python数据分析实战-第9章-数据分析实例气象数据

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import numpy as npimport pandas as pdimport datetime

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ferrara = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Ferrara,IT‘)

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torino = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Torino,IT‘)

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mantova = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Mantova,IT‘)

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milano = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Milano,IT‘)

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ravenna = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Ravenna,IT‘)

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asti = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Asti,IT‘)

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bologna = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Bologna,IT‘)

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piacenza = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Piacenza,IT‘)

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cesena = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Cesena,IT‘)

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faenza = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Faenza,IT‘)

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def prepare(city_list,city_name):    temp = [ ]    humidity = [ ]    pressure = [ ]    description = [ ]    dt = [ ]    wind_speed = [ ]    wind_deg = [ ]    for row in city_list:       temp.append(row[‘main‘][‘temp‘]-273.15)       humidity.append(row[‘main‘][‘humidity‘])       pressure.append(row[‘main‘][‘pressure‘])       description.append(row[‘weather‘][0][‘description‘])       dt.append(row[‘dt‘])       wind_speed.append(row[‘wind‘][‘speed‘])       wind_deg.append(row[‘wind‘][‘deg‘])    headings = [‘temp‘,‘humidity‘,‘pressure‘,‘description‘,‘dt‘,‘wind_speed‘,‘wind_deg‘]    data = [temp,humidity,pressure,description,dt,wind_speed,wind_deg]    df = pd.DataFrame(data,index=headings)    city = df.T    city[‘city‘] = city_name    city[‘day‘] = city[‘dt‘].apply(datetime.datetime.fromtimestamp)    return city

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df_ferrara = prepare(ferrara.list,‘Ferrara‘)df_milano = prepare(milano.list,‘Milano‘)df_mantova = prepare(mantova.list,‘Mantova‘)df_ravenna = prepare(ravenna.list,‘Ravenna‘)df_torino = prepare(torino.list,‘Torino‘)#df_alessandria = prepare(alessandria.list,‘Alessandria‘)df_asti = prepare(asti.list,‘Asti‘)df_bologna = prepare(bologna.list,‘Bologna‘)df_piacenza = prepare(piacenza.list,‘Piacenza‘)df_cesena = prepare(cesena.list,‘Cesena‘)df_faenza = prepare(faenza.list,‘Faenza‘)

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print df_ferrara.shapeprint df_milano.shapeprint df_mantova.shapeprint df_ravenna.shapeprint df_torino.shapeprint df_asti.shapeprint df_bologna.shapeprint df_piacenza.shapeprint df_cesena.shapeprint df_faenza.shape

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#http://it.thetimenow.com/distance-calculator.php#(Comacchio)df_ravenna[‘dist‘] = 8df_cesena[‘dist‘] = 14df_faenza[‘dist‘] = 37df_ferrara[‘dist‘] = 47df_bologna[‘dist‘] = 71df_mantova[‘dist‘] = 121 df_piacenza[‘dist‘] = 200df_milano[‘dist‘] = 250df_asti[‘dist‘] = 315df_torino[‘dist‘] = 357

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import pandas as pd#df_ferrara.to_csv(‘ferrara_270615.csv‘)#df_milano.to_csv(‘milano_270615.csv‘)#df_mantova.to_csv(‘mantova_270615.csv‘)#df_ravenna.to_csv(‘ravenna_270615.csv‘)#df_torino.to_csv(‘torino_270615.csv‘)#df_asti.to_csv(‘asti_270615.csv‘)#df_bologna.to_csv(‘bologna_270615.csv‘)#df_piacenza.to_csv(‘piacenza_270615.csv‘)#df_cesena.to_csv(‘cesena_270615.csv‘)#df_faenza.to_csv(‘faenza_270615.csv‘)df_ferrara = pd.read_csv(‘ferrara_270615.csv‘)df_milano = pd.read_csv(‘milano_270615.csv‘)df_mantova = pd.read_csv(‘mantova_270615.csv‘)df_ravenna = pd.read_csv(‘ravenna_270615.csv‘)df_torino = pd.read_csv(‘torino_270615.csv‘)df_asti = pd.read_csv(‘asti_270615.csv‘)df_bologna = pd.read_csv(‘bologna_270615.csv‘)df_piacenza = pd.read_csv(‘piacenza_270615.csv‘)df_cesena = pd.read_csv(‘cesena_270615.csv‘)df_faenza = pd.read_csv(‘faenza_270615.csv‘)

