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_gcs_csv()`	Google Cloud Storage Bucket	Write a table to a csv stored in Google Cloud Storage
`to_sftp_csv()`	SFTP Server	Write a table to a csv stored on an SFTP server
`to_redshift()`	A Redshift Database	Write a table to a Redshift database
`to_postgres()`	A Postgres Database	Write a table to a Postgres database
`to_civis()`	Civis Redshift Database	Write a table to Civis platform database
`to_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
`append_csv()`	CSV file	Appends table to an existing CSV
`to_zip_csv()`	ZIP file	Writes a table to a CSV in a zip archive
`to_dicts()`	Dicts	Write a table as a list of dicts

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
`from_csv_string()`	File like object, local path, url, ftp.	Load a CSV string into a Table

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
`head()`	Get the first n rows of a table
`tail()`	Get the last n rows of a table
`add_column()`	Add a column
`remove_column()`	Remove a column
`rename_column()`	Rename a column
`rename_columns()`	Rename multiple columns
`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
`deduplicate()`	Removes duplicate rows based on optional key(s), and optionally sorts

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, parsons.Table.table, which will allow you to perform any petl-supported ETL operations. Additionally, you can use the helper method, use_petl(), to conveniently perform the same operations on a parsons Table. For example:

import petl
...

tbl = Table()
tbl.table = petl.skipcomments(tbl.table, '#')

or

tbl = Table()
tbl.use_petl('skipcomments', '#', update_table=True)

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. There may also be cases where it’s possible to get faster execution by caching a table, especially in situations where a single table will be used as the base for several subsequent calculations.

For these cases Parsons provides two utility functions to materialize a Table and all of its transformations.

Method	Description
`materialize()`	Load all data from the Table into memory and apply any transformations
`materialize_to_file()`	Load all data from the Table and apply any transformations, then save to a local temp file.

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[source]

to_dataframe(index=None, exclude=None, columns=None, coerce_float=False)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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()[source]

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)[source]

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, use_env_token=True, **csvargs)[source]

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
use_env_token: boolean: Controls use of the AWS_SESSION_TOKEN environment variable for S3. Defaults to True. Set to False in order to ignore the AWS_SESSION_TOKEN env variable even if the aws_session_token argument was not passed in.
**csvargs: kwargs: csv_writer optional arguments

Returns:

Public url if specified. If not None.

to_gcs_csv(bucket_name, blob_name, app_creds=None, project=None, compression=None, encoding=None, errors='strict', write_header=True, public_url=False, public_url_expires=60, **csvargs)[source]

Writes the table to a Google Cloud Storage blob as a CSV.

Args:

bucket_name: str: The bucket to upload to
blob_name: str: The blob to name the file. If it ends in ‘.gz’ or ‘.zip’, the file will be compressed.
app_creds: str: A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.
project: str: The project which the client is acting on behalf of. If not passed then will use the default inferred environment.
compression: str: The compression type for the csv. 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: 60: The time, in minutes, until the url expires if public_url set to True.
**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)[source]

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)[source]

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_bigquery(table_name: str, app_creds: str | None = None, project: str | None = None, **kwargs)[source]

Write a table to BigQuery

Args:

table_name: str: Table name to write to in BigQuery; this should be in schema.table format
app_creds: str: A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.
project: str: The project which the client is acting on behalf of. If not passed then will use the default inferred environment.
**kwargs: kwargs: Additional keyword arguments passed into the .copy() function (if_exists, max_errors, etc.)

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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_bigquery(sql: str, app_creds: str = None, project: str = None)[source]

Create a parsons table from a BigQuery statement.

To pull an entire BigQuery table, use a query like SELECT * FROM {{ table }}.

Args:

sql: str: A valid SQL statement
app_creds: str: A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.
project: str: The project which the client is acting on behalf of. If not passed then will use the default inferred environment.

TODO - Should users be able to pass in kwargs here? For parameters?

Returns:

Parsons Table: See Parsons Table for output options.

classmethod from_dataframe(dataframe, include_index=False)[source]

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[source]

head(n=5)[source]

Return the first n rows of the table

Args:

n: int: The number of rows to return. Defaults to 5.

Returns:

Parsons Table

tail(n=5)[source]

Return the last n rows of the table. Defaults to 5.

