Python Dict Comprehension retrieve value from 1 dataframe column if match another column value
Question:
I have a dataframe and there are 2 columns ["country"] and ["city"] which basically informs of the country and their cities.
I need to create a dict using dict comprehensions, to get as a key, the country and as values, a list of the city/cities (some of them only have one city, others many).
I’m able to define the keys and create a list but all the cities existing appears a values, I am not able to create the condition that the country of the value should be the key:
Dic = {k: list(megacities["city"]) for k,f in megacities.groupby('country')}
for k in Dic:
print("{}:{}n".format(k, Dic[k]))
Part of the output that I receive is:
Argentina:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
Bangladesh:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
Brazil:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
So basically the expect output would be:
Argentina:['Buenos Aires']
Bangladesh:['Dhaka']
Brazil:['São Paulo', 'Rio de Janeiro']
How can I should proceed in terms of syntaxis to stablish that condition for the value in the dict comprehension?
Lastly, the dataframe:
city city_ascii lat lng country iso2 iso3 admin_name capital population id
0 Tokyo Tokyo 35.6839 139.7744 Japan JP JPN Tōkyō primary 39105000 1392685764
1 Jakarta Jakarta -6.2146 106.8451 Indonesia ID IDN Jakarta primary 35362000 1360771077
2 Delhi Delhi 28.6667 77.2167 India IN IND Delhi admin 31870000 1356872604
3 Manila Manila 14.6000 120.9833 Philippines PH PHL Manila primary 23971000 1608618140
4 São Paulo Sao Paulo -23.5504 -46.6339 Brazil BR BRA São Paulo admin 22495000 1076532519
5 Seoul Seoul 37.5600 126.9900 South Korea KR KOR Seoul primary 22394000 1410836482
6 Mumbai Mumbai 19.0758 72.8775 India IN IND Mahārāshtra admin 22186000 1356226629
7 Shanghai Shanghai 31.1667 121.4667 China CN CHN Shanghai admin 22118000 1156073548
8 Mexico City Mexico City 19.4333 -99.1333 Mexico MX MEX Ciudad de México primary 21505000 1484247881
9 Guangzhou Guangzhou 23.1288 113.2590 China CN CHN Guangdong admin 21489000 1156237133
10 Cairo Cairo 30.0444 31.2358 Egypt EG EGY Al Qāhirah primary 19787000 1818253931
11 Beijing Beijing 39.9040 116.4075 China CN CHN Beijing primary 19437000 1156228865
12 New York New York 40.6943 -73.9249 United States US USA New York NaN 18713220 1840034016
13 Kolkāta Kolkata 22.5727 88.3639 India IN IND West Bengal admin 18698000 1356060520
14 Moscow Moscow 55.7558 37.6178 Russia RU RUS Moskva primary 17693000 1643318494
15 Bangkok Bangkok 13.7500 100.5167 Thailand TH THA Krung Thep Maha Nakhon primary 17573000 1764068610
16 Dhaka Dhaka 23.7289 90.3944 Bangladesh BD BGD Dhaka primary 16839000 1050529279
17 Buenos Aires Buenos Aires -34.5997 -58.3819 Argentina AR ARG Buenos Aires, Ciudad Autónoma de primary 16216000 1032717330
18 Ōsaka Osaka 34.7520 135.4582 Japan JP JPN Ōsaka admin 15490000 1392419823
19 Lagos Lagos 6.4500 3.4000 Nigeria NG NGA Lagos minor 15487000 1566593751
