BUG: pd.Categorical turns all values into NaN
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- [x] I have confirmed this bug exists on the latest version of pandas.

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---

#### Code Sample

```python
import pandas as pd
data = pd.read_excel('titanic.xlsx')

data['Survived'] = data['Survived'].astype('category')
data['Sex'] = data['Sex'].astype('category')

data.Survived.cat.categories = ['No', 'Yes']
data.Sex.cat.categories = ['female','male']

data.Survived = pd.Categorical(data.Survived, categories=['No', 'Yes'], ordered=False)
data.Sex = pd.Categorical(data.Sex, categories=['female','male'], ordered=False)

print(data.head(3))
```

#### Problem description

This code sample reads the popular Kaggle titanic file. When reading the titanic.xlsx file, the following Data Set is generated:

| |PassengerId | **Survived** |   Pclass | ...  |   Fare | Cabin |  Embarked |
|-|-----|------|---| ---- |  ------ | ---        | - |
|0| 1          | **0**      | 3 | ... |  7.2500 | NaN        | S |
|1| 2          | **1**      | 1 | ... | 71.2833 | C85        | C |
|2| 3          | **1**      | 3 | ... |  7.9250 | NaN        | S |

When I execute the code above, the result displayed on the terminal is as follows:

| |PassengerId | **Survived** |   Pclass | ...  |   Fare | Cabin |  Embarked |
|-|-----|------|---| ---- |  ------ | ---        | - |
|0| 1          |**NaN**      | 3 | ... |  7.2500 | NaN        | S |
|1| 2          | **NaN**      | 1 | ... | 71.2833 | C85        | C |
|2| 3          | **NaN**      | 3 | ... |  7.9250 | NaN        | S |

As can be seen, all values in the "Survived" Series are now NaN. The expected behavior, however, would be for the values to become "Yes" or "No". Strangely, if I invert the penultimate and anti-penultimate lines generating the following code sample:

```python
import pandas as pd
data = pd.read_excel('titanic.xlsx')

data['Survived'] = data['Survived'].astype('category')
data['Sex'] = data['Sex'].astype('category')

data.Survived.cat.categories = ['No', 'Yes']
data.Sex.cat.categories = ['female','male']

data.Sex = pd.Categorical(data.Sex, categories=['female','male'], ordered=False)
data.Survived = pd.Categorical(data.Survived, categories=['No', 'Yes'], ordered=False)

print(data.head(3))
```
The result generated by the code sample above is the expected one, as shown below.

| |PassengerId | **Survived** |   Pclass | ...  |   Fare | Cabin |  Embarked |
|-|-----|------|---| ---- |  ------ | ---        | - |
|0| 1          | **No**      | 3 | ... |  7.2500 | NaN        | S |
|1| 2          | **Yes**      | 1 | ... | 71.2833 | C85        | C |
|2| 3          | **Yes**      | 3 | ... |  7.9250 | NaN        | S |

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

<details>

INSTALLED VERSIONS
------------------
commit           : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python           : 3.9.6.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : pt_BR.UTF-8
LOCALE           : pt_BR.UTF-8

pandas           : 1.3.2
numpy            : 1.21.1
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.1.3
setuptools       : 57.0.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.0.1
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.4.3
numexpr          : None
odfpy            : None
openpyxl         : 3.0.7
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : 1.4.15
tables           : None
tabulate         : None
xarray           : None
xlrd             : 2.0.1
xlwt             : None
numba            : None

</details>
