Parsons Table

Overview

Most methods and functions in Parsons return a Table, which is a 2D list-like object similar to a Pandas Dataframe. You can call the following methods on the Table object to output it into a variety of formats or storage types. A full list of Table methods can be found in the API section.

From Parsons Table

Method	Destination Type	Description
to_csv()	CSV File	Write a table to a local csv file
to_s3_csv()	AWS s3 Bucket	Write a table to a csv stored in S3
to_sftp_csv()	SFTP Server	Write a table to a csv stored on an SFTP server
from_csv()	A Redshift Database	Write a table to a Redshift database
from_postgres()	A Postgres Database	Write a table to a Postgres database
to_civis()	Civis Redshift Database	Write a table to Civis platform database
from_petl()	Petl table object	Convert a table a Petl table object
to_json()	JSON file	Write a table to a local JSON file
to_html()	HTML formatted table	Write a table to a local html file
to_dataframe()	Pandas Dataframe [1]	Return a Pandas dataframe

[1]

Requires optional installation of Pandas package by running pip install pandas.

To Parsons Table

Create Parsons Table object using the following methods.

Method	Source Type	Description
from_csv()	File like object, local path, url, ftp.	Loads a csv object into a Table
from_json()	File like object, local path, url, ftp.	Loads a json object into a Table
from_columns()	List object	Loads lists organized as columns in Table
from_redshift()	Redshift table	Loads a Redshift query into a Table
from_postgres()	Postgres table	Loads a Postgres query into a Table
from_dataframe()	Pandas Dataframe [2]	Load a Parsons table from a Pandas Dataframe
from_s3_csv()	S3 CSV	Load a Parsons table from a csv file on S3

[2]

Requires optional installation of Pandas package by running pip install pandas.

You can also use the Table constructor to create a Table from a python list or petl table:

# From a list of dicts
tbl = Table([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])

# From a list of lists, the first list holding the field names
tbl = Table([['a', 'b'], [1, 2], [3, 4]])

# From a petl table
tbl = Table(petl_tbl)

Parsons Table Attributes

Tables have a number of convenience attributes.

Attribute	Description
.num_rows	The number of rows in the table
.columns	A list of column names in the table
.data	The actual data (rows) of the table, as a list of tuples (without field names)
.first	The first value in the table. Use for database queries where a single value is returned.

Parsons Table Transformations

Parsons tables have many methods that allow you to easily transform tables. Below is a selection of commonly used methods. The full list can be found in the API section.

Column Transformations

Method	Description
add_column()	Add a column
remove_column()	Remove a column
rename_column()	Rename a column
move_column()	Move a column within a table
cut()	Return a table with a subset of columns
fill_column()	Provide a fixed value to fill a column
fillna_column()	Provide a fixed value to fill all null values in a column
get_column_types()	Get the python type of values for a given column
convert_column()	Transform the values of a column via arbitrary functions
coalesce_columns()	Coalesce values from one or more source columns
map_columns()	Standardizes column names based on multiple possible values

Row Transformations

Method	Description
select_rows()	Return a table of a subset of rows based on filters
stack()	Stack a number of tables on top of one another
chunk()	Divide tables into smaller tables based on row count
remove_null_rows()	Removes rows with null values in specified columns

Extraction and Reshaping

Method	Description
unpack_dict()	Unpack dictionary values from one column to top level columns
unpack_list()	Unpack list values from one column and add to top level columns
long_table()	Take a column with nested data and create a new long table
unpack_nested_columns_as_rows()	Unpack list or dict values from one column into separate rows

Parsons Table Indexing

To access rows and columns of data within a Parsons table, you can index on them. To access a column pass in the column name as a string (e.g. tbl['a']) and to access a row, pass in the row index as an integer (e.g. tbl[1]).

tbl = Table([{'a': 1, 'b': 2}, {'a': 3, 'b': 4}])

