"Large data" workflows using pandas
I routinely use tens of gigabytes of data in just this fashione.g. I have tables on disk that I read via queries, create data and append back.
Details which will affect how you store your data, like:
Give as much detail as you can; and I can help you develop a structure.
- Size of data, # of rows, columns, types of columns; are you appendingrows, or just columns?
- What will typical operations look like. E.g. do a query on columns to select a bunch of rows and specific columns, then do an operation (in-memory), create new columns, save these.
(Giving a toy example could enable us to offer more specific recommendations.)
- After that processing, then what do you do? Is step 2 ad hoc, or repeatable?
- Input flat files: how many, rough total size in Gb. How are these organized e.g. by records? Does each one contains different fields, or do they have some records per file with all of the fields in each file?
- Do you ever select subsets of rows (records) based on criteria (e.g. select the rows with field A > 5)? and then do something, or do you just select fields A, B, C with all of the records (and then do something)?
- Do you 'work on' all of your columns (in groups), or are there a good proportion that you may only use for reports (e.g. you want to keep the data around, but don't need to pull in that column explicity until final results time)?
Ensure you have pandas at least
Since pytables is optimized to operate on row-wise (which is what you query on), we will create a table for each group of fields. This way it's easy to select a small group of fields (which will work with a big table, but it's more efficient to do it this way... I think I may be able to fix this limitation in the future... this is more intuitive anyhow):
(The following is pseudocode.)
import numpy as npimport pandas as pd# create a storestore = pd.HDFStore('mystore.h5')# this is the key to your storage:# this maps your fields to a specific group, and defines # what you want to have as data_columns.# you might want to create a nice class wrapping this# (as you will want to have this map and its inversion) group_map = dict( A = dict(fields = ['field_1','field_2',.....], dc = ['field_1',....,'field_5']), B = dict(fields = ['field_10',...... ], dc = ['field_10']), ..... REPORTING_ONLY = dict(fields = ['field_1000','field_1001',...], dc = ),)group_map_inverted = dict()for g, v in group_map.items(): group_map_inverted.update(dict([ (f,g) for f in v['fields'] ]))
Reading in the files and creating the storage (essentially doing what
for f in files: # read in the file, additional options may be necessary here # the chunksize is not strictly necessary, you may be able to slurp each # file into memory in which case just eliminate this part of the loop # (you can also change chunksize if necessary) for chunk in pd.read_table(f, chunksize=50000): # we are going to append to each table by group # we are not going to create indexes at this time # but we *ARE* going to create (some) data_columns # figure out the field groupings for g, v in group_map.items(): # create the frame for this group frame = chunk.reindex(columns = v['fields'], copy = False) # append it store.append(g, frame, index=False, data_columns = v['dc'])
Now you have all of the tables in the file (actually you could store them in separate files if you wish, you would prob have to add the filename to the group_map, but probably this isn't necessary).
This is how you get columns and create new ones:
frame = store.select(group_that_I_want)# you can optionally specify:# columns = a list of the columns IN THAT GROUP (if you wanted to# select only say 3 out of the 20 columns in this sub-table)# and a where clause if you want a subset of the rows# do calculations on this framenew_frame = cool_function_on_frame(frame)# to 'add columns', create a new group (you probably want to# limit the columns in this new_group to be only NEW ones# (e.g. so you don't overlap from the other tables)# add this info to the group_mapstore.append(new_group, new_frame.reindex(columns = new_columns_created, copy = False), data_columns = new_columns_created)
When you are ready for post_processing:
# This may be a bit tricky; and depends what you are actually doing.# I may need to modify this function to be a bit more general:report_data = store.select_as_multiple([groups_1,groups_2,.....], where =['field_1>0', 'field_1000=foo'], selector = group_1)
About data_columns, you don't actually need to define ANY data_columns; they allow you to sub-select rows based on the column. E.g. something like:
store.select(group, where = ['field_1000=foo', 'field_1001>0'])
They may be most interesting to you in the final report generation stage (essentially a data column is segregated from other columns, which might impact efficiency somewhat if you define a lot).
You also might want to:
- create a function which takes a list of fields, looks up the groups in the groups_map, then selects these and concatenates the results so you get the resulting frame (this is essentially what select_as_multiple does). This way the structure would be pretty transparent to you.
- indexes on certain data columns (makes row-subsetting much faster).
- enable compression.
Let me know when you have questions!
I think the answers above are missing a simple approach that I've found very useful.
When I have a file that is too large to load in memory, I break up the file into multiple smaller files (either by row or cols)
Example: In case of 30 days worth of trading data of ~30GB size, I break it into a file per day of ~1GB size. I subsequently process each file separately and aggregate results at the end
One of the biggest advantages is that it allows parallel processing of the files (either multiple threads or processes)
The other advantage is that file manipulation (like adding/removing dates in the example) can be accomplished by regular shell commands, which is not be possible in more advanced/complicated file formats
This approach doesn't cover all scenarios, but is very useful in a lot of them
There is now, two years after the question, an 'out-of-core' pandas equivalent: dask. It is excellent! Though it does not support all of pandas functionality, you can get really far with it. Update: in the past two years it has been consistently maintained and there is substantial user community working with Dask.
And now, four years after the question, there is another high-performance 'out-of-core' pandas equivalent in Vaex. It "uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted)." It can handle data sets of billions of rows and does not store them into memory (making it even possible to do analysis on suboptimal hardware).