Geopandas Connect PointsLimiting floats to two decimal pointsPeak detection in a 2D arrayHow can I replace all the NaN values with Zero's in a column of a pandas dataframe“Large data” work flows using pandasHow to load mapinfo file into geopandasPass Series of colors/markers to matplotlib with geopandasExtracting geometry in geopandas (worldmap) into my dataframeConvert Points to Lines GeopandasMatplotlib map and subplot with different text at each data pointgeopandas cloropeth with two variables on map
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Geopandas Connect Points
Limiting floats to two decimal pointsPeak detection in a 2D arrayHow can I replace all the NaN values with Zero's in a column of a pandas dataframe“Large data” work flows using pandasHow to load mapinfo file into geopandasPass Series of colors/markers to matplotlib with geopandasExtracting geometry in geopandas (worldmap) into my dataframeConvert Points to Lines GeopandasMatplotlib map and subplot with different text at each data pointgeopandas cloropeth with two variables on map
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I am trying to create a basic origin/destination map of lines. My data has this structure:
ID lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
Any ideas on how to connect the origins & destinations? I have tried zipping them but I can't get geopandas to recognize two geometry columns at once.
python pandas gis geopandas
asked Mar 7 at 23:58
bcparker21bcparker21
286
add a comment |
I am trying to create a basic origin/destination map of lines. My data has this structure:
ID lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
Any ideas on how to connect the origins & destinations? I have tried zipping them but I can't get geopandas to recognize two geometry columns at once.
python pandas gis geopandas
asked Mar 7 at 23:58
bcparker21bcparker21
286
What output do you expect?
– giser_yugang
Mar 8 at 3:31
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I getTypeError: input must be an array, list, tuple or scalar.
– bcparker21
Mar 8 at 19:22
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars
– bcparker21
Mar 8 at 19:46
add a comment |
I am trying to create a basic origin/destination map of lines. My data has this structure:
ID lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
Any ideas on how to connect the origins & destinations? I have tried zipping them but I can't get geopandas to recognize two geometry columns at once.
python pandas gis geopandas
asked Mar 7 at 23:58
bcparker21bcparker21
286
I am trying to create a basic origin/destination map of lines. My data has this structure:
ID lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
Any ideas on how to connect the origins & destinations? I have tried zipping them but I can't get geopandas to recognize two geometry columns at once.
python pandas gis geopandas
python pandas gis geopandas
asked Mar 7 at 23:58
bcparker21bcparker21
286
asked Mar 7 at 23:58
bcparker21bcparker21
286
asked Mar 7 at 23:58
bcparker21bcparker21
286
asked Mar 7 at 23:58
bcparker21bcparker21
286
asked Mar 7 at 23:58
bcparker21bcparker21
286
286
What output do you expect?
– giser_yugang
Mar 8 at 3:31
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I getTypeError: input must be an array, list, tuple or scalar.
– bcparker21
Mar 8 at 19:22
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars
– bcparker21
Mar 8 at 19:46
add a comment |
What output do you expect?
– giser_yugang
Mar 8 at 3:31
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I getTypeError: input must be an array, list, tuple or scalar.
– bcparker21
Mar 8 at 19:22
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars
– bcparker21
Mar 8 at 19:46
What output do you expect?
– giser_yugang
Mar 8 at 3:31
What output do you expect?
– giser_yugang
Mar 8 at 3:31
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I get
TypeError: input must be an array, list, tuple or scalar.– bcparker21
Mar 8 at 19:22
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I get
TypeError: input must be an array, list, tuple or scalar.– bcparker21
Mar 8 at 19:22
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:
for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars– bcparker21
Mar 8 at 19:46
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:
for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars– bcparker21
Mar 8 at 19:46
add a comment |
2 Answers
2
active
oldest
votes
if you just want to calculate the distance, here is one solution :
from geopy.distance import lonlat, distance
data = """
id lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
"""
df = pd.read_csv(pd.compat.StringIO(data), sep='s+')
df['KM'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).km), axis=1)
print(df)
output:
id lon_origin lat_origin lon_destination lat_destination KM
0 0 -116.284 43.617 -116.304 43.608 1.89910
1 1 -116.291 43.610 -116.304 43.608 1.07277
if you want to use Miles, just change the metric:
df['MILES'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).miles), axis=1)
id lon_origin lat_origin lon_destination lat_destination MILES
0 0 -116.284 43.617 -116.304 43.608 1.180046
1 1 -116.291 43.610 -116.304 43.608 0.666588
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
add a comment |
I wound up forgoing the drawgreatcircle function because it wouldn't be very great or circular at the scale I was using. plt.plot did what I was after:
Code & Output
