In Pandas, does .iloc method give a copy or view?

Question

I find the result is a little bit random. Sometimes it\'s a copy sometimes it\'s a view. For example:

df = pd.DataFrame([{\'name\':\'Marry\', \'age\':21},{\'         


        

Answer 1

You are starting with a DataFrame that has two columns with two different dtypes:

df.dtypes
Out: 
age      int64
name    object
dtype: object

Since different dtypes are stored in different numpy arrays under the hood, you have two different blocks for them:

df.blocks

Out: 
{'int64':           age
 student1   21
 student2   24, 'object':            name
 student1  Marry
 student2   John}

If you attempt to slice the first row of this DataFrame, it has to get one value from each different block which makes it necessary to create a copy.

df2.is_copy
Out[40]: 

In the second attempt, you are changing the dtypes. Since 'old' cannot be stored in an integer array, it casts the Series as an object Series.

df.loc['student3'] = ['old','Tom']

df.dtypes
Out: 
age     object
name    object
dtype: object

Now all data for this DataFrame is stored in a single block (and in a single numpy array):

df.blocks

Out: 
{'object':           age   name
 student1   21  Marry
 student2   24   John
 student3  old    Tom}

At this step, slicing the first row can be done on the numpy array without creating a copy, so it returns a view.

df3._is_view
Out: True