Convert pandas dataframe column labels from float to integer

Question

I have the following snippet of the pandas dataframe 'GDP', where the column labels are floats.

3        2013.0        2014.0        2015.0  
4           NaN           NaN           NaN  
5  3.127550e+09           NaN           NaN  
6  1.973134e+10  1.999032e+10  2.029415e+10  
7  9.626143e+10  1.008863e+11  1.039106e+11  
8  1.254247e+10  1.279331e+10  1.312082e+10  

I have tried to convert these float labels to integers with the following but did not succeed:

GDP.columns = GDP.columns.astype(int)

I keep getting the error: TypeError: Cannot cast Index to dtype

I then want to convert this to a string (like the following):

years = np.arange(2006, 2016).astype(str)
GDP = GDP[np.append(['Country'],years)]

Thus, my overall dataframe should look something like this:

Could anyone give me a helping hand?

print(GDP.columns) looks like this:

Index([       'Country',   'Country Code', 'Indicator Name', 'Indicator Code',
                 1960.0,           1961.0,           1962.0,           1963.0,
                 1964.0,           1965.0,           1966.0,           1967.0,
                 1968.0,           1969.0,           1970.0,           1971.0,
                 1972.0,           1973.0,           1974.0,           1975.0,
                 1976.0,           1977.0,           1978.0,           1979.0,
                 1980.0,           1981.0,           1982.0,           1983.0,
                 1984.0,           1985.0,           1986.0,           1987.0,
                 1988.0,           1989.0,           1990.0,           1991.0,
                 1992.0,           1993.0,           1994.0,           1995.0,
                 1996.0,           1997.0,           1998.0,           1999.0,
                 2000.0,           2001.0,           2002.0,           2003.0,
                 2004.0,           2005.0,           2006.0,           2007.0,
                 2008.0,           2009.0,           2010.0,           2011.0,
                 2012.0,           2013.0,           2014.0,           2015.0],
      dtype='object', name=3)

Answer 1

Convert only numeric columns, here it means all columns after first 4. column and join together:

GDP.columns = GDP.columns[:4].tolist() + GDP.columns[4:].astype(int).astype(str).tolist()

And then:

years = np.arange(2006, 2016).astype(str)
GDP = GDP[np.append(['Country'],years)]