Useful Python Libraries

Useful Python Libraries#

The following libraries are available and recommended for use by Cal-ITP data analysts. Our JupyterHub environment comes with all of these installed already, except for calitp-data-infra. A full list of the packages included in the system image that underpins our JupyterHub environment can be found (and updated when needed) here.

Table of Contents#

  1. shared utils

  2. calitp-data-analysis

  3. siuba
    - Basic Query
    - Collect Query Results
    - Show Query SQL
    - More siuba Resources

  4. pandas

  5. Add New Packages

  6. Updating calitp-data-analysis

  7. Appendix: calitp-data-infra

shared utils#

A set of shared utility functions can also be installed, similarly to any Python library. The shared_utils are stored in two places: here in data-analyses, which houses functions that are more likely to be updated. Shared functions that are updated less frequently are housed here in the calitp_data_analysis package in data-infra. Generalized functions for analysis are added as collaborative work evolves so we aren’t constantly reinventing the wheel.

In terminal#

  • Navigate to the package folder: cd data-analyses/_shared_utils

  • Use the make command to run through conda install and pip install: make setup_env

    • Note: you may need to select Kernel -> Restart Kernel from the top menu after make setup_env in order to successfully import shared_utils

  • Alternative: add an alias to your .bash_profile:

    • In terminal use cd to navigate to the home directory (not a repository)

    • Type nano .bash_profile to open the .bash_profile in a text editor

    • Add a line at end: alias go='cd ~/data-analyses/portfolio && pip install -r requirements.txt && cd ../_shared_utils && make setup_env && cd ..'

    • Exit with Ctrl+X, hit yes, then hit enter at the filename prompt

    • Restart your server; you can check your changes with cat .bash_profile

In notebook#

from calitp_data_analysis import geography_utils

geography_utils.WGS84

See data-analyses/starter_kit for examples on how to use shared_utils for general functions, charts, and maps.

calitp-data-analysis#

calitp-data-analysis is an internal library of utility functions used to access our warehouse data for analysis purposes.

import tbls#

Most notably, you can include import tbls at the top of your notebook to import a table from the warehouse in the form of a tbls:

from calitp_data_analysis.tables import tbls

Example:

from calitp_data_analysis.tables import tbls

tbls.mart_gtfs.dim_agency()

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1721856642.604971    1990 config.cc:230] gRPC experiments enabled: call_status_override_on_cancellation, event_engine_dns, event_engine_listener, http2_stats_fix, monitoring_experiment, pick_first_new, trace_record_callops, work_serializer_clears_time_cache

# Source: lazy query
# DB Conn: Engine(bigquery://cal-itp-data-infra/?maximum_bytes_billed=5000000000)
# Preview:
key _gtfs_key feed_key agency_id agency_name agency_url agency_timezone agency_lang agency_phone agency_fare_url agency_email base64_url _dt _feed_valid_from _line_number feed_timezone
0 282b4e07171c48ac08162e0dc8749066 18fcf47148228c7b56c19726c854054b 9b8e9b8befe560293a6c5b38dc19ffbb MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2021-07-28 2021-07-28 13:56:16+00:00 1 America/Los_Angeles
1 a196a8996bb74b7bd6d92f0cc2802620 00e77c29f2ce1986407cb93f53936dff 25915c089571c49be33b59e10c25d2ac LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 00:01:13+00:00 1 America/Los_Angeles
2 033d018b031002914fbfa48c872d4a65 8f5e204872e6b7034479a6d11502547b 3d30b02b9008f788eb89c41c137786c1 MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2022-01-19 2022-01-19 00:11:01+00:00 1 America/Los_Angeles
3 56112d81d62d71d408ea0247309178e4 090343c89ab8fe1d5e3b79e5687bbcca 21fa0b125d801eb5058da2ec5d748bda LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 14:59:54.274779+00:00 1 America/Los_Angeles
4 1d48492cd90930d6612fc70ef18bf8d4 93c44e614219c41696a8148018c1d83b f87ac9abe81a7548325423ede69c3b86 LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-09 2022-09-09 00:00:56+00:00 1 America/Los_Angeles

# .. may have more rows

query_sql#

query_sql is another useful function to use inside of JupyterHub notebooks to turn a SQL query into a pandas DataFrame.