# Return a column as a list
tbl['a']
>> [1, 3]

# Return a row as a dict
tbl[1]
>> {'a': 3, 'b': 4}

A note on indexing and iterating over a table’s data: If you need to iterate over the data, make sure to use the python iterator syntax, so any data transformations can be applied efficiently. An example:

# Some data transformations
table.add_column('newcol', 'some value')

# Efficient way to grab all the data (applying the data transformations only once)
rows_list = [row for row in table]

Warning

If you must index directly into a table’s data, you can do so, but note that data transformations will be applied each time you do so. So this code will be very inefficient on a large table…

# Inefficient way to grab all the data
rows_list = []
for i in range(0, table.num_rows):
  # Data transformations will be applied each time through this loop!
  rows_list.append(table[i])

PETL

The Parsons Table relies heavily on the petl Python package. You can always access the underlying petl table with my_parsons_table.table, which will allow you to perform any petl-supported ETL operations.

Lazy Loading

The Parsons Table makes use of “lazy” loading and “lazy” transformations. What this means is that it tries not to load and process your data until absolutely necessary.

An example:

# Specify where to load the data
tbl = Table.from_csv('name_data.csv')

# Specify data transformations
tbl.add_column('full_name', lambda row: row['first_name'] + ' ' + row['last_name'])
tbl.remove_column(['first_name', 'last_name'])

# Save the table elsewhere
# IMPORTANT - The CSV won't actually be loaded and transformed until this step,
# since this is the first time it's actually needed.
tbl.to_redshift('main.name_table')

This “lazy” loading can be very convenient and performant. However, it can make issues hard to debug. Eg. if your data transformations are time-consuming, you won’t actually notice that performance hit until you try to use the data, potentially much later in your code.

So just be aware of this behavior.

Examples

Basic Pipelines

# S3 to Civis
s3 = S3()
csv = s3.get_file('tmc-bucket', 'my_ids.csv')
Table.from_csv(csv).to_civis('TMC','ids.my_ids')

#VAN Activist Codes to a Dataframe
van = VAN(db='MyVoters')
van.activist_codes().to_dataframe()

#VAN Events to an s3 bucket
van = VAN(db='MyVoters')
van.events().to_s3_csv('my-van-bucket','myevents.csv')

To & From API

class parsons.etl.tofrom.ToFrom

to_dataframe(index=None, exclude=None, columns=None, coerce_float=False)

Outputs table as a Pandas Dataframe

Args:

index: str, list

Field of array to use as the index, alternately a specific set of input labels to use

exclude: list

Columns or fields to exclude

columns: list

Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns)

Returns:

dataframe

Pandas DataFrame object

to_html(local_path=None, encoding=None, errors='strict', index_header=False, caption=None, tr_style=None, td_styles=None, truncate=None)

Outputs table to html.

Warning

If a file already exists at the given location, it will be overwritten.

Args:

local_path: str

The path to write the html locally. If not specified, a temporary file will be created and returned, and that file will be removed automatically when the script is done running.

encoding: str

The encoding type for csv.writer()

errors: str

Raise an Error if encountered

index_header: boolean

Prepend index to column names; Defaults to False.

caption: str

A caption to include with the html table.

tr_style: str or callable

Style to be applied to the table row.

td_styles: str, dict or callable

Styles to be applied to the table cells.

truncate: int

Length of cell data.

Returns:

str

The path of the new file

to_csv(local_path=None, temp_file_compression=None, encoding=None, errors='strict', write_header=True, csv_name=None, **csvargs)

Outputs table to a CSV. Additional key word arguments are passed to csv.writer(). So, e.g., to override the delimiter from the default CSV dialect, provide the delimiter keyword argument.

Warning

If a file already exists at the given location, it will be overwritten.

