What do low_memory and memory_map flags do in pd.read_csv

Question

the function signature for pandas.read_csv gives, among others, the following options:

read_csv(filepath_or_buffer, low_memory=True, memory_map=False, iterator=False, chunksize=None, ...)

I couldn't find any documentation for either low_memoryor memory_map flags. I am confused about whether these features are implemented yet and if so how do they work.

Specifically,

1. memory_map: If implemented does it use np.memmap and if so does it store the individual columns as memmap or the rows.
2. low_memory: Does it specify something like cache to store in memory?
3. can we convert an existing DataFrame to a memmapped DataFrame

P.S. : versions of relevant modules

pandas==0.14.0
scipy==0.14.0
numpy==1.8.1

Answer 1

I will attempt to sum up the comments to this question and also add my own research into one comprehensive answer.

1. low_memory option is kind of depricated, as in that it does not actually do anything anymore (source).

2. memory_map does not seem to use the numpy memory map as far as I can tell from the source code It seems to be an option for how to parse the incoming stream of data, not something that matters for how the dataframe you receive works.

3. Since my assumption in point 2 is that this is only for parsing, this question is kind of irrelevant.