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df_cesena.columns

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dist = [df_ravenna[‘dist‘][0],     df_cesena[‘dist‘][0],     df_faenza[‘dist‘][0],     df_ferrara[‘dist‘][0],     df_bologna[‘dist‘][0],     df_mantova[‘dist‘][0],     df_piacenza[‘dist‘][0],     df_milano[‘dist‘][0],     df_asti[‘dist‘][0],     df_torino[‘dist‘][0]]temp_max = [df_ravenna[‘temp‘].max(),     df_cesena[‘temp‘].max(),     df_faenza[‘temp‘].max(),     df_ferrara[‘temp‘].max(),     df_bologna[‘temp‘].max(),     df_mantova[‘temp‘].max(),     df_piacenza[‘temp‘].max(),     df_milano[‘temp‘].max(),     df_asti[‘temp‘].max(),     df_torino[‘temp‘].max()]temp_min = [df_ravenna[‘temp‘].min(),     df_cesena[‘temp‘].min(),     df_faenza[‘temp‘].min(),     df_ferrara[‘temp‘].min(),     df_bologna[‘temp‘].min(),     df_mantova[‘temp‘].min(),     df_piacenza[‘temp‘].min(),     df_milano[‘temp‘].min(),     df_asti[‘temp‘].min(),     df_torino[‘temp‘].min()]hum_min = [df_ravenna[‘humidity‘].min(),     df_cesena[‘humidity‘].min(),     df_faenza[‘humidity‘].min(),     df_ferrara[‘humidity‘].min(),     df_bologna[‘humidity‘].min(),     df_mantova[‘humidity‘].min(),     df_piacenza[‘humidity‘].min(),     df_milano[‘humidity‘].min(),     df_asti[‘humidity‘].min(),     df_torino[‘humidity‘].min()]hum_max = [df_ravenna[‘humidity‘].max(),     df_cesena[‘humidity‘].max(),     df_faenza[‘humidity‘].max(),     df_ferrara[‘humidity‘].max(),     df_bologna[‘humidity‘].max(),     df_mantova[‘humidity‘].max(),     df_piacenza[‘humidity‘].max(),     df_milano[‘humidity‘].max(),     df_asti[‘humidity‘].max(),     df_torino[‘humidity‘].max()]

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%matplotlib inlineimport matplotlib.pyplot as pltimport matplotlib.dates as mdates

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#temperatura massimaplt.plot(dist,temp_max,‘ro‘)

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x = np.array(dist)y = np.array(temp_max)x1 = x[x<100]x1 = x1.reshape((x1.size,1))y1 = y[x<100]x2 = x[x>50]x2 = x2.reshape((x2.size,1))y2 = y[x>50]

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from sklearn.svm import SVRsvr_lin1 = SVR(kernel=‘linear‘, C=1e3)svr_lin2 = SVR(kernel=‘linear‘, C=1e3)svr_lin1.fit(x1, y1)svr_lin2.fit(x2, y2)xp1 = np.arange(10,100,10).reshape((9,1))xp2 = np.arange(50,400,50).reshape((7,1))yp1 = svr_lin1.predict(xp1)yp2 = svr_lin2.predict(xp2)

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plt.plot(xp1, yp1, c=‘r‘, label=‘Strong sea effect‘)plt.plot(xp2, yp2, c=‘b‘, label=‘Light sea effect‘)plt.axis((0,400,20,40))plt.scatter(x, y, c=‘k‘, label=‘data‘)

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from scipy.optimize import fsolve

def line1(x):    a1 = svr_lin1.coef_[0][0]    b1 = svr_lin1.intercept_[0]    return -a1*x + b1def line2(x):    a2 = svr_lin2.coef_[0][0]    b2 = svr_lin2.intercept_[0]    return -a2*x + b2def findIntersection(fun1,fun2,x0): return fsolve(lambda x : fun1(x) - fun2(x),x0)

result = findIntersection(line1,line2,0.0)print "[x,y] = [ %d , %d ]" % (result,line1(result))x = numpy.linspace(0,300,31)plt.plot(x,line1(x),x,line2(x),result,line1(result),‘ro‘)

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#temperatures minplt.axis((0,400,15,25))plt.plot(dist,temp_min,‘bo‘)

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#min humidityplt.plot(dist,hum_min,‘bo‘)

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#max humidityplt.plot(dist,hum_max,‘bo‘)

