Create nested list from Pandas dataframe

Question

i have a simple pandas dataframe with two columns. i would like to generate a nested list of those two columns.

geo = pd.DataFrame({'lat': [40.672304, 40.777169, 40.712196], 
                'lon': [-73.935385, -73.988911, -73.957649]})

my solution to this problem is the following:

X = [[i] for i in geo['lat'].tolist()]
Y = [i for i in geo['lon'].tolist()]

for key, value in enumerate(X):
    X[key].append(Y[key])

however, i feel there must be a better way than this.

thanks!

Are you after geo.values.tolist()?

MaxU - stand with Ukraine
– MaxU - stand with Ukraine

2016-10-22 22:38:14 +00:00
Commented Oct 22, 2016 at 22:38 — MaxU - stand with Ukraine
– MaxU - stand with Ukraine, Commented Oct 22, 2016 at 22:38
@MaxU pundit worthy

piRSquared
– piRSquared

2016-10-23 00:23:28 +00:00
Commented Oct 23, 2016 at 0:23 — piRSquared
– piRSquared, Commented Oct 23, 2016 at 0:23
@piRSquared I agree

phillyooo
– phillyooo

2016-11-10 10:09:20 +00:00
Commented Nov 10, 2016 at 10:09 — phillyooo
– phillyooo, Commented Nov 10, 2016 at 10:09

timgeb · Accepted Answer · 2018-05-14 18:11:43Z

6

pandas is built on top of numpy. A DataFrame stores its values in a numpy array, which has a tolist method.

>>> geo = pd.DataFrame({'lat': [40.672304, 40.777169, 40.712196],
    ...:                 'lon': [-73.935385, -73.988911, -73.957649]})
    ...:
>>> geo.values
>>>
array([[ 40.672304, -73.935385],
       [ 40.777169, -73.988911],
       [ 40.712196, -73.957649]])
>>>
>>> geo.values.tolist()
[[40.672304, -73.935385], [40.777169, -73.988911], [40.712196, -73.957649]]

answered May 14, 2018 at 18:11

timgeb

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Comments

Jonathan Windridge · Accepted Answer · 2016-10-23 00:09:51Z

-1

How about

out_list = []
for index, row in geo.iterrows():
    out_list.append([row.lat, row.long])

answered Oct 23, 2016 at 0:09

Jonathan Windridge

4074 silver badges13 bronze badges

3 Comments

phillyooo Over a year ago

thanks! that is more concise! @MaxU's was even more concise though.

AMC Over a year ago

Note: .iterrows() is notoriously sluggish, use it only if absolutely necessary.

Jonathan Windridge Over a year ago

Agreed - I've learnt a lot more about pandas in the years since and the accepted answer is far, far better.

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