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)

@jezrael I have ammended the code above to show print(GDP.columns) — Caledonian26
– Caledonian26, Commented May 16, 2020 at 12:01

jezrael · Accepted Answer · 2020-05-16 12:17:08Z

2

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)]

edited May 16, 2020 at 12:17

answered May 16, 2020 at 12:04

jezrael

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7 Comments

Caledonian26 Over a year ago

thanks so much! In python should the data type of every column label be the same?

jezrael Over a year ago

@Caledonian26 - I think no, each columns should be different type.

jezrael Over a year ago

@Caledonian26 - But if need convert all columns togetehr, is necessary same type.

Caledonian26 Over a year ago

Yes I then converted: GDP.columns = GDP.columns.astype(str) and then continued with my formula and it worked nicely!

Caledonian26 Over a year ago

see my answer overall below!

|

mcp · Accepted Answer · 2022-06-21 23:21:29Z

0

GDP.columns = GDP.columns.map(int)

If you have numeric column labels mixed in with string labels:

for ind, col in enumerate(df.columns):
    if col.isdigit():
        df.columns.values[ind] = int(col)

edited Jun 21, 2022 at 23:21

answered Jun 21, 2022 at 22:56

mcp

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