Examples

import numpy as np
import pandas as pd

dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
data = dict([(t, np.ones(shape=3000).astype(t))
             for t in dtypes])

df = pd.DataFrame(data)
df.head()

	int64	float64	complex128	object	bool
0	1	1.0	(1+0j)	1	True
1	1	1.0	(1+0j)	1	True
2	1	1.0	(1+0j)	1	True
3	1	1.0	(1+0j)	1	True
4	1	1.0	(1+0j)	1	True

df.memory_usage()

Index            80
int64         24000
float64       24000
complex128    48000
object        24000
bool           3000
dtype: int64

df.memory_usage(index=False)

int64         24000
float64       24000
complex128    48000
object        24000
bool           3000
dtype: int64

The memory footprint of object dtype columns is ignored by default:

df.memory_usage(deep=True)

Index            80
int64         24000
float64       24000
complex128    48000
object        96000
bool           3000
dtype: int64

Use a Categorical for efficient storage of an object-dtype column with many repeated values.

df['object'].astype('category').memory_usage(deep=True)

3168