- Hands-On Data Science and Python Machine Learning
- Frank Kane
- 217字
- 2021-07-15 17:14:56
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can measure that using the r2_score() function from sklearn.metrics."
A block of code is set as follows:
import numpy as np import pandas as pd from sklearn import tree input_file = "c:/spark/DataScience/PastHires.csv" df = pd.read_csv(input_file, header = 0)
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
import numpy as np
import pandas as pd
from sklearn import tree
input_file = "c:/spark/DataScience/PastHires.csv"
df = pd.read_csv(input_file, header = 0)
Any command-line input or output is written as follows:
spark-submit SparkKMeans.py
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "On Windows 10, you'll need to open up the Start menu and go to Windows System | Control Panel to open up Control Panel."
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