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#temperaturey1 = df_milano[‘temp‘]x1 = df_milano[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)ax.plot(x1,y1,‘r‘)

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#humidityy1 = df_milano[‘humidity‘]x1 = df_milano[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)ax.plot(x1,y1,‘r‘)

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y1 = df_ravenna[‘temp‘]x1 = df_ravenna[‘day‘]y2 = df_ferrara[‘temp‘]x2 = df_ferrara[‘day‘]y3 = df_milano[‘temp‘]x3 = df_milano[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)plt.plot(x1,y1,‘r‘,x2,y2,‘b‘,x3,y3,‘g‘)

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y1 = df_ravenna[‘humidity‘]x1 = df_ravenna[‘day‘]y2 = df_ferrara[‘humidity‘]x2 = df_ferrara[‘day‘]y3 = df_milano[‘humidity‘]x3 = df_milano[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)plt.plot(x1,y1,‘r‘,x2,y2,‘b‘,x3,y3,‘g‘)

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y1 = df_ravenna[‘humidity‘]x1 = df_ravenna[‘day‘]y2 = df_faenza[‘humidity‘]x2 = df_faenza[‘day‘]y3 = df_cesena[‘humidity‘]x3 = df_cesena[‘day‘]y4 = df_milano[‘humidity‘]x4 = df_milano[‘day‘]y5 = df_asti[‘humidity‘]x5 = df_asti[‘day‘]y6 = df_torino[‘humidity‘]x6 = df_torino[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)plt.plot(x1,y1,‘r‘,x2,y2,‘r‘,x3,y3,‘r‘)plt.plot(x4,y4,‘g‘,x5,y5,‘g‘,x6,y6,‘g‘)

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y1 = df_ravenna[‘temp‘]x1 = df_ravenna[‘day‘]y2 = df_faenza[‘temp‘]x2 = df_faenza[‘day‘]y3 = df_cesena[‘temp‘]x3 = df_cesena[‘day‘]y4 = df_milano[‘temp‘]x4 = df_milano[‘day‘]y5 = df_asti[‘temp‘]x5 = df_asti[‘day‘]y6 = df_torino[‘temp‘]x6 = df_torino[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)plt.plot(x1,y1,‘r‘,x2,y2,‘r‘,x3,y3,‘r‘)plt.plot(x4,y4,‘g‘,x5,y5,‘g‘,x6,y6,‘g‘)

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hum_mean = [df_ravenna[‘humidity‘].mean(),     df_cesena[‘humidity‘].mean(),     df_faenza[‘humidity‘].mean(),     df_ferrara[‘humidity‘].mean(),     df_bologna[‘humidity‘].mean(),     df_mantova[‘humidity‘].mean(),     df_piacenza[‘humidity‘].mean(),     df_milano[‘humidity‘].mean(),     df_asti[‘humidity‘].mean(),     df_torino[‘humidity‘].mean()]plt.plot(dist,hum_mean,‘bo‘)

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y1 = df_ravenna[‘wind_speed‘]*20y2 = df_ravenna[‘humidity‘]x = df_ravenna[‘day‘]fig, ax = plt.subplots()plt.xticks(rotation=70)hours = mdates.DateFormatter(‘%H:%M‘)ax.xaxis.set_major_formatter(hours)plt.plot(x,y1,‘r‘,x,y2,‘b‘)

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plt.plot(df_ravenna[‘wind_deg‘],df_ravenna[‘wind_speed‘],‘ro‘)

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plt.subplot(211)plt.plot(df_cesena[‘wind_deg‘],df_cesena[‘humidity‘],‘bo‘)plt.subplot(212)plt.plot(df_cesena[‘wind_deg‘],df_cesena[‘wind_speed‘],‘bo‘)

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hist, bins = np.histogram(df_ravenna[‘wind_deg‘],8,[0,360])print histprint bins

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def showRoseWind(values,city_name,max_value):   N = 8   theta = np.arange(0.,2 * np.pi, 2 * np.pi / N)   radii = np.array(values)   plt.axes([0.025, 0.025, 0.95, 0.95], polar=True)   colors = [(1-x/max_value, 1-x/max_value, 0.75) for x in radii]   plt.bar(theta, radii, width=(2*np.pi/N), bottom=0.0, color=colors)   plt.title(city_name,x=0.2, fontsize=20)

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hist, bin = np.histogram(df_ravenna[‘wind_deg‘],8,[0,360])print histshowRoseWind(hist,‘Ravenna‘, 15.0)