answered Mar 8 at 20:08
bcparker21bcparker21
286
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
if you just want to calculate the distance, here is one solution :
from geopy.distance import lonlat, distance
data = """
id lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
"""
df = pd.read_csv(pd.compat.StringIO(data), sep='s+')
df['KM'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).km), axis=1)
print(df)
output:
id lon_origin lat_origin lon_destination lat_destination KM
0 0 -116.284 43.617 -116.304 43.608 1.89910
1 1 -116.291 43.610 -116.304 43.608 1.07277
if you want to use Miles, just change the metric:
df['MILES'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).miles), axis=1)
id lon_origin lat_origin lon_destination lat_destination MILES
0 0 -116.284 43.617 -116.304 43.608 1.180046
1 1 -116.291 43.610 -116.304 43.608 0.666588
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
add a comment |
if you just want to calculate the distance, here is one solution :
from geopy.distance import lonlat, distance
data = """
id lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
"""
df = pd.read_csv(pd.compat.StringIO(data), sep='s+')
df['KM'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).km), axis=1)
print(df)
output:
id lon_origin lat_origin lon_destination lat_destination KM
0 0 -116.284 43.617 -116.304 43.608 1.89910
1 1 -116.291 43.610 -116.304 43.608 1.07277
if you want to use Miles, just change the metric:
df['MILES'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).miles), axis=1)
id lon_origin lat_origin lon_destination lat_destination MILES
0 0 -116.284 43.617 -116.304 43.608 1.180046
1 1 -116.291 43.610 -116.304 43.608 0.666588
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
add a comment |
if you just want to calculate the distance, here is one solution :
from geopy.distance import lonlat, distance
data = """
id lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
"""
df = pd.read_csv(pd.compat.StringIO(data), sep='s+')
df['KM'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).km), axis=1)
print(df)
output:
id lon_origin lat_origin lon_destination lat_destination KM
0 0 -116.284 43.617 -116.304 43.608 1.89910
1 1 -116.291 43.610 -116.304 43.608 1.07277
if you want to use Miles, just change the metric:
df['MILES'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).miles), axis=1)
id lon_origin lat_origin lon_destination lat_destination MILES
0 0 -116.284 43.617 -116.304 43.608 1.180046
1 1 -116.291 43.610 -116.304 43.608 0.666588
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
if you just want to calculate the distance, here is one solution :
from geopy.distance import lonlat, distance
data = """
id lon_origin lat_origin lon_destination lat_destination
0 -116.284 43.617 -116.304 43.608
1 -116.291 43.610 -116.304 43.608
"""
df = pd.read_csv(pd.compat.StringIO(data), sep='s+')
df['KM'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).km), axis=1)
print(df)
output:
id lon_origin lat_origin lon_destination lat_destination KM
0 0 -116.284 43.617 -116.304 43.608 1.89910
1 1 -116.291 43.610 -116.304 43.608 1.07277
if you want to use Miles, just change the metric:
df['MILES'] = df.apply(
(lambda row:distance(lonlat(row['lon_origin'], row['lat_origin']),
lonlat(row['lon_destination'], row['lat_destination'])).miles), axis=1)
id lon_origin lat_origin lon_destination lat_destination MILES
0 0 -116.284 43.617 -116.304 43.608 1.180046
1 1 -116.291 43.610 -116.304 43.608 0.666588
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
answered Mar 8 at 8:07
FrenchyFrenchy
1,9882516
1,9882516
add a comment |
add a comment |
I wound up forgoing the drawgreatcircle function because it wouldn't be very great or circular at the scale I was using. plt.plot did what I was after:
Code & Output
answered Mar 8 at 20:08
bcparker21bcparker21
286
add a comment |
I wound up forgoing the drawgreatcircle function because it wouldn't be very great or circular at the scale I was using. plt.plot did what I was after:
Code & Output
answered Mar 8 at 20:08
bcparker21bcparker21
286
add a comment |
I wound up forgoing the drawgreatcircle function because it wouldn't be very great or circular at the scale I was using. plt.plot did what I was after:
Code & Output
answered Mar 8 at 20:08
bcparker21bcparker21
286
I wound up forgoing the drawgreatcircle function because it wouldn't be very great or circular at the scale I was using. plt.plot did what I was after:
Code & Output
answered Mar 8 at 20:08
bcparker21bcparker21
286
answered Mar 8 at 20:08
bcparker21bcparker21
286
answered Mar 8 at 20:08
bcparker21bcparker21
286
answered Mar 8 at 20:08
bcparker21bcparker21
286
286
add a comment |
add a comment |
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What output do you expect?
– giser_yugang
Mar 8 at 3:31
Something like the .drawgreatcircle tool iterrated over each row in the dataframe. When I try that, I get
TypeError: input must be an array, list, tuple or scalar.– bcparker21
Mar 8 at 19:22
Solved the TypeError problem for a single row by calling .values after each column name. Now iterating is giving me the following:
for index, row in df.iterrows(): m.drawgreatcircle(df['lon_orig'].values,df['lat_orig'].values,df['lon_dest'].values,df['lat_dest'].values, linewidth=2, color='orange') --------------------------------------------------------------------------- TypeError: only size-1 arrays can be converted to Python scalars– bcparker21
Mar 8 at 19:46