard library, eg decimal.Decimal, IPAddress object, ... or your other favorite python object)

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Or, maybe also useful, to just use some strings. Because of storing them as object (typical gotcha in pandas), there memory is not reflected well by default in this

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Yes, I like the idea of using strings.

You'll need to make sure the string is is not interned by the interprepter, so don't use just pd.Series(['a', 'b'])

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@lebigot lebigot Mar 10, 2018 •
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Why not use interned strings? They are longer than their pointer, so they also are a good example no?

>>> s = pd.Series(["a", "b"])
>>> s.memory_usage()
96
>>> s.memory_usage(deep=True)
212

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so apparantly it is giving the memory of that object, so for the example that looks fine (but I would expect that if you have repeated interned strings, it might then not give the correct result)

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@lebigot lebigot Mar 11, 2018 •
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A repeated interned string does not give the real memory footprint indeed:

>>> id("a")
4397773352
>>> id("a")
4397773352
>>> s = pd.Series(["a"]*1000)
>>> s.memory_usage()
8080
>>> s.memory_usage(deep=True)
66080

>>> s
0 <__main__.MyClass object at ...>
dtype: object
>>> s.memory_usage()
88
>>> s.memory_usage(deep=True)
120
"""
v = super(Series, self).memory_usage(deep=deep)
if index:
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