前言 本系列教程基本就是摘抄<Python机器学习基础教程>中的例子内容. 为了便于跟踪和学习,本系列教程在Github上提供了jupyter notebook 版本: Github仓库:https://github.com/Holy-Shine/Introduciton-2-ML-with-Python-notebook 系列教程总目录 Python机器学习基础教程 引子 假设有一名植物学爱好者对她发现的鸢尾花的品种很感兴趣.她收集了每朵鸢尾花的一些测量数据:花瓣的长度和宽度以及花萼的长度和

错误 错误1:An asset is marked as dont save, but is included in the build: unityEditor.HostView:OnGUI() 错误2:Building - Failed to write file: sharedassets0.assetsUnityEditor.HostView:OnGUI() 错误3:Error building Player: Couldn't build player because of unsup

假设现在有图像数据imgs和对应标签targets.数据维度分别如下 imgs.shape = (num, channel, width, height) targets.shape = (num, class) 因为通常我们需要将数据打散,这样的好处是可以让模型训练更具鲁棒性,那么如何同时打散data和target,而且还需要保持对应顺序不变呢?方法如下 # 得到打乱后的index from random import shuffle index = [i for i in range(len

Coursera课程<Python Data Structures> 密歇根大学 Charles Severance Week6 Tuple 10 Tuples 10.1 Tuples Are Like Lists 元组是另外一种序列,它的方法和list挺像的.它的元素也是从0开始计数. >>> x = ('Glenn', 'Sally', 'Joseph') >>> print(x[2]) Joseph >>> y = (1, 9, 2)

Coursera课程<Python Data Structures> 密歇根大学 Charles Severance Week5 Dictionary 9.1 Dictionaries 字典就像是一个包,而这个包里的每样东西都整整齐齐地贴好了标签,于是我们可以通过标签来找到我们想要的东西.而且注意,字典这个包是无序的,所以它不能根据顺序索引,只能根据标签. >>> purse = dict() >>> purse['money'] = 12 >>

Coursera课程<Python Data Structures> 密歇根大学 Charles Severance Week4 List 8.2 Manipulating Lists 8.2.1 Concatenating Lists Using + 使用"+"可以把存在的两个list加在一起.如: >>> a = [1, 2, 3] >>> b = [4, 5, 6] >>> c = a + b >>&g

Python : Data Encapsulation The following table shows the different behaviour: Name Notation Behaviour name Public Can be accessed from inside and outside _name Protected Like a public member, but they shouldn't be directly accessed from outside. __n

import csv filename = 'ch02-data.csv' data = [] try: with open(filename) as f://用with语句将数据文件绑定到对象f reader = csv.reader(f) header = next(reader)//Python 3.X 用的是next() data = [row for row in reader] except csv.Error as e: print('Error reading CSV file

原文:http://www.jianshu.com/p/94516a58314d Dataset transformations| 数据转换 Combining estimators|组合学习器 Feature extration|特征提取 Preprocessing data|数据预处理 1 Dataset transformations scikit-learn provides a library of transformers, which may clean (see Preproce

User-defined functions from:https://campus.datacamp.com/courses/python-data-science-toolbox-part-1/writing-your-own-functions?ex=1 Strings in Python To assign the string company = 'DataCamp' You've also learned to use the operations + and * with stri

In this article, we dicuss some main steps in data preparation. Drop Labels Firstly, we drop labels for train set. Here we use drop() method in Pandas library. housing = strat_train_set.drop("median_house_value", axis=1) # drop labels for traini

In the former article "Data Preparation by Pandas and Scikit-Learn", we discussed about a series of steps in data preparation. Scikit-Learn provides the Pipeline class to help with such sequences of transformations. The Pipeline constructor take

Python - Algorithm Design - Tutorialspoint https://www.tutorialspoint.com/python_data_structure/python_algorithm_design.htm

>>> repr(111.5) '111.5' >>> repr(10) ' >>> int(") 11 >>> long(") 22L >>> float("1.22") 1.22 >>> hex(19) '0x13' >>> oct(20) ' >>> tuple("1,2,3,4,5") (') &

import numpy as np import matplotlib.pyplot as plt def is_outlier(points, threshold=3.5): if len(points.shape) == 1: points = points[:, None] # Find the median number of points median = np.median(points, axis=0) diff = np.sum((points - median)**2, ax

import numpy as np import matplotlib.pyplot as plt import seaborn; seaborn.set() rand = np.random.RandomState(42) x = rand.rand(10,2) #数组 plt.scatter(x[:,0],x[:,1],s=100) #数组第一列为横坐标,第二列为纵坐标 s=100:散点大小 plt.show() any list of dictionaries can be made i

Before you can plot anything, you need to specify which backend Matplotlib should use. The simplest option is to use Jupyter’s magic command %matplotlib inline. This tells Jupyter to set up Matplotlib so it uses Jupyter’s own backend. Scatter Plot ho

A Complete Tutorial to Learn Data Science with Python from Scratch Introduction It happened few years back. After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools w

Pandas is a great lib to process BIg Data. 1) pandas.pivot_table(data,values=None,columns=None,aggfunc=func) func can be any function in python 2) pandas.merge(left,right,hpw='inner') combine left with right based on the inner columns. 3) pandas.read

1. Background Though it's always difficult to give child a perfect name, parent never give up trying. One of my friends met a problem. his baby girl just came to the world, he want to make a perfect name for her. he found a web page, in which he can

From: http://interactivepython.org/courselib/static/pythonds/Introduction/GettingStartedwithData.html Built-in Collection Data Types Python has a number of very powerful built-in collection classes. Lists, strings, and tuples are ordered collections