From 53514f4d77b1c3e319aa5bce953615a54b22fc11 Mon Sep 17 00:00:00 2001 From: amandaha8 Date: Wed, 6 Nov 2024 19:12:51 +0000 Subject: [PATCH 1/2] copyediting starter kit --- gtfs_digest/_operators_prep_og.py | 90 +++++++++++++++++++++++++++++++ starter_kit/2024_basics_01.ipynb | 2 +- 2 files changed, 91 insertions(+), 1 deletion(-) create mode 100644 gtfs_digest/_operators_prep_og.py diff --git a/gtfs_digest/_operators_prep_og.py b/gtfs_digest/_operators_prep_og.py new file mode 100644 index 000000000..289adc996 --- /dev/null +++ b/gtfs_digest/_operators_prep_og.py @@ -0,0 +1,90 @@ +from shared_utils import catalog_utils +import pandas as pd +import yaml + +GTFS_DATA_DICT = catalog_utils.get_catalog("gtfs_analytics_data") +# Readable Dictionary +with open("readable.yml") as f: + readable_dict = yaml.safe_load(f) + +def operator_profiles()->pd.DataFrame: + # Load operator profiles + op_profiles_url = f"{GTFS_DATA_DICT.digest_tables.dir}{GTFS_DATA_DICT.digest_tables.operator_profiles}.parquet" + op_cols = ["organization_name", "name", "service_date", "schedule_gtfs_dataset_key"] + op_profiles_df = pd.read_parquet(op_profiles_url)[op_cols] + + # Keep the name with the most recent service date + op_profiles2 = (op_profiles_df.sort_values( + by=["name", "service_date"], + ascending=[True, False]) + ) + # Drop duplicated names + op_profiles3 = op_profiles2.drop_duplicates(subset=["name"]) + + # Drop duplicated organization names + op_profiles4 = (op_profiles3 + .drop_duplicates(subset = ['organization_name']) + .reset_index(drop = True)) + return op_profiles4 + + +def operators_schd_vp_rt()->pd.DataFrame: + """ + Operators who have schedule only OR have + both schedule and realtime data. + """ + schd_vp_url = f"{GTFS_DATA_DICT.digest_tables.dir}{GTFS_DATA_DICT.digest_tables.route_schedule_vp}.parquet" + + schd_vp_df = (pd.read_parquet(schd_vp_url, + filters=[[("sched_rt_category", "in", ["schedule_and_vp", "schedule_only"])]], + columns = [ "schedule_gtfs_dataset_key", + "caltrans_district", + "organization_name", + "name", + "sched_rt_category", + "service_date",] + ) + ) + + + schd_vp_df2 = ( + schd_vp_df.dropna(subset="caltrans_district") + .sort_values( + by=[ + "caltrans_district", + "organization_name", + "service_date", + ], + ascending=[True, True, False], + ) + .drop_duplicates( + subset=[ + "organization_name", + "caltrans_district", + ] + ) + .reset_index(drop=True) + ) + + schd_vp_df3 = ( + schd_vp_df2.sort_values( + by=["caltrans_district", "name", "service_date"], ascending=[True, False, False] + ) + .drop_duplicates(subset=["caltrans_district", "name"]) + .reset_index(drop=True) + ) + + schd_vp_df3 = schd_vp_df3[["caltrans_district","organization_name"]] + + op_profile = operator_profiles() + + # Merge + final = pd.merge( + schd_vp_df3, op_profile, on=["organization_name"], + how="left") + + final = (final + .sort_values(by = ["caltrans_district","organization_name"]) + .reset_index(drop = True) + ) + return final diff --git a/starter_kit/2024_basics_01.ipynb b/starter_kit/2024_basics_01.ipynb index 3431b92ed..956547687 100644 --- a/starter_kit/2024_basics_01.ipynb +++ b/starter_kit/2024_basics_01.ipynb @@ -8,7 +8,7 @@ "# Exercise 1: Familiarize yourself with `pandas` and `python`\n", "If you are new to Python, there are many resources!\n", "* There are introductory Python courses available through [Caltrans's LinkedIn Learning Library](https://www.linkedin.com/learning/search?keywords=python&u=36029164).\n", - "* [Practical Python for Data Science](https://www.practicalpythonfordatascience.com/00_python_crash_course) is an incredibly helpful book and material from this resource are linked throughout.\n", + "* [Practical Python for Data Science](https://www.practicalpythonfordatascience.com/00_python_crash_course) is an incredibly helpful resource. Material from it is linked throughout.\n", "\n", "## Skills \n", "* `pandas` is one of the base Python packages for working with tabular data.\n", From bd6af9169e68c5572415d7bad15c6dc088e4773b Mon Sep 17 00:00:00 2001 From: amandaha8 Date: Wed, 6 Nov 2024 23:39:56 +0000 Subject: [PATCH 2/2] corrected typos and made instructions less longwinded --- starter_kit/2024_basics_01.ipynb | 715 ++--------- starter_kit/2024_basics_02.ipynb | 1595 ++----------------------- starter_kit/2024_basics_03.ipynb | 1482 ++--------------------- starter_kit/2024_basics_04.ipynb | 1906 ++---------------------------- starter_kit/2024_basics_05.ipynb | 33 +- 5 files changed, 454 insertions(+), 5277 deletions(-) diff --git a/starter_kit/2024_basics_01.ipynb b/starter_kit/2024_basics_01.ipynb index 956547687..9a838649e 100644 --- a/starter_kit/2024_basics_01.ipynb +++ b/starter_kit/2024_basics_01.ipynb @@ -5,27 +5,22 @@ "id": "247e773f-0e29-4ed6-ab4d-5856325611b4", "metadata": {}, "source": [ - "# Exercise 1: Familiarize yourself with `pandas` and `python`\n", - "If you are new to Python, there are many resources!