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Draw a Map of cities in python

I have a ranking of countries across the world in a variable called rank_2000 that looks like this:
Seoul
Tokyo
Paris
New_York_Greater
Shizuoka
Chicago
Minneapolis
Boston
Austin
Munich
Salt_Lake
Greater_Sydney
Houston
Dallas
London
San_Francisco_Greater
Berlin
Seattle
Toronto
Stockholm
Atlanta
Indianapolis
Fukuoka
San_Diego
Phoenix
Frankfurt_am_Main
Stuttgart
Grenoble
Albany
Singapore
Washington_Greater
Helsinki
Nuremberg
Detroit_Greater
TelAviv
Zurich
Hamburg
Pittsburgh
Philadelphia_Greater
Taipei
Los_Angeles_Greater
Miami_Greater
MannheimLudwigshafen
Brussels
Milan
Montreal
Dublin
Sacramento
Ottawa
Vancouver
Malmo
Karlsruhe
Columbus
Dusseldorf
Shenzen
Copenhagen
Milwaukee
Marseille
Greater_Melbourne
Toulouse
Beijing
Dresden
Manchester
Lyon
Vienna
Shanghai
Guangzhou
San_Antonio
Utrecht
New_Delhi
Basel
Oslo
Rome
Barcelona
Madrid
Geneva
Hong_Kong
Valencia
Edinburgh
Amsterdam
Taichung
The_Hague
Bucharest
Muenster
Greater_Adelaide
Chengdu
Greater_Brisbane
Budapest
Manila
Bologna
Quebec
Dubai
Monterrey
Wellington
Shenyang
Tunis
Johannesburg
Auckland
Hangzhou
Athens
Wuhan
Bangalore
Chennai
Istanbul
Cape_Town
Lima
Xian
Bangkok
Penang
Luxembourg
Buenos_Aires
Warsaw
Greater_Perth
Kuala_Lumpur
Santiago
Lisbon
Dalian
Zhengzhou
Prague
Changsha
Chongqing
Ankara
Fuzhou
Jinan
Xiamen
Sao_Paulo
Kunming
Jakarta
Cairo
Curitiba
Riyadh
Rio_de_Janeiro
Mexico_City
Hefei
Almaty
Beirut
Belgrade
Belo_Horizonte
Bogota_DC
Bratislava
Dhaka
Durban
Hanoi
Ho_Chi_Minh_City
Kampala
Karachi
Kuwait_City
Manama
Montevideo
Panama_City
Quito
San_Juan
What I would like to do is a map of the world where those cities are colored according to their position on the ranking above. I am opened to further solutions for the representation (such as bubbles of increasing dimension according to the position of the cities in the rank or, if necessary, representing only a sample of countries taken from the top rank, the middle and the bottom).
Thank you,
Federico

Your question has two parts; finding the location of each city and then drawing them on the map. Assuming you have the latitude and longitude of each city, here's how you'd tackle the latter part.
I like Folium (https://pypi.org/project/folium/) for drawing maps. Here's an example of how you might draw a circle for each city, with it's position in the list is used to determine the size of that circle.
import folium
cities = [
{'name':'Seoul', 'coodrs':[37.5639715, 126.9040468]},
{'name':'Tokyo', 'coodrs':[35.5090627, 139.2094007]},
{'name':'Paris', 'coodrs':[48.8588787,2.2035149]},
{'name':'New York', 'coodrs':[40.6976637,-74.1197631]},
# etc. etc.
]
m = folium.Map(zoom_start=15)
for counter, city in enumerate(cities):
circle_size = 5 + counter
folium.CircleMarker(
location=city['coodrs'],
radius=circle_size,
popup=city['name'],
color="crimson",
fill=True,
fill_color="crimson",
).add_to(m)
m.save('map.html')
Output:
You may need to adjust the circle_size calculation a little to work with the number of cities you want to include.

