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Clipping shapefile with multi-polygon shapefile in geopandas

I have a shapefile (will be called source-file hereafter), which I need to clip by a multi-polygon shapefile so that I can have a clipped shapefile for each polygon. I tried the geopandas, though I am able to clip the source-file by individually clipping the it by selecting the polygons separately from the multi-polygon shapefile, but when I try to loop over the polygons to automate the clipping process I get the following error:
Error:
TypeError: 'mask' should be GeoDataFrame, GeoSeries or(Multi)Polygon, got <class 'tuple'>
Code:
import geopandas as gpd
source = ('source-shapefile.shp')
mask = ('mask_shapefile.shp')
sourcefile = gpd.read_file(source)
maskfile = gpd.read_file(mask)
for row in maskfile.iterrows():
gpd.clip(sourcefile, row)

Two points
https://geopandas.org/en/stable/docs/reference/api/geopandas.clip.html mask can be a GeoDataFrame hence no need for looping
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html yields a tuple of the index value and named tuple of row. Hence your error is the fact your are passing this tuple to clip()
Have constructed an example. It is far simpler to clip using a GeoDataFrame as the mask.
import geopandas as gpd
import pandas as pd
# lets build a mask for use in clip, multipolygons and polygons
maskfile = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
maskfile = maskfile.loc[maskfile["continent"].eq("Europe") & maskfile["name"].ne("Russia")].pipe(
lambda d: d.assign(gdp_grp=pd.cut(d["gdp_md_est"], bins=4, labels=list("abcd")))
).dissolve("gdp_grp").reset_index()
sourcefile = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
# now clip, no looping needed
gpd.clip(sourcefile, maskfile)

Finally, after 5 hours of research, I am now able clip the shapefile by a multi-polygon shapefile and save the clipped polygons separately with their respective names. Following code may be dirty
but it works.
code:
import geopandas as gpd
import pandas as pd
import os, sys
source = ('source-shapefile.shp')
mask = ('mask_shapefile.shp')
outpath = ('/outpath')
sourcefile = gpd.read_file(source)
maskfile = gpd.read_file(mask)
clipshape = maskfile.explode()
clipshape.set_index('CATCH_NAME', inplace=True) # CATCH_NAME is attribute column name
for index, row in clipshape['geometry'].iteritems():
clipped = gpd.clip(sourcefile, row)
clipped.to_file(os.path.join(outpath, f'{index}.shp'))

Ipyleaflet will not add layer with the use of geopandas

I am trying to load in a shapefile with the use of geopandas. I have read the file in, but when I go to add the layer to the ipyleaflet map, an error comes up that says 'GeoDataFrame' object has no attribute 'model_id'.
I have tried many files and it just will not read with the geodataframe.
from ipyleaflet import Map, basemaps
fire_was = gpd.read_file('fire_wa.shp')
#add map
center = [47.409824923593575,-120.43401014843751]zoom = 7
m = Map(basemap=basemaps.OpenStreetMap.Mapnik, center=center, zoom=zoom)
m.add_layer(fire_was)
m
Any ideas?

I figured it out. The example I had seen did not show the below but checked more of the documentation of ipyleaflet. You must use GeoData to load the geodataframe.
from ipyleaflet import Map, GeoData, basemaps, LayersControl
fire_was = gpd.read_file('fire_wa.shp') firewa = GeoData(geo_dataframe = fire_was)
m = Map(center =(47.409824923593575,-120.43401014843751), zoom =7,basemap= basemaps.Esri.WorldTopoMap)
m.add_layer(firewa)
m

Plotting with folium

The task is to make an adress popularity map for Moscow. Basically, it should look like this:
https://nbviewer.jupyter.org/github/python-visualization/folium/blob/master/examples/GeoJSON_and_choropleth.ipynb
For my map I use public geojson: http://gis-lab.info/qa/moscow-atd.html
The only data I have - points coordinates and there's no information about the district they belong to.
Question 1:
Do I have to manually calculate for each disctrict if the point belongs to it, or there is more effective way to do this?
Question 2:
If there is no way to do this easier, then, how can I get all the coordinates for each disctrict from the geojson file (link above)?

