Timeseries pandas changing name columns IndexError: too many indices for array

IMAGE OF THE DATASET I had dates in one column, set them as a time series, and then transposed them so that each year has its own column. Some columns have the correct year, but the wrong date. I would like to set them all to the same month and day, but when I try to modify them, I get an error "IndexError: too many indices for array".

I tried these 2 methods:

df_top100.rename(columns={'2012-12-31 00:00:00': '2012-01-01 00:00:00'}, inplace=True)
#df_top100.rename(columns={'2013-12-31': '2013-01-01'}, inplace=True)
#df_top100.rename(columns={'2016-12-31': '2016-01-01'}, inplace=True)

Dataset, first line:

year    2005-01-01  2006-01-01  2007-01-01  2008-01-01  2009-01-01  2010-01-01  2011-01-01  2012-12-31  2013-12-31  2014-01-01  2015-01-01  2016-12-31
country                                             
Australia   2.0 3.0 3.0 3.0 3.0 3.0 9.0 6.0 8.0 11.0    11.0    6.0

Last column:

print print(df_top100.columns[-1])
2016-12-31 00:00:00

>Solution :

Solution for DatetimeIndex in columns – extract years and convert to datetimes:

d = pd.to_datetime(['2012-12-31 00:00:00','2013-12-31','2016-12-31'])
df_top100 = pd.DataFrame(columns=d)

df_top100.columns = pd.to_datetime(df_top100.columns.year, format='%Y')
print (df_top100)
Empty DataFrame
Columns: [2012-01-01 00:00:00, 2013-01-01 00:00:00, 2016-01-01 00:00:00]
Index: []

For strings use DatetimeIndex.strftime:

df_top100.columns = df_top100.columns.strftime('%Y-01-01')
print (df_top100)
Empty DataFrame
Columns: [2012-01-01, 2013-01-01, 2016-01-01]
Index: []