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
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[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, **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
**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_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_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]
add_column(column, value=None, index=None)[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
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
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:
Fixed value only
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)[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
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=1000, 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