机器学习with python.zip

import os import pandas as pd import requests import matplotlib.pyplot as plt plt.style.use('ggplot') import numpy as np # r = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data') # with open('iris.data','w') as f: # f.write(r.text) # print("数据获取成功") df = pd.read_csv('iris.data',names=["sepal length","sepal width","petal length","petal width","class"]) # print(df.head()) # # sepal length sepal width ... petal width class # # 0 5.1 3.5 ... 0.2 Iris-setosa # # 1 4.9 3.0 ... 0.2 Iris-setosa # # 2 4.7 3.2 ... 0.2 Iris-setosa # # 3 4.6 3.1 ... 0.2 Iris-setosa # # 4 5.0 3.6 ... 0.2 Iris-setosa # print(df["sepal width"]) #通过列名索引数据 # # 0 3.5 # # 1 3.0 # # 2 3.2 # # 3 3.1 # # 4 3.6 # # 5 3.9 # # 6 3.4 # # 7 3.4 # # 8 2.9 # # 9 3.1 # # 10 3.7 # # 11 3.4 # # 12 3.0 # # 13 3.0 # # 14 4.0 # # 15 4.4 # # 16 3.9 # # 17 3.5 # # 18 3.8 # # 19 3.8 # # 20 3.4 # # 21 3.7 # # 22 3.6 # # 23 3.3 # # 24 3.4 # # 25 3.0 # # 26 3.4 # # 27 3.5 # # 28 3.4 # # 29 3.2 # # ... # # 120 3.2 # # 121 2.8 # # 122 2.8 # # 123 2.7 # # 124 3.3 # # 125 3.2 # # 126 2.8 # # 127 3.0 # # 128 2.8 # # 129 3.0 # # 130 2.8 # # 131 3.8 # # 132 2.8 # # 133 2.8 # # 134 2.6 # # 135 3.0 # # 136 3.4 # # 137 3.1 # # 138 3.0 # # 139 3.1 # # 140 3.1 # # 141 3.1 # # 142 2.7 # # 143 3.2 # # 144 3.3 # # 145 3.0 # # 146 2.5 # # 147 3.0 # # 148 3.4 # # 149 3.0 # # Name: sepal width, Length: 150, dtype: float64 # print(df.ix[:3,:2]) #df.ix[row,column] 通过切片索引 # # sepal length sepal width # # 0 5.1 3.5 # # 1 4.9 3.0 # # 2 4.7 3.2 # # 3 4.6 3.1 # print(df.ix[:3,[x for x in df.columns if "width" in x]]) # # sepal width petal width # # 0 3.5 0.2 # # 1 3.0 0.2 # # 2 3.2 0.2 # # 3 3.1 0.2 # print([x for x in df.columns if "width" in x]) # #['sepal width', 'petal width'] # print(df["class"].unique()) #列出符合他特定条件的所有内容 # #['Iris-setosa' 'Iris-versicolor' 'Iris-virginica'] # print(df[df["class"] == "Iris-virginica"]) # # sepal length sepal width ... petal width class # # 100 6.3 3.3 ... 2.5 Iris-virginica # # 101 5.8 2.7 ... 1.9 Iris-virginica # # 102 7.1 3.0 ... 2.1 Iris-virginica # # 103 6.3 2.9 ... 1.8 Iris-virginica # # 104 6.5 3.0 ... 2.2 Iris-virginica # # 105 7.6 3.0 ... 2.1 Iris-virginica # # 106 4.9 2.5 ... 1.7 Iris-virginica # # 107 7.3 2.9 ... 1.8 Iris-virginica # # 108 6.7 2.5 ... 1.8 Iris-virginica # # 109 7.2 3.6 ... 2.5 Iris-virginica # # 110 6.5 3.2 ... 2.0 Iris-virginica # # 111 6.4 2.7 ... 1.9 Iris-virginica # # 112 6.8 3.0 ... 2.1 Iris-virginica # # 113 5.7 2.5 ... 2.0 Iris-virginica # # 114 5.8 2.8 ... 2.4 Iris-virginica # # 115 6.4 3.2 ... 2.3 Iris-virginica # # 116 6.5 3.0 ... 1.8 Iris-virginica # # 117 7.7 3.8 ... 2.2 Iris-virginica # # 118 7.7 2.6 ... 2.3 Iris-virginica # # 119 6.0 2.2 ... 1.5 Iris-virginica # # 120 6.9 3.2 ... 2.3 Iris-virginica # # 121 5.6 2.8 ... 2.0 Iris-virginica # # 122 7.7 2.8 ... 2.0 Iris-virginica # # 123 6.3 2.7 ... 1.8 Iris-virginica # # 124 6.7 3.3 ... 2.1 Iris-virginica # # 125 7.2 3.2 ... 1.8 Iris-virginica # # 126 6.2 2.8 ... 1.8 Iris-virginica # # 127 6.1 3.0 ... 1.8 Iris-virginica # # 128 6.4 2.8 ... 2.1 Iris-virginica # # 129 7.2 3.0 ... 1.6 Iris-virginica # # 130 7.4 2.8 ... 1.9 Iris-virginica # # 131 7.9 3.8 ... 2.0 Iris-virginica # # 132 6.4 2.8 ... 2.2 Iris-virginica # # 133 6.3 2.8 ... 1.5 Iris-virginica # # 134 6.1 2.6 ... 1.4 Iris-virginica # # 135 7.7 3.0 ... 2.3 Iris-virginica # # 136 6.3 3.4 ... 2.4 Iris-virginica # # 137 6.4 3.1 ... 1.8 Iris-virginica # # 138 6.0 3.0 ... 1.8 Iris-virginica # # 139 6.9 3.1 ... 2.1 Iris-virginica # # 140 6.7 3.1 ... 2.4 Iris-virginica # # 141 6.9 3.1 ... 2.3 Iris-virginica # # 142 5.8 2.7 ... 1.9 Iris-virginica # # 143 6.8 3.2 ... 2.3 Iris-virginica # # 144 6.7 3.3 ... 2.5 Iris-virginica # # 145 6.7 3.0 ... 2.3 Iris-virginica # # 146 6.3 2.5 ... 1.9 Iris-virginica # # 147 6.5 3.0 ... 2.0 Iris-virginica # # 148 6.2 3.4 ... 2.3 Iris-virginica # # 149 5.9 3.0 ... 1.8 Iris-virginica # # # # # virginica = df[df["class"] == "Iris-virginica"].reset_index(drop=True) # #重新set为一个dataframe，并重新排序 # print(virginica) # # sepal length sepal width ... petal width class # # 0 6.3 3.3 ... 2.5 Iris-virginica # # 1 5.8 2.7 ... 1.9 Iris-virginica # # 2 7.1 3.0 ... 2.1 Iris-virginica # # 3 6.3 2.9 ... 1.8 Iris-virginica # # 4 6.5 3.0 ... 2.2 Iris-virginica # # 5 7.6 3.0 ... 2.1 Iris-virginica # # 6 4.9 2.5 ... 1.7 Iris-virginica # # 7 7.3 2.9 ... 1.8 Iris-virginica # # 8 6.7 2.5 ... 1.8 Iris-virginica # # 9 7.2 3.6 ... 2.5 Iris-virginica # # 10 6.5 3.2 ... 2.0 Iris-virginica # # 11 6.4 2.7 ... 1.9 Iris-virginica # # 12 6.8 3.0 ... 2.1 Iris-virginica # # 13 5.7 2.5 ... 2.0 Iris