Disk increase extremly after modify DataFrame instance and copy its np.ndarray to list
#### Code Sample
Versions: `python 3.6.5`, `pandas 0.23.0`, `numpy 1.14.3`, `macOS 10.14.1`

```python
from scipy.stats import zscore

def generate_dataset(df_data, feature_name, continuous_range, normalize=True, time_step=3):
    """
    Args:
         df_data: DataFrame, shape=(20000, 61)
         feature_name: list of string, length is 60
         continuous_range: list of tuple (int, int), you can simply set it to [(0, 19990)]
    """
    if normalize:
        # modify every column by z-score normalization
        df_data.loc[:, feature_name] = df_data[feature_name].apply(zscore)

    feats = []  # list of ndarray, shape of ndarray is (time_step, 60)
    for start, end in continuous_range:
        for i in range(start, end - time_step + 1):
            feats.append(df_data[feature_name].iloc[i: i + time_step].values)
```

#### Problem description
As for-loop code above, I copy numpy.ndarray values from DataFrame instance to a list, then my computer disk increase rapidly from 50G left to 2G left, finally my laptop crashed!

However, if I don't modify `df_data.loc[:, feature_name]`(It means I don't execute if block codes above), everything is OK. If there are not so many rows in `df_data`, I think everything ganna be ok.

Could you guys please explain why this happened? How to write this function in a right way, especially handle large DataFrame object? I just want to modify df_data and copy a series values to a list.

THANKS.

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

<details>

INSTALLED VERSIONS
------------------

commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.0
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
