Frequently Asked Questions (FAQ)

DataFrame memory usage

As of pandas version 0.15.0, the memory usage of a dataframe (including the index) is shown when accessing the info method of a dataframe. A configuration option, display.memory_usage (see Options and Settings), 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 df.info():

In [1]: dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
   ...:           'complex128', 'object', 'bool']
   ...: 

In [2]: n = 5000

In [3]: data = dict([ (t, np.random.randint(100, size=n).astype(t))
   ...:                 for t in dtypes])
   ...: 

In [4]: df = pd.DataFrame(data)

In [5]: df['categorical'] = df['object'].astype('category')

In [6]: df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 8 columns):
bool               5000 non-null bool
complex128         5000 non-null complex128
datetime64[ns]     5000 non-null datetime64[ns]
float64            5000 non-null float64
int64              5000 non-null int64
object             5000 non-null object
timedelta64[ns]    5000 non-null timedelta64[ns]
categorical        5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
memory usage: 284.1+ KB

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, that accounts for the full usage of the contained objects. This is optional as it can be expensive to do this deeper introspection.

In [7]: df.info(memory_usage='deep')
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 8 columns):
bool               5000 non-null bool
complex128         5000 non-null complex128
datetime64[ns]     5000 non-null datetime64[ns]
float64            5000 non-null float64
int64              5000 non-null int64
object             5000 non-null object
timedelta64[ns]    5000 non-null timedelta64[ns]
categorical        5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int64(1), object(1), timedelta64[ns](1)
memory usage: 401.2 KB

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 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 of the dataframe can be found with the memory_usage method:

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

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:

In [10]: df.memory_usage(index=False)
Out[10]: 
bool                5000
complex128         80000
datetime64[ns]     40000
float64            40000
int64              40000
object             40000
timedelta64[ns]    40000
categorical         5800
dtype: int64

The memory usage displayed by the info method utilizes the 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 Categorical Memory Usage.

Byte-Ordering Issues

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. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar to the following:

In [11]: x = np.array(list(range(10)), '>i4') # big endian

In [12]: newx = x.byteswap().newbyteorder() # force native byteorder

In [13]: s = pd.Series(newx)

See the NumPy documentation on byte order for more details.

Visualizing Data in Qt applications

There is no support for such visualization in pandas. However, the external package pandas-qt does provide this functionality.