airflow/contrib/hooks/salesforce_hook.py
def _to_timestamp(cls, column):
        """
        Convert a column of a dataframe to UNIX timestamps if applicable

        :param column: A Series object representing a column of a dataframe.
        :type column: pd.Series
        :return: a new series that maintains the same index as the original
        :rtype: pd.Series
        """
        # try and convert the column to datetimes
        # the column MUST have a four digit year somewhere in the string
        # there should be a better way to do this,
        # but just letting pandas try and convert every column without a format
        # caused it to convert floats as well
        # For example, a column of integers
        # between 0 and 10 are turned into timestamps
        # if the column cannot be converted,
        # just return the original column untouched
        try:
            column = pd.to_datetime(column)
        except ValueError:
            log = LoggingMixin().log
            log.warning("Could not convert field to timestamps: %s", column.name)
            return column

        # now convert the newly created datetimes into timestamps
        # we have to be careful here
        # because NaT cannot be converted to a timestamp
        # so we have to return NaN
        converted = []
        for value in column:
            try:
                converted.append(value.timestamp())
            except (ValueError, AttributeError):
                converted.append(pd.np.NaN)

        return pd.Series(converted, index=column.index)
def _to_timestamp(cls, column):
        # try and convert the column to datetimes
        # the column MUST have a four digit year somewhere in the string
        # there should be a better way to do this,
        # but just letting pandas try and convert every column without a format
        # caused it to convert floats as well
        # For example, a column of integers
        # between 0 and 10 are turned into timestamps
        # if the column cannot be converted,
        # just return the original column untouched
        try:
            column = pd.to_datetime(column)
        except ValueError:
            log = LoggingMixin().log
            log.warning("Could not convert field to timestamps: %s", column.name)
            return column

        # now convert the newly created datetimes into timestamps
        # we have to be careful here
        # because NaT cannot be converted to a timestamp
        # so we have to return NaN
        converted = []
        for value in column:
            try:
                converted.append(value.timestamp())
            except (ValueError, AttributeError):
                converted.append(pd.np.NaN)

        return pd.Series(converted, index=column.index)
def _to_timestamp ( cls , column ) : # try and convert the column to datetimes # the column MUST have a four digit year somewhere in the string # there should be a better way to do this, # but just letting pandas try and convert every column without a format # caused it to convert floats as well # For example, a column of integers # between 0 and 10 are turned into timestamps # if the column cannot be converted, # just return the original column untouched try : column = pd . to_datetime ( column ) except ValueError : log = LoggingMixin ( ) . log log . warning ( "Could not convert field to timestamps: %s" , column . name ) return column # now convert the newly created datetimes into timestamps # we have to be careful here # because NaT cannot be converted to a timestamp # so we have to return NaN converted = [ ] for value in column : try : converted . append ( value . timestamp ( ) ) except ( ValueError , AttributeError ) : converted . append ( pd . np . NaN ) return pd . Series ( converted , index = column . index )
Convert a column of a dataframe to UNIX timestamps if applicable

        :param column: A Series object representing a column of a dataframe.
        :type column: pd.Series
        :return: a new series that maintains the same index as the original
        :rtype: pd.Series
Convert a column of a dataframe to UNIX timestamps if applicable