ترسیم داده های جغرافیایی در پایتون — راهنمای جامع

1pip install pyshp

1import numpy as np
2import pandas as pd
3import shapefile as shp
4import matplotlib.pyplot as plt
5import seaborn as sns

1sns.set(style=”whitegrid”, palette=”pastel”, color_codes=True)
2sns.mpl.rc(“figure”, figsize=(10,6))

1%matplotlib inline

1shp_path = “./Comunas_RM_Mapas_Vectoriales/Comuna.shp”
2sf = shp.Reader(shp_path)

1len(sf.shapes())

1sf.records()[1]

1Andes

1sf.records()[25]

1def read_shapefile(sf):
2    """
3    Read a shapefile into a Pandas dataframe with a 'coords' 
4    column holding the geometry information. This uses the pyshp
5    package
6    """
7    fields = [x[0] for x in sf.fields][1:]
8    records = sf.records()
9    shps = [s.points for s in sf.shapes()]
10    df = pd.DataFrame(columns=fields, data=records)
11    df = df.assign(coords=shps)
12    return df

1df = read_shapefile(sf)
2df.shape

1df.sample(5)

1df[df.NOM_COMUNA == ‘SANTIAGO’]

1df.NOM_COMUNA

1def plot_shape(id, s=None):
2    """ PLOTS A SINGLE SHAPE """
3    plt.figure()
4    ax = plt.axes()
5    ax.set_aspect('equal')
6    shape_ex = sf.shape(id)
7    x_lon = np.zeros((len(shape_ex.points),1))
8    y_lat = np.zeros((len(shape_ex.points),1))
9    for ip in range(len(shape_ex.points)):
10        x_lon[ip] = shape_ex.points[ip][0]
11        y_lat[ip] = shape_ex.points[ip][1]
12    plt.plot(x_lon,y_lat) 
13    x0 = np.mean(x_lon)
14    y0 = np.mean(y_lat)
15    plt.text(x0, y0, s, fontsize=10)
16    # use bbox (bounding box) to set plot limits
17    plt.xlim(shape_ex.bbox[0],shape_ex.bbox[2])
18    return x0, y0

1sf.shape(id)

1def plot_map(sf, x_lim = None, y_lim = None, figsize = (11,9)):
2    '''
3    Plot map with lim coordinates
4    '''
5    plt.figure(figsize = figsize)
6    id=0
7    for shape in sf.shapeRecords():
8        x = [i[0] for i in shape.shape.points[:]]
9        y = [i[1] for i in shape.shape.points[:]]
10        plt.plot(x, y, 'k')
11        
12        if (x_lim == None) & (y_lim == None):
13            x0 = np.mean(x)
14            y0 = np.mean(y)
15            plt.text(x0, y0, id, fontsize=10)
16        id = id+1
17    
18    if (x_lim != None) & (y_lim != None):     
19        plt.xlim(x_lim)
20        plt.ylim(y_lim)

1plot_map(sf)

1y_lim = (-33.7,-33.3) # latitude 
2x_lim = (-71, -70.25) # longitude
3plot_map(sf, x_lim, y_lim)

1def plot_map2(id, sf, x_lim = None, y_lim = None, figsize=(11,9)):
2    '''
3    Plot map with lim coordinates
4    '''
5   
6    plt.figure(figsize = figsize)
7    for shape in sf.shapeRecords():
8        x = [i[0] for i in shape.shape.points[:]]
9        y = [i[1] for i in shape.shape.points[:]]
10        plt.plot(x, y, 'k')
11        
12    shape_ex = sf.shape(id)
13    x_lon = np.zeros((len(shape_ex.points),1))
14    y_lat = np.zeros((len(shape_ex.points),1))
15    for ip in range(len(shape_ex.points)):
16        x_lon[ip] = shape_ex.points[ip][0]
17        y_lat[ip] = shape_ex.points[ip][1]
18    plt.plot(x_lon,y_lat, 'r', linewidth=3) 
19    
20    if (x_lim != None) & (y_lim != None):     
21        plt.xlim(x_lim)
22        plt.ylim(y_lim)

1plot_map2(25, sf, x_lim, y_lim)