20 Istanbul Istanbul 41.0100 28.9603 Turkey TR TUR İstanbul admin 15311000 1792756324
21 Karachi Karachi 24.8600 67.0100 Pakistan PK PAK Sindh admin 15292000 1586129469
22 Kinshasa Kinshasa -4.3317 15.3139 Congo (Kinshasa) CD COD Kinshasa primary 15056000 1180000363
23 Shenzhen Shenzhen 22.5350 114.0540 China CN CHN Guangdong minor 14678000 1156158707
24 Bangalore Bangalore 12.9791 77.5913 India IN IND Karnātaka admin 13999000 1356410365
25 Ho Chi Minh City Ho Chi Minh City 10.8167 106.6333 Vietnam VN VNM Hồ Chí Minh admin 13954000 1704774326
26 Tehran Tehran 35.7000 51.4167 Iran IR IRN Tehrān primary 13819000 1364305026
27 Los Angeles Los Angeles 34.1139 -118.4068 United States US USA California NaN 12750807 1840020491
28 Rio de Janeiro Rio de Janeiro -22.9083 -43.1964 Brazil BR BRA Rio de Janeiro admin 12486000 1076887657
29 Chengdu Chengdu 30.6600 104.0633 China CN CHN Sichuan admin 11920000 1156421555
30 Baoding Baoding 38.8671 115.4845 China CN CHN Hebei NaN 11860000 1156256829
31 Chennai Chennai 13.0825 80.2750 India IN IND Tamil Nādu admin 11564000 1356374944
32 Lahore Lahore 31.5497 74.3436 Pakistan PK PAK Punjab admin 11148000 1586801463
33 London London 51.5072 -0.1275 United Kingdom GB GBR London, City of primary 11120000 1826645935
34 Paris Paris 48.8566 2.3522 France FR FRA Île-de-France primary 11027000 1250015082
35 Tianjin Tianjin 39.1467 117.2056 China CN CHN Tianjin admin 10932000 1156174046
36 Linyi Linyi 35.0606 118.3425 China CN CHN Shandong NaN 10820000 1156086320
37 Shijiazhuang Shijiazhuang 38.0422 114.5086 China CN CHN Hebei admin 10784600 1156217541
38 Zhengzhou Zhengzhou 34.7492 113.6605 China CN CHN Henan admin 10136000 1156183137
39 Nanyang Nanyang 32.9987 112.5292 China CN CHN Henan NaN 10013600 1156192287
Many thanks!
Answers:
Since you are doing the groupby, You need to fetch city from the group
Dic = {k: f['city'].unique() for k,f in megacities.groupby('country')}
Try:
d = {i: g["city"].to_list() for i, g in df.groupby("country")}
print(d)
Prints:
{
"Argentina": ["Buenos Aires"],
"Bangladesh": ["Dhaka"],
"Brazil": ["São Paulo", "Rio de Janeiro"],
"China": [
"Shanghai",
"Guangzhou",
"Beijing",
"Shenzhen",
"Chengdu",
"Baoding",
"Tianjin",
"Linyi",
"Shijiazhuang",
"Zhengzhou",
"Nanyang",
],
"Congo (Kinshasa)": ["Kinshasa"],
"Egypt": ["Cairo"],
"France": ["Paris"],
"India": ["Delhi", "Mumbai", "Kolkāta", "Bangalore", "Chennai"],
"Indonesia": ["Jakarta"],
"Iran": ["Tehran"],
"Japan": ["Tokyo", "Ōsaka"],
"Mexico": ["Mexico City"],
"Nigeria": ["Lagos"],
"Pakistan": ["Karachi", "Lahore"],
"Philippines": ["Manila"],
"Russia": ["Moscow"],
"South Korea": ["Seoul"],
"Thailand": ["Bangkok"],
"Turkey": ["Istanbul"],
"United Kingdom": ["London"],
"United States": ["New York", "Los Angeles"],
"Vietnam": ["Ho Chi Minh City"],
}
I have a dataframe and there are 2 columns ["country"] and ["city"] which basically informs of the country and their cities.
I need to create a dict using dict comprehensions, to get as a key, the country and as values, a list of the city/cities (some of them only have one city, others many).