Args:

local_path: str

The path to write the csv locally. If it ends in “.gz” or “.zip”, the file will be compressed. If not specified, a temporary file will be created and returned, and that file will be removed automatically when the script is done running.

temp_file_compression: str

If a temp file is requested (ie. no local_path is specified), the compression type for that file. Currently “None”, “gzip” or “zip” are supported. If a local_path is specified, this argument is ignored.

encoding: str

The CSV encoding type for csv.writer()

errors: str

Raise an Error if encountered

write_header: boolean

Include header in output

csv_name: str

If zip compression (either specified or inferred), the name of csv file within the archive.

**csvargs: kwargs

csv_writer optional arguments

Returns:

str

The path of the new file

append_csv(local_path, encoding=None, errors='strict', **csvargs)

Appends table to an existing CSV.

Additional additional key word arguments are passed to csv.writer(). So, e.g., to override the delimiter from the default CSV dialect, provide the delimiter keyword argument.

Args:

local_path: str

The local path of an existing CSV file. If it ends in “.gz”, the file will be compressed.

encoding: str

The CSV encoding type for csv.writer()

errors: str

Raise an Error if encountered

**csvargs: kwargs

csv_writer optional arguments

Returns:

str

The path of the file

to_zip_csv(archive_path=None, csv_name=None, encoding=None, errors='strict', write_header=True, if_exists='replace', **csvargs)

Outputs table to a CSV in a zip archive. Additional key word arguments are passed to csv.writer(). So, e.g., to override the delimiter from the default CSV dialect, provide the delimiter keyword argument. Use thismethod if you would like to write multiple csv files to the same archive.

Warning

If a file already exists in the archive, it will be overwritten.

Args:

archive_path: str

The path to zip achive. If not specified, a temporary file will be created and returned, and that file will be removed automatically when the script is done running.

csv_name: str

The name of the csv file to be stored in the archive. If None will use the archive name.

encoding: str

The CSV encoding type for csv.writer()

errors: str

Raise an Error if encountered

write_header: boolean

Include header in output

if_exists: str

If archive already exists, one of ‘replace’ or ‘append’

**csvargs: kwargs

csv_writer optional arguments

Returns:

str

The path of the archive

to_json(local_path=None, temp_file_compression=None, line_delimited=False)

Outputs table to a JSON file

Warning

If a file already exists at the given location, it will be overwritten.

Args:

local_path: str

The path to write the JSON locally. If it ends in “.gz”, it will be compressed first. If not specified, a temporary file will be created and returned, and that file will be removed automatically when the script is done running.

temp_file_compression: str

If a temp file is requested (ie. no local_path is specified), the compression type for that file. Currently “None” and “gzip” are supported. If a local_path is specified, this argument is ignored.

line_delimited: bool

Whether the file will be line-delimited JSON (with a row on each line), or a proper JSON file.

Returns:

str

The path of the new file

to_dicts()

Output table as a list of dicts.

Returns:

list

to_sftp_csv(remote_path, host, username, password, port=22, encoding=None, compression=None, errors='strict', write_header=True, rsa_private_key_file=None, **csvargs)

Writes the table to a CSV file on a remote SFTP server

Args:

remote_path: str

The remote path of the file. If it ends in ‘.gz’, the file will be compressed.

host: str

The remote host

username: str

The username to access the SFTP server

password: str

The password to access the SFTP server

port: int

The port number of the SFTP server

encoding: str

The CSV encoding type for csv.writer()

errors: str

Raise an Error if encountered

write_header: boolean

Include header in output

rsa_private_key_file str

Absolute path to a private RSA key used to authenticate stfp connection

**csvargs: kwargs

csv_writer optional arguments

to_s3_csv(bucket, key, aws_access_key_id=None, aws_secret_access_key=None, compression=None, encoding=None, errors='strict', write_header=True, acl='bucket-owner-full-control', public_url=False, public_url_expires=3600, **csvargs)

Writes the table to an s3 object as a CSV

Args:

bucket: str

The s3 bucket to upload to

key: str

The s3 key to name the file. If it ends in ‘.gz’ or ‘.zip’, the file will be compressed.

