pandas
python
import numpy as np np.random.seed(123456) np.set_printoptions(precision=4, suppress=True) import pandas as pd pd.options.display.max_rows = 15 import matplotlib matplotlib.style.use('ggplot') import matplotlib.pyplot as plt plt.close('all')
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), 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():
python
- dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
'complex128', 'object', 'bool']
n = 5000 data = dict([ (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.
0.17.1
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.
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 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:
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:
python
df.memory_usage(index=False)
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 <categorical.memory>.
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:
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.
There is no support for such visualization in pandas. However, the external package pandas-qt does provide this functionality.