Convert a pandas dataframe to geojson

I was struggling with converting plain sheet data with coordinates info into spatial feature data that is applicable for web application for a long time, tried many ways but had a hard time configuring the environment on different platforms. It's true you can use ArcGIS or QGIS to easily convert the data, but sometimes an automation way with Python code is a better option when you need to bulk process a huge amount of data.

Now, pandas and pyproj are the only packages you need.

Define the coordinate projection of input data

import pandas as pd
from pyproj import CRS, Transformer
df = pd.read_csv('./data_security.csv')
projection_code = "2232"
df = df[0:5]

Define a function on converting coordinates

def convertXY(row, latlng=None):
    proj_in = CRS("EPSG:"+projection_code)
    proj_out = CRS("EPSG:4326")
    transformer = Transformer.from_crs(proj_in, proj_out)
    lat, long = transformer.transform(row['X_COORD'], row['Y_COORD'])
    if latlng == "lat":
        return lat
    elif latlng == "lng":
        return long
    else:
        return lat, long

Check number of datasets and get the field names

print('We have {} rows'.format(len(df)))
str(df.columns.tolist())

Turn data into a GeoJSON formatted python dictionary

def df_to_geojson(df, properties, lat='latitude', lon='longitude'):
    """
    df : the dataframe to convert to geojson
    properties : a list of columns in the dataframe to turn into geojson feature properties
    lat : the name of the column in the dataframe that contains latitude data
    lon : the name of the column in the dataframe that contains longitude data
    """
    # create a new python dict to contain our geojson data, using geojson format
    geojson = {'type':'FeatureCollection', 'features':[]}
    # loop through each row in the dataframe and convert each row to geojson format
    for _, row in df.iterrows():
        # create a feature template to fill in
        feature = {'type':'Feature',
                   'properties':{},
                   'geometry':{'type':'Point',
                               'coordinates':[]}}
        # fill in the coordinates
        feature['geometry']['coordinates'] = [row[lon],row[lat]]
        # for each column, get the value and add it as a new feature property
        for prop in properties:
            feature['properties'][prop] = row[prop]
        # add this feature (aka, converted dataframe row) to the list of features inside our dict
        geojson['features'].append(feature)
    return geojson

Choose the columns for GeoJSON

useful_columns = ['MONTH', 'YEAR', 'District', 'Source', 'LOCID', 'GPM_RATE', 'Date']
geojson_dict = df_to_geojson(df, properties=useful_columns)
geojson_str = json.dumps(geojson_dict, indent=2)
geojson_str

Output the data in .js format

# save the geojson result to a file
output_filename = './dataset.js'
with open(output_filename, 'w') as output_file:
    output_file.write('var dataset = {};'.format(geojson_str))

# how many features did we save to the geojson file?
print('{} geotagged features saved to file'.format(len(geojson_dict['features'])))

Output the data in GeoJSON format

# save the geojson result to a file
output_filename = './dataset.geojson'
with open(output_filename, 'w') as output_file:
    output_file.write(geojson_str)

# how many features did we save to the geojson file?
print('{} geotagged features saved to file'.format(len(geojson_dict['features'])))