PERF: Performance regression with memory_usage(deep=True) on `object` columns
#### Code Sample, a copy-pastable example if possible

```python
import time

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "a": np.empty(10000000),
    "b": np.empty(10000000),
})

df["a"] = df["a"].astype("object")

s = time.time()
mem = df.memory_usage(deep=True)
print("memory_usage(deep=True) took %.4fsecs" % (time.time() - s))
```

#### Problem description

Performance of `memory_usage(deep=True)` on `object` columns seems to have regressed significantly since v0.23.4. Once in v0.24.0, and another regression in v1.0.0 that remains in v1.0.3.

**Output**
v1.0.3
```
memory_usage(deep=True) took 26.4566secs
```
v0.24.0
```
memory_usage(deep=True) took 6.0479secs
```
v0.23.4
```
memory_usage(deep=True) took 0.4633secs
```

Removing `df["a"] = df["a"].astype("object")` reverts it back to the expected magnitude of speed in v1.0.3:
```
memory_usage(deep=True) took 0.0024secs
```

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.16.0-77-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_AU.UTF-8
LOCALE           : en_AU.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 46.1.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None

</details>
