8 examples of 'pandas drop duplicated columns' in Python

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def remove_duplicates(df_or_series):
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    """ Remove duplicate rows or values by keeping the first of each duplicate.
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    Parameters
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    ----------
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    df_or_series : :any:`pandas.DataFrame` or :any:`pandas.Series`
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        Pandas object from which to drop duplicate index values.
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    Returns
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    -------
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    deduplicated : :any:`pandas.DataFrame` or :any:`pandas.Series`
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        The deduplicated pandas object.
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    """
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    # CalTrack 2.3.2.2
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    return df_or_series[~df_or_series.index.duplicated(keep="first")]

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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 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)

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def _unique(df, columns=None):
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    if isinstance(columns, str):
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        columns = [columns]
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    if not columns:
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        columns = df.columns.tolist()
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    info = {}
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    for col in columns:
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        values = df[col].dropna().values
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        uniques = np.unique(list(_flatten_list(values))).tolist()
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        info[col] = {'count': len(uniques), 'values': uniques}
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    return info

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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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@property
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def drop_columns(self):
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    drop_col=self.uni_table.query('Iv<%s'%(self._iv_threshold))
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    return pd.concat([drop_col,self.uni_table[self.uni_table['Iv'].isnull()]])

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def drop_duplicate_events(df):
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    """
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    Function to group dataframe, use all new information from the latest row
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    but keep the ``event_index`` from the first one
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    """
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    df = df.sort_values('event_index', na_position='last')
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    event_index = df.event_index.iloc[0]
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    r = df.iloc[-1].to_dict()
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    r['event_index'] = event_index
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    return r

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def drop_some(df_: pd.DataFrame, thresh: int) -> pd.DataFrame:
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    # thresh is the minimum number of NA, the 1 indicates that columns should be dropped not rows
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    return df_.dropna(1, thresh=thresh)