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

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本文标题: python绘制地震散点图

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