aws_access_key_id: str

Required if not included as environmental variable

aws_secret_access_key: str

Required if not included as environmental variable

compression: str

The compression type for the s3 object. Currently “None”, “zip” and “gzip” are supported. If specified, will override the key suffix.

encoding: str

The CSV encoding type for csv.writer()

errors: str

Raise an Error if encountered

write_header: boolean

Include header in output

public_url: boolean

Create a public link to the file

public_url_expire: 3600

The time, in seconds, until the url expires if public_url set to True.

acl: str

The S3 permissions on the file

**csvargs: kwargs

csv_writer optional arguments

Returns:

Public url if specified. If not None.

to_redshift(table_name, username=None, password=None, host=None, db=None, port=None, **copy_args)

Write a table to a Redshift database. Note, this requires you to pass AWS S3 credentials or store them as environmental variables.

Args:

table_name: str

The table name and schema (my_schema.my_table) to point the file.

username: str

Required if env variable REDSHIFT_USERNAME not populated

password: str

Required if env variable REDSHIFT_PASSWORD not populated

host: str

Required if env variable REDSHIFT_HOST not populated

db: str

Required if env variable REDSHIFT_DB not populated

port: int

Required if env variable REDSHIFT_PORT not populated. Port 5439 is typical.

**copy_args: kwargs

See copy`() for options.

Returns:

None

to_postgres(table_name, username=None, password=None, host=None, db=None, port=None, **copy_args)

Write a table to a Postgres database.

Args:

table_name: str

The table name and schema (my_schema.my_table) to point the file.

username: str

Required if env variable PGUSER not populated

password: str

Required if env variable PGPASSWORD not populated

host: str

Required if env variable PGHOST not populated

db: str

Required if env variable PGDATABASE not populated

port: int

Required if env variable PGPORT not populated.

**copy_args: kwargs

See copy`() for options.

Returns:

None

to_civis(table, api_key=None, db=None, max_errors=None, existing_table_rows='fail', diststyle=None, distkey=None, sortkey1=None, sortkey2=None, wait=True, **civisargs)

Write the table to a Civis Redshift cluster. Additional key word arguments can passed to civis.io.dataframe_to_civis() # noqa: E501

Args

table: str

The schema and table you want to upload to. E.g., ‘scratch.table’. Schemas or tablenames with periods must be double quoted, e.g. ‘scratch.”my.table”’.

api_key: str

Your Civis API key. If not given, the CIVIS_API_KEY environment variable will be used.

db: str or int

The Civis Database. Can be database name or ID

max_errors: int

The maximum number of rows with errors to remove from the import before failing.

diststyle: str

The distribution style for the table. One of ‘even’, ‘all’ or ‘key’.

existing_table_rows: str

The behaviour if a table with the requested name already exists. One of ‘fail’, ‘truncate’, ‘append’ or ‘drop’. Defaults to ‘fail’.

distkey: str

The column to use as the distkey for the table.

sortkey1: str

The column to use as the sortkey for the table.

sortkey2: str

The second column in a compound sortkey for the table.

wait: boolean

Wait for write job to complete before exiting method.

classmethod from_csv(local_path, **csvargs)

Create a parsons table object from a CSV file

Args:

local_path: obj

A csv formatted local path, url or ftp. If this is a file path that ends in “.gz”, the file will be decompressed first.

**csvargs: kwargs

csv_reader optional arguments

Returns:

Parsons Table

See Parsons Table for output options.

classmethod from_csv_string(str, **csvargs)

Create a parsons table object from a string representing a CSV.