\n", + "# Exercise 1: `pandas`,`python`, `f-strings`, Importing and Exporting data.\n", + "If you are new to Python, there are many resources to help you! Below is just a small sample of what is available.\n", "* There are introductory Python courses available through [Caltrans's LinkedIn Learning Library](https://www.linkedin.com/learning/search?keywords=python&u=36029164).\n", "* [Practical Python for Data Science](https://www.practicalpythonfordatascience.com/00_python_crash_course) is an incredibly helpful resource. Material from it is linked throughout.\n", "\n", - "## Skills \n", - "* `pandas` is one of the base Python packages for working with tabular data.\n", - "* F-strings\n", - "* Export to Google Cloud Storage\n", - "* Practice committing on GitHub\n", - "\n", "## How to use these tutorials\n", "* The tutorials are divided by skills/concepts we are going to learn.\n", "* There are hints and instructions on the top.\n", - "* There are links to references. **It is highly recommended to read through them and practice them in this notebook, in addition to these exercises.**\n", + "* There are links to references. \n", + "**It is highly recommended to read through them and practice them in this notebook.**\n", "\n", "## What are we working with today? \n", - "* Today we will be working on Caltrans System Investment Strategy (CSIS) today. Per this [description](https://dot.ca.gov/programs/transportation-planning/division-of-transportation-planning/corridor-and-system-planning/csis)\n", + "* Today we will be working on Caltrans System Investment Strategy (CSIS) data. Per this [description](https://dot.ca.gov/programs/transportation-planning/division-of-transportation-planning/corridor-and-system-planning/csis)\n", "> The California Department of Transportation (Caltrans) is committed to leading climate action and advancing social equity in the transportation sector set forth by the California State Transportation Agency (CalSTA) Climate Action Plan for Transportation Infrastructure (CAPTI, 2021)...Caltrans is in a significant leadership role to carry out meaningful measures that advance state’s goals and priorities through the development and implementation of the Caltrans System Investment Strategy (CSIS). The CSIS, which implements one of CAPTI’s key actions, is envisioned to be an investment framework through a data and performance-driven approach that guides transportation investments and decisions.\n", - "* DDS is working on CSIS is by automating the scoring of projects using Python. We score each project based on how well they do in various categories, aka metrics such as Zero Emmission Vehicles, Vehicle Miles Traveled, and more. \n", - "* While the values in we are working with today are all fake, the exercise is based on actual datasets and assignments. " + "* The Data Science Branch is working on CSIS is by automating the scoring of projects using Python. We score each project based on how well they do on various metrics such as Zero Emmission Vehicles, Vehicle Miles Traveled Reduction, and more. \n", + "* While the values in we are working with today are all fake, the exercise is based on the actual data and work we've done. " ] }, { @@ -34,20 +29,18 @@ "metadata": {}, "source": [ "## Import Packages\n", - "* Before doing some data cleaning and analyzing, we need to equip ourselves with the right tools to get started.\n", - "* Part of our \"toolbox\" are packages. \n", - "\n", + "* Before doing some data cleaning and analyzing, we need to equip ourselves with the right tools.\n", + "* Part of our \"toolbox\" are importing packages. \n", "* **Resource**: [Importing Dependencies via Practical Python for Data Science](https://www.practicalpythonfordatascience.com/05_data_exploration.html?highlight=dependencies#importing-our-dependencies)\n", "\n", "### `Pandas`\n", - "* You are importing the package `pandas` that is the backbone of the majority of our data analysis work. \n", - "* You can import countless packages. \n", - "* We commonly use `geopandas` for geospatial data work. We use `altair` for making charts." + "* Below, you are importing the package `pandas` that is the backbone of our data analysis work. \n", + "* Other packages DDS commonly uses are `geopandas` for geospatial data work and `altair` for making charts." