熊猫数据框中的随机行选择

问题:熊猫数据框中的随机行选择

有没有一种方法可以从Pandas的DataFrame中选择随机行。

在R中,使用汽车包装,有一个有用的功能some(x, n),它类似于head,但在此示例中,从x中随机选择10行。

我也看过切片文档,似乎没有什么等效的。

更新资料

现在使用版本20。有一个示例方法。

df.sample(n)

Is there a way to select random rows from a DataFrame in Pandas.

In R, using the car package, there is a useful function some(x, n) which is similar to head but selects, in this example, 10 rows at random from x.

I have also looked at the slicing documentation and there seems to be nothing equivalent.

Update

Now using version 20. There is a sample method.

df.sample(n)


回答 0

像这样吗

import random

def some(x, n):
    return x.ix[random.sample(x.index, n)]

注:由于熊猫v0.20.0的,ix 已被弃用,赞成loc基于标签索引。

Something like this?

import random

def some(x, n):
    return x.ix[random.sample(x.index, n)]

Note: As of Pandas v0.20.0, ix has been deprecated in favour of loc for label based indexing.


回答 1

随着pandas版本0.16.1及更高版本,现在DataFrame.sample 内置了一个方法

import pandas

df = pandas.DataFrame(pandas.np.random.random(100))

# Randomly sample 70% of your dataframe
df_percent = df.sample(frac=0.7)

# Randomly sample 7 elements from your dataframe
df_elements = df.sample(n=7)

对于上述两种方法,您都可以通过执行以下操作获得其余的行:

df_rest = df.loc[~df.index.isin(df_percent.index)]

With pandas version 0.16.1 and up, there is now a DataFrame.sample method built-in:

import pandas

df = pandas.DataFrame(pandas.np.random.random(100))

# Randomly sample 70% of your dataframe
df_percent = df.sample(frac=0.7)

# Randomly sample 7 elements from your dataframe
df_elements = df.sample(n=7)

For either approach above, you can get the rest of the rows by doing:

df_rest = df.loc[~df.index.isin(df_percent.index)]

回答 2

sample

从v0.20.0开始,您可以使用pd.DataFrame.sample,它可用于返回固定数量的行或行百分比的随机样本:

df = df.sample(n=k)     # k rows
df = df.sample(frac=k)  # int(len(df.index) * k) rows

为了重现性,您可以指定一个整数random_state,等效于使用np.ramdom.seed。因此,不用设置,例如np.random.seed = 0,您可以:

df = df.sample(n=k, random_state=0)

sample

As of v0.20.0, you can use pd.DataFrame.sample, which can be used to return a random sample of a fixed number rows, or a percentage of rows:

df = df.sample(n=k)     # k rows
df = df.sample(frac=k)  # int(len(df.index) * k) rows

For reproducibility, you can specify an integer random_state, equivalent to using np.ramdom.seed. So, instead of setting, for example, np.random.seed = 0, you can:

df = df.sample(n=k, random_state=0)

回答 3

最好的方法是使用随机模块中的样本函数,

import numpy as np
import pandas as pd
from random import sample

# given data frame df

# create random index
rindex =  np.array(sample(xrange(len(df)), 10))

# get 10 random rows from df
dfr = df.ix[rindex]

The best way to do this is with the sample function from the random module,

import numpy as np
import pandas as pd
from random import sample

# given data frame df

# create random index
rindex =  np.array(sample(xrange(len(df)), 10))

# get 10 random rows from df
dfr = df.ix[rindex]

回答 4

实际上,这将为您提供重复的索引np.random.random_integers(0, len(df), N),其中的索引N很大。

Actually this will give you repeated indices np.random.random_integers(0, len(df), N) where N is a large number.


回答 5

下面的行将从数据帧df的现有行总数中随机选择n个行,而不进行替换。

df=df.take(np.random.permutation(len(df))[:n])

Below line will randomly select n number of rows out of the total existing row numbers from the dataframe df without replacement.

df=df.take(np.random.permutation(len(df))[:n])