andrew andrew - 1 year ago 253
Python Question

Creating large Pandas DataFrames: preallocation vs append vs concat

I am confused by the performance in Pandas when building a large dataframe chunk by chunk. In Numpy, we (almost) always see better performance by preallocating a large empty array and then filling in the values. As I understand it, this is due to Numpy grabbing all the memory it needs at once instead of having to reallocate memory with every


In Pandas, I seem to be getting better performance by using the
df = df.append(temp)

Here is an example with timing. The definition of the
class follows. As you, see I find that preallocating is roughly 10x slower than using
! Preallocating a dataframe with
values of the appropriate dtype helps a great deal, but the
method is still the fastest.

import numpy as np
from numpy.random import rand
import pandas as pd

from timer import Timer

# Some constants
num_dfs = 10 # Number of random dataframes to generate
n_rows = 2500
n_cols = 40
n_reps = 100 # Number of repetitions for timing

# Generate a list of num_dfs dataframes of random values
df_list = [pd.DataFrame(rand(n_rows*n_cols).reshape((n_rows, n_cols)), columns=np.arange(n_cols)) for i in np.arange(num_dfs)]

# Define two methods of growing a large dataframe

# Method 1 - append dataframes
def method1():
out_df1 = pd.DataFrame(columns=np.arange(4))
for df in df_list:
out_df1 = out_df1.append(df, ignore_index=True)
return out_df1

def method2():
# # Create an empty dataframe that is big enough to hold all the dataframes in df_list
out_df2 = pd.DataFrame(columns=np.arange(n_cols), index=np.arange(num_dfs*n_rows))
#EDIT_1: Set the dtypes of each column
for ix, col in enumerate(out_df2.columns):
out_df2[col] = out_df2[col].astype(df_list[0].dtypes[ix])
# Fill in the values
for ix, df in enumerate(df_list):
out_df2.iloc[ix*n_rows:(ix+1)*n_rows, :] = df.values
return out_df2

# EDIT_2:
# Method 3 - preallocate dataframe with np.empty data of appropriate type
def method3():
# Create fake data array
data = np.transpose(np.array([np.empty(n_rows*num_dfs, dtype=dt) for dt in df_list[0].dtypes]))
# Create placeholder dataframe
out_df3 = pd.DataFrame(data)
# Fill in the real values
for ix, df in enumerate(df_list):
out_df3.iloc[ix*n_rows:(ix+1)*n_rows, :] = df.values
return out_df3

# Time both methods

# Time Method 1
times_1 = np.empty(n_reps)
for i in np.arange(n_reps):
with Timer() as t:
df1 = method1()
times_1[i] = t.secs
print 'Total time for %d repetitions of Method 1: %f [sec]' % (n_reps, np.sum(times_1))
print 'Best time: %f' % (np.min(times_1))
print 'Mean time: %f' % (np.mean(times_1))

#>> Total time for 100 repetitions of Method 1: 2.928296 [sec]
#>> Best time: 0.028532
#>> Mean time: 0.029283

# Time Method 2
times_2 = np.empty(n_reps)
for i in np.arange(n_reps):
with Timer() as t:
df2 = method2()
times_2[i] = t.secs
print 'Total time for %d repetitions of Method 2: %f [sec]' % (n_reps, np.sum(times_2))
print 'Best time: %f' % (np.min(times_2))
print 'Mean time: %f' % (np.mean(times_2))

#>> Total time for 100 repetitions of Method 2: 32.143247 [sec]
#>> Best time: 0.315075
#>> Mean time: 0.321432

# Time Method 3
times_3 = np.empty(n_reps)
for i in np.arange(n_reps):
with Timer() as t:
df3 = method3()
times_3[i] = t.secs
print 'Total time for %d repetitions of Method 3: %f [sec]' % (n_reps, np.sum(times_3))
print 'Best time: %f' % (np.min(times_3))
print 'Mean time: %f' % (np.mean(times_3))

#>> Total time for 100 repetitions of Method 3: 6.577038 [sec]
#>> Best time: 0.063437
#>> Mean time: 0.065770

I use a nice
courtesy of Huy Nguyen:

# credit:

import time

class Timer(object):
def __init__(self, verbose=False):
self.verbose = verbose

def __enter__(self):
self.start = time.clock()
return self

def __exit__(self, *args):
self.end = time.clock()
self.secs = self.end - self.start
self.msecs = self.secs * 1000 # millisecs
if self.verbose:
print 'elapsed time: %f ms' % self.msecs

If you are still following, I have two questions:

1) Why is the
method faster? (NOTE: for very small dataframes, i.e.
n_rows = 40
, it is actually slower).

2) What is the most efficient way to build a large dataframe out of chunks? (In my case, the chunks are all large csv files).

Thanks for your help!

In my real world project, the columns have different dtypes. So I cannot use the
pd.DataFrame(.... dtype=some_type)
trick to improve the performance of preallocation, per BrenBarn's recommendation. The dtype parameter forces all the columns to be the same dtype [Ref. issue 4464]

I added some lines to
in my code to change the dtypes column-by-column to match in the input dataframes. This operation is expensive and negates the benefits of having the appropriate dtypes when writing blocks of rows.

EDIT_2: Try preallocating a dataframe using placeholder array
np.empty(... dtyp=some_type)
. Per @Joris's suggestion.

Answer Source

Your benchmark is actually too small to show the real difference. Appending, copies EACH time, so you are actually doing copying a size N memory space N*(N-1) times. This is horribly inefficient as the size of your dataframe grows. This certainly might not matter in a very small frame. But if you have any real size this matters a lot. This is specifically noted in the docs here, though kind of a small warning.

In [97]: df = DataFrame(np.random.randn(100000,20))

In [98]: df['B'] = 'foo'

In [99]: df['C'] = pd.Timestamp('20130101')

In [103]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 100000 entries, 0 to 99999
Data columns (total 22 columns):
0     100000 non-null float64
1     100000 non-null float64
2     100000 non-null float64
3     100000 non-null float64
4     100000 non-null float64
5     100000 non-null float64
6     100000 non-null float64
7     100000 non-null float64
8     100000 non-null float64
9     100000 non-null float64
10    100000 non-null float64
11    100000 non-null float64
12    100000 non-null float64
13    100000 non-null float64
14    100000 non-null float64
15    100000 non-null float64
16    100000 non-null float64
17    100000 non-null float64
18    100000 non-null float64
19    100000 non-null float64
B     100000 non-null object
C     100000 non-null datetime64[ns]
dtypes: datetime64[ns](1), float64(20), object(1)
memory usage: 17.5+ MB


In [85]: def f1():
   ....:     result = df
   ....:     for i in range(9):
   ....:         result = result.append(df)
   ....:     return result


In [86]: def f2():
   ....:     result = []
   ....:     for i in range(10):
   ....:         result.append(df)
   ....:     return pd.concat(result)

In [100]: f1().equals(f2())
Out[100]: True

In [101]: %timeit f1()
1 loops, best of 3: 1.66 s per loop

In [102]: %timeit f2()
1 loops, best of 3: 220 ms per loop

Note that I wouldn't even bother trying to pre-allocate. Its somewhat complicated, especially since you are dealing with multiple dtypes (e.g. you could make a giant frame and simply .loc and it would work). But pd.concat is just dead simple, works reliably, and fast.

And timing of your sizes from above

In [104]: df = DataFrame(np.random.randn(2500,40))

In [105]: %timeit f1()
10 loops, best of 3: 33.1 ms per loop

In [106]: %timeit f2()
100 loops, best of 3: 4.23 ms per loop
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