Yes, that can be done with shapely and geopandas.
Supposed that your pandas dataframe kind of looks like this:
import pandas as pd
data = [
{'some_attribute': 'abc', 'lat': '50.1234', 'lon': '10.4023'},
{'some_attribute': 'def', 'lat': '40.5678', 'lon': '8.3365'},
{'some_attribute': 'ghi', 'lat': '60.9012', 'lon': '6.2541'},
{'some_attribute': 'jkl', 'lat': '45.3456', 'lon': '12.5478'},
{'some_attribute': 'mno', 'lat': '35.7890', 'lon': '14.3957'},
]
df = pd.DataFrame(data)
print(df)
=>
lat lon some_attribute
0 50.1234 10.4023 abc
1 40.5678 8.3365 def
2 60.9012 6.2541 ghi
3 45.3456 12.5478 jkl
4 35.7890 14.3957 mno
First, make sure that geopandas and shapely are installed properly which sometimes is not easy because they come with some dependencies (e.g. GEOS and GDAL). If does not work at first try via pip install geopandas shapely, search for the error on Google or StackOverflow/Gis.Stackexchange because most probably there will be an answer available solving that problem for you.
Then, it is just a matter of creating a new geometry column in your dataframe which combines the lat and lon values into a shapely Point() object. Note that the Point() constructor expects a tuple of float values, so conversion must be included if the dataframe's column dtypes are not already set to float.
from shapely.geometry import Point
# combine lat and lon column to a shapely Point() object
df['geometry'] = df.apply(lambda x: Point((float(x.lon), float(x.lat))), axis=1)
Now, convert the pandas DataFrame into a GeoDataFrame. The geopandas constructor expects a geometry column which can consist of shapely geometry objects, so the column we created is just fine:
import geopandas
df = geopandas.GeoDataFrame(df, geometry='geometry')
To dump this GeoDataFrame into a shapefile, use geopandas' to_file() method (other drivers supported by Fiona such as GeoJSON should also work):
df.to_file('MyGeometries.shp', driver='ESRI Shapefile')
And that is what the resulting shapefile looks like when visualized with QGIS:
Convert the DataFrame's content (e.g. Lat and Lon columns) into appropriate Shapely geometries first and then use them together with the original DataFrame to create a GeoDataFrame.
from geopandas import GeoDataFrame
from shapely.geometry import Point
geometry = [Point(xy) for xy in zip(df.Lon, df.Lat)]
df = df.drop(['Lon', 'Lat'], axis=1)
gdf = GeoDataFrame(df, crs="EPSG:4326", geometry=geometry)
Result:
Date/Time ID geometry
0 4/1/2014 0:11:00 140 POINT (-73.95489999999999 40.769)
1 4/1/2014 0:17:00 NaN POINT (-74.03449999999999 40.7267)
Since the geometries often come in the WKT format, I thought I'd include an example for that case as well:
import geopandas as gpd
import shapely.wkt
geometry = df['wktcolumn'].map(shapely.wkt.loads)
df = df.drop('wktcolumn', axis=1)
gdf = gpd.GeoDataFrame(df, crs="EPSG:4326", geometry=geometry)
Best Answer
Here is a possible solution:
Your data doesn't seem to be regular so it will not be straightforward to build an image out of this, unless you do some interpolation:
You will end up with this raster close to the south pole:
Documentation is here: https://corteva.github.io/geocube/html/geocube.html