从 pandas.DataFrame 中绘制引导样本
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Drawing a bootstrap sample from a pandas.DataFrame
提问by Till Hoffmann
I would like to draw a bootstrap sample of a pandas.DataFrameas efficiently as possible. Using the builtin iloctogether with a list of integers seems to be slow:
我想pandas.DataFrame尽可能有效地绘制 a 的引导样本。将内置iloc函数与整数列表一起使用似乎很慢：
import pandas
import numpy as np
# Generate some data
n = 5000
values = np.random.uniform(size=(n, 5))
# Construct a pandas.DataFrame
columns = ['a', 'b', 'c', 'd', 'e']
df = pandas.DataFrame(values, columns=columns)
# Bootstrap
%timeit df.iloc[np.random.randint(n, size=n)]
# Out: 1000 loops, best of 3: 1.46 ms per loop
Indexing the numpyarray is of course much faster:
索引numpy数组当然要快得多：
%timeit values[np.random.randint(n, size=n)]
# Out: 10000 loops, best of 3: 159 μs per loop
But even extracting the values, sampling the numpyarray, and constructing a new pandas.DataFrameis faster:
但即使提取值、对numpy数组进行采样并构造一个新的值pandas.DataFrame也更快：
%timeit pandas.DataFrame(df.values[np.random.randint(n, size=n)], columns=columns)
# Out: 1000 loops, best of 3: 302 μs per loop
@JohnE suggested samplewhich is unfortunately even slower:
@JohnE 建议sample不幸的是，它甚至更慢：
%timeit df.sample(n, replace=True)
# Out: 100 loops, best of 3: 5.14 ms per loop
@firelynx suggested merge:
@firelynx 建议merge：
%timeit df.merge(pandas.DataFrame(index=np.random.randint(n, size=n)), left_index=True, right_index=True, how='right')
# Out: 1000 loops, best of 3: 1.23 ms per loop
Does anyone have an idea why ilocis so slow and/or whether there are better alternatives than extracting the values, sampling and then constructing a new pandas.DataFrame?
有没有人知道为什么iloc这么慢和/或是否有比提取值、采样然后构建新值更好的替代方法pandas.DataFrame？
回答by firelynx
The merge method in pandas is fairly optimized, so I tried my luck with it and it gave me a significant speed increase. Given my machine is a bit slower than yours, I'm also using pandas 0.15.2 Things may be a bit different.
pandas 中的合并方法相当优化，所以我用它试试运气，它给了我显着的速度提升。鉴于我的机器比你的慢一点，我也在使用 Pandas 0.15.2 事情可能有点不同。
%timeit df.iloc[np.random.randint(n, size=n)]
# 100 loops, best of 3: 2.41 ms per loop
randlist = pandas.DataFrame(index=np.random.randint(n, size=n))
%timeit df.merge(randlist, left_index=True, right_index=True, how='right')
# 1000 loops, best of 3: 1.87 ms per loop
%timeit df.merge(pandas.DataFrame(index=np.random.randint(n, size=n)), left_index=True, right_index=True, how='right')
# 100 loops, best of 3: 2.29 ms per loop
回答by tmthydvnprt
Indexing Speeds
索引速度
Boolean Indexing tested to be slightly faster for me:
布尔索引测试对我来说稍微快一点：
Boolean Indexing
布尔索引
%timeit -n10000 df[np.random.randint(2, size=n).astype(bool)]
# 10000 loops, best of 3: 307 μs per loop
numpysampling & reDataFrameing
numpy抽样和DataFrame
%timeit -n10000 pandas.DataFrame(df.values[np.random.randint(n, size=n)], columns=columns)
# 10000 loops, best of 3: 380 μs per loop