Args:

n: int: The number of rows to return

Returns:

Parsons Table

add_column(column, value=None, index=None, if_exists='fail')[source]

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
if_exists: str (options: ‘fail’, ‘replace’): If set replace, this function will call fill_column if the column already exists, rather than raising a ValueError

Returns:

Parsons Table and also updates self

remove_column(*columns)[source]

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)[source]

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

rename_columns(column_map)[source]

Rename multiple columns

Args:

column_map: dict

A dictionary of columns and new names. The key is the old name and the value is the new name.

Example dictionary: {‘old_name’: ‘new_name’, ‘old_name2’: ‘new_name2’}

Returns:

Parsons Table and also updates self

fill_column(column_name, fill_value)[source]

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)[source]

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

move_column(column, index)[source]

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)[source]

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)[source]

Return the maximum width of the column.

Args:

column: str: The column name.

Returns:

int

convert_columns_to_str()[source]

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)[source]

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, exact_match=True)[source]

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

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)[source]

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)[source]

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()[source]

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)[source]

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=5000, missing=None, prepend=True, prepend_value=None)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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')[source]

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)[source]

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)[source]

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)[source]

Replace the header row of the table.

Args:

new_header: list: List of new header column names

Returns:

Parsons Table and also updates self

use_petl(petl_method, *args, **kwargs)[source]

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.

>>> # 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

deduplicate(keys=None, presorted=False)[source]

Deduplicates table based on an optional keys argument, which can contain any number of keys or None.

Method considers all keys specified in the keys argument when deduplicating, not each key individually. For example, if keys=['a', 'b'], the method will not remove a record unless it’s identical to another record in both columns a and b.

>>> tbl = Table([['a', 'b'], [1, 3], [1, 2], [1, 2], [2, 3]])
>>> tbl.table
+---+---+
| a | b |
+===+===+
| 1 | 3 |
+---+---+
| 1 | 2 |
+---+---+
| 1 | 2 |
+---+---+
| 2 | 3 |
+---+---+

>>> tbl.deduplicate('a')
>>> # removes all subsequent rows with {'a': 1}
>>> tbl.table
+---+---+
| a | b |
+===+===+
| 1 | 3 |
+---+---+
| 2 | 3 |
+---+---+

>>> tbl = Table([['a', 'b'], [1, 3], [1, 2], [1, 2], [2, 3]]) # reset
>>> tbl.deduplicate(['a', 'b'])
>>> # sorted on both ('a', 'b') so (1, 2) was placed before (1, 3)
>>> # did not remove second instance of {'a': 1} or {'b': 3}
>>> tbl.table
+---+---+
| a | b |
+===+===+
| 1 | 2 |
+---+---+
| 1 | 3 |
+---+---+
| 2 | 3 |
+---+---+


>>> tbl = Table([['a', 'b'], [1, 3], [1, 2], [1, 2], [2, 3]]) # reset
>>> tbl.deduplicate('a').deduplicate('b')
>>> # can chain method to sort/dedupe on 'a', then sort/dedupe on 'b'
>>> tbl.table
+---+---+
| a | b |
+===+===+
| 1 | 3 |
+---+---+

>>> tbl = Table([['a', 'b'], [1, 3], [1, 2], [1, 2], [2, 3]]) # reset
>>> tbl.deduplicate('b').deduplicate('a')
>>> # Order DOES matter when deduping on one column at a time
>>> tbl.table
+---+---+
| a | b |
+===+===+
| 1 | 2 |
+---+---+

Args:

keys: str or list[str] or None: keys to deduplicate (and optionally sort) on.
presorted: bool: If false, the row will be sorted.

Returns:

Parsons Table and also updates self

Materialize API

class parsons.etl.table.Table(lst: list | tuple | Table | _EmptyDefault = _EmptyDefault.token)[source]

Create a Parsons Table. Accepts one of the following: - A list of lists, with list[0] holding field names, and the other lists holding data - A list of dicts - A petl table

Args:

lst: list: See above for accepted list formats
source: str: The original data source from which the data was pulled (optional)
name: str: The name of the table (optional)

materialize()[source]

“Materializes” a Table, meaning all data is loaded into memory and all pending transformations are applied.

Use this if petl’s lazy-loading behavior is causing you problems, eg. if you want to read data from a file immediately.

materialize_to_file(file_path=None)[source]

“Materializes” a Table, meaning all pending transformations are applied.

Unlike the original materialize function, this method does not bring the data into memory, but instead loads the data into a local temp file.

This method updates the current table in place.

Args:

file_path: str: The path to the file to materialize the table to; if not specified, a temp file will be created.

Returns:

str: Path to the temp file that now contains the table