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本项目是利用五年左右的世界地震数据,通过python的pandas库、matplotlib库、basemap库等进行数据可视化,绘制出地震散点图。主要代码如下所示
from __future__ import division import pandas as pd from pandas import Series,DataFrame import numpy as np from matplotlib.patches import Polygon chi_provinces = ['北京','天津','上海','重庆', '河北','山西','辽宁','吉林', '黑龙江','江苏','浙江','安徽', '福建','江西','山东','河南', '湖北','湖南','广东','海南', '四川','贵州','云南','陕西', '甘肃','青海','台湾','内蒙古', '广西','西藏','宁夏','新疆', '香港','澳门'] #list of chinese provinces def is_in_china(str): if str[:2] in chi_provinces: return True else: return False def convert_data_2014(x): try: return float(x.strip()) except ValueError: return x except AttributeError: return x def format_lat_lon(x): try: return x/100 except(TypeError): return np.nan df = pd.read_excel(r'C:/Users/GGWS/Desktop/shuju/201601-12.xls') df = df.append(pd.read_excel(r'C:/Users/GGWS/Desktop/shuju/201201-12.xls'),ignore_index = True) df = df.append(pd.read_excel(r'C:/Users/GGWS/Desktop/shuju/shuju.xls'),ignore_index = True) df = df.append(pd.read_excel(r'C:/Users/GGWS/Desktop/shuju/201501-12.xls'),ignore_index = True) df_2014 = pd.read_excel(r'C:/Users/GGWS/Desktop/shuju/201401-12.xls') #have to introduce statics of 2014 independently because the format and the type of data of specific column in this data set are different from others df['longitude'] = df['longitude'].apply(convert_data_2014) df['latitude'] = df['latitude'].apply(convert_data_2014) df_2014['longitude'] = df_2014['longitude'].apply(convert_data_2014) df_2014['latitude'] = df_2014['latitude'].apply(convert_data_2014) df = df.append(df_2014,ignore_index = True) df = df[['latitude','longitude','magnitude','referenced place','time']] #only save four columns as valuable statics df[['longitude','latitude']] = df[['longitude','latitude']].applymap(format_lat_lon) #use function "applymap" to convert the format of the longitude and latitude statics df = df.dropna(axis=0,how='any') #drop all rows that have any NaN values format_magnitude = lambda x: float(str(x).strip('ML')) df['magnitude'] = df['magnitude'].apply(format_magnitude) #df = df[df['referenced place'].apply(is_in_china)] lon_mean = (df['longitude'].groupby(df['referenced place'])).mean() lat_mean = (df['latitude'].groupby(df['referenced place'])).mean() group_counts = (df['magnitude'].groupby(df['referenced place'])).count() after_agg_data = pd.concat([lon_mean,lat_mean,group_counts], axis = 1 ) after_agg_data.rename(columns = {'magnitude':'counts'} , inplace = True) #aggregate after grouping the data after_sorted_data = after_agg_data.sort_values(by = 'counts',ascending = False) new_index = np.arange(len(after_sorted_data.index)) after_sorted_data.index = new_index paint_data = after_sorted_data[after_sorted_data['counts']>=after_sorted_data['counts'][80]] import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap plt.figure(figsize=(16,8)) m = Basemap() m.readshapefile(r'C:/Users/GGWS/Desktop/jb/gadm36_CHN_1', 'states', drawbounds=True) ax = plt.gca() ''' for nshape,seg in enumerate (m.states): poly = Polygon(seg,facecolor = 'r') ax.add_patch(poly) ''' m.drawcoastlines(linewidth=0.5) m.drawcountries(linewidth=0.5) m.shadedrelief() for indexs in df.index: lon2,lat2 = df.loc[indexs].values[1],df.loc[indexs].values[0] x,y = m(lon2,lat2) m.plot(x,y,'ro',markersize = 0.5) #获取经度值 ''' for indexs in after_sorted_data.index[:80]: lon,lat = after_sorted_data.loc[indexs].values[0],after_sorted_data.loc[indexs].values[1] x,y = m(lon,lat) m.plot(x,y,'wo',markersize = 10*(after_sorted_data.loc[indexs].values[2]/after_sorted_data.loc[0].values[2])) ''' plt.title("Worldwide Earthquake") plt.show() #indexs-len(df.index)+80
效果如下
到此这篇关于python绘制地震散点图就介绍到这了。造成人与人之间巨大差异的根本原因是自律程度的高低。更多相关python绘制地震散点图内容请查看相关栏目,小编编辑不易,再次感谢大家的支持!