As an example, in a notebook:

from calitp_data_analysis.sql import query_sql

df_dim_agency = query_sql("""
SELECT
    *
FROM `mart_gtfs.dim_agency`
LIMIT 10""", as_df=True)

df_dim_agency.head()
key _gtfs_key feed_key agency_id agency_name agency_url agency_timezone agency_lang agency_phone agency_fare_url agency_email base64_url _dt _feed_valid_from _line_number feed_timezone
0 282b4e07171c48ac08162e0dc8749066 18fcf47148228c7b56c19726c854054b 9b8e9b8befe560293a6c5b38dc19ffbb MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2021-07-28 2021-07-28 13:56:16+00:00 1 America/Los_Angeles
1 a196a8996bb74b7bd6d92f0cc2802620 00e77c29f2ce1986407cb93f53936dff 25915c089571c49be33b59e10c25d2ac LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 00:01:13+00:00 1 America/Los_Angeles
2 033d018b031002914fbfa48c872d4a65 8f5e204872e6b7034479a6d11502547b 3d30b02b9008f788eb89c41c137786c1 MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2022-01-19 2022-01-19 00:11:01+00:00 1 America/Los_Angeles
3 56112d81d62d71d408ea0247309178e4 090343c89ab8fe1d5e3b79e5687bbcca 21fa0b125d801eb5058da2ec5d748bda LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 14:59:54.274779+00:00 1 America/Los_Angeles
4 1d48492cd90930d6612fc70ef18bf8d4 93c44e614219c41696a8148018c1d83b f87ac9abe81a7548325423ede69c3b86 LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-09 2022-09-09 00:00:56+00:00 1 America/Los_Angeles

siuba#

siuba is a tool that allows the same analysis code to run on a pandas DataFrame, as well as generate SQL for different databases. It supports most pandas Series methods analysts use. See the siuba docs for more information.

The examples below go through the basics of using siuba, collecting a database query to a local DataFrame, and showing SQL test queries that siuba code generates.

Basic query#

from calitp_data_analysis.tables import tbls
from siuba import _, filter, count, collect, show_query

# query agency information, then filter for a single gtfs feed,
# and then count how often each feed key occurs
(tbls.mart_gtfs.dim_agency()
    >> filter(_.agency_id == 'BA', _.base64_url == 'aHR0cHM6Ly9hcGkuNTExLm9yZy90cmFuc2l0L2RhdGFmZWVkcz9vcGVyYXRvcl9pZD1SRw==')
    >> count(_.feed_key)
)

# Source: lazy query
# DB Conn: Engine(bigquery://cal-itp-data-infra/?maximum_bytes_billed=5000000000)
# Preview:
feed_key n
0 e839b5cbcd5b29a16c2ac4d40cd4439d 1
1 7eded7cbbf072a2fd9dd812f80bf2d2b 1
2 8f5949d03a0cb1243ac9301df8adef14 1
3 cae827cd30737d76600b13970dde458a 1
4 023ec6a2b6246ad996abcb5aefedc068 1

# .. may have more rows

Collect query results#

Note that siuba by default prints out a preview of the SQL query results. In order to fetch the results of the query as a pandas DataFrame, run collect().

tbl_agency_names = tbls.mart_gtfs.dim_agency() >> collect()

# Use pandas .head() method to show first 5 rows of data
tbl_agency_names.head()
key _gtfs_key feed_key agency_id agency_name agency_url agency_timezone agency_lang agency_phone agency_fare_url agency_email base64_url _dt _feed_valid_from _line_number feed_timezone
0 282b4e07171c48ac08162e0dc8749066 18fcf47148228c7b56c19726c854054b 9b8e9b8befe560293a6c5b38dc19ffbb MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2021-07-28 2021-07-28 13:56:16+00:00 1 America/Los_Angeles
1 a196a8996bb74b7bd6d92f0cc2802620 00e77c29f2ce1986407cb93f53936dff 25915c089571c49be33b59e10c25d2ac LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 00:01:13+00:00 1 America/Los_Angeles
2 033d018b031002914fbfa48c872d4a65 8f5e204872e6b7034479a6d11502547b 3d30b02b9008f788eb89c41c137786c1 MT None http://www.mantecatransit.com America/Los_Angeles en (209) 456-8000 None None aHR0cHM6Ly93d3cuY2kubWFudGVjYS5jYS51cy9Db21tdW... 2022-01-19 2022-01-19 00:11:01+00:00 1 America/Los_Angeles
3 56112d81d62d71d408ea0247309178e4 090343c89ab8fe1d5e3b79e5687bbcca 21fa0b125d801eb5058da2ec5d748bda LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-15 2022-09-15 14:59:54.274779+00:00 1 America/Los_Angeles
4 1d48492cd90930d6612fc70ef18bf8d4 93c44e614219c41696a8148018c1d83b f87ac9abe81a7548325423ede69c3b86 LAX FlyAway None http://www.LAXFlyAway.org America/Los_Angeles en (714) 507-1170 None None aHR0cHM6Ly93d3cuZmx5bGF4LmNvbS8tL21lZGlhL2ZseW... 2022-09-09 2022-09-09 00:00:56+00:00 1 America/Los_Angeles