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "50199af7-04a8-43c5-ba1b-4127940749bd", "metadata": {}, "outputs": [], @@ -60,19 +53,18 @@ "id": "19b42c5d-4f2b-4d66-a7a7-98ab74a6591e", "metadata": {}, "source": [ - "* This block of code below adjusts the notebook.\n", - "* I am setting the maximum number of columns to be displayed to be 100.\n", + "* This block of code below adjusts the notebook's settings.\n", + "* I am setting the maximum number of columns to be displayed to be 100 because the default number of columns shown is much smaller.\n", "* I want any `float` columns to be rounded to 2 decimal points.\n", "* I want all of the rows in the dataframe to display. \n", - "* I don't want my columns to be truncated.\n", - " * If you have a column with `strings` that is very long, it will automatically cut off.\n", - " * Example: The California Department of Transportation (Caltrans) is committed to leading climate action and advancing social equity... would be displayed something like this The California Department of Transportation (Caltrans) is... without this line of code.\n", - "* Adjust some of these settings if you wish " + "* I don't want my string columns to be truncated.\n", + " * A long string value will display like this The California Department of Transportation (Caltrans) is committed to leading climate action and advancing social equity... would be displayed something like this The California Department of Transportation (Caltrans) is... without this line of code.\n", + "* Adjust some of these settings if you wish to make this notebook the proper environment for you." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "8e18d8d7-2cce-4854-b6c4-56a7e7bdf636", "metadata": {}, "outputs": [], @@ -90,14 +82,15 @@ "source": [ "### `calitp_data_analysis`\n", "* DDS also has our own [internal library of functions](https://docs.calitp.org/data-infra/analytics_tools/python_libraries.html#calitp-data-analysis).\n", - "* You can check out all the functions [here](https://github.com/cal-itp/data-infra/tree/main/packages/calitp-data-analysis/calitp_data_analysis).\n", - "* Below, we are importing only one function called `to_snakecase` from the python submodule `sql` in our package `calitp_data_analysis`. `to_snakecase` allows us to change the column names of our dataset from something like `Project Description` to `project_description`. \n", - "* By turning the column names to lower case and replacing the spaces with underscores, this makes referencing specific columns much easier." + " * You can check out all the functions [here](https://github.com/cal-itp/data-infra/tree/main/packages/calitp-data-analysis/calitp_data_analysis).\n", + "* Below, we are importing only one function called `to_snakecase` from the python submodule `sql` in our package `calitp_data_analysis`. \n", + "* `to_snakecase` allows us to change the column names of our dataset from something like `Project Description` to `project_description`. \n", + " * Turning the column names to lower case and replacing the spaces with underscores, this makes referencing specific columns much easier." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "bd388d88-d2d6-4dd6-9870-22c14db7a44a", "metadata": {}, "outputs": [], @@ -112,8 +105,13 @@ "source": [ "## Jupyter Notebook\n", "* You're using a Jupyter Notebook right now.\n", - "* There are many benefits listed here in our [DDS Docs](https://docs.calitp.org/data-infra/analytics_new_analysts/04-notebooks.html).\n", - "* Take some time to get used to this interface. There are many tutorials available on Youtube that shows tips and tricks, just skip the installation portion. \n", + "* There are many benefits of using a notebook for our analysis, which you can read about here in our [DDS Docs](https://docs.calitp.org/data-infra/analytics_new_analysts/04-notebooks.html).\n", + "* Take some time to get used to this interface. \n", + " * Press ctrl+enter to run a cell\n", + " * Go up to the Kernel and rerun all the cells.\n", + " * Use the scissors at the top to cut out the cell.\n", + " * Adjust your settings to be dark instead of light.\n", + "* There are many tutorials available on Youtube, just skip the installation portion. \n", " * [This one looks promising](https://youtu.be/LW2Rye_l8L0?si=B8kojobCe3OIF3xg)." ] }, @@ -124,16 +122,17 @@ "source": [ "## Check out the data \n", "* Download the Excel workbook containing all the CSIS data from Google Cloud Storage [here](https://console.cloud.google.com/storage/browser/_details/calitp-analytics-data/data-analyses/starter_kit/starter_kit_csis_scoring_workbook.xlsx;tab=live_object?project=cal-itp-data-infra). \n", - " * Open it up in Excel and take a look at how many sheets and the data structure.\n", + "* Open the workbook up in Excel and take a look at how many sheetsit contains.