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hist, bin = np.histogram(df_piacenza[‘wind_deg‘],8,[0,360])print histshowRoseWind(hist,‘Piacenza‘, 15.0)

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print df_milano[df_milano[‘wind_deg‘]<45][‘wind_speed‘]print df_milano[df_milano[‘wind_deg‘]<45][‘wind_speed‘].mean()

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print df_milano[df_milano[‘wind_deg‘]<45][‘wind_speed‘].mean()#print df_milano[(df_milano[‘wind_deg‘]>0) & (df_milano[‘wind_deg‘]<45)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>44) & (df_milano[‘wind_deg‘]<90)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>89) & (df_milano[‘wind_deg‘]<135)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>134) & (df_milano[‘wind_deg‘]<180)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>179) & (df_milano[‘wind_deg‘]<225)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>224) & (df_milano[‘wind_deg‘]<270)][‘wind_speed‘].mean()print df_milano[(df_milano[‘wind_deg‘]>269) & (df_milano[‘wind_deg‘]<315)][‘wind_speed‘].mean()#print df_milano[(df_milano[‘wind_deg‘]>314) & (df_milano[‘wind_deg‘]<360)][‘wind_speed‘].mean()print df_milano[df_milano[‘wind_deg‘]>314][‘wind_speed‘].mean()

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degs = np.arange(45,361,45)print degs

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tmp =  []for deg in degs:    #print df_milano[(df_milano[‘wind_deg‘]>(deg-46)) & (df_milano[‘wind_deg‘]<deg)][‘wind_speed‘].mean()    tmp.append(df_milano[(df_milano[‘wind_deg‘]>(deg-46)) & (df_milano[‘wind_deg‘]<deg)][‘wind_speed‘].mean())speeds = np.array(tmp)print speeds

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N = 8theta = np.arange(0.,2 * np.pi, 2 * np.pi / N)radii = np.array(speeds)plt.axes([0.025, 0.025, 0.95, 0.95], polar=True)colors = [(1-x/10.0, 1-x/10.0, 0.75) for x in radii]bars = plt.bar(theta, radii, width=(2*np.pi/N), bottom=0.0, color=colors)plt.title(‘Milano‘,x=0.2, fontsize=20)

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def RoseWind_Speed(df_city):   degs = np.arange(45,361,45)   tmp =  []   for deg in degs:      tmp.append(df_city[(df_city[‘wind_deg‘]>(deg-46)) & (df_city[‘wind_deg‘]<deg)][‘wind_speed‘].mean())   return np.array(tmp)

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def showRoseWind_Speed(speeds,city_name):   N = 8   theta = np.arange(0.,2 * np.pi, 2 * np.pi / N)   radii = np.array(speeds)   plt.axes([0.025, 0.025, 0.95, 0.95], polar=True)   colors = [(1-x/10.0, 1-x/10.0, 0.75) for x in radii]   bars = plt.bar(theta, radii, width=(2*np.pi/N), bottom=0.0, color=colors)   plt.title(city_name,x=0.2, fontsize=20)

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showRoseWind(RoseWind_Speed(df_milano),‘Milano‘,10)

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showRoseWind_Speed(RoseWind_Speed(df_ravenna),‘Ravenna‘)

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showRoseWind_Speed(RoseWind_Speed(df_faenza),‘Faenza‘)

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showRoseWind_Speed(RoseWind_Speed(df_cesena),‘Cesena‘)

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showRoseWind_Speed(RoseWind_Speed(df_ferrara),‘Ferrara‘)

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showRoseWind_Speed(RoseWind_Speed(df_torino),‘Torino‘)

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showRoseWind_Speed(RoseWind_Speed(df_mantova),‘Mantova‘)

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ferrara = pd.read_json(‘http://api.openweathermap.org/data/2.5/history/city?q=Ferrara,IT‘)

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df_ferrara.to_csv(‘ferrara.csv‘)df_milano.to_csv(‘milano.csv‘)df_mantova.to_csv(‘mantova.csv‘)df_ravenna.to_csv(‘ravenna.csv‘)df_torino.to_csv(‘torino.csv‘)df_asti.to_csv(‘asti.csv‘)df_bologna.to_csv(‘bologna.csv‘)df_piacenza.to_csv(‘piacenza.csv‘)df_cesena.to_csv(‘cesena.csv‘)df_faenza.to_csv(‘faenza.csv‘)