1def plot_map_fill(id, sf, x_lim = None, 
2                          y_lim = None, 
3                          figsize = (11,9), 
4                          color = 'r'):
5    '''
6    Plot map with lim coordinates
7    '''
8    
9    plt.figure(figsize = figsize)
10    fig, ax = plt.subplots(figsize = figsize)
11    for shape in sf.shapeRecords():
12        x = [i[0] for i in shape.shape.points[:]]
13        y = [i[1] for i in shape.shape.points[:]]
14        ax.plot(x, y, 'k')
15        
16    shape_ex = sf.shape(id)
17    x_lon = np.zeros((len(shape_ex.points),1))
18    y_lat = np.zeros((len(shape_ex.points),1))
19    for ip in range(len(shape_ex.points)):
20        x_lon[ip] = shape_ex.points[ip][0]
21        y_lat[ip] = shape_ex.points[ip][1]
22    ax.fill(x_lon,y_lat, color)
23    
24    if (x_lim != None) & (y_lim != None):     
25        plt.xlim(x_lim)
26        plt.ylim(y_lim)

1plot_map_fill(0, sf, x_lim, y_lim, color='g')

1def plot_map_fill_multiples_ids(title, comuna, sf, 
2                                               x_lim = None, 
3                                               y_lim = None, 
4                                               figsize = (11,9), 
5                                               color = 'r'):
6    '''
7    Plot map with lim coordinates
8    '''
9    
10    plt.figure(figsize = figsize)
11    fig, ax = plt.subplots(figsize = figsize)
12    fig.suptitle(title, fontsize=16)
13    for shape in sf.shapeRecords():
14        x = [i[0] for i in shape.shape.points[:]]
15        y = [i[1] for i in shape.shape.points[:]]
16        ax.plot(x, y, 'k')
17            
18    for id in comuna:
19        shape_ex = sf.shape(id)
20        x_lon = np.zeros((len(shape_ex.points),1))
21        y_lat = np.zeros((len(shape_ex.points),1))
22        for ip in range(len(shape_ex.points)):
23            x_lon[ip] = shape_ex.points[ip][0]
24            y_lat[ip] = shape_ex.points[ip][1]
25        ax.fill(x_lon,y_lat, color)
26             
27        x0 = np.mean(x_lon)
28        y0 = np.mean(y_lat)
29        plt.text(x0, y0, id, fontsize=10)
30    
31    if (x_lim != None) & (y_lim != None):     
32        plt.xlim(x_lim)
33        plt.ylim(y_lim)

1comuna_id = [0, 1, 2, 3, 4, 5, 6]
2plot_map_fill_multiples_ids("Multiple Shapes", 
3                            comuna_id, sf, color = 'r')

1def plot_comunas_2(sf, title, comunas, color):
2    '''
3    Plot map with selected comunes, using specific color
4    '''
5    
6    df = read_shapefile(sf)
7    comuna_id = []
8    for i in comunas:
9        comuna_id.append(df[df.NOM_COMUNA == i.upper()]
10                         .index.get_values()[0])
11    plot_map_fill_multiples_ids(title, comuna_id, sf, 
12                                       x_lim = None, 
13                                       y_lim = None, 
14                                       figsize = (11,9), 
15                                       color = color);

1south = ['alhué', 'calera de tango', 'buin', 'isla de maipo', 'el bosque', 'paine', 'la granja', 'pedro aguirre cerda', 'lo espejo', 'puente alto', 'san joaquín', 'san miguel', 'pirque', 'san bernardo', 'san ramón', 'la cisterna', 'talagante', 'la pintana']
2plot_comunas_2(sf, 'South', south, 'c')

1def calc_color(data, color=None):
2        if color   == 1: color_sq =  
3                        ['#dadaebFF','#bcbddcF0','#9e9ac8F0',
4                        '#807dbaF0','#6a51a3F0','#54278fF0']; 
5                        colors = 'Purples';
6        elif color == 2: color_sq = 
7                        ['#c7e9b4','#7fcdbb','#41b6c4',
8                        '#1d91c0','#225ea8','#253494']; 
9                        colors = 'YlGnBu';
10        elif color == 3: color_sq = 
11                        ['#f7f7f7','#d9d9d9','#bdbdbd',
12                        '#969696','#636363','#252525']; 
13                        colors = 'Greys';
14        elif color == 9: color_sq = 
15                        ['#ff0000','#ff0000','#ff0000',
16                        '#ff0000','#ff0000','#ff0000']
17        else:            color_sq = 
18                        ['#ffffd4','#fee391','#fec44f',
19                        '#fe9929','#d95f0e','#993404']; 
20                        colors = 'YlOrBr';
21        new_data, bins = pd.qcut(data, 6, retbins=True, 
22        labels=list(range(6)))
23        color_ton = []
24        for val in new_data:
25            color_ton.append(color_sq[val]) 
26        if color != 9:
27            colors = sns.color_palette(colors, n_colors=6)
28            sns.palplot(colors, 0.6);
29            for i in range(6):
30                print ("\n"+str(i+1)+': '+str(int(bins[i]))+
31                       " => "+str(int(bins[i+1])-1), end =" ")
32            print("\n\n   1   2   3   4   5   6")    
33        return color_ton, bins;

