readabs 0.0.7

Get ABS timeseries data in pandas DataFrames

readabs

readabs is an open-source python package to download and work with timeseries data from the Australian Bureau of Statistics (ABS), using pandas DataFrames.

readabs automates the downloading of zip files and excel excel files and the capture of that data using pandas. It does not use the ABS APIs (see: here).

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Usage:

Standand import arrangements. Metacol is a Namedtuple that allows just a couple of keystrokes to access the column names in the meta data (did='Data Item Description', stype='Series Type', id='Series ID', start='Series Start', end='Series End', num='No. Obs.', unit='Unit', dtype='Data Type', freq='Freq.', cmonth='Collection Month', table='Table', tdesc='Table Description', cat='Catalogue number').

import readabs as ra
from readabs import metacol as mc

Print a list of available catalogue identifiers from the ABS. You may need this to get the catalogue identifier/number for the data you want to download.

ra.print_abs_catalogue()

Get the ABS catalogue map as a pandas DataFrame.

cat_map = ra.catalogue_map()

Get all of the data tables associated with a particular catalogue identifier. The catalogue identifier is a string with the standard ABS identifier. For example, the cataloge identifier for the monthly labour force survey is "6202.0". Returns a tuple. The first element of the tuple is a dictionary of DataFrames. The dictionary is indexed by table names (which can be found in the meta data). The second element is a DataFrame for the meta data. Note: with some ABS catalogues, a specific series may be repeated in more than one table.

abs_dict, meta = ra.read_abs_cat(cat="id")

Get two DataFrames in a tuple, the first containing the actual data, and the second containing the meta data for one or more specified ABS series identifiers.

data, meta = ra.read_abs_series(cat="id", series="id1")
data, meta = ra.read_abs_series(cat="id", series=("id1", "id2", ...))

Search the metadata for one or more matching data items. Note:

• The search terms are strings placed in a dictionary with the form {"search phrase": "meta data column name", ...}.
• Additional optional arguments are:
  • exact_match - bool - whether to match using == (exact) or .str.contains() (inexact) [But note that the table name is always matched exactly].
  • regex - bool - for .str.contains() - whether to use regular expressions.
  • validate_unique - bool - raise a ValueError if the search result is not a single unique match.
  • verbose - bool - print additional information while searching; which can be useful when diagnosing problems with search terms.
• Returns a pandas DataFrame (subseted from meta), Note: The index for the returned meta data will be ABS series_ids. Duplicate indexes will be removed from the meta data (ie. where the ABS has a series in more than one table, this function will only report the first match.)

found_meta = ra.search_meta(meta, search_terms, **kwargs)

The find_id function uses the search_mete function to return a tuple of three strings: the table name, the series identifier, and the units of measurement. The keyword arguments are the same for search_meta.

table, series_id, units = find_id(meta, search_terms, **kwargs)

Additional utility functions

While not necessary for working with ABS data, the package includes some useful functions for manipulating ABS data:

Calculate percentage change over n_periods.

change_data = percentage_change(data, n_periods)

Annualise monthly or quarterly percentage rates.

annualised = annualise_percentages(data, periods_per_year)

Convert a pandas timeseries with a Quarterly PeriodIndex to an timeseries with a Monthly PeriodIndex.

monthly_data = qtly_to_monthly(
    quarterly_data, 
    interpolate, # default is True
    limit,  # default is 2, only used if interpolate is True
    dropna,  # default is True,
)

Convert monthly data to quarterly data by taking the mean or sum of the three months in each quarter. Ignore quarters with less than three months data. Drop NA items.

quarterly_data = monthly_to_qtly(
    monthly_data,
    q_ending,  # default is "DEC"
    f, # the function to apply ("sum" or "mean"), the default is "mean"
)

Recalibrate a DataFrame or a Series so that its values are within the range -1000 to +1000. Adjust the units to match the recalibrated series.

series, units = ra.recalibrate(series, units)

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Notes:

• This package does not manipulate the ABS data. The data is returned as it was downloaded. This includes any NA-only (ie. empty) columns where they occur.
• This package only downloads timeseries data tables. Other data tables (for example, pivot tables) are ignored.
• The index for all of the downloaded tables should be a pandas PeriodIndex, with an appropriately selected frequency.
• In the process of data retrieval, ABS zip and excel files are downloaded and stored in a local cache. By default, the cache directory is "./.readabs_cache/". You can change the default directory name by setting the environemnt variable "READABS_CACHE_DIR" with the name of the preferred directory.
• the "read" functions have a number of standard keyword arguments (with default settings as follows):
  • history="" - provide a month-year string to extract historical ABS data.
    For example, you can set history="dec-2023" to the get the ABS data for a catalogue identifier that was originally published in respect of Q4 of 2023. Note: not all ABS data sources are structured so that this technique works in every case; but most are.
  • verbose=False - Do not print detailed information on the data retrieval process. Setting this to true may help diagnose why something might be going wrong with the data retrieval process.
  • ignore_errors=False - Cease downloading when an error in encounted. However, sometimes the ABS website has malformed links, and changing this setting is necessitated. (Note: if you drop a message to the ABS, they will usually fix broken links with a business day).
  • get_zip=True - Download the excel files in .zip files.
  • get_excel_if_no_zip=True Only try to download .xlsx files if there are no zip files available to be downloaded.
  • get_excel=False - Do not automatically download .xlsx files. Note at least one of get_zip, get_excel_if_no_zip, or get_excel must be true. For most ABS catalogue items, it is sufficient to just download the one zip file. But note, some catalogue items do not have a zip file. Others have quite a number of zip files.
  • single_excel_only="" - if this argument is set to a table name (without the .xlsx extention), only that excel file will be downloaded. If set, and only a limited subset of available data is needed, this can speed up download times significantly. Note: overrides get_zip, get_excel_if_no_zip, get_excel and single_zip_only.
  • single_zip_only="" - if this argument is set to a zip file name (without the .zip extention), only that zip file will be downloaded. If set, and only a limited subset of available data is needed, this can speed up download times significantly. Note: overrides get_zip, get_excel_if_no_zip, and get_excel.