Pandas Groupby Head by Percentage of Row Counts

I have a dataframe:
state city score
CA San Francisco 80
CA San Francisco 90
...
NC Raleigh 44
NY New York City 22
I want to do a groupby.head(), but instead of an integer value, I want to select the top 80%, sorted by Score, of each state-city combo.
So if CA, San Francisco has 100 rows, and NC, Raleigh has 20 rows, the final dataframe would have the top 80 score rows for CA, San Francisco, and the top 16 score rows for NC, Raleigh.
So the final result code might look something like:
df.sort_values('score', ascending=False).groupby(['State', 'City']).head(80%)
Thanks!

from io import StringIO
import pandas as pd
# sample data
s = """state,city,score
CA,San Francisco,80
CA,San Francisco,90
CA,San Francisco,30
CA,San Francisco,10
CA,San Francisco,70
CA,San Francisco,60
CA,San Francisco,50
CA,San Francisco,40
NC,Raleigh,44
NC,Raleigh,54
NC,Raleigh,64
NC,Raleigh,14
NY,New York City,22
NY,New York City,12
NY,New York City,32
NY,New York City,42
NY,New York City,52"""
df = pd.read_csv(StringIO(s))
sample = .8 # 80%
# sort the values and create a groupby object
g = df.sort_values('score', ascending=False).groupby(['state', 'city'])
# use list comprehension to iterate over each group
# for each group, calculate what 80% is
# in other words, the length of each group multiplied by .8
# you then use int to round down to the whole number
new_df = pd.concat([data.head(int(len(data)*sample)) for _,data in g])
state city score
1 CA San Francisco 90
0 CA San Francisco 80
4 CA San Francisco 70
5 CA San Francisco 60
6 CA San Francisco 50
7 CA San Francisco 40
10 NC Raleigh 64
9 NC Raleigh 54
8 NC Raleigh 44
16 NY New York City 52
15 NY New York City 42
14 NY New York City 32
12 NY New York City 22

Use nlargest and compute the number of selected rows per group based on its length, i.e. 0.8 * len(group)
res = (
df.groupby(['State', 'City'], group_keys=False)
.apply(lambda g: g.nlargest(int(0.8*len(g)), "Score"))
)

Pythons 'DataFrame' object is not callable - FOR ERROR

Good Morning, My df(df_part3) is above:
Postal Code Borough Neighbourhood Latitude Longitude
0 M5A Downtown Toronto Regent Park, Harbourfront 43.654260 -79.360636
1 M7A Downtown Toronto Queen's Park, Ontario Provincial Government 43.662301 -79.389494
2 M5B Downtown Toronto Garden District, Ryerson 43.657162 -79.378937
3 M5C Downtown Toronto St. James Town 43.651494 -79.375418
4 M4E East Toronto The Beaches 43.676357 -79.293031
... ... ... ... ... ...
34 M5W Downtown Toronto Stn A PO Boxes 43.646435 -79.374846
35 M4X Downtown Toronto St. James Town, Cabbagetown 43.667967 -79.367675
36 M5X Downtown Toronto First Canadian Place, Underground city 43.648429 -79.382280
37 M4Y Downtown Toronto Church and Wellesley 43.665860 -79.383160
38 M7Y East Toronto Business reply mail Processing Centre, South C... 43.662744 -79.321558
And My Code is Here:
map_toronto = folium.Map(location=[latitude, longitude], zoom_start=11)
# add markers to map
for lat, lng, label in zip(df_part3['Latitude'], df_part3['Longitude'], df_part3['Neighbourhood']):
label = folium.Popup(label, parse_html=True)
folium.CircleMarker(
[lat, lng],
radius=5,
popup=label,
color='blue',
fill=True,
fill_color='#3186cc',
fill_opacity=0.7,
parse_html=False).add_to(map_toronto)
map_toronto
But When i Run it i get:
TypeError: 'DataFrame' object is not callable
----> 5 for lat, lng, label in zip(df_part3['Latitude'], df_part3['Longitude'], df_part3['Neighbourhood']):
Does anyone knows how to help-me?

