csv文件转换为shp(Python实现) python——csv文件转换为shp


python——csv文件转换为shp

转载自https://blog.csdn.net/qq_23926575/article/details/82316965

学地信的经常会有这样的需求,即将csv格式的文件转换为shapefile格式加载到arcMap中进行显示与分析,arcMap本身提供了这样的功能,即

文件->添加数据->添加XY数据:选择文件路径,指定X字段(longitude)、Y字段(latitude)即可,默认坐标系为WGS84坐标系。

arcMap的这个功能非常方便,但因为我的数据量较大,有接近100万条数据,故通过代码进行格式转换效率更高点。
下面的代码参考如下链接:Using pyshp to convert .csv file to .shp?
需要用到pyshp模块、csv模块、codecs模块,pip install 安装即可。
下面贴上代码:
目前只能转换point类型数据

#-*-coding:utf-8-*-
import shapefile as shp
import csv
import codecs
import os

def trans_point(folder, fn, idlng, idlat, delimiter=','):
    # create a point shapefile
    output_shp = shp.Writer(shp.POINT)
    # for every record there must be a corresponding geometry.
    output_shp.autoBalance = 1
    # create the field names and data type for each.you can omit fields here
    # output_shp.field('id','N') # number    
    output_shp.field('longitude', 'F', 10, 8) # float
    output_shp.field('latitude', 'F', 10, 8) # float
    output_shp.field('locname','C',100) # string, max-length
    # access the CSV file
    with codecs.open(folder + fn, 'rb', 'utf-8') as csvfile:
        reader = csv.reader(csvfile, delimiter=delimiter)
        # skip the header
        next(reader, None)
        #loop through each of the rows and assign the attributes to variables
        for row in reader:
            # idx = row[0]
            locname= row[1]
            lng= float(row[idlng])
            lat = float(row[idlat])
            print lng, lat
            # create the point geometry
            output_shp.point(lng, lat)
            # add attribute data
            output_shp.record(lng, lat, locname)
    output_shp.save(folder + "%s.shp"%fn.split('.')[0]) # save the Shapefile

if __name__ == '__main__':
    folder = 'C:\Users\MaMQ\Desktop' + os.sep
    fn = 'test.csv'
    trans_point(folder, fn, 2, 3)


示例数据

id locname x y
1 Haryana, Haryana, India 76.9806 29.6161
2 Mumbai, Maharashtra, India 72.8258 18.975

结果
这里写图片描述


参考资料
[1] Using pyshp to convert .csv file to .shp?

转载自:https://blog.csdn.net/knkn123/article/details/85601607

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