In [1]:

# Uncomment to install
#!pip install leafmap spatialcsv geopandas

import leafmap
import spatialcsv
import requests
import geopandas


# This function will allow us to preview the csv before processing
def preview(url):
    response = requests.get(url)
    for line in response.text.splitlines()[:4]:
        print(line)

# Uncomment to install #!pip install leafmap spatialcsv geopandas import leafmap import spatialcsv import requests import geopandas # This function will allow us to preview the csv before processing def preview(url): response = requests.get(url) for line in response.text.splitlines()[:4]: print(line)

This package takes csv files and standardizes them for web mapping. It will automatically change coordidantes from degree,minute format to decimal.

If using streamlit, it will change the column headers to what streamlit uses, and remove any empty coordinate fields (which causes streamlit to error out). To view in streamlit, go to https://spatialcsv.streamlit.app/

If using leafmap, you can add the following arguments:

• tags = [ ] Will select what columns are included in the pin popup
• epsg = '####' Will reproject from given crs to lat/long coordinates

If you don't want to use a web map, but only want to convert all the coordinates, there is also a function updated_csv() that will export it back to an csv file.

Basic Example¶

In [2]:

m1 = leafmap.Map()
fp1 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/test.csv'
preview(fp1)

m1 = leafmap.Map() fp1 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/test.csv' preview(fp1)

City,State,lat,long
Arizona,Phoenix,33.448143,-112.096962
Arkansas,Little Rock,34.746613,-92.288986
California,Sacramento,38.576668,-121.493629

In [3]:

points1 = spatialcsv.Points(fp1, ['lat', 'long'])
points1.df.head()

points1 = spatialcsv.Points(fp1, ['lat', 'long']) points1.df.head()

Out[3]:

	City	State	lat	long
0	Arizona	Phoenix	33.448143	-112.096962
1	Arkansas	Little Rock	34.746613	-92.288986
2	California	Sacramento	38.576668	-121.493629
3	Colorado	Denver	39.739227	-104.984856
4	Connecticut	Hartford<br>	41.764046	-72.682198

In [4]:

data1 = points1.to_leafmap()
m1.add_points_from_xy(data1, x='long', y='lat')
m1

data1 = points1.to_leafmap() m1.add_points_from_xy(data1, x='long', y='lat') m1

Example changing from degree to decimal format¶

In [5]:

m2 = leafmap.Map()
fp2 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/dms.csv'
preview(fp2)

m2 = leafmap.Map() fp2 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/dms.csv' preview(fp2)

State Capital,lat,long
Tallahassee,30°27'N, 84°16'W
Raleigh,35°46'N, 78°39'W
Austin,30°14'N, 97°45'W

In [6]:

points2 = spatialcsv.Points(fp2, ['lat', 'long'])
points2.df.head()

points2 = spatialcsv.Points(fp2, ['lat', 'long']) points2.df.head()

Out[6]:

	State Capital	lat	long
0	Tallahassee	30.450000	-84.266667
1	Raleigh	35.766667	-78.650000
2	Austin	30.233333	-97.750000
3	Sacramento	38.900000	-121.483333
4	Richmond	37.533333	-77.450000

In [7]:

data2 = points2.to_leafmap()
m2.add_points_from_xy(data2, x='long', y='lat')
m2

data2 = points2.to_leafmap() m2.add_points_from_xy(data2, x='long', y='lat') m2

Example changing projection¶

In [8]:

m3 = leafmap.Map()
fp3 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/projected_2274.csv'
preview(fp3)

m3 = leafmap.Map() fp3 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/projected_2274.csv' preview(fp3)

ObjectID,X,Y,School,City,GradeLevels
1,767637.23167577,315674.50617952,1ST CLASS MONTESSORI - MEMPHIS,Memphis,"PK,K"
2,828609.47361461,327549.00838232,1ST CLASS MONTESSORI SCHOOL - CORDOVA,Cordova,"PK,K,1,2,3"
3,757231.10957349,307890.57621781,A B HILL ELEMENTARY,Memphis,"PK,K,1,2,3,4,5"

In [9]:

points3 = spatialcsv.Points(fp3, ['X', 'Y'], epsg='2274')
points3.df.head()

points3 = spatialcsv.Points(fp3, ['X', 'Y'], epsg='2274') points3.df.head()

Out[9]:

	ObjectID	X	Y	School	City	GradeLevels
0	1	35.132828	-90.017079	1ST CLASS MONTESSORI - MEMPHIS	Memphis	PK,K
1	2	35.172125	-89.814876	1ST CLASS MONTESSORI SCHOOL - CORDOVA	Cordova	PK,K,1,2,3
2	3	35.110283	-90.050772	A B HILL ELEMENTARY	Memphis	PK,K,1,2,3,4,5
3	4	36.387252	-85.319641	A H ROBERTS ELEMENTARY	Livingston	PK,K,1,2,3,4
4	5	35.882628	-84.083299	A L LOTTS ELEMENTARY	Knoxville	K,1,2,3,4,5

In [10]:

data3 = points3.to_leafmap()
m3.add_points_from_xy(data3, x='Y', y='X')
m3

data3 = points3.to_leafmap() m3.add_points_from_xy(data3, x='Y', y='X') m3

Example assigning tags¶

In [11]:

m4 = leafmap.Map()
fp4 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/tags.csv'
preview(fp4)

m4 = leafmap.Map() fp4 = 'https://github.com/TJHomer/spatialcsv/raw/main/spatialcsv/example_files/tags.csv' preview(fp4)

name,sov_a3,latitude,longitude,pop_max,region
San Bernardino,USA,34.12038,-117.30003,1745000,West
Bridgeport,USA,41.17998,-73.19996,1018000,Northeast
Rochester,USA,43.17043,-77.61995,755000,Northeast

In [12]:

points4 = spatialcsv.Points(fp4, ['latitude', 'longitude'], tags=['name', 'pop_max'])
points4.df.head()

points4 = spatialcsv.Points(fp4, ['latitude', 'longitude'], tags=['name', 'pop_max']) points4.df.head()

Out[12]:

	name	sov_a3	latitude	longitude	pop_max	region
0	San Bernardino	USA	34.12038	-117.30003	1745000	West
1	Bridgeport	USA	41.17998	-73.19996	1018000	Northeast
2	Rochester	USA	43.17043	-77.61995	755000	Northeast
3	St. Paul	USA	44.94399	-93.08497	734854	Midwest
4	Billings	USA	45.78830	-108.54000	104552	West

In [13]:

data4 = points4.to_leafmap()
m4.add_points_from_xy(data4, x='longitude', y='latitude')
m4
# Click on the location icon to see selected tags

data4 = points4.to_leafmap() m4.add_points_from_xy(data4, x='longitude', y='latitude') m4 # Click on the location icon to see selected tags

Downloading updated csv¶

In [14]:

#enter a filepath and uncomment to download one of the updated csvs

filepath = 'updated.csv'

points2.updated_csv(filepath)

#enter a filepath and uncomment to download one of the updated csvs filepath = 'updated.csv' points2.updated_csv(filepath)

In [ ]:

---

Last update: 2023-05-08