7 examples of 'remove index column pandas' in Python

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def _drop_col(self, df):
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    '''
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    Drops last column, which was added in the parsing procedure due to a
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    trailing white space for each sample in the text file
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    Arguments:
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        df: pandas dataframe
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    Return:
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        df: original df with last column dropped
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    '''
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    return df.drop(df.columns[-1], axis=1)

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def set_index_post_series(df, index_name, drop, column_dtype):
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    df2 = df.drop("_partitions", axis=1).set_index("_index", drop=True)
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    df2.index.name = index_name
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    df2.columns = df2.columns.astype(column_dtype)
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    return df2

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def _rm_pd_index(col_names, col_types):
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    """remove pandas index if found in columns
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    """
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    try:
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        pd_index_loc = col_names.index('__index_level_0__')
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        del col_names[pd_index_loc]
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        del col_types[pd_index_loc]
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    except ValueError:
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        pass

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def _clean_columns(df, keep_colnames):
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    new_colnames = []
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    for i,colname in enumerate(df.columns):
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        if colname not in keep_colnames:
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            new_colnames.append(i)
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        else:
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            new_colnames.append(colname)
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    return new_colnames

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def _clean_column(self, column):
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    if not isinstance(column, (int, str, unicode)):
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        raise ValueError('{} is not a valid column'.format(column))
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    return column in self.df.columns

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def remove_column(A, removed_col_index):
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    return [
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        [entry for j, entry in enumerate(row) if j != removed_col_index]
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        for row in A
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    ]

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def dropcols(df, start=None, end=None):
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    """Drop columns that contain NaN within [start, end] inclusive.
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    A wrapper around DataFrame.dropna() that builds an easier *subset*
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    syntax for tseries-indexed DataFrames.
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    Parameters
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    ----------
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    df : DataFrame
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    start : str or datetime, default None
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        start cutoff date, inclusive
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    end : str or datetime, default None
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        end cutoff date, inclusive
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    Example
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    -------
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    df = DataFrame(np.random.randn(10,3),
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                   index=pd.date_range('2017', periods=10))
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    # Drop in some NaN
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    df.set_value('2017-01-04', 0, np.nan)
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    df.set_value('2017-01-02', 2, np.nan)
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    df.loc['2017-01-05':, 1] = np.nan
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    # only col2 will be kept--its NaN value falls before `start`
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    print(dropcols(df, start='2017-01-03'))
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                      2
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    2017-01-01  0.12939
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    2017-01-02      NaN
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    2017-01-03  0.16596
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    2017-01-04  1.06442
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    2017-01-05 -1.87040
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    2017-01-06 -0.17160
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    2017-01-07  0.94588
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    2017-01-08  1.49246
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    2017-01-09  0.02042
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    2017-01-10  0.75094
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    """
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    if isinstance(df, Series):
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        raise ValueError("func only applies to `pd.DataFrame`")
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    if start is None:
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        start = df.index[0]
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    if end is None:
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        end = df.index[-1]
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    subset = df.index[(df.index >= start) & (df.index <= end)]
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    return df.dropna(axis=1, subset=subset)