BUG: Inconsistent replacement of values
- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

#### Problem description

Value at a given position don't change using "values" indexing when the column in cuestion has object data type. However, the value change using "at".

I known that values return numpy array, then I test the same behaviour on numpy and everything is ok.

#### Normal Behaviour:

```python
import pandas as pd

#Create DataFrame
columns = ["A", "B"]

data = [[0, 1], [2, 3]]

dataset = pd.DataFrame(data=data, columns=columns)

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)

#Modify value
dataset.values[0, 1] = "4"

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)

#Modify value
dataset.values[0, 1] = 4

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
```

#### Output:

```python
   A  B
0  0  1
1  2  3

 A    int64
B    int64
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
   A  B
0  0  4
1  2  3

 A    int64
B    int64
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
   A  B
0  0  4
1  2  3

 A    int64
B    int64
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False
```

#### Inconsistent Behaviour:

```python
import pandas as pd

#Create DataFrame
columns = ["A", "B"]

data = [[0, 1], [2, "3"]]

dataset = pd.DataFrame(data=data, columns=columns)

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)

#Modify value
dataset.values[0, 1] = "4"

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)

#Modify value
dataset.values[0, 1] = 4

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
print("="*25)

#Modify value
dataset.at[0, "B"] = "4"

print(dataset)
print("\n", dataset.dtypes)
print("\n", dataset.values.flags)
```

#### Output:

```python
   A  B
0  0  1
1  2  3

 A     int64
B    object
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
   A  B
0  0  1
1  2  3

 A     int64
B    object
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
   A  B
0  0  1
1  2  3

 A     int64
B    object
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
   A  B
0  0  4
1  2  3

 A     int64
B    object
dtype: object

   C_CONTIGUOUS : False
  F_CONTIGUOUS : True
  OWNDATA : False
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False
```

#### Normal Behaviour on Numpy:

```python
import numpy as np

data = [[0, 1], [2, "3"]]

#Create array
dataset = np.array(data)

print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)

#Modify value
dataset[0, 1] = 4

print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)

#Create array
dataset = np.array(data, dtype=object)

print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
print("="*25)

#Modify value
dataset[0, 1] = 4

print(dataset)
print("\n", dataset.dtype)
print("\n", dataset.flags)
```

#### Output:

```python
[['0' '1']
 ['2' '3']]

 <U21

   C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
[['0' '4']
 ['2' '3']]

 <U21

   C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
[[0 1]
 [2 '3']]

 object

   C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False

=========================
[[0 4]
 [2 '3']]

 object

   C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  WRITEBACKIFCOPY : False
  UPDATEIFCOPY : False
```

#### Output of ``pd.__version__``

```python
'1.0.3'
```

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

<details>

```python
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
  """)
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  from pandas.util.testing import assert_frame_equal

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.19.104+
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2018.9
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 46.0.0
Cython           : 0.29.16
pytest           : 3.6.4
hypothesis       : None
sphinx           : 1.8.5
blosc            : None
feather          : 0.4.0
xlsxwriter       : None
lxml.etree       : 4.2.6
html5lib         : 1.0.1
pymysql          : None
psycopg2         : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2           : 2.11.1
IPython          : 5.5.0
pandas_datareader: 0.8.1
bs4              : 4.6.3
bottleneck       : 1.3.2
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.2.6
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 2.5.9
pandas_gbq       : 0.11.0
pyarrow          : 0.14.1
pytables         : None
pytest           : 3.6.4
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.4.1
sqlalchemy       : 1.3.15
tables           : 3.4.4
tabulate         : 0.8.7
xarray           : 0.15.1
xlrd             : 1.1.0
xlwt             : 1.3.0
xlsxwriter       : None
numba            : 0.47.0
```

</details>
