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How to fix inefficient nested dictionary with haversine distances between locations

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I have made a nested dictionary which contains the distances between locations and all other distances.

for example:

city1 :city1 : 0, city2: 2, city3: 3, city4: 4
city2 : city1: 2, city2: 0, city3: 1, city4:2
city3 : …

My code does deliver this, although it works extremely inefficiently.
I am wondering why and how to improve it, since when I try changing it, the code gives an error saying that tuple indices can not be strings or lists or tuples.

So the code now does deliver, however it takes way too long.

I am looking for a way to improve it & to understand why this works but takes this long.

The data is a dataframe containing city numbers and lattitude and longitude coordinates.

 def distances_dict(data):
 all_distances = 
 for row in data.iterrows():
 all_distances[row[1]['City Nr.']] = 
 for row2 in data.iterrows():
 all_distances[row[1]['City Nr.']][row2[1]['City Nr.']]= 
 haversine(row[1][['Lat','Long']],row2[1][['Lat','Long']])
 return all_distances

dictionary nested haversine coding-efficiency

share|improve this question

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

add a comment | 

0

I have made a nested dictionary which contains the distances between locations and all other distances.

for example:

city1 :city1 : 0, city2: 2, city3: 3, city4: 4
city2 : city1: 2, city2: 0, city3: 1, city4:2
city3 : …

My code does deliver this, although it works extremely inefficiently.
I am wondering why and how to improve it, since when I try changing it, the code gives an error saying that tuple indices can not be strings or lists or tuples.

So the code now does deliver, however it takes way too long.

I am looking for a way to improve it & to understand why this works but takes this long.

The data is a dataframe containing city numbers and lattitude and longitude coordinates.

 def distances_dict(data):
 all_distances = 
 for row in data.iterrows():
 all_distances[row[1]['City Nr.']] = 
 for row2 in data.iterrows():
 all_distances[row[1]['City Nr.']][row2[1]['City Nr.']]= 
 haversine(row[1][['Lat','Long']],row2[1][['Lat','Long']])
 return all_distances

dictionary nested haversine coding-efficiency

share|improve this question

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

add a comment | 

I have made a nested dictionary which contains the distances between locations and all other distances.

for example:

city1 :city1 : 0, city2: 2, city3: 3, city4: 4
city2 : city1: 2, city2: 0, city3: 1, city4:2
city3 : …

My code does deliver this, although it works extremely inefficiently.
I am wondering why and how to improve it, since when I try changing it, the code gives an error saying that tuple indices can not be strings or lists or tuples.

So the code now does deliver, however it takes way too long.

I am looking for a way to improve it & to understand why this works but takes this long.

The data is a dataframe containing city numbers and lattitude and longitude coordinates.

 def distances_dict(data):
 all_distances = 
 for row in data.iterrows():
 all_distances[row[1]['City Nr.']] = 
 for row2 in data.iterrows():
 all_distances[row[1]['City Nr.']][row2[1]['City Nr.']]= 
 haversine(row[1][['Lat','Long']],row2[1][['Lat','Long']])
 return all_distances

dictionary nested haversine coding-efficiency

share|improve this question

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

city1 :city1 : 0, city2: 2, city3: 3, city4: 4
city2 : city1: 2, city2: 0, city3: 1, city4:2
city3 : …

 def distances_dict(data):
 all_distances = 
 for row in data.iterrows():
 all_distances[row[1]['City Nr.']] = 
 for row2 in data.iterrows():
 all_distances[row[1]['City Nr.']][row2[1]['City Nr.']]= 
 haversine(row[1][['Lat','Long']],row2[1][['Lat','Long']])
 return all_distances

share|improve this question

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

share|improve this question

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge

asked Mar 25 at 14:47

ValérieValérie

111 bronze badge