Pandas apply to dateframe produces '

问题

I'm trying to build a GeoJSON object. My input is a csv with an address column, a lat column, and a lon column. I then created Shapely points out of the coordinates , buffer them out by a given radius, and get the dictionary of coordinates via the mapping option- so far, so good. Then, after referring to this question, I wrote the following function to get a Series of dictionaries:

def make_geojson(row): return {'geometry':row['geom'], 'properties':{'address':row['address']}}

and I applied it thusly:

data['new_output'] = data.apply(make_geojson, axis=1)

My resulting column is full of these: <built-in method values of dict object at 0x10...

The weirdest part is, when I directly call the function (i.e. make_geojson(data.loc[0]) I do in fact get the dictionary I'm expecting. Perhaps even weirder is that, when I call the functions I'm getting from the apply (e.g. data.output[0](), data.loc[0]['output']()) I get the equivalent of the following list: [data.loc[0]['geom'], {'address':data.loc[0]['address']}], i.e. the values (but not the keys) of the dictionary I'm trying to get.

For those of you playing along at home, here's a toy example:

from shapely.geometry import Point, mapping
import pandas as pd

def make_geojson(row):
    return {'geometry':row['geom'], 'properties':{'address':row['address']}}

data = pd.DataFrame([{'address':'BS', 'lat':34.017, 'lon':-117.959}, {'address':'BS2', 'lat':33.989, 'lon':-118.291}])
data['point'] = map(Point, zip(data['lon'], data['lat']))
data['buffer'] = data['point'].apply(lambda x: x.buffer(.1))
data['geom'] = data.buffer.apply(mapping)
data['output'] = data.apply(make_geojson, axis=1)

回答1:

Thanks, DSM, for pointing that out. Lesson learned: pandas is not good for arbitrary Python objects

So this is what I wound up doing:

temp = zip(list(data.geom), list(data.address))
output = map(lambda x: {'geometry': x[0], 'properties':{'address':x[1]}}, temp)

回答2:

I got to this post because I ran into a similar issue, but when running a PySpark DataFrame instead of Pandas.

In case someone ends up here, like myself, I'll explain how I fixed it for a PySpark DataFrame.

The reason why I was getting the error (built-in method of Row object, in my case), was because my field name count was colliding with the inherited method count from python tuples (as seen here).

The solution was simply change the name of the field to something like my_count and it worked fine.

来源：https://stackoverflow.com/questions/27993058/pandas-apply-to-dateframe-produces-built-in-method-values-of

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python