Using read_excel with converters for reading Excel file into Pandas DataFrame results in a numeric column of object type

Question

I am reading this Excel file United Nations Energy Indicators using the code snippet here:

def convert_energy(energy):
    if isinstance(energy, float):
             


        

Answer 1

One of the values for energy in your excel file is a string "..." and when in your coverter function, you just return energy as is if it is a string datatype.

Therefore you are getting a string returned along with your numbers which then changes the dtype of you column to 'object.

You could try something like this:

def convert_energy(energy):
    if energy == "...":
        return np.nan
    elif isinstance(energy, float):
        return float(energy*1000000)
    else:
        return float(energy)

df = pd.read_excel('http://unstats.un.org/unsd/environment/excel_file_tables/2013/Energy%20Indicators.xls', 
                   skiprows=17, skip_footer=38, 
                   usecols=[2,3,4,5], na_values=['...'], 
                   names=['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable'],
                   converters={1: convert_energy}).set_index('Country')

df.info()

Output:

Index: 227 entries, Afghanistan to Zimbabwe
Data columns (total 3 columns):
Energy Supply               222 non-null float64
Energy Supply per Capita    222 non-null float64
% Renewable                 227 non-null float64
dtypes: float64(3)
memory usage: 6.2+ KB