DataFrame: from string dictionnary in one columns to floats in two column {'latitude': '34.04', 'longitude': '-118.24'}

Question

I have a pandas DataFrame df with a column latlng. The rows in this column have the format

{'latitude': '34.041005', 'longitude': '-118.249569'}.

In order to be able to add markers to a map (using folium librairie), I would like to create two columns 'latitude' and longitude which in this example would contain respectively 34.041005 and -118.249569.

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EDIT: Managed to have it working with this first step: df['latlng'] = df['latlng'].map(eval)

Answer 1

You can use pd.json_normalize to avoid apply which is costly:

In [684]: df_out = pd.json_normalize(df.latlong)
In [686]: df_out
Out[686]: 
    latitude    longitude
0  34.041005  -118.249569
1  30.041005  -120.249569

Then you can concat these columns back to df like below:

pd.concat([df.drop('latlong', axis=1), df_out], axis=1)

Answer 2

This should do the job for you:

df['blatlong'].apply(pd.Series)

Answer 3

The following should work:

df['latitude']=[i['latitude'] for i in eval(df['latlong'])]
df['longtitude']=[i['longtitude'] for i in eval(df['longtitude'])]

Answer 4

Try this:

df_new = pd.DataFrame(df['latlng'].values.tolist(), index=df.index)