10 examples of 'change column name in pandas' in Python

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def df_column_types_rename(df):
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    result = [df[x].dtype.name for x in list(df.columns)]
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    result[:] = [x if x != 'object' else 'string' for x in result]
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    result[:] = [x if x != 'int64' else 'integer' for x in result]
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    result[:] = [x if x != 'float64' else 'double' for x in result]
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    result[:] = [x if x != 'bool' else 'boolean' for x in result]
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    return result

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def matchColumnNames(df):
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    return df.rename(
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        columns={
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            "id": "vehicle_id",
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            "heading": "direction",
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            "secsSinceReport": "seconds_since_report",
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            "lat": "latitude",
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            "lon": "longitude",
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            "routeTag": "line"
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        }
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    )

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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 _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 extractCols(df, colnames):
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    extracted = df[colnames]
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    df.drop(extracted.columns, axis=1, inplace=True)
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    return extracted

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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 convert_columns_dtype(df, old_dtype, new_dtype):
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    """
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    Parameters
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    ----------
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    df: pandas.DataFrame
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    old_dtype: numpy dtype
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    new_dtype: numpy dtype
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    """
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    changed = []
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    for column in df.columns:
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        if df[column].dtype == old_dtype:
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            df[column] = df[column].astype(new_dtype)
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            changed.append(column)
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    return changed

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def to_unique(self, column_or_columns):
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    self.__convert_column(column_or_columns, FType.unique, None)

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def copy_column(self, to, fro):
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    """Copy column data
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    Args:
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        fro (str): column name to copy data from
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        to (str): column name to copy to
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    """
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    logging.debug("copying {} to {}".format(fro, to))
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    self.df[to] = self.df[fro]