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The memory usage of a DataFrame (including the index) is shown when calling
the :meth:`~DataFrame.info`. A configuration option, display.memory_usage
(see :ref:`the list of options <options.available>`), specifies if the
DataFrame's memory usage will be displayed when invoking the df.info()
method.
For example, the memory usage of the DataFrame below is shown
when calling :meth:`~DataFrame.info`:
.. ipython:: python
dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
'complex128', 'object', 'bool']
n = 5000
data = {t: np.random.randint(100, size=n).astype(t) for t in dtypes}
df = pd.DataFrame(data)
df['categorical'] = df['object'].astype('category')
df.info()
The + symbol indicates that the true memory usage could be higher, because
pandas does not count the memory used by values in columns with
dtype=object.
Passing memory_usage='deep' will enable a more accurate memory usage report,
accounting for the full usage of the contained objects. This is optional
as it can be expensive to do this deeper introspection.
.. ipython:: python df.info(memory_usage='deep')
By default the display option is set to True but can be explicitly
overridden by passing the memory_usage argument when invoking df.info().
The memory usage of each column can be found by calling the
:meth:`~DataFrame.memory_usage` method. This returns a Series with an index
represented by column names and memory usage of each column shown in bytes. For
the DataFrame above, the memory usage of each column and the total memory
usage can be found with the memory_usage method:
.. ipython:: python
df.memory_usage()
# total memory usage of dataframe
df.memory_usage().sum()
By default the memory usage of the DataFrame's index is shown in the
returned Series, the memory usage of the index can be suppressed by passing
the index=False argument:
.. ipython:: python
df.memory_usage(index=False)
The memory usage displayed by the :meth:`~DataFrame.info` method utilizes the
:meth:`~DataFrame.memory_usage` method to determine the memory usage of a
DataFrame while also formatting the output in human-readable units (base-2
representation; i.e. 1KB = 1024 bytes).
See also :ref:`Categorical Memory Usage <categorical.memory>`.
pandas follows the NumPy convention of raising an error when you try to convert
something to a bool. This happens in an if-statement or when using the
boolean operations: and, or, and not. It is not clear what the result
of the following code should be:
>>> if pd.Series([False, True, False]):
... passShould it be True because it's not zero-length, or False because there
are False values? It is unclear, so instead, pandas raises a ValueError:
>>> if pd.Series([False, True, False]):
... print("I was true")
Traceback
...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().You need to explicitly choose what you want to do with the DataFrame, e.g.
use :meth:`~DataFrame.any`, :meth:`~DataFrame.all` or :meth:`~DataFrame.empty`.
Alternatively, you might want to compare if the pandas object is None:
>>> if pd.Series([False, True, False]) is not None:
... print("I was not None")
I was not NoneBelow is how to check if any of the values are True:
>>> if pd.Series([False, True, False]).any():
... print("I am any")
I am anyTo evaluate single-element pandas objects in a boolean context, use the method :meth:`~DataFrame.bool`:
.. ipython:: python pd.Series([True]).bool() pd.Series([False]).bool() pd.DataFrame([[True]]).bool() pd.DataFrame([[False]]).bool()
Bitwise boolean operators like == and != return a boolean Series,
which is almost always what you want anyways.
>>> s = pd.Series(range(5))
>>> s == 4
0 False
1 False
2 False
3 False
4 True
dtype: boolSee :ref:`boolean comparisons<basics.compare>` for more examples.
Using the Python in operator on a Series tests for membership in the
index, not membership among the values.
.. ipython:: python
s = pd.Series(range(5), index=list('abcde'))
2 in s
'b' in s
If this behavior is surprising, keep in mind that using in on a Python
dictionary tests keys, not values, and Series are dict-like.
To test for membership in the values, use the method :meth:`~pandas.Series.isin`:
.. ipython:: python
s.isin([2])
s.isin([2]).any()
For DataFrames, likewise, in applies to the column axis,
testing for membership in the list of column names.
For lack of NA (missing) support from the ground up in NumPy and Python in
general, we were given the difficult choice between either:
- A masked array solution: an array of data and an array of boolean values indicating whether a value is there or is missing.
- Using a special sentinel value, bit pattern, or set of sentinel values to
denote
NAacross the dtypes.
