标签:shape desc 机器 学习 lis 评价 没有 log red
导入包
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
%matplotlib inline
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
1.导入数据各个海滨城市数据
ferrara1 = pd.read_csv('./ferrara_150715.csv')
ferrara2 = pd.read_csv('./ferrara_250715.csv')
ferrara3 = pd.read_csv('./ferrara_270615.csv')
ferrara=pd.concat([ferrara1,ferrara2,ferrara3],ignore_index=True)
# ignore_index是否忽略行索引(显式索引)
torino1 = pd.read_csv('./torino_150715.csv')
torino2 = pd.read_csv('./torino_250715.csv')
torino3 = pd.read_csv('./torino_270615.csv')
torino = pd.concat([torino1,torino2,torino3],ignore_index=True)
mantova1 = pd.read_csv('./mantova_150715.csv')
mantova2 = pd.read_csv('./mantova_250715.csv')
mantova3 = pd.read_csv('./mantova_270615.csv')
mantova = pd.concat([mantova1,mantova2,mantova3],ignore_index=True)
milano1 = pd.read_csv('./milano_150715.csv')
milano2 = pd.read_csv('./milano_250715.csv')
milano3 = pd.read_csv('./milano_270615.csv')
milano = pd.concat([milano1,milano2,milano3],ignore_index=True)
ravenna1 = pd.read_csv('./ravenna_150715.csv')
ravenna2 = pd.read_csv('./ravenna_250715.csv')
ravenna3 = pd.read_csv('./ravenna_270615.csv')
ravenna = pd.concat([ravenna1,ravenna2,ravenna3],ignore_index=True)
asti1 = pd.read_csv('./asti_150715.csv')
asti2 = pd.read_csv('./asti_250715.csv')
asti3 = pd.read_csv('./asti_270615.csv')
asti = pd.concat([asti1,asti2,asti3],ignore_index=True)
bologna1 = pd.read_csv('./bologna_150715.csv')
bologna2 = pd.read_csv('./bologna_250715.csv')
bologna3 = pd.read_csv('./bologna_270615.csv')
bologna = pd.concat([bologna1,bologna2,bologna3],ignore_index=True)
piacenza1 = pd.read_csv('./piacenza_150715.csv')
piacenza2 = pd.read_csv('./piacenza_250715.csv')
piacenza3 = pd.read_csv('./piacenza_270615.csv')
piacenza = pd.concat([piacenza1,piacenza2,piacenza3],ignore_index=True)
cesena1 = pd.read_csv('./cesena_150715.csv')
cesena2 = pd.read_csv('./cesena_250715.csv')
cesena3 = pd.read_csv('./cesena_270615.csv')
cesena = pd.concat([cesena1,cesena2,cesena3],ignore_index=True)
faenza1 = pd.read_csv('./faenza_150715.csv')
faenza2 = pd.read_csv('./faenza_250715.csv')
faenza3 = pd.read_csv('./faenza_270615.csv')
faenza = pd.concat([faenza1,faenza2,faenza3],ignore_index=True)
2.去除没用的列
faenza.head()
Unnamed: 0 | temp | humidity | pressure | description | dt | wind_speed | wind_deg | city | day | dist | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 29.40 | 83 | 1015 | moderate rain | 1436863177 | 3.62 | 94.001 | Faenza | 2015-07-14 10:39:37 | 37 |
1 | 1 | 30.12 | 78 | 1015 | moderate rain | 1436866759 | 3.10 | 80.000 | Faenza | 2015-07-14 11:39:19 | 37 |
2 | 2 | 30.10 | 78 | 1015 | moderate rain | 1436870510 | 3.60 | 70.000 | Faenza | 2015-07-14 12:41:50 | 37 |
3 | 3 | 30.75 | 74 | 1015 | moderate rain | 1436874099 | 4.60 | 90.000 | Faenza | 2015-07-14 13:41:39 | 37 |
4 | 4 | 30.71 | 66 | 1015 | moderate rain | 1436877646 | 5.10 | 100.000 | Faenza | 2015-07-14 14:40:46 | 37 |
city_list = [ferrara,torino,mantova,milano,ravenna,asti,bologna,piacenza,cesena,faenza]
for city in city_list:
city.drop(labels='Unnamed: 0',axis=1,inplace=True)
3.显示最高温度于离海远近的关系(观察多个城市)
max_temp = [] # 10个城市的最高温度
cities_dist = [] # 10个城市距离海洋的距离
for city in city_list:
max_temp.append(city['temp'].max())
cities_dist.append(city['dist'].max())
max_temp
[33.43000000000001,
34.69,
34.18000000000001,
34.81,
32.79000000000002,
34.31,
33.850000000000016,
33.920000000000016,
32.81,
32.74000000000001]
cities_dist
[47, 357, 121, 250, 8, 315, 71, 200, 14, 37]
plt.scatter(cities_dist,max_temp,c='rbyg')
plt.xlabel('距离')
plt.ylabel('最高温度')
plt.title('距离和最高温度之间的关系图')
Text(0.5,1,'距离和最高温度之间的关系图')
机器学习
# 0.提取样本数据(特征数据,目标数据)
feature = np.array(cities_dist) # 特征数据
feature = feature.reshape(-1, 1) # 二维形式的特征数据
target = np.array(max_temp) # 目标数据
# 1.选择一个模型对象进行实例化
from sklearn.linear_model import LinearRegression
linner = LinearRegression()
# 2.训练模型
linner.fit(feature,target) #X,y
# 3.使用相关的评价指标来评价模型
linner.score(feature,target)
# 4.实现预测
linner.predict([[222],[333]]) #调用方程:y = 3x + 6
array([34.17277041, 34.75520186])
# 画出回归曲线
x = np.linspace(0,350,num=100)
y = linner.predict(x.reshape(-1,1))
plt.scatter(cities_dist,max_temp,c='rbyg')
plt.xlabel('距离')
plt.ylabel('最高温度')
plt.title('距离和最高温度之间的关系图')
plt.scatter(x,y)
<matplotlib.collections.PathCollection at 0xd0014e0>
查看最低温度与海洋距离的关系
min_temp = []
for city in city_list:
min_temp.append(city['temp'].min())
# 0.提取样本数据(特征数据,目标数据)
feature = np.array(cities_dist) # 特征数据
feature = feature.reshape(-1, 1) # 二维形式的特征数据
target = np.array(min_temp) # 目标数据
# 1.选择一个模型对象进行实例化
from sklearn.linear_model import LinearRegression
linner = LinearRegression()
# 2.训练模型
linner.fit(feature,target) #X,y
# 3.使用相关的评价指标来评价模型
linner.score(feature,target)
# 4.实现预测
linner.predict([[222],[333]])
# 画出回归曲线
x = np.linspace(0,350,num=100)
y = linner.predict(x.reshape(-1,1))
plt.scatter(cities_dist,min_temp,c='rbyg')
plt.xlabel('距离')
plt.ylabel('最低温度')
plt.title('距离和最低温度之间的关系图')
plt.scatter(x,y)
<matplotlib.collections.PathCollection at 0x16129f776d8>
标签:shape desc 机器 学习 lis 评价 没有 log red
原文地址:https://www.cnblogs.com/zyyhxbs/p/11708559.html