How to create a view of dataframe in pandas?

Question

I have a large dataframe (10m rows, 40 columns, 7GB in memory). I would like to create a view in order to have a shorthand name for a view that is complicated to express, without adding another 2-4 GB to memory usage. In other words, I would rather type:

df2

Than:

df.loc[complicated_condition, some_columns]

The documentation states that, while using .loc ensures that setting values modifies the original dataframe, there is still no guarantee as to whether the object returned by .loc is a view or a copy.

I know I could assign the condition and column list to variables (e.g. df.loc[cond, cols]), but I'm generally curious to know whether it is possible to create a view of a dataframe.

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Edit: Related questions:

• What rules does Pandas use to generate a view vs a copy?
• Pandas: Subindexing dataframes: Copies vs views

Answer 1

You generally can't return a view.

Your answer lies in the pandas docs: returning-a-view-versus-a-copy.

Whenever an array of labels or a boolean vector are involved in the indexing operation, the result will be a copy. With single label / scalar indexing and slicing, e.g. df.ix[3:6] or df.ix[:, 'A'], a view will be returned.

This answer was found in the following post: Link.