How to estimate how much memory a Pandas' DataFrame will need?

Question

I have been wondering... If I am reading, say, a 400MB csv file into a pandas dataframe (using read_csv or read_table), is there any way to guesstimate how much memory this

Answer 1

You have to do this in reverse.

In [4]: DataFrame(randn(1000000,20)).to_csv('test.csv')

In [5]: !ls -ltr test.csv
-rw-rw-r-- 1 users 399508276 Aug  6 16:55 test.csv

Technically memory is about this (which includes the indexes)

In [16]: df.values.nbytes + df.index.nbytes + df.columns.nbytes
Out[16]: 168000160

So 168MB in memory with a 400MB file, 1M rows of 20 float columns

DataFrame(randn(1000000,20)).to_hdf('test.h5','df')

!ls -ltr test.h5
-rw-rw-r-- 1 users 168073944 Aug  6 16:57 test.h5

MUCH more compact when written as a binary HDF5 file

In [12]: DataFrame(randn(1000000,20)).to_hdf('test.h5','df',complevel=9,complib='blosc')

In [13]: !ls -ltr test.h5
-rw-rw-r-- 1 users 154727012 Aug  6 16:58 test.h5

The data was random, so compression doesn't help too much