I’m able to define the keys and create a list but all the cities existing appears a values, I am not able to create the condition that the country of the value should be the key:
Dic = {k: list(megacities["city"]) for k,f in megacities.groupby('country')}
for k in Dic:
print("{}:{}n".format(k, Dic[k]))
Part of the output that I receive is:
Argentina:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
Bangladesh:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
Brazil:['Tokyo', 'Jakarta', 'Delhi', 'Manila', 'São Paulo', 'Seoul', 'Mumbai', 'Shanghai', 'Mexico City', 'Guangzhou', 'Cairo', 'Beijing', 'New York', 'Kolkāta', 'Moscow', 'Bangkok', 'Dhaka', 'Buenos Aires', 'Ōsaka', 'Lagos', 'Istanbul', 'Karachi', 'Kinshasa', 'Shenzhen', 'Bangalore', 'Ho Chi Minh City', 'Tehran', 'Los Angeles', 'Rio de Janeiro', 'Chengdu', 'Baoding', 'Chennai', 'Lahore', 'London', 'Paris', 'Tianjin', 'Linyi', 'Shijiazhuang', 'Zhengzhou', 'Nanyang']
So basically the expect output would be:
Argentina:['Buenos Aires']
Bangladesh:['Dhaka']
Brazil:['São Paulo', 'Rio de Janeiro']
How can I should proceed in terms of syntaxis to stablish that condition for the value in the dict comprehension?
Lastly, the dataframe:
city city_ascii lat lng country iso2 iso3 admin_name capital population id
0 Tokyo Tokyo 35.6839 139.7744 Japan JP JPN Tōkyō primary 39105000 1392685764
1 Jakarta Jakarta -6.2146 106.8451 Indonesia ID IDN Jakarta primary 35362000 1360771077
2 Delhi Delhi 28.6667 77.2167 India IN IND Delhi admin 31870000 1356872604
3 Manila Manila 14.6000 120.9833 Philippines PH PHL Manila primary 23971000 1608618140
4 São Paulo Sao Paulo -23.5504 -46.6339 Brazil BR BRA São Paulo admin 22495000 1076532519
5 Seoul Seoul 37.5600 126.9900 South Korea KR KOR Seoul primary 22394000 1410836482
6 Mumbai Mumbai 19.0758 72.8775 India IN IND Mahārāshtra admin 22186000 1356226629
7 Shanghai Shanghai 31.1667 121.4667 China CN CHN Shanghai admin 22118000 1156073548
8 Mexico City Mexico City 19.4333 -99.1333 Mexico MX MEX Ciudad de México primary 21505000 1484247881
9 Guangzhou Guangzhou 23.1288 113.2590 China CN CHN Guangdong admin 21489000 1156237133
10 Cairo Cairo 30.0444 31.2358 Egypt EG EGY Al Qāhirah primary 19787000 1818253931
11 Beijing Beijing 39.9040 116.4075 China CN CHN Beijing primary 19437000 1156228865
12 New York New York 40.6943 -73.9249 United States US USA New York NaN 18713220 1840034016
13 Kolkāta Kolkata 22.5727 88.3639 India IN IND West Bengal admin 18698000 1356060520
14 Moscow Moscow 55.7558 37.6178 Russia RU RUS Moskva primary 17693000 1643318494
15 Bangkok Bangkok 13.7500 100.5167 Thailand TH THA Krung Thep Maha Nakhon primary 17573000 1764068610
16 Dhaka Dhaka 23.7289 90.3944 Bangladesh BD BGD Dhaka primary 16839000 1050529279
17 Buenos Aires Buenos Aires -34.5997 -58.3819 Argentina AR ARG Buenos Aires, Ciudad Autónoma de primary 16216000 1032717330
