Pandas dataframe pivot not fitting in memory

Question

I have a dataframe df with the following structure:

        val          newidx    Code
Idx                             
0       1.0      1220121127    706
1       1.0      1220121030    706
2       1.0      1620120122    565

It has 1000000 lines. In total we have 600 unique Code value and 200000 unique newidx values.

If I perform the following operation

df.pivot_table(values='val', index='newidx', columns='Code', aggfunc='max')

I get a MemoryError . but this sounds strange as the size of the resulting dataframe should be sustainable: 200000x600.

How much memory requires such operation? Is there a way to fix this memory error?

Answer 1

Try to see if this fits in your memory:

df.groupby(['newidx', 'Code'])['val'].max().unstack()

pivot_table is unfortunately very memory intensive as it may make multiple copies of data.

---

If the groupby does not work, you will have to split your DataFrame into smaller pieces. Try not to assign multiple times. For example, if reading from csv:

df = pd.read_csv('file.csv').groupby(['newidx', 'Code'])['val'].max().unstack()

avoids multiple assignments.

Answer 2

I've had a very similar problem when carrying out a merge between 4 dataframes recently.

What worked for me was disabling the index during the groupby, then merging.

if @Kartiks answer doesn't work, try this before chunking the DataFrame.

df.groupby(['newidx', 'Code'], as_index=False)['val'].max().unstack()