This article documents how to use Python, pandas, numpy, and scikit-learn to randomly disrupt, extract, and cut data. The main methods are included:
- sample
- shuffle
- train_test_split
Import data
In [1]:
import pandas as pd import numpy as np import random # Random module import plotly_express as px # Visualization libraries import plotly.graph_objects as go
built-in data
A consumption dataset built into the plotly library was used:
In [2]:
df = () ()
Basic Information
In [3]:
Out[3]:
(244, 7)
In [4]:
columns = columns
Out[4]:
Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')
sample realization
direction of travel
In [5]:
Randomly select a row of records:
() # Randomly select a row of records
Randomly select multiple rows of data:
Proportional random sampling is achieved through the parameter frac:
(frac=0.05)
direction of travel
The main point is to choose different numbers or proportions of attributes; the overall number of rows is constant
In [8]:
(3, axis=1) # Extract on column attributes
shuffle implementation
scikit-earn's shuffle
In [9]:
from import shuffle
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shuffle(df) # Disrupt the data
random module shuffle
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length = list(range(len(df))) # The original length as an index length[:5]
Out[11]:
[0, 1, 2, 3, 4]
In [12]:
(length) # Disrupt the index
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length[:5]
Out[13]:
[136, 35, 207, 127, 29] # Disrupted results
In [14]:
[length] # Fetch data through a mangled index
numpy implementation
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# First, we're gonna break up each index (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])
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# Selection of data by disrupted indexes [(len(df))]
train_test_split implementation
from sklearn.model_selection import train_test_split
data = []
for i in train_test_split(df, test_size=0.2):
(i)
In [18]:
The first figure is 80%:
data[0] # 80% of data
The remaining 20% of the data:
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