I have a simple GeoPandas Dataframe:

enter image description here

I would like to upload this GeoDataframe to a PostGIS table. I have a Database setup with the PostGIS extension already but can't seem to add this Dataframe as a table.

I have tried the following:

engine = <>
meta = MetaData(engine)
eld_test = Table('eld_test', meta, Column('id', Integer, primary_key=True), Column('key_comb_drvr', Text), 
                 Column('geometry', Geometry('Point', srid=4326))) 
conn = engine.connect() 
conn.execute(eld_test.insert(), df.to_dict('records'))
  • I have tried the following: engine = <> # create table meta = MetaData(engine) eld_test = Table('eld_test', meta, Column('id', Integer, primary_key=True), Column('key_comb_drvr', Text), Column('geometry', Geometry('Point', srid=4326)) ) eld_test.create(engine) # DBAPI's executemany with list of dicts conn = engine.connect() conn.execute(eld_test.insert(), df.to_dict('records')) – thecornman May 4 '17 at 15:38
  • 1
    Welcome to GIS SE, please have a read of our tour! Could you edit your post to include your code posted in the comments? – GISKid May 4 '17 at 16:26

Using Panda's to_sql method and SQLAlchemy you can store a dataframe in Postgres. And since you're storing a Geodataframe, GeoAlchemy will handle the geom column for you. Here's a code sample:

# Imports
from geoalchemy2 import Geometry, WKTElement
from sqlalchemy import *
import pandas as pd
import geopandas as gpd

# Creating SQLAlchemy's engine to use
engine = create_engine('postgresql://username:password@host:socket/database')

geodataframe = gpd.GeoDataFrame(pd.DataFrame.from_csv('<your dataframe source>'))
#... [do something with the geodataframe]

geodataframe['geom'] = geodataframe['geometry'].apply(lambda x: WKTElement(x.wkt, srid=<your_SRID>)

#drop the geometry column as it is now duplicative
geodataframe.drop('geometry', 1, inplace=True)

# Use 'dtype' to specify column's type
# For the geom column, we will use GeoAlchemy's type 'Geometry'
geodataframe.to_sql(table_name, engine, if_exists='append', index=False, 
                         dtype={'geom': Geometry('POINT', srid= <your_srid>)})

Worth noting that 'if_exists' parameter allows you to handle the way the dataframe will be added to your postgres table:

    if_exists = replace: If table exists, drop it, recreate it, and insert data.
    if_exists = fail: If table exists, do nothing.
    if_exists = append: If table exists, insert data. Create if does not exist.

I have also had the same question you've asked and have spent many, many days on it (more than I care to admit) looking for a solution. Assuming the following postgreSQL table with the postGIS extension,

postgres=> \d cldmatchup.geo_points;
Table "cldmatchup.geo_points"
Column   |         Type         |                               Modifiers                                
gridid    | bigint               | not null default nextval('cldmatchup.geo_points_gridid_seq'::regclass)
lat       | real                 | 
lon       | real                 | 
the_point | geography(Point,4326) | 

"geo_points_pkey" PRIMARY KEY, btree (gridid)

this is what I finally got working:

import geopandas as gpd
from geoalchemy2 import Geography, Geometry
from sqlalchemy import create_engine, MetaData, Table
from sqlalchemy.orm import sessionmaker
from shapely.geometry import Point
from psycopg2.extensions import adapt, register_adapter, AsIs

# From http://initd.org/psycopg/docs/advanced.html#adapting-new-types but 
# modified to accomodate postGIS point type rather than a postgreSQL 
# point type format
def adapt_point(point):
    from psycopg2.extensions import adapt, AsIs
    x = adapt(point.x).getquoted()
    y = adapt(point.y).getquoted()
    return AsIs("'POINT (%s %s)'" % (x, y))

register_adapter(Point, adapt_point)

engine = create_engine('postgresql://<yourUserName>:postgres@localhost:5432/postgres', echo=False)
Session = sessionmaker(bind=engine)
session = Session()
meta = MetaData(engine, schema='cldmatchup')

# Create reference to pre-existing "geo_points" table in schema "cldmatchup"
geoPoints = Table('geo_points', meta, autoload=True, schema='cldmatchup', autoload_with=engine)

df = gpd.GeoDataFrame({'lat':[45.15, 35., 57.], 'lon':[-35, -150, -90.]})

# Create a shapely.geometry point 
the_point = [Point(xy) for xy in zip(df.lon, df.lat)]

# Create a GeoDataFrame specifying 'the_point' as the column with the 
# geometry data
crs = {'init': 'epsg:4326'}
geo_df = gpd.GeoDataFrame(df.copy(), crs=crs, geometry=the_point)

# Rename the geometry column to match the database table's column name.
# From https://media.readthedocs.org/pdf/geopandas/latest/geopandas.pdf,
# Section 1.2.2 p 7
geo_df = geo_df.rename(columns{'geometry':'the_point'}).set_geometry('the_point')

# Write to sql table 'geo_points'
geo_df.to_sql(geoPoints.name, engine, if_exists='append', schema='cldmatchup', index=False)


I can't say if my database connection logic is the best since I basically copied that from another link and was just happy that I was able to successfully automap (or reflect) my existing table with the geometry definition recognized. I've been writing python to sql spatial code for only a few months, so I know there is much to learn.

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