dropna after shift in same cell vs not, issues SettingWithCopyWarning

# for https://stackoverflow.com/q/66327949/1431750

In [1]: import pandas as pd

In [2]: pd.__version__
Out[2]: '1.2.2'

In [3]: df = pd.DataFrame({
   ...:     'Fruits': ['apple', 'orange', 'banana', 'pineapple', 'watermelon'],
   ...:     'Volume 1': [10, 20, 30, 700, 800],
   ...:     'Volume 2': [100, 200, 300, 7000, 8000],
   ...:     'Volume 3': [1, 2, 3, 70, 8],
   ...:     'Volume 4': [9, 8, 7, 6, 5],
   ...:     'Other': [-1, -2, -3, -4, -5]
   ...: })
   ...: df.loc[:, 'Volume 2':'Volume 4'] = df.loc[:, 'Volume 2':'Volume 4'].shift(1)
   ...: df
Out[3]:
       Fruits  Volume 1  Volume 2  Volume 3  Volume 4  Other
0       apple        10       NaN       NaN       NaN     -1
1      orange        20     100.0       1.0       9.0     -2
2      banana        30     200.0       2.0       8.0     -3
3   pineapple       700     300.0       3.0       7.0     -4
4  watermelon       800    7000.0      70.0       6.0     -5

In [4]: df = df.dropna()
   ...: df
Out[4]:
       Fruits  Volume 1  Volume 2  Volume 3  Volume 4  Other
1      orange        20     100.0       1.0       9.0     -2
2      banana        30     200.0       2.0       8.0     -3
3   pineapple       700     300.0       3.0       7.0     -4
4  watermelon       800    7000.0      70.0       6.0     -5

In [5]: # will issue warning
   ...: df.loc[:, 'Volume 2':'Volume 4'] = df.loc[:, 'Volume 2':'Volume 4'].astype(int)
   ...: df
c:\program files\python38\lib\site-packages\pandas\core\indexing.py:1787: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(loc, val, pi)
Out[5]:
       Fruits  Volume 1  Volume 2  Volume 3  Volume 4  Other
1      orange        20       100         1         9     -2
2      banana        30       200         2         8     -3
3   pineapple       700       300         3         7     -4
4  watermelon       800      7000        70         6     -5

In [6]: # let's start over but this time, `dropna` after `shift` **in the same cell**:
   ...:
   ...: df = pd.DataFrame({
   ...:     'Fruits': ['apple', 'orange', 'banana', 'pineapple', 'watermelon'],
   ...:     'Volume 1': [10, 20, 30, 700, 800],
   ...:     'Volume 2': [100, 200, 300, 7000, 8000],
   ...:     'Volume 3': [1, 2, 3, 70, 8],
   ...:     'Volume 4': [9, 8, 7, 6, 5],
   ...:     'Other': [-1, -2, -3, -4, -5]
   ...: })
   ...: df.loc[:, 'Volume 2':'Volume 4'] = df.loc[:, 'Volume 2':'Volume 4'].shift(1)
   ...: df = df.dropna()
   ...: df
Out[6]:
       Fruits  Volume 1  Volume 2  Volume 3  Volume 4  Other
1      orange        20     100.0       1.0       9.0     -2
2      banana        30     200.0       2.0       8.0     -3
3   pineapple       700     300.0       3.0       7.0     -4
4  watermelon       800    7000.0      70.0       6.0     -5

In [7]: # NO WARNINGS next...
   ...: df.loc[:, 'Volume 2':'Volume 4'] = df.loc[:, 'Volume 2':'Volume 4'].astype(int)
   ...: df
Out[7]:
       Fruits  Volume 1  Volume 2  Volume 3  Volume 4  Other
1      orange        20       100         1         9     -2
2      banana        30       200         2         8     -3
3   pineapple       700       300         3         7     -4
4  watermelon       800      7000        70         6     -5

In [8]: