Source code for parsons.etl.etl

import petl
import logging

logger = logging.getLogger(__name__)


[docs]class ETL(object): def __init__(self): pass
[docs] def add_column(self, column, value=None, index=None): """ Add a column to your table `Args:` column: str Name of column to add value: A fixed or calculated value index: int The position of the new column in the table `Returns:` `Parsons Table` and also updates self """ if column in self.columns: raise ValueError(f"Column {column} already exists") self.table = self.table.addfield(column, value, index) return self
[docs] def remove_column(self, *columns): """ Remove a column from your table `Args:` \*columns: str Column names `Returns:` `Parsons Table` and also updates self """ # noqa: W605 self.table = petl.cutout(self.table, *columns) return self
[docs] def rename_column(self, column_name, new_column_name): """ Rename a column `Args:` column_name: str The current column name new_column_name: str The new column name `Returns:` `Parsons Table` and also updates self """ if new_column_name in self.columns: raise ValueError(f"Column {new_column_name} already exists") self.table = petl.rename(self.table, column_name, new_column_name) return self
[docs] def fill_column(self, column_name, fill_value): """ Fill a column in a table `Args:` column_name: str The column to fill fill_value: A fixed or calculated value `Returns:` `Parsons Table` and also updates self """ if callable(fill_value): self.table = petl.convert(self.table, column_name, lambda _, r: fill_value(r), pass_row=True) else: self.table = petl.update(self.table, column_name, fill_value) return self
[docs] def fillna_column(self, column_name, fill_value): """ Fill None values in a column in a table `Args:` column_name: str The column to fill fill_value: A fixed or calculated value `Returns:` `Parsons Table` and also updates self """ if callable(fill_value): self.table = petl.convert(self.table, column_name, lambda _, r: fill_value(r), where=lambda r: r[column_name] is None, pass_row=True) else: self.table = petl.update(self.table, column_name, fill_value, where=lambda r: r[column_name] is None) return self
[docs] def move_column(self, column, index): """ Move a column `Args:` column: str The column name to move index: The new index for the column `Returns:` `Parsons Table` and also updates existing object. """ self.table = petl.movefield(self.table, column, index) return self
[docs] def convert_column(self, *column, **kwargs): """ Transform values under one or more fields via arbitrary functions, method invocations or dictionary translations. This leverages the petl ``convert()`` method. Example usage can be found `here <https://petl.readthedocs.io/en/v0.24/transform.html#petl.convert>`_. `Args:` *column: str A single column or multiple columns passed as a list **kwargs: str, method or variable The update function, method, or variable to process the update `Returns:` `Parsons Table` and also updates self """ # noqa: E501,E261 self.table = petl.convert(self.table, *column, **kwargs) return self
[docs] def get_column_max_width(self, column): """ Return the maximum width of the column. `Args:` column: str The column name. `Returns:` int """ max_width = 0 for v in petl.values(self.table, column): if len(str(v).encode('utf-8')) > max_width: max_width = len(str(v).encode('utf-8')) return max_width
[docs] def convert_columns_to_str(self): """ Convenience function to convert all non-string or mixed columns in a Parsons table to string (e.g. for comparison) `Returns:` `Parsons Table` and also updates self """ # If we don't have any rows, don't bother trying to convert things if self.num_rows == 0: return self cols = self.get_columns_type_stats() for col in cols: # If there's more than one type (or no types), convert to str # Also if there is one type and it's not str, convert to str if len(col['type']) != 1 or col['type'][0] != 'str': self.convert_column(col['name'], str) return self
[docs] def coalesce_columns(self, dest_column, source_columns, remove_source_columns=True): """ Coalesces values from one or more source columns into a destination column, by selecting the first non-empty value. If the destination column doesn't exist, it will be added. `Args:` dest_column: str Name of destination column source_columns: list List of source column names remove_source_columns: bool Whether to remove the source columns after the coalesce. If the destination column is also one of the source columns, it will not be removed. `Returns:` `Parsons Table` and also updates self """ if dest_column in self.columns: def convert_fn(value, row): for source_col in source_columns: if row.get(source_col): return row[source_col] logger.debug(f"Coalescing {source_columns} into {dest_column}") self.convert_column(dest_column, convert_fn, pass_row=True) else: def add_fn(row): for source_col in source_columns: if row.get(source_col): return row[source_col] logger.debug(f"Creating new column {dest_column} from {source_columns}") self.add_column(dest_column, add_fn) if remove_source_columns: for source_col in source_columns: if source_col != dest_column: self.remove_column(source_col) return self
[docs] def map_columns(self, column_map, exact_match=True): """ Standardizes column names based on multiple possible values. This method is helpful when your input table might have multiple and unknown column names. `Args:` column_map: dict A dictionary of columns and possible values that map to it exact_match: boolean If ``True`` will only map if an exact match. If ``False`` will ignore case, spaces and underscores. `Returns:` `Parsons Table` and also updates self .. code-block:: python tbl = [{'fn': 'Jane'}, {'lastname': 'Doe'}, {'dob': '1980-01-01'}] column_map = {'first_name': ['fn', 'first', 'firstname'], 'last_name': ['ln', 'last', 'lastname'], 'date_of_birth': ['dob', 'birthday']} tbl.map_columns(column_map) print (tbl) >> {{'first_name': 'Jane', 'last_name': 'Doe', 'date_of_birth': '1908-01-01'}} """ for col in self.columns: if not exact_match: cleaned_col = col.lower().replace('_', '').replace(' ', '') else: cleaned_col = col for k, v in column_map.items(): for i in v: if cleaned_col == i: self.rename_column(col, k) return self
[docs] def map_and_coalesce_columns(self, column_map): """ Coalesces columns based on multiple possible values. The columns in the map do not need to be in your table, so you can create a map with all possibilities. The coalesce will occur in the order that the columns are listed, unless the destination column name already exists in the table, in which case that value will be preferenced. This method is helpful when your input table might have multiple and unknown column names. `Args:` column_map: dict A dictionary of columns and possible values that map to it `Returns:` `Parsons Table` and also updates self .. code-block:: python tbl = [{'first': None}, {'fn': 'Jane'}, {'lastname': 'Doe'}, {'dob': '1980-01-01'}] column_map = {'first_name': ['fn', 'first', 'firstname'], 'last_name': ['ln', 'last', 'lastname'], 'date_of_birth': ['dob', 'birthday']} tbl.map_and_coalesce_columns(column_map) print (tbl) >> {{'first_name': 'Jane', 'last_name': 'Doe', 'date_of_birth': '1908-01-01'}} """ for key, value in column_map.items(): coalesce_list = value # if the column in the mapping dict isn't actually in the table, # remove it from the list of columns to coalesce for item in coalesce_list: if item not in self.columns: coalesce_list.remove(item) # if the key from the mapping dict already exists in the table, # rename it so it can be coalesced with other possible columns if key in self.columns: self.rename_column(key, f'{key}_temp') coalesce_list.insert(0, f'{key}_temp') # coalesce columns self.coalesce_columns(key, coalesce_list, remove_source_columns=True) return self
[docs] def get_column_types(self, column): """ Return all of the Python types for values in a given column `Args:` column: str Name of the column to analyze `Returns:` list A list of Python types """ return list(petl.typeset(self.table, column))
[docs] def get_columns_type_stats(self): """ Return descriptive stats for all columns `Returns:` list A list of dicts `Returns:` list A list of dicts, each containing a column 'name' and a 'type' list """ return [{'name': col, 'type': self.get_column_types(col)} for col in self.table.columns()]
[docs] def convert_table(self, *args): """ Transform all cells in a table via arbitrary functions, method invocations or dictionary translations. This method is useful for cleaning fields and data hygiene functions such as regex. This method leverages the petl ``convert()`` method. Example usage can be found `here` <https://petl.readthedocs.io/en/v0.24/transform.html#petl.convert>`_. `Args:` \*args: str, method or variable The update function, method, or variable to process the update. Can also `Returns:` `Parsons Table` and also updates self """ # noqa: W605 self.convert_column(self.columns, *args) return self
[docs] def unpack_dict(self, column, keys=None, include_original=False, sample_size=5000, missing=None, prepend=True, prepend_value=None): """ Unpack dictionary values from one column into separate columns `Args:` column: str The column name to unpack keys: list The dict keys in the column to unpack. If ``None`` will unpack all. include_original: boolean Retain original column after unpacking sample_size: int Number of rows to sample before determining columns missing: str If a value is missing, the value to fill it with prepend: Prepend the column name of the unpacked values. Useful for avoiding duplicate column names prepend_value: Value to prepend new columns if ``prepend=True``. If None, will set to column name. """ if prepend: if prepend_value is None: prepend_value = column self.table = petl.convert( self.table, column, lambda v: self._prepend_dict(v, prepend_value)) self.table = petl.unpackdict( self.table, column, keys=keys, includeoriginal=include_original, samplesize=sample_size, missing=missing) return self
[docs] def unpack_list(self, column, include_original=False, missing=None, replace=False, max_columns=None): """ Unpack list values from one column into separate columns. Numbers the columns. .. code-block:: python # Begin with a list in column json = [{'id': '5421', 'name': 'Jane Green', 'phones': ['512-699-3334', '512-222-5478'] } ] tbl = Table(json) print (tbl) >>> {'id': '5421', 'name': 'Jane Green', 'phones': ['512-699-3334', '512-222-5478']} tbl.unpack_list('phones', replace=True) print (tbl) >>> {'id': '5421', 'name': 'Jane Green', 'phones_0': '512-699-3334', 'phones_1': '512-222-5478'} # noqa: E501 `Args:` column: str The column name to unpack include_original: boolean Retain original column after unpacking sample_size: int Number of rows to sample before determining columns missing: str If a value is missing, the value to fill it with replace: boolean Return new table or replace existing max_columns: int The maximum number of columns to unpack `Returns:` None """ # Convert all column values to list to avoid unpack errors self.table = petl.convert( self.table, column, lambda v: [v] if not isinstance(v, list) else v ) # Find the max number of values in list for all rows col_count = 0 for row in self.cut(column): if len(row[column]) > col_count: col_count = len(row[column]) # If max columns provided, set max columns if col_count > 0 and max_columns: col_count = max_columns # Create new column names "COL_01, COL_02" new_cols = [] for i in range(col_count): new_cols.append(column + '_' + str(i)) tbl = petl.unpack(self.table, column, new_cols, include_original=include_original, missing=missing) if replace: self.table = tbl else: return tbl
[docs] def unpack_nested_columns_as_rows(self, column, key='id', expand_original=False): """ Unpack list or dict values from one column into separate rows. Not recommended for JSON columns (i.e. lists of dicts), but can handle columns with any mix of types. Makes use of PETL's `melt()` method. `Args:` column: str The column name to unpack key: str The column to use as a key when unpacking. Defaults to `id` expand_original: boolean or int If `True`: Add resulting unpacked rows (with all other columns) to original If `int`: Add to original unless the max added per key is above the given number If `False` (default): Return unpacked rows (with `key` column only) as standalone Removes packed list and dict rows from original either way. `Returns:` If `expand_original`, original table with packed rows replaced by unpacked rows Otherwise, standalone table with key column and unpacked values only """ if isinstance(expand_original, int) and expand_original is not True: lengths = {len(row[column]) for row in self if isinstance(row[column], (dict, list))} max_len = sorted(lengths, reverse=True)[0] if max_len > expand_original: expand_original = False if expand_original: # Include all columns and filter out other non-dict types in table_list table = self table_list = table.select_rows(lambda row: isinstance(row[column], list)) else: # Otherwise, include only key and column, but keep all non-dict types in table_list table = self.cut(key, column) table_list = table.select_rows(lambda row: not isinstance(row[column], dict)) # All the columns other than column to ignore while melting ignore_cols = table.columns ignore_cols.remove(column) # Unpack lists as separate columns table_list.unpack_list(column, replace=True) # Rename the columns to retain only the number for col in table_list.columns: if f'{column}_' in col: table_list.rename_column(col, col.replace(f'{column}_', "")) # Filter dicts and unpack as separate columns table_dict = table.select_rows(lambda row: isinstance(row[column], dict)) table_dict.unpack_dict(column, prepend=False) from parsons.etl.table import Table # Use melt to pivot both sets of columns into their own Tables and clean out None values melted_list = Table(petl.melt(table_list.table, ignore_cols)) melted_dict = Table(petl.melt(table_dict.table, ignore_cols)) melted_list.remove_null_rows('value') melted_dict.remove_null_rows('value') melted_list.rename_column('variable', column) melted_dict.rename_column('variable', column) # Combine the list and dict Tables melted_list.concat(melted_dict) import hashlib if expand_original: # Add unpacked rows to the original table (minus packed rows) orig = self.select_rows(lambda row: not isinstance(row[column], (dict, list))) orig.concat(melted_list) # Add unique id column by hashing all the other fields if 'uid' not in self.columns: orig.add_column('uid', lambda row: hashlib.md5( str.encode( ''.join([str(x) for x in row]) ) ).hexdigest()) orig.move_column('uid', 0) # Rename value column in case this is done again to this Table orig.rename_column('value', f'{column}_value') # Keep column next to column_value orig.move_column(column, -1) output = orig else: orig = self.remove_column(column) # Add unique id column by hashing all the other fields melted_list.add_column('uid', lambda row: hashlib.md5( str.encode( ''.join([str(x) for x in row]) ) ).hexdigest()) melted_list.move_column('uid', 0) output = melted_list self = orig return output
[docs] def long_table(self, key, column, key_rename=None, retain_original=False, prepend=True, prepend_value=None): """ Create a new long parsons table from a column, including the foreign key. .. code-block:: python # Begin with nested dicts in a column json = [{'id': '5421', 'name': 'Jane Green', 'emails': [{'home': 'jane@gmail.com'}, {'work': 'jane@mywork.com'} ] } ] tbl = Table(json) print (tbl) >>> {'id': '5421', 'name': 'Jane Green', 'emails': [{'home': 'jane@gmail.com'}, {'work': 'jane@mywork.com'}]} # noqa: E501 >>> {'id': '5421', 'name': 'Jane Green', 'emails': [{'home': 'jane@gmail.com'}, {'work': 'jane@mywork.com'}]} # noqa: E501 # Create skinny table of just the nested dicts email_skinny = tbl.long_table(['id'], 'emails') print (email_skinny) >>> {'id': '5421', 'emails_home': 'jane@gmail.com', 'emails_work': None} >>> {'id': '5421', 'emails_home': None, 'emails_work': 'jane@mywork.com'} `Args:` key: lst The columns to retain in the long table (e.g. foreign keys) column: str The column name to make long key_rename: dict The new name for the foreign key to better identify it. For example, you might want to rename ``id`` to ``person_id``. Ex. {'KEY_NAME': 'NEW_KEY_NAME'} retain_original: boolean Retain the original column from the source table. prepend: Prepend the column name of the unpacked values. Useful for avoiding duplicate column names prepend_value: Value to prepend new columns if ``prepend=True``. If None, will set to column name. `Returns:` Parsons Table The new long table """ if type(key) == str: key = [key] lt = self.cut(*key, column) # Create a table of key and column lt.unpack_list(column, replace=True) # Unpack the list lt.table = petl.melt(lt.table, key) # Melt into a long table lt = lt.cut(*key, 'value') # Get rid of column names created in unpack lt.rename_column('value', column) # Rename 'value' to old column name lt.remove_null_rows(column) # Remove null values # If a new key name is specified, rename if key_rename: for k, v in key_rename.items(): lt.rename_column(k, v) # If there is a nested dict in the column, unpack it if lt.num_rows > 0 and isinstance(lt.table[column][0], dict): lt.unpack_dict(column, prepend=prepend, prepend_value=prepend_value) if not retain_original: self.remove_column(column) return lt
[docs] def cut(self, *columns): """ Return a table of selection of columns `Args:` \*columns: str Columns in the parsons table `Returns:` A new parsons table containing the selected columnns """ # noqa: W605 from parsons.etl.table import Table return Table(petl.cut(self.table, *columns))
[docs] def select_rows(self, *filters): """ Select specific rows from a Parsons table based on the passed filters. Example filters: .. code-block:: python tbl = Table([['foo', 'bar', 'baz'], ['c', 4, 9.3], ['a', 2, 88.2], ['b', 1, 23.3],]) # You can structure the filter in multiple wayss # Lambda Function tbl2 = tbl.select_rows(lambda row: row.foo == 'a' and row.baz > 88.1) tbl2 >>> {'foo': 'a', 'bar': 2, 'baz': 88.1} # Expression String tbl3 = tbl.select_rows("{foo} == 'a' and {baz} > 88.1") tbl3 >>> {'foo': 'a', 'bar': 2, 'baz': 88.1} `Args:` \*filters: function or str `Returns:` A new parsons table containing the selected rows """ # noqa: W605 from parsons.etl.table import Table return Table(petl.select(self.table, *filters))
[docs] def remove_null_rows(self, columns, null_value=None): """ Remove rows if the values in a column are ``None``. If multiple columns are passed as list, it will remove all rows with null values in any of the passed columns. `Args:` column: str or list The column or columns to analyze null_value: int or float or str The null value `Returns:` ``None`` """ if isinstance(columns, str): columns = [columns] for col in columns: self.table = petl.selectisnot(self.table, col, null_value) return self
def _prepend_dict(self, dict_obj, prepend): # Internal method to rename dict keys new_dict = {} for k, v in dict_obj.items(): new_dict[prepend + '_' + k] = v return new_dict
[docs] def stack(self, *tables, missing=None): """ Stack Parsons tables on top of one another. Similar to ``table.concat()``, except no attempt is made to align fields from different tables. `Args:` tables: Parsons Table or list A single table, or a list of tables missing: bool The value to use when padding missing values `Returns:` ``None`` """ if type(tables) not in [list, tuple]: tables = [tables] petl_tables = [tbl.table for tbl in tables] self.table = petl.stack(self.table, *petl_tables, missing=missing)
[docs] def concat(self, *tables, missing=None): """ Concatenates one or more tables onto this one. Note that the tables do not need to share exactly the same fields. Any missing fields will be padded with None, or whatever is provided via the ``missing`` keyword argument. `Args:` tables: Parsons Table or list A single table, or a list of tables missing: bool The value to use when padding missing values `Returns:` ``None`` """ if type(tables) not in [list, tuple]: tables = [tables] petl_tables = [tbl.table for tbl in tables] self.table = petl.cat(self.table, *petl_tables, missing=missing)
[docs] def chunk(self, rows): """ Divides a Parsons table into smaller tables of a specified row count. If the table cannot be divided evenly, then the final table will only include the remainder. `Args:` rows: int The number of rows of each new Parsons table `Returns:` List of Parsons tables """ from parsons.etl import Table return [Table(petl.rowslice(self.table, i, i+rows)) for i in range(0, self.num_rows, rows)]
[docs] @staticmethod def get_normalized_column_name(column_name): """ Returns a column name with whitespace removed, non-alphanumeric characters removed, and everything lowercased. `Returns:` str Normalized column name """ column_name = column_name.lower().strip() return ''.join(c for c in column_name if c.isalnum())
[docs] def match_columns(self, desired_columns, fuzzy_match=True, if_extra_columns='remove', if_missing_columns='add'): """ Changes the column names and ordering in this Table to match a list of desired column names. `Args:` desired_columns: list Ordered list of desired column names fuzzy_match: bool Whether to normalize column names when matching against the desired column names, removing whitespace and non-alphanumeric characters, and lowercasing everything. Eg. With this flag set, "FIRST NAME" would match "first_name". If the Table has two columns that normalize to the same string (eg. "FIRST NAME" and "first_name"), the latter will be considered an extra column. if_extra_columns: string If the Table has columns that don't match any desired columns, either 'remove' them, 'ignore' them, or 'fail' (raising an error). if_missing_columns: string If the Table is missing some of the desired columns, either 'add' them (with a value of None), 'ignore' them, or 'fail' (raising an error). `Returns:` `Parsons Table` and also updates self """ from parsons.etl import Table # Just trying to avoid recursive imports. normalize_fn = Table.get_normalized_column_name if fuzzy_match else (lambda s: s) # Create a mapping of our "normalized" name to the original column name current_columns_normalized = { normalize_fn(col): col for col in self.columns } # Track any columns we need to add to our current table from our desired columns columns_to_add = [] # We are going to do a "cut" later to trim our table and re-order the columns, but # we won't have renamed our columns yet, so we need to remember their un-normalized # form cut_columns = [] # We are going to also rename our columns AFTER we cut, so we want to remember their # normalized names final_header = [] # Loop through our desired columns -- the columns we want to see in our final table for desired_column in desired_columns: normalized_desired = normalize_fn(desired_column) # Try to find our desired column in our Table if normalized_desired not in current_columns_normalized: # If we can't find our desired column in our current columns, then it's "missing" if if_missing_columns == 'fail': # If our missing strategy is to fail, raise an exception raise TypeError(f"Table is missing column {desired_column}") elif if_missing_columns == 'add': # We have to add to our table columns_to_add.append(desired_column) # We will need to remember this column when we cut down to desired columns cut_columns.append(desired_column) # This will be in the final table final_header.append(desired_column) elif if_missing_columns != 'ignore': # If it's not ignore, add, or fail, then it's not a valid strategy raise TypeError(f"Invalid option {if_missing_columns} for " "argument `if_missing_columns`") else: # We have found this in our current columns, so take it out of our list to search current_column = current_columns_normalized.pop(normalized_desired) # Add the column to our intermediate table as the old column name cut_columns.append(current_column) # Add to our final header list as the "desired" name final_header.append(desired_column) # Look for any "extra" columns from our current table that aren't in our desired columns for current_column in current_columns_normalized.values(): # Figure out what to do with our "extra" columns if if_extra_columns == 'fail': # If our missing strategy is to fail, raise an exception raise TypeError(f"Table has extra column {current_column}") elif if_extra_columns == 'ignore': # If we're "ignore"ing our extra columns, we should keep them by adding them to # our intermediate and final columns list cut_columns.append(current_column) final_header.append(current_column) elif if_extra_columns != 'remove': # If it's not ignore, add, or fail, then it's not a valid strategy raise TypeError(f"Invalid option {if_extra_columns} for " "argument `if_extra_columns`") # Add any columns we need to add for column in columns_to_add: self.table = petl.addfield(self.table, column, None) # Cut down to just the columns we care about self.table = petl.cut(self.table, *cut_columns) # Rename any columns self.table = petl.setheader(self.table, final_header) return self
[docs] def reduce_rows(self, columns, reduce_func, headers, presorted=False, **kwargs): """ Group rows by a column or columns, then reduce the groups to a single row. Based on the `rowreduce petl function <https://petl.readthedocs.io/en/stable/transform.html#petl.transform.reductions.rowreduce>`_. For example, the output from the query to get a table's definition is returned as one component per row. The `reduce_rows` method can be used to reduce all those to a single row containg the entire query. .. code-block:: python >>> ddl = rs.query(sql_to_get_table_ddl) >>> ddl.table +--------------+--------------+----------------------------------------------------+ | schemaname | tablename | ddl | +==============+==============+====================================================+ | 'db_scratch' | 'state_fips' | '--DROP TABLE db_scratch.state_fips;' | +--------------+--------------+----------------------------------------------------+ | 'db_scratch' | 'state_fips' | 'CREATE TABLE IF NOT EXISTS db_scratch.state_fips' | +--------------+--------------+----------------------------------------------------+ | 'db_scratch' | 'state_fips' | '(' | +--------------+--------------+----------------------------------------------------+ | 'db_scratch' | 'state_fips' | '\\tstate VARCHAR(1024) ENCODE RAW' | +--------------+--------------+----------------------------------------------------+ | 'db_scratch' | 'state_fips' | '\\t,stusab VARCHAR(1024) ENCODE RAW' | +--------------+--------------+----------------------------------------------------+ >>> reducer_fn = lambda columns, rows: [ ... f"{columns[0]}.