18 Ōsaka Osaka 34.7520 135.4582 Japan JP JPN Ōsaka admin 15490000 1392419823
19 Lagos Lagos 6.4500 3.4000 Nigeria NG NGA Lagos minor 15487000 1566593751
20 Istanbul Istanbul 41.0100 28.9603 Turkey TR TUR İstanbul admin 15311000 1792756324
21 Karachi Karachi 24.8600 67.0100 Pakistan PK PAK Sindh admin 15292000 1586129469
22 Kinshasa Kinshasa -4.3317 15.3139 Congo (Kinshasa) CD COD Kinshasa primary 15056000 1180000363
23 Shenzhen Shenzhen 22.5350 114.0540 China CN CHN Guangdong minor 14678000 1156158707
24 Bangalore Bangalore 12.9791 77.5913 India IN IND Karnātaka admin 13999000 1356410365
25 Ho Chi Minh City Ho Chi Minh City 10.8167 106.6333 Vietnam VN VNM Hồ Chí Minh admin 13954000 1704774326
26 Tehran Tehran 35.7000 51.4167 Iran IR IRN Tehrān primary 13819000 1364305026
27 Los Angeles Los Angeles 34.1139 -118.4068 United States US USA California NaN 12750807 1840020491
28 Rio de Janeiro Rio de Janeiro -22.9083 -43.1964 Brazil BR BRA Rio de Janeiro admin 12486000 1076887657
29 Chengdu Chengdu 30.6600 104.0633 China CN CHN Sichuan admin 11920000 1156421555
30 Baoding Baoding 38.8671 115.4845 China CN CHN Hebei NaN 11860000 1156256829
31 Chennai Chennai 13.0825 80.2750 India IN IND Tamil Nādu admin 11564000 1356374944
32 Lahore Lahore 31.5497 74.3436 Pakistan PK PAK Punjab admin 11148000 1586801463
33 London London 51.5072 -0.1275 United Kingdom GB GBR London, City of primary 11120000 1826645935
34 Paris Paris 48.8566 2.3522 France FR FRA Île-de-France primary 11027000 1250015082
35 Tianjin Tianjin 39.1467 117.2056 China CN CHN Tianjin admin 10932000 1156174046
36 Linyi Linyi 35.0606 118.3425 China CN CHN Shandong NaN 10820000 1156086320
37 Shijiazhuang Shijiazhuang 38.0422 114.5086 China CN CHN Hebei admin 10784600 1156217541
38 Zhengzhou Zhengzhou 34.7492 113.6605 China CN CHN Henan admin 10136000 1156183137
39 Nanyang Nanyang 32.9987 112.5292 China CN CHN Henan NaN 10013600 1156192287
Many thanks!
Since you are doing the groupby, You need to fetch city from the group
Dic = {k: f['city'].unique() for k,f in megacities.groupby('country')}
Try:
d = {i: g["city"].to_list() for i, g in df.groupby("country")}
print(d)
Prints:
{
"Argentina": ["Buenos Aires"],
"Bangladesh": ["Dhaka"],
"Brazil": ["São Paulo", "Rio de Janeiro"],
"China": [
"Shanghai",
"Guangzhou",
"Beijing",
"Shenzhen",
"Chengdu",
"Baoding",
"Tianjin",
"Linyi",
"Shijiazhuang",
"Zhengzhou",
"Nanyang",
],
"Congo (Kinshasa)": ["Kinshasa"],
"Egypt": ["Cairo"],
"France": ["Paris"],
"India": ["Delhi", "Mumbai", "Kolkāta", "Bangalore", "Chennai"],
"Indonesia": ["Jakarta"],
"Iran": ["Tehran"],
"Japan": ["Tokyo", "Ōsaka"],
"Mexico": ["Mexico City"],
"Nigeria": ["Lagos"],
"Pakistan": ["Karachi", "Lahore"],
"Philippines": ["Manila"],
"Russia": ["Moscow"],
"South Korea": ["Seoul"],
"Thailand": ["Bangkok"],
"Turkey": ["Istanbul"],
"United Kingdom": ["London"],
"United States": ["New York", "Los Angeles"],
"Vietnam": ["Ho Chi Minh City"],
}