CODEWITHSUNDEEP
pythonpandas
Nilani Algiriyage
Nilani Algiriyage

Reputation: 35636

Split a large pandas dataframe

I have a large dataframe with 423244 lines. I want to split this in to 4. I tried the following code which gave an error? ValueError: array split does not result in an equal division

for item in np.split(df, 4):
    print item

How to split this dataframe in to 4 groups?

Upvotes: 199

Views: 346982

Answers (11)

Richard M
Richard M

Reputation: 25

Numpy can do the job since pandas are built on top of Numpy

import numpy as np

df_split = np.split(df, number_of_times)

Upvotes: 1

raphael
raphael

Reputation: 2950

building on @elixir's answer...
I'd suggest using a generator to avoid loading all the chunks in memory:

def chunkit(df, chunk_size = 10000): 
    num_chunks = len(df) // chunk_size
    if len(df) % chunk_size != 0 or len(df) == 0:
        num_chunks += 1
    for i in range(num_chunks):
        yield df[i*chunk_size:(i + 1) * chunk_size]

Upvotes: 7

root
root

Reputation: 80346

Use np.array_split:

Docstring:
Split an array into multiple sub-arrays.

Please refer to the ``split`` documentation.  The only difference
between these functions is that ``array_split`` allows
`indices_or_sections` to be an integer that does *not* equally
divide the axis.

In [1]: import pandas as pd

In [2]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ...:                           'foo', 'bar', 'foo', 'foo'],
   ...:                    'B' : ['one', 'one', 'two', 'three',
   ...:                           'two', 'two', 'one', 'three'],
   ...:                    'C' : randn(8), 'D' : randn(8)})

In [3]: print df
     A      B         C         D
0  foo    one -0.174067 -0.608579
1  bar    one -0.860386 -1.210518
2  foo    two  0.614102  1.689837
3  bar  three -0.284792 -1.071160
4  foo    two  0.843610  0.803712
5  bar    two -1.514722  0.870861
6  foo    one  0.131529 -0.968151
7  foo  three -1.002946 -0.257468

In [4]: import numpy as np
In [5]: np.array_split(df, 3)
Out[5]: 
[     A    B         C         D
0  foo  one -0.174067 -0.608579
1  bar  one -0.860386 -1.210518
2  foo  two  0.614102  1.689837,
      A      B         C         D
3  bar  three -0.284792 -1.071160
4  foo    two  0.843610  0.803712
5  bar    two -1.514722  0.870861,
      A      B         C         D
6  foo    one  0.131529 -0.968151
7  foo  three -1.002946 -0.257468]

Upvotes: 352

drkr
drkr

Reputation: 436

I like a one-liners, so @LucyDrops answer works for me.

However, there is one important thing: add a .copy() if chunks should be COPY of original df parts:

chunks = [df[i:i+n].copy() for i in range(0,df.shape[0],n)]

Otherwise there is a high chance to receive the next warning during the further processing of chunks (in loop for example):

A value is trying to be set on a copy of a slice from a DataFrame.

(see the details in the Pandas documentation)

Upvotes: 7

Martin Alexandersson
Martin Alexandersson

Reputation: 1427

I also experienced np.array_split not working with Pandas DataFrame. My solution was to only split the index of the DataFrame and then introduce a new column with the "group" label:

indexes = np.array_split(df.index,N, axis=0)
for i,index in enumerate(indexes):
   df.loc[index,'group'] = i

This makes groupby operations very convenient, such as for calculating the mean value of each group:

df.groupby(by='group').mean()

Upvotes: 1

LucyDrops
LucyDrops

Reputation: 609

you can use list comprehensions to do this in a single line

n = 4
chunks = [df[i:i+n] for i in range(0,df.shape[0],n)]

Upvotes: 11

Gilberto
Gilberto

Reputation: 863

Be aware that np.array_split(df, 3) splits the dataframe into 3 sub-dataframes, while the split_dataframe function defined in @elixir's answer, when called as split_dataframe(df, chunk_size=3), splits the dataframe every chunk_size rows.

Example:

With np.array_split:

df = pd.DataFrame([1,2,3,4,5,6,7,8,9,10,11], columns=['TEST'])
df_split = np.array_split(df, 3)

...you get 3 sub-dataframes:

df_split[0] # 1, 2, 3, 4
df_split[1] # 5, 6, 7, 8
df_split[2] # 9, 10, 11

With split_dataframe:

df_split2 = split_dataframe(df, chunk_size=3)

...you get 4 sub-dataframes:

df_split2[0] # 1, 2, 3
df_split2[1] # 4, 5, 6
df_split2[2] # 7, 8, 9
df_split2[3] # 10, 11

Hope I'm right, and that this is useful.

Upvotes: 50

elixir
elixir

Reputation: 979

I wanted to do the same, and I had first problems with the split function, then problems with installing pandas 0.15.2, so I went back to my old version, and wrote a little function that works very well. I hope this can help!

# input - df: a Dataframe, chunkSize: the chunk size
# output - a list of DataFrame
# purpose - splits the DataFrame into smaller chunks
def split_dataframe(df, chunk_size = 10000): 
    chunks = list()
    num_chunks = len(df) // chunk_size + 1
    for i in range(num_chunks):
        chunks.append(df[i*chunk_size:(i+1)*chunk_size])
    return chunks

Upvotes: 87

pratpor
pratpor

Reputation: 2104

I guess now we can use plain iloc with range for this.

chunk_size = int(df.shape[0] / 4)
for start in range(0, df.shape[0], chunk_size):
    df_subset = df.iloc[start:start + chunk_size]
    process_data(df_subset)
    ....

Upvotes: 34

rumpel
rumpel

Reputation: 8290

You can use groupby, assuming you have an integer enumerated index:

import math
df = pd.DataFrame(dict(sample=np.arange(99)))
rows_per_subframe = math.ceil(len(df) / 4.)

subframes = [i[1] for i in df.groupby(np.arange(len(df))//rows_per_subframe)]

Note: groupby returns a tuple in which the 2nd element is the dataframe, thus the slightly complicated extraction.

>>> len(subframes), [len(i) for i in subframes]
(4, [25, 25, 25, 24])

Upvotes: 5

yemu
yemu

Reputation: 28259

Caution:

np.array_split doesn't work with numpy-1.9.0. I checked out: It works with 1.8.1.

Error:

Dataframe has no 'size' attribute

Upvotes: 8

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