For many reasons we chose the latter. After years of production use it has
proven, at least in my opinion, to be the best decision given the state of
affairs in NumPy and Python in general. The special value NaN
(Not-A-Number) is used everywhere as the NA value, and there are API
functions isna and notna which can be used across the dtypes to
detect NA values.
However, it comes with it a couple of trade-offs which I most certainly have not ignored.
In the absence of high performance NA support being built into NumPy from
the ground up, the primary casualty is the ability to represent NAs in integer
arrays. For example:
.. ipython:: python
s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))
s
s.dtype
s2 = s.reindex(['a', 'b', 'c', 'f', 'u'])
s2
s2.dtype
This trade-off is made largely for memory and performance reasons, and also so
that the resulting Series continues to be "numeric".
If you need to represent integers with possibly missing values, use one of the nullable-integer extension dtypes provided by pandas
.. ipython:: python
s_int = pd.Series([1, 2, 3, 4, 5], index=list('abcde'),
dtype=pd.Int64Dtype())
s_int
s_int.dtype
s2_int = s_int.reindex(['a', 'b', 'c', 'f', 'u'])
s2_int
s2_int.dtype
See :ref:`integer_na` for more.
When introducing NAs into an existing Series or DataFrame via
:meth:`~Series.reindex` or some other means, boolean and integer types will be
promoted to a different dtype in order to store the NAs. The promotions are
summarized in this table:
| Typeclass | Promotion dtype for storing NAs |
|---|---|
floating |
no change |
object |
no change |
integer |
cast to float64 |
boolean |
cast to object |
While this may seem like a heavy trade-off, I have found very few cases where this is an issue in practice i.e. storing values greater than 2**53. Some explanation for the motivation is in the next section.
Many people have suggested that NumPy should simply emulate the NA support
present in the more domain-specific statistical programming language R. Part of the reason is the NumPy type hierarchy:
| Typeclass | Dtypes |
|---|---|
numpy.floating |
float16, float32, float64, float128 |
numpy.integer |
int8, int16, int32, int64 |
numpy.unsignedinteger |
uint8, uint16, uint32, uint64 |
numpy.object_ |
object_ |
numpy.bool_ |
bool_ |
numpy.character |
string_, unicode_ |
The R language, by contrast, only has a handful of built-in data types:
integer, numeric (floating-point), character, and
boolean. NA types are implemented by reserving special bit patterns for
each type to be used as the missing value. While doing this with the full NumPy
type hierarchy would be possible, it would be a more substantial trade-off
(especially for the 8- and 16-bit data types) and implementation undertaking.
An alternate approach is that of using masked arrays. A masked array is an
array of data with an associated boolean mask denoting whether each value
should be considered NA or not. I am personally not in love with this
approach as I feel that overall it places a fairly heavy burden on the user and
the library implementer. Additionally, it exacts a fairly high performance cost
when working with numerical data compared with the simple approach of using
NaN. Thus, I have chosen the Pythonic "practicality beats purity" approach
and traded integer NA capability for a much simpler approach of using a
special value in float and object arrays to denote NA, and promoting
integer arrays to floating when NAs must be introduced.
For Series and DataFrame objects, :meth:`~DataFrame.var` normalizes by
N-1 to produce unbiased estimates of the sample variance, while NumPy's
var normalizes by N, which measures the variance of the sample. Note that
:meth:`~DataFrame.cov` normalizes by N-1 in both pandas and NumPy.
As of pandas 0.11, pandas is not 100% thread safe. The known issues relate to
the :meth:`~DataFrame.copy` method. If you are doing a lot of copying of
DataFrame objects shared among threads, we recommend holding locks inside
the threads where the data copying occurs.
See this link for more information.
Occasionally you may have to deal with data that were created on a machine with a different byte order than the one on which you are running Python. A common symptom of this issue is an error like::
Traceback
...
ValueError: Big-endian buffer not supported on little-endian compiler
To deal
with this issue you should convert the underlying NumPy array to the native
system byte order before passing it to Series or DataFrame
constructors using something similar to the following:
.. ipython:: python x = np.array(list(range(10)), '>i4') # big endian newx = x.byteswap().newbyteorder() # force native byteorder s = pd.Series(newx)
See the NumPy documentation on byte order for more details.