1def plot_comunas_data(sf, title, comunas, data=None, 
2                      color=None, print_id=False):
3    '''
4    Plot map with selected comunes, using specific color
5    '''
6    
7    color_ton, bins = calc_color(data, color)
8    df = read_shapefile(sf)
9    comuna_id = []
10    for i in comunas:
11        i = conv_comuna(i).upper()
12        comuna_id.append(df[df.NOM_COMUNA == 
13                            i.upper()].index.get_values()[0])
14    plot_map_fill_multiples_ids_tone(sf, title, comuna_id, 
15                                     print_id, 
16                                     color_ton, 
17                                     bins, 
18                                     x_lim = None, 
19                                     y_lim = None, 
20                                     figsize = (11,9));

1def plot_map_fill_multiples_ids_tone(sf, title, comuna,  
2                                     print_id, color_ton, 
3                                     bins, 
4                                     x_lim = None, 
5                                     y_lim = None, 
6                                     figsize = (11,9)):
7    '''
8    Plot map with lim coordinates
9    '''
10        
11    plt.figure(figsize = figsize)
12    fig, ax = plt.subplots(figsize = figsize)
13    fig.suptitle(title, fontsize=16)
14for shape in sf.shapeRecords():
15        x = [i[0] for i in shape.shape.points[:]]
16        y = [i[1] for i in shape.shape.points[:]]
17        ax.plot(x, y, 'k')
18            
19    for id in comuna:
20        shape_ex = sf.shape(id)
21        x_lon = np.zeros((len(shape_ex.points),1))
22        y_lat = np.zeros((len(shape_ex.points),1))
23        for ip in range(len(shape_ex.points)):
24            x_lon[ip] = shape_ex.points[ip][0]
25            y_lat[ip] = shape_ex.points[ip][1]
26        ax.fill(x_lon,y_lat, color_ton[comuna.index(id)])
27        if print_id != False:
28            x0 = np.mean(x_lon)
29            y0 = np.mean(y_lat)
30            plt.text(x0, y0, id, fontsize=10)
31    if (x_lim != None) & (y_lim != None):     
32        plt.xlim(x_lim)
33        plt.ylim(y_lim)

1south = ['alhué', 'calera de tango', 'buin', 'isla de maipo', 'el bosque', 'paine', 'la granja', 'pedro aguirre cerda', 'lo espejo', 'puente alto', 'san joaquín', 'san miguel', 'pirque', 'san bernardo', 'san ramón', 'la cisterna', 'talagante', 'la pintana']
2data = [100, 2000, 300, 400000, 500, 600, 100, 2000, 300, 400, 500, 600, 100, 2000, 300, 400, 500, 600]
3print_id = True # The shape id will be printed
4color_pallete = 1 # 'Purples'
5plot_comunas_data(sf, 'South', south, data, color_pallete, print_id)

1census_17 = pd.read_excel('./data/CENSO_2017_COMUNAS_RM.xlsx')
2census_17.shape
3Out:
4(52,7)

1title = 'Population Distrubution on Santiago Metropolitan Region'
2data = census_17.PERSONAS
3names = census_17.NOM_COMUNA
4plot_comunas_data(sf, title, names, data, 4, True)

1title = 'Percentual of immigrants over total population'
2data = census_17.INM_PERC
3names = census_17.NOM_COMUNA
4plot_comunas_data(sf, title, names, data, 2, True)

1plot_comunas_data() .... that calls:
2plot_map_fill_multiples_ids_tone() ... that calls:
3calc_color()

1shp_path = "./cb_2017_us_state_5m/cb_2017_us_state_5m.shp"
2sf = shp.Reader(shp_path)
3# Continental US
4y_lim = (23, 50) # lat 
5x_lim = (-128, -65) # long
6state_id = [0, 10, 3, 5, 6, 7, 8, 30]
7plot_map_fill_multiples_ids("US - States", state_id, sf, x_lim, 
8                             y_lim, color = 'r', figsize = (15,9))