Split column in DataFrame based on item in list

I have the following table and would like to split each row into three columns: state, postcode and city. State and postcode are easy, but I'm unable to extract the city. I thought about splitting each string after the street synonyms and before the state, but I seem to be getting the loop wrong as it will only use the last item in my list.
Input data:
Address Text
0 11 North Warren Circle Lisbon Falls ME 04252
1 227 Cony Street Augusta ME 04330
2 70 Buckner Drive Battle Creek MI
3 718 Perry Street Big Rapids MI
4 14857 Martinsville Road Van Buren MI
5 823 Woodlawn Ave Dallas TX 75208
6 2525 Washington Avenue Waco TX 76710
7 123 South Main St Dallas TX 75201
The output I'm trying to achieve (for all rows, but I only wrote out the first two to save time)
City State Postcode
0 Lisbon Falls ME 04252
1 Augusta ME 04330
My code:
# Extract postcode and state
df["Zip"] = df["Address Text"].str.extract(r'(\d{5})', expand = True)
df["State"] = df["Address Text"].str.extract(r'([A-Z]{2})', expand = True)
# Split after these substrings
street_synonyms = ["Circle", "Street", "Drive", "Road", "Ave", "Avenue", "St"]
# This is where I got stuck
df["Syn"] = df["Address Text"].apply(lambda x: x.split(syn))
df

Here's a way to do that:
import pandas as pd
# data
df = pd.DataFrame(
['11 North Warren Circle Lisbon Falls ME 04252',
'227 Cony Street Augusta ME 04330',
'70 Buckner Drive Battle Creek MI',
'718 Perry Street Big Rapids MI',
'14857 Martinsville Road Van Buren MI',
'823 Woodlawn Ave Dallas TX 75208',
'2525 Washington Avenue Waco TX 76710',
'123 South Main St Dallas TX 75201'],
columns=['Address Text'])
# Extract postcode and state
df["Zip"] = df["Address Text"].str.extract(r'(\d{5})', expand=True)
df["State"] = df["Address Text"].str.extract(r'([A-Z]{2})', expand=True)
# Split after these substrings
street_synonyms = ["Circle", "Street", "Drive", "Road", "Ave", "Avenue", "St"]
def find_city(address, state, street_synonyms):
for syn in street_synonyms:
if syn in address:
# remove street
city = address.split(syn)[-1]
# remove State and postcode
city = city.split(state)[0]
return city
df['City'] = df.apply(lambda x: find_city(x['Address Text'], x['State'], street_synonyms), axis=1)
print(df[['City', 'State', 'Zip']])
"""
City State Zip
0 Lisbon Falls ME 04252
1 Augusta ME 04330
2 Battle Creek MI NaN
3 Big Rapids MI NaN
4 Van Buren MI 14857
5 Dallas TX 75208
6 nue Waco TX 76710
7 Dallas TX 75201
"""

Group a dataframe by a column and concactenate strings in another

I know this should be easy but it's driving me mad...
I am trying to turn a dataframe into a grouped dataframe.
df outputs:
Postcode Borough Neighbourhood
0 M3A North York Parkwoods
1 M4A North York Victoria Village
2 M5A Downtown Toronto Harbourfront
3 M5A Downtown Toronto Regent Park
4 M6A North York Lawrence Heights
5 M6A North York Lawrence Manor
6 M7A Queen's Park Not assigned
7 M9A Etobicoke Islington Avenue
8 M1B Scarborough Rouge
9 M1B Scarborough Malvern
10 M3B North York Don Mills North
...
I want to make a grouped dataframe where the Neighbourhood is grouped by Postcode and all neighborhoods then become a concatenated string of Neighbourhoods as grouped by Postcode...
something like:
Postcode Borough Neighbourhood
0 M3A North York Parkwoods
1 M4A North York Victoria Village
2 M5A Downtown Toronto Harbourfront, Regent Park
...
I am trying to use:
df.groupby(['Postcode'])['Neighbourhood'].apply(lambda strs: ', '.join(strs))
But this does not return a new dataframe .. it outputs the same original dataframe when I use df after running.
if I use:
df = df.groupby(['Postcode'])['Neighbourhood'].apply(lambda strs: ', '.join(strs))
it turns df into an object?

Use this code
new_df = df.groupby(['Postcode', 'Borough']).agg({'Neighbourhood':lambda x:', '.join(x)}).reset_index()
reset_index() will take your group by columns out of the index and return it as a column to the dataframe and create a new integer index.