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
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from __future__ import divisionimport pandas as pdfrom pandas import series,dataframeimport numpy as npfrom 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 othersdf['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 staticsdf = df.dropna(axis=0,how='any') #drop all rows that have any nan valuesformat_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_indexpaint_data = after_sorted_data[after_sorted_data['counts']>=after_sorted_data['counts'][80]] import matplotlib.pyplot as pltfrom 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
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_36228216/article/details/86680246
