# how can i solve error ''float' object is not iterable'

## Question:

im new at python, and try to categorize places an df1 by distance to places in df2, but something gonna wrong

i have 2 dataframes whith coordinate of places

``````import pandas as pd
import geopy.distance

df1 = pd.DataFrame([['a', 55.88, 37.48],
['b', 55.88, 37.53],
['c', 55.89, 37.45]],
columns=['name', 'lat', 'lng']

df1 = pd.DataFrame([['f', 55.81, 37.12],
['g', 55.79, 37.23],
['h', 55.23, 37.21]],
columns=['name', 'lat', 'lng']
print(df1)
print(df2)
``````

df1

name lat lng
a 55.88 37.48
b 55.88 37.53
c 55.89 37.45

df2

name lat lng
f 55.81 37.12
g 55.79 37.23
h 55.23 37.21

so, i try to calculate distance between a and f,g,h and if distance to one of this place less than 1000m, append category
"close" and else category ‘far’, and do it for each name in df1

i want this df

print(df1)

name lat lng dist_to_palce
a 55.88 37.48 far
b 55.88 37.53 close
c 55.89 37.45 far

i try this construction

``````def dist(df1):
for i in range(len(df1)):
for j in range(len(df2)):
if geopy.distance.geodesic(
tuple(data[['lat','lng']].iloc[i]),
tuple(metro[['lat','lng']].iloc[j])).m <1000:
return 'close'
else: return 'far'

df1['dist_to_place'] = df1.apply(dist, axis=1)
``````

but i got error ‘float’ object is not iterable

solution

``````def dist(df1_row):
for j in range(len(df2)):
if geopy.distance.geodesic(
tuple(df1_row[['lat','lng']]),
tuple(df2[['lat','lng']].iloc[j])).m <1000:
return 'close'
return 'far'

df1['dist_to_place'] = df1.apply(dist, axis=1)
``````

The error is happening because you are returning a string value ‘close’ or ‘far’ from the dist function and trying to assign it to the entire row of the df1 dataframe using df1.apply(dist, axis=1). Instead of returning a string, you should create a list of values with length equal to the number of rows in df1, and then assign the list to a new column in df1.

``````import geopy.distance

def dist(row):
result = []
for j in range(len(df2)):
if geopy.distance.geodesic(
(row['lat'], row['lng']),
(df2.loc[j, 'lat'], df2.loc[j, 'lng'])).m < 1000:
result.append('close')
else: result.append('far')
return result
``````

df1[‘dist_to_place’] = df1.apply(dist, axis=1).apply(lambda x: x)

above code calculates the distance between each place in df1 and all the places in df2, but only the first occurrence of ‘close’ or ‘far’ is returned and assigned to the new column in df1.

``````import pandas as pd
import geopy.distance

df1 = pd.DataFrame({'name':['a', 'b'],'lat':[56.34, 76.56], 'lng':[23.42, 45.34]})
df2 = pd.DataFrame({'name':['f', 'g'],'lat':[56.45, 76.55], 'lng':[27.42, 40.34]})

def dist(df1_row):
for j in range(len(df2)):
if geopy.distance.geodesic(
tuple(df1_row[['lat','lng']]),
tuple(df2[['lat','lng']].iloc[j])).m <1000:
return 'close'
return 'far'

df1['dist_to_place'] = df1.apply(dist, axis=1)
``````
1. when you use apply, row is given to your function, not the whole dataframe
2. you need to return `far` after the cycle, not inside
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