Show query SQL#

While collect() fetches query results, show_query() prints out the SQL code that siuba generates.

(tbls.mart_gtfs.dim_agency()
  >> filter(_.agency_name.str.contains("Metro"))
  >> show_query(simplify=True)
)

SELECT * 
FROM `mart_gtfs.dim_agency` AS `mart_gtfs.dim_agency_1` 
WHERE regexp_contains(`mart_gtfs.dim_agency_1`.`agency_name`, 'Metro')

# Source: lazy query
# DB Conn: Engine(bigquery://cal-itp-data-infra/?maximum_bytes_billed=5000000000)
# Preview:
key _gtfs_key feed_key agency_id agency_name agency_url agency_timezone agency_lang agency_phone agency_fare_url agency_email base64_url _dt _feed_valid_from _line_number feed_timezone
0 5510fd5cfd5718d36c5cea9efc61b05e 267a1b2a19b0526a5086cded02ba3325 2716d91a7c0f05036fd7d593a6980ec4 960 Madera Metro https://www.cityofmadera.ca.gov/home/departmen... America/Los_Angeles en (559) 661-RIDE (7433) https://www.cityofmadera.ca.gov/wp-content/upl... None aHR0cHM6Ly9kYXRhLnRyaWxsaXVtdHJhbnNpdC5jb20vZ3... 2022-12-16 2022-12-16 03:00:27.604217+00:00 1 America/Los_Angeles
1 57317be7e3f5249578e73e95f7dc5965 5c81c258fbc5971b313c4bf898146f3d fbbfef3d80a43f31213adacbedcea27d 960 Madera Metro https://www.madera.gov/home/departments/transi... America/Los_Angeles en (559) 661-RIDE (7433) https://www.madera.gov/home/departments/transi... None aHR0cHM6Ly9kYXRhLnRyaWxsaXVtdHJhbnNpdC5jb20vZ3... 2024-02-02 2024-02-02 03:00:31.039579+00:00 1 America/Los_Angeles
2 149712407d0b279211c623c2e52a3d3c 513def24eef056315fb6ef128714f584 726a476a7c2917a2cd7c94a409c5d0c5 960 Madera Metro https://www.madera.gov/home/departments/transi... America/Los_Angeles en (559) 661-RIDE (7433) https://www.madera.gov/home/departments/transi... None aHR0cHM6Ly9kYXRhLnRyaWxsaXVtdHJhbnNpdC5jb20vZ3... 2024-02-06 2024-02-06 03:00:59.720405+00:00 1 America/Los_Angeles
3 6ef00206641f97e0e6d2fd19f21ac9c0 56b61b5baea7c4768dd8939d3612296f 1819d162df24993e08dd9d308edbad84 960 Madera Metro https://www.madera.gov/home/departments/transi... America/Los_Angeles en (559) 661-RIDE (7433) https://www.madera.gov/home/departments/transi... None aHR0cHM6Ly9kYXRhLnRyaWxsaXVtdHJhbnNpdC5jb20vZ3... 2024-02-01 2024-02-01 03:00:21.894200+00:00 1 America/Los_Angeles
4 0cd97b3be5aeb3c1d6caeaebf6eb823b b2cb4c2659f278a891f69cc26cd89efa a0c77b0ae8cb6c4d2b0786b878aa975e 960 Madera Metro https://www.cityofmadera.ca.gov/home/departmen... America/Los_Angeles en (559) 661-RIDE (7433) https://www.cityofmadera.ca.gov/wp-content/upl... None aHR0cHM6Ly9kYXRhLnRyaWxsaXVtdHJhbnNpdC5jb20vZ3... 2021-08-07 2021-08-07 00:16:25+00:00 1 America/Los_Angeles

# .. may have more rows

Note that here the pandas Series method str.contains corresponds to regexp_contains in Google BigQuery.