import pandas as pd
import numpy as np
import geopandas as gpd
from shapely.geometry import Point
Reading in the Moscow area shape file with geopandas
districts = gpd.read_file('mo-shape/mo.shp')
Construct a mock user dataset
moscow = [55.7, 37.6]
data = (
np.random.normal(size=(100, 2)) *
np.array([[.25, .25]]) +
np.array([moscow])
)
my_df = pd.DataFrame(data, columns=['lat', 'lon'])
my_df['pop'] = np.random.randint(500, 100000, size=len(data))
Create Point objects from the user data
geom = [Point(x, y) for x,y in zip(my_df['lon'], my_df['lat'])]
# and a geopandas dataframe using the same crs from the shape file
my_gdf = gpd.GeoDataFrame(my_df, geometry=geom)
my_gdf.crs = districts.crs
Then the join using default value of 'inner'
gpd.sjoin(districts, my_gdf, op='contains')

Thanks to #BobHaffner, I tried to solve the problem using geopandas.
Here are my steps:
I download a shape-files for Moscow using this link click
From a list of tuples containing x and y (latitude and logitude) coordinates I create list of Points (docs)
Assuming that in the dataframe from the first link I have polygons I can write a simple loop for checking if the Point is inside this polygon. For details read this.

Geopandas: not able to change the crs of a geopandas object

I am trying to set the crs of a geopandas object as described here.
The example file can be downloaded from here
import geopandas as gdp
df = pd.read_pickle('myShp.pickle')
I upload the screenshot to show the values of the coordinates
then if I try to change the crs the values of the polygon don't change
tmp = gpd.GeoDataFrame(df, geometry='geometry')
tmp.crs = {'init' :'epsg:32618'}
I show again the screenshot
If I try:
import geopandas as gdp
df = pd.read_pickle('myShp.pickle')
df = gpd.GeoDataFrame(df, geometry='geometry')
dfNew=df.to_crs(epsg=32618)
I get:
ValueError: Cannot transform naive geometries. Please set a crs on the object first.

Setting the crs like:
gdf.crs = {'init' :'epsg:32618'}
does not transform your data, it only sets the CRS (it basically says: "my data is represented in this CRS"). In most cases, the CRS is already set while reading the data with geopandas.read_file (if your file has CRS information). So you only need the above when your data has no CRS information yet.
If you actually want to convert the coordinates to a different CRS, you can use the to_crs method:
gdf_new = gdf.to_crs(epsg=32618)
See https://geopandas.readthedocs.io/en/latest/projections.html

super late answer, but it's:
tmp.set_crs(...)
Use this to define whatever coordinate system the data is in, i.e. 'telling the computer what coordinate system we started in'
Then;
tmp.to_crs(...)
Use this to change to your new preferred crs.

How to generate Folium map using GeoDataFrame?

I have created a geoDataFrame using, and would like to create a Folium Map, plotting the population eat for each country. Do I have to create the Json file, or I can directly use the geoDataFrame file?
import folium
import fiona
import geopandas as gpd
world = fiona.open(gpd.datasets.get_path('naturalearth_lowres'))
world = gpd.GeoDataFrame.from_features([feature for feature in world])
world = world[(world.pop_est > 0) & (world.name != "Antarctica")]
I used folium.map and geojson function, but it failed to create correct JSON files.
Thanks for the help!

The m.cholopleth() code in #joris's answer is now deprecated. The following code produces the same result using the new folium.Chloropleth() function:
m = folium.Map()
folium.Choropleth(world, data=world,
key_on='feature.properties.name',
columns=['name', 'pop_est'],
fill_color='YlOrBr').add_to(m)
folium.LayerControl().add_to(m)
m

In recent releases of folium, you don't need to convert the GeoDataFrame to geojson, but you can pass it directly. Connecting the population column to color the polygons is still somewhat tricky to get correct:
m = folium.Map()
m.choropleth(world, data=world, key_on='feature.properties.name',
columns=['name', 'pop_est'], fill_color='YlOrBr')
m