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Merge pull request #1418 from cal-itp/jan_ntd
jan 2025 monthly ntd ridership report publish
2 parents 91fb528 + 902c28c commit cedce52

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‎_shared_utils/shared_utils/schedule_gtfs_keys_multi_orgs.py

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@@ -1,23 +1,25 @@
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"""
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Create the GTFS Digest yaml that
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Create the GTFS Digest yaml that
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sets the parameterization for the analysis site.
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"""
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import sys
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import pandas as pd
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import yaml
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# import yaml
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from shared_utils import portfolio_utils
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from update_vars import GTFS_DATA_DICT
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import sys
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sys.path.append("../../gtfs_digest/")
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from update_vars import GTFS_DATA_DICT
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def count_orgs(df: pd.DataFrame) -> list:
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"""
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Count the number of unique organization_names
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to schedule_gtfs_dataset_keys. Filter out any
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schedule_gtfs_dataset_keys with less than 2 unique
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organization_names. Return these schedule_gtfs_dataset_keys
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in a list.
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in a list.
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"""
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agg1 = (
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df.groupby(["caltrans_district", "schedule_gtfs_dataset_key"])
@@ -31,12 +33,13 @@ def count_orgs(df: pd.DataFrame) -> list:
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multi_org_list = list(agg1.schedule_gtfs_dataset_key.unique())
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return multi_org_list
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def find_schd_keys_multi_ops() -> pd.DataFrame:
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"""
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Return a dataframe with all the schedule_gtfs_dataset_keys
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that have more than one organization_name that corresponds to it.
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that have more than one organization_name that corresponds to it.
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This way, we won't include duplicate organizations when publishing
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our GTFS products.
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our GTFS products.
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"""
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schd_vp_url = f"{GTFS_DATA_DICT.digest_tables.dir}{GTFS_DATA_DICT.digest_tables.route_schedule_vp}.parquet"
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@@ -60,10 +63,8 @@ def find_schd_keys_multi_ops() -> pd.DataFrame:
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)
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# Sort dataframe to keep the row for district/gtfs_key for the most
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# current date
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schd_vp_df2 = schd_vp_df.dropna(subset="caltrans_district").sort_values(
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by=sort_cols, ascending=[True, False, True]
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)
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# current date
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schd_vp_df2 = schd_vp_df.dropna(subset="caltrans_district").sort_values(by=sort_cols, ascending=[True, False, True])
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schd_vp_df3 = schd_vp_df2.drop_duplicates(
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subset=[
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"organization_name",
@@ -77,30 +78,25 @@ def find_schd_keys_multi_ops() -> pd.DataFrame:
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multi_orgs_list = count_orgs(schd_vp_df3)
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# Filter out the dataframe to only include schedule_gtfs_keys with multiple orgs
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schd_vp_df4 = schd_vp_df3.loc[
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schd_vp_df3.schedule_gtfs_dataset_key.isin(multi_orgs_list)
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].reset_index(drop=True)
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schd_vp_df4 = schd_vp_df3.loc[schd_vp_df3.schedule_gtfs_dataset_key.isin(multi_orgs_list)].reset_index(drop=True)
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# Drop duplicates for organization_name
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schd_vp_df5 = schd_vp_df4.drop_duplicates(
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subset=["caltrans_district", "organization_name"]
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).reset_index(drop=True)
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schd_vp_df5 = schd_vp_df4.drop_duplicates(subset=["caltrans_district", "organization_name"]).reset_index(drop=True)
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# Aggregate the dataframe to find schedule_gtfs_dataset_keys
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# with multiple organization_names once more.
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multi_orgs_list2 = count_orgs(schd_vp_df5)
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# Filter one last time to only include schedule_gtfs_keys with multiple orgs
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schd_vp_df6 = schd_vp_df5.loc[
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schd_vp_df5.schedule_gtfs_dataset_key.isin(multi_orgs_list2)
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].reset_index(drop=True)
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schd_vp_df6 = schd_vp_df5.loc[schd_vp_df5.schedule_gtfs_dataset_key.isin(multi_orgs_list2)].reset_index(drop=True)
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# Clean
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schd_vp_df6= schd_vp_df6.drop(columns = ["service_date"])
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schd_vp_df6["combo"] = schd_vp_df6.caltrans_district + " (" + schd_vp_df6.schedule_gtfs_dataset_key + ")"
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schd_vp_df6 = schd_vp_df6.drop(columns=["service_date"])
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schd_vp_df6["combo"] = schd_vp_df6.caltrans_district + " (" + schd_vp_df6.schedule_gtfs_dataset_key + ")"
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return schd_vp_df6
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SITE_YML = "./schedule_gtfs_dataset_key_multi_operator.yml"
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if __name__ == "__main__":
@@ -109,13 +105,13 @@ def find_schd_keys_multi_ops() -> pd.DataFrame:
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portfolio_utils.create_portfolio_yaml_chapters_with_sections(
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SITE_YML,
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df,
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chapter_info = {
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chapter_info={
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"column": "combo",
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"name": "District/Key",
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"caption_prefix": "",
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"caption_suffix": "",
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},
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section_info = {
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section_info={
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"column": "organization_name",
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"name": "organization_name",
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},

‎ntd/ntd_dates.py

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@@ -42,4 +42,5 @@ def get_public_filename(monthyear_string: str) -> str:
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"oct2024": "2024-12",
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"nov2024": "2025-01",
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"dec2024": "2025-02",
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"jan2025": "2025-03"
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}

‎ntd/update_vars.py

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GCS_FILE_PATH = "gs://calitp-analytics-data/data-analyses/ntd/"
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current_month = "dec2024"
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current_month = "jan2025"
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YEAR, MONTH = ntd_dates.parse_monthyear_string(current_month)
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PUBLIC_FILENAME = ntd_dates.get_public_filename(current_month)
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MONTH_CREATED = ntd_dates.DATES[current_month]
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# Check this url each month
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# https://www.transit.dot.gov/ntd/data-product/monthly-module-adjusted-data-release
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# Depending on if they fixed the Excel, there may be an additional suffix
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suffix = "_250203"
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suffix = "_250303"
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FULL_URL = (
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"https://www.transit.dot.gov/sites/fta.dot.gov/files/"
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f"{MONTH_CREATED}/{MONTH}%20{YEAR}%20"

‎portfolio/ntd_monthly_ridership/_config.yml

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title: NTD Monthly Ridership by RTPA
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author: Cal-ITP
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copyright: "2024"
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copyright: "2025"
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#logo: calitp_logo_MAIN.png
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# Force re-execution of notebooks on each build.

‎portfolio/ntd_monthly_ridership/rtpa_alpine-county-transportation-commission/00__monthly_ridership_report__rtpa_alpine-county-transportation-commission.ipynb

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‎portfolio/ntd_monthly_ridership/rtpa_butte-county-association-of-governments/00__monthly_ridership_report__rtpa_butte-county-association-of-governments.ipynb

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‎portfolio/ntd_monthly_ridership/rtpa_el-dorado-county-transportation-commission/00__monthly_ridership_report__rtpa_el-dorado-county-transportation-commission.ipynb

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‎portfolio/ntd_monthly_ridership/rtpa_fresno-county-council-of-governments/00__monthly_ridership_report__rtpa_fresno-county-council-of-governments.ipynb

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‎portfolio/ntd_monthly_ridership/rtpa_imperial-county-transportation-commission/00__monthly_ridership_report__rtpa_imperial-county-transportation-commission.ipynb

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‎portfolio/ntd_monthly_ridership/rtpa_kern-council-of-governments/00__monthly_ridership_report__rtpa_kern-council-of-governments.ipynb

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