More siuba Resources#

pandas#

The library pandas is very commonly used in data analysis, and the external resources below provide a brief overview of it’s use.

Add New Packages#

While most Python packages an analyst uses come in JupyterHub, there may be additional packages you’ll want to use in your analysis.

  • Install shared utility functions,

  • Change directory into the project task’s subfolder and add requirements.txt and/or conda-requirements.txt

  • Run pip install -r requirements.txt and/or conda install --yes -c conda-forge --file conda-requirements.txt

Updating calitp-data-analysis#

calitp-data-analysis is a package that lives here in the data-infra repo. Follow the steps below update to the package.

Steps

Adapted from this and this Slack thread.

  1. Make the changes you want in the calitp-data-analysis folder inside packages here. If you are only changing package metadata (author information, package description, etc.) without changing the function of the package itself, that information lives in pyproject.toml rather than in the calitp-data-analysis subfolder.

    • If you are adding a new function that relies on a package that isn’t already a dependency, run poetry add <package name> after changing directories to data-infra/packages/calitp_data_analysis. Check this Jupyter image file for the version number associated with the package, because you should specify the version.

    • For example, your function relies on dask. In the Jupyter image file, the version is dask = "~2022.8" so run poetry add dask==~2022.8 in the terminal.

    • You may also have run poetry install mypy. mypy is a package that audits Python files for information related to data types, and you can read more about it here.

    • mypy is a package that audits Python files for information related to data types, and you can read more about it here. mypy is one of the standard development dependencies for the calitp-data-analysis package, defined in the pyproject.toml file for the package, so to install it you can run poetry install in packages/calitp-data-analysis/ (which will also install the other package dependencies). To use mypy, run poetry run mypy [file or directory name you're interested in checking].

    • mypy is generally run in CI when a PR is opened, as part of build tasks. You can see it called here for this package in build-calitp-data-analysis.yml. Within the PR, the “Test, visualize, and build calitp-data-analysis” CI workflow will fail if problems are found.

    • More helpful hints for passing mypy in our repo.

  2. Each time you update the package, you must also update the version number. We use dates to reflect which version we are on. Update the version in pyproject.toml that lives in calitp-data-analysis to either today’s date or a future date.

  3. Open a new pull request and make sure the new version date appears on the test version page.

    • The new version date may not show up on the test page due to errors. Check the GitHub Action page of your pull request to see the errors that have occurred.

    • If you run into the error message like this, error: Skipping analyzing "dask_geopandas": module is installed, but missing library stubs or py.typed marker  [import] go to your .py file and add # type: ignore behind the package import.

    • To fix the error above, change import dask_geopandas as dg to import dask_geopandas as dg  # type: ignore.

    • It is encouraged to make changes in a set of smaller commits. For example, add all the necessary packages with poetry run <package first, fix any issues flagged by mypy, and finally address any additional issues.

  4. Merge the PR. Once it is merged in, the actual package will display the new version number. To make sure everything works as expected, run pip install calitp-data-analysis==<new version here> in a cell of Jupyter notebook and import a package (or two) such as from calitp_data_analysis import styleguide.

  5. Update the new version number in the data-infra repository here, here, here, and anywhere else you find a reference to the old version of the package. You’ll also want to do the same for any other Cal-ITP repositories that reference the calitp-data-analysis package.

    • When yu update the jupyter-singleuser toml, make sure to run poetry add calitp-data-analysis==<new version here> and commit the updated poetry.lock file.

    • As of writing, the only other repository that references to the package version is reports.

Resources

Appendix: calitp-data-infra#

The calitp-data-infra package, used primarily by warehouse mainainers and data pipeline developers, includes utilities that analysts will likely not need need to interact with directly (and therefore generally won’t need to install), but which may be helpful to be aware of. For instance, the get_secret_by_name() and get_secrets_by_label() functions in the package’s auth module are used to interact with Google’s Secret Manager, the service that securely stores API keys and other sensitive information that underpins many of our data syncs.

You can read more about the calitp-data-infra Python package here.

Contents