python数据分析——城市气候与海洋的关系研究+机器学习【实例】

城市气候与海洋的关系研究

 

导入包

In [2]:

import numpy as np
import pandas as pd
from pandas import Series,DataFrame

import matplotlib.pyplot as plt


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

 

导入数据各个海滨城市数据

In [4]:

# ignore_index忽略行索引
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)

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