{columns[1]}", ... '\\n'.join([row[2] for row in rows])] >>> ddl.reduce_rows( ... ['schemaname', 'tablename'], ... reducer_fn, ... ['tablename', 'ddl'], ... presorted=True) >>> ddl.table +-------------------------+-----------------------------------------------------------------------+ | tablename | ddl | +=========================+=======================================================================+ | 'db_scratch.state_fips' | '--DROP TABLE db_scratch.state_fips;\\nCREATE TABLE IF NOT EXISTS | | | db_scratch.state_fips\\n(\\n\\tstate VARCHAR(1024) ENCODE RAW\\n\\t | | | ,db_scratch.state_fips\\n(\\n\\tstate VARCHAR(1024) ENCODE RAW | | | \\n\\t,stusab VARCHAR(1024) ENCODE RAW\\n\\t,state_name | | | VARCHAR(1024) ENCODE RAW\\n\\t,statens VARCHAR(1024) ENCODE | | | RAW\\n)\\nDISTSTYLE EVEN\\n;' | +-------------------------+-----------------------------------------------------------------------+ `Args:` columns: list The column(s) by which to group the rows. reduce_func: fun The function by which to reduce the rows. Should take the 2 arguments, the columns list and the rows list and return a list. `reducer(columns: list, rows: list) -> list;` headers: list The list of headers for modified table. The length of `headers` should match the length of the list returned by the reduce function. presorted: bool If false, the row will be sorted. `Returns:` `Parsons Table` and also updates self """ # noqa: E501,E261 self.table = petl.rowreduce( self.table, columns, reduce_func, header=headers, presorted=presorted, **kwargs) return self
[docs] def sort(self, columns=None, reverse=False): """ Sort the rows a table. `Args:` sort_columns: list or str Sort by a single column or a list of column. If ``None`` then will sort columns from left to right. reverse: boolean Sort rows in reverse order. `Returns:` `Parsons Table` and also updates self """ self.table = petl.sort(self.table, key=columns, reverse=reverse) return self
[docs] def set_header(self, new_header): """ Replace the header row of the table. `Args:` new_header: list List of new header column names `Returns:` `Parsons Table` and also updates self """ self.table = petl.setheader(self.table, new_header) return self
[docs] def use_petl(self, petl_method, *args, **kwargs): """ Call a petl function on the current table. This convenience method exposes the petl functions to the current Table. This is useful in cases where one might need a ``petl`` function that has not yet been implemented for ``parsons.Table``. .. code-block:: python >>> # https://petl.readthedocs.io/en/v1.6.0/transform.html#petl.transform.basics.skipcomments >>> tbl = Table([ ... ['col1', 'col2'], ... ['# this is a comment row',], ... ['a', 1], ... ['#this is another comment', 'this is also ignored'], ... ['b', 2] ... ]) >>> tbl.use_petl('skipcomments', '#', update_table=True) {'col1': 'a', 'col2': 1} {'col1': 'b', 'col2': 2} >>> tbl.table +------+------+ | col1 | col2 | +======+======+ | 'a' | 1 | +------+------+ | 'b' | 2 | +------+------+ `Args:` petl_method: str The ``petl`` function to call update_table: bool If ``True``, updates the ``parsons.Table``. Defaults to ``False``. to_petl: bool If ``True``, returns a petl table, otherwise a ``parsons.Table``. Defaults to ``False``. *args: Any The arguements to pass to the petl function. **kwargs: Any The keyword arguements to pass to the petl function. `Returns:` `parsons.Table` or `petl` table """ # noqa: E501 update_table = kwargs.pop('update_table', False) to_petl = kwargs.pop('to_petl', False) if update_table: self.table = getattr(petl, petl_method)(self.table, *args, **kwargs) if to_petl: return getattr(petl, petl_method)(self.table, *args, **kwargs) from parsons.etl.table import Table return Table(getattr(petl, petl_method)(self.table, *args, **kwargs))