Remove Unnamed columns in pandas dataframe [duplicate]

Answer 1

df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

In [162]: df
Out[162]:
   colA  ColB  colC  colD  colE  colF  colG
0    44    45    26    26    40    26    46
1    47    16    38    47    48    22    37
2    19    28    36    18    40    18    46
3    50    14    12    33    12    44    23
4    39    47    16    42    33    48    38

if the first column in the CSV file has index values, then you can do this instead:

df = pd.read_csv('data.csv', index_col=0)

Answer 2

First, find the columns that have 'unnamed', then drop those columns. Note: You should Add inplace = True to the .drop parameters as well.

df.drop(df.columns[df.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)

Answer 3

The pandas.DataFrame.dropna function removes missing values (e.g. NaN, NaT).

For example the following code would remove any columns from your dataframe, where all of the elements of that column are missing.

df.dropna(how='all', axis='columns')

Answer 4

The approved solution doesn't work in my case, so my solution is the following one:

    ''' The column name in the example case is "Unnamed: 7"
 but it works with any other name ("Unnamed: 0" for example). '''

        df.rename({"Unnamed: 7":"a"}, axis="columns", inplace=True)

        # Then, drop the column as usual.

        df.drop(["a"], axis=1, inplace=True)

Hope it helps others.