Pandas随机取数

本文记录的是如何使用Python、pandas、numpy、scikit-learn来实现随机打乱、抽取和切割数据。主要的方法包含：

sample
shuffle
np.random.permutation
train_test_split

导入数据

In [1]:

import pandas as pd
import numpy as np
import random  # 随机模块

import plotly_express as px  # 可视化库
import plotly.graph_objects as go

内置数据

采用的是plotly库中内置的一份消费数据集：

In [2]:

1 2	df = px.data.tips() df.head()

基本信息

In [3]:

df.shape

Out[3]:

(244, 7)

In [4]:

1 2	columns = df.columns columns

Out[4]:

1	Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')

sample实现

行方向

In [5]:

随机抽取一行记录：

1	df.sample() # 随机抽取一行记录

随机抽取多行数据：

通过参数frac实现按照比例随机抽样：

1	df.sample(frac=0.05)

列方向

主要是选择不同数量或者比例的属性；整体的行数量是不变的

In [8]:

1	df.sample(3, axis=1) # 在列属性上抽取

shuffle实现

scikit-Learn的shuffle

In [9]:

1	from sklearn.utils import shuffle

In [10]:

1	shuffle(df) # 打乱数据

random模块的shuffle

In [11]:

1 2	length = list(range(len(df))) # 原始的长度作为索引 length[:5]

Out[11]:

1	[0, 1, 2, 3, 4]

In [12]:

1	random.shuffle(length) # 打乱索引

In [13]:

1	length[:5]

Out[13]:

1	[136, 35, 207, 127, 29] # 打乱后的结果

In [14]:

1	df.iloc[length] # 通过打乱后的索引获取数据

numpy实现

In [15]:

1 2	# 先打乱每个索引 np.random.permutation(len(df))

Out[15]:

array([223,  98, 238,  17, 101,  26, 122, 212,  27,  79, 210, 147, 176,
        82, 164, 142, 141, 219,   6,  63, 185, 112, 158, 188, 242, 207,
        45,  55, 178, 150, 217,  32,  16, 160, 157, 234,  95, 174,  93,
        52,  57, 220, 216, 230,  35,  86, 125, 114, 100,  73,  83,  88,
        34,   7,  40, 115,  97, 165,  84,  18, 197, 151, 135, 121,  72,
       173, 228, 143, 227,   9, 183,  56,  23, 237, 136, 106, 133, 189,
       139,   0, 208,  74, 166,   4,  68,  12,  71,  85, 172, 138, 149,
       144, 232, 186,  99, 130,  41, 201, 204,  10, 167, 195,  66, 159,
       213,  87, 103, 117,  31, 211, 190,  24, 243, 127,  48, 218, 233,
       113,  81, 235, 229, 206,  96,  46, 222,  50, 156, 180, 214, 124,
       240, 140,  89, 225,   2, 120,  58, 169, 193,  39, 102, 104, 148,
       184, 170, 152, 153, 146, 179, 137, 129,  64,   3,  65, 128,  90,
       110,  14, 226, 181, 131, 203, 221,  80,  51,  94, 231,  44, 108,
        43, 145,  47,  75, 162, 163,  69, 126, 200,   1, 123,  37, 205,
       111,  25,  91,  11,  42,  67, 118, 196, 161,  28, 116, 105,  33,
        38,  78,  76, 224,  20, 202, 171, 177, 107,   8, 209, 239,  77,
       241, 154,   5, 198,  92,  61, 182,  36,  70,  22,  54, 187, 175,
       119, 215,  49, 134,  21,  60,  62, 168,  59, 155, 194, 109, 132,
        19, 199,  29, 191,  13,  30, 192, 236,  15,  53])

In [16]:

1
2
3

# 通过打乱后的索引来选择数据

df.iloc[np.random.permutation(len(df))]

train_test_split实现

from sklearn.model_selection import train_test_split

data = []

for i in train_test_split(df, test_size=0.2):
    data.append(i)

In [18]:

第一份数据是80%的：

1	data[0] # 80%的数据

剩余的20%的数据：