Args:

str: str

The string object to convert to a table

**csvargs: kwargs

csv_reader optional arguments

Returns:

Parsons Table

See Parsons Table for output options.

classmethod from_columns(cols, header=None)

Create a parsons table from a list of lists organized as columns

Args:

cols: list

A list of lists organized as columns

header: list

List of column names. If not specified, will use dummy column names

Returns:

Parsons Table

See Parsons Table for output options.

classmethod from_json(local_path, header=None, line_delimited=False)

Create a parsons table from a json file

Args:

local_path: list

A JSON formatted local path, url or ftp. If this is a file path that ends in “.gz”, the file will be decompressed first.

header: list

List of columns to use for the destination table. If omitted, columns will be inferred from the initial data in the file.

line_delimited: bool

Whether the file is line-delimited JSON (with a row on each line), or a proper JSON file.

Returns:

Parsons Table

See Parsons Table for output options.

classmethod from_redshift(sql, username=None, password=None, host=None, db=None, port=None)

Create a parsons table from a Redshift query.

To pull an entire Redshift table, use a query like SELECT * FROM tablename.

Args:

sql: str

A valid SQL statement

username: str

Required if env variable REDSHIFT_USERNAME not populated

password: str

Required if env variable REDSHIFT_PASSWORD not populated

host: str

Required if env variable REDSHIFT_HOST not populated

db: str

Required if env variable REDSHIFT_DB not populated

port: int

Required if env variable REDSHIFT_PORT not populated. Port 5439 is typical.

Returns:

Parsons Table

See Parsons Table for output options.

classmethod from_postgres(sql, username=None, password=None, host=None, db=None, port=None)

Args:

sql: str

A valid SQL statement

username: str

Required if env variable PGUSER not populated

password: str

Required if env variable PGPASSWORD not populated

host: str

Required if env variable PGHOST not populated

db: str

Required if env variable PGDATABASE not populated

port: int

Required if env variable PGPORT not populated.

classmethod from_s3_csv(bucket, key, from_manifest=False, aws_access_key_id=None, aws_secret_access_key=None, **csvargs)

Create a parsons table from a key in an S3 bucket.

Args:

bucket: str

The S3 bucket.

key: str

The S3 key

from_manifest: bool

If True, treats key as a manifest file and loads all urls into a parsons.Table. Defaults to False.

aws_access_key_id: str

Required if not included as environmental variable.

aws_secret_access_key: str

Required if not included as environmental variable.

**csvargs: kwargs

csv_reader optional arguments

Returns:

parsons.Table object

classmethod from_dataframe(dataframe, include_index=False)

Create a parsons table from a Pandas dataframe.

Args:

dataframe: dataframe

A valid Pandas dataframe objectt

include_index: boolean

Include index column

Transformation API

The following methods allow you to manipulate the Parsons table data.

class parsons.etl.etl.ETL

add_column(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

remove_column(*columns)

Remove a column from your table

Args:

*columns: str

Column names

Returns:

Parsons Table and also updates self

rename_column(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

fill_column(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

fillna_column(column_name, fill_value)

Fill None values in a column in a table

Args:

column_name: str

The column to fill

fill_value:

Fixed value only

Returns:

Parsons Table and also updates self

move_column(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.

convert_column(*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.

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

get_column_max_width(column)

Return the maximum width of the column.

Args:

column: str

The column name.

Returns:

int

convert_columns_to_str()

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

coalesce_columns(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

map_columns(column_map)

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

Returns:

Parsons Table and also updates self

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'}}

map_and_coalesce_columns(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

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'}}

get_column_types(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

get_columns_type_stats()

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

convert_table(*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

unpack_dict(column, keys=None, include_original=False, sample_size=1000, 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.

unpack_list(column, include_original=False, missing=None, replace=False, max_columns=None)

Unpack list values from one column into separate columns. Numbers the columns.

# 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

unpack_nested_columns_as_rows(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

long_table(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.

# 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

cut(*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

select_rows(*filters)

Select specific rows from a Parsons table based on the passed filters.

Example filters:

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

remove_null_rows(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

stack(*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

concat(*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

chunk(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

static 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

match_columns(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

reduce_rows(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.

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.

>>> 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

sort(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

set_header(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