\n", + "\n", "### Read in the data\n", "* We are reading our Excel Workbook into a Pandas dataframe.\n", "* While there is a very [technical definition](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) of what a dataframe is, you can think of it as an Excel sheet that holds your data. \n", - "* Resource: [This page of the Practical Python for Data Science](https://www.practicalpythonfordatascience.com/02_loading_data)" + "* Resource: [Practical Python for Data Science](https://www.practicalpythonfordatascience.com/02_loading_data)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "5950cb87-75ab-4871-ab4b-a8f1c41f0a4a", "metadata": {}, "outputs": [], @@ -146,12 +145,12 @@ "id": "88d79cea-c017-454e-a2aa-85c0bf511d85", "metadata": {}, "source": [ - "* Read in the dataframe without `to_snakecase()` first to see what happens." + "* Read in the dataframe without the function `to_snakecase()` first to see what happens." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "67ba9264-65d9-453b-a800-a91bd365e43e", "metadata": {}, "outputs": [], @@ -161,78 +160,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "e2d886b4-c207-41e5-8325-7275619b60e6", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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14Bunny Hop Bike BoulevardA Class II bike lane with charming streetlights, benches, and bike racks designed to resemble carrot sticks, connecting residential neighborhoods to local schools and parks.6929368Unicorn Fairy Express Bus (UFX)
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01Meadow Magic Multi-Use PathA 2-mile Class I bike lane and multi-use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife.5245734Meadow Bunny Public Transportation (MBPT)
14Bunny Hop Bike BoulevardA Class II bike lane with charming streetlights, benches, and bike racks designed to resemble carrot sticks, connecting residential neighborhoods to local schools and parks.6929368Unicorn Fairy Express Bus (UFX)
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" - ], - "text/plain": [ - " ct_district project_name \\\n", - "0 1 Meadow Magic Multi-Use Path \n", - "1 4 Bunny Hop Bike Boulevard \n", - "\n", - " scope_of_work \\\n", - "0 A 2-mile Class I bike lane and multi-use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife. \n", - "1 A Class II bike lane with charming streetlights, benches, and bike racks designed to resemble carrot sticks, connecting residential neighborhoods to local schools and parks. \n", - "\n", - " project_cost lead_agency \n", - "0 5245734 Meadow Bunny Public Transportation (MBPT) \n", - "1 6929368 Unicorn Fairy Express Bus (UFX) " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head(2)" ] @@ -436,40 +229,11 @@ "* `df.shape` gives you the number of rows and columns in your dataset.\n", "* `df.columns` returns all of the column names.\n", "* `df.info()` per the [pandas docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html#pandas.DataFrame.info) prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.\n", - "* Experiment below. \n", + "* **Experiment below.** \n", "* More food for thought:\n", " * `Dtype` is critical. There are integers, objects, booleans, floats...\n", " * Does the `dtype` of each column below make sense to you? \n", - " * The `dtype` of `object` is a catchall term." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "7f55b33e-d402-473b-815a-92ad935d35d7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 44 entries, 0 to 43\n", - "Data columns (total 5 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ct_district 44 non-null int64 \n", - " 1 project_name 44 non-null object\n", - " 2 scope_of_work 44 non-null object\n", - " 3 project_cost 44 non-null int64 \n", - " 4 lead_agency 44 non-null object\n", - "dtypes: int64(2), object(3)\n", - "memory usage: 1.8+ KB\n" - ] - } - ], - "source": [ - "df.info()" + " * The `dtype` of `object` is a catchall term. It can either contain all string values like \"muffins\" and \"apples\" or a mix of string and other data types like \"6 muffins\" and \"3 apples.\"" ] }, { @@ -478,46 +242,16 @@ "metadata": {}, "source": [ "### Deeper Dive\n", - "* We now know a good amount about our dataset, but the # of rows and columns are not always so thrilling. \n", "* Let's take a closer look at some columns.\n", "* `.value_counts()` helps you see how many times the same value appears. " ] }, - { - "cell_type": "markdown", - "id": "55cece73-c3d5-4cd7-8896-f97d43fc1114", - "metadata": {}, - "source": [] - }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "63f21ab5-0920-4310-afce-2ea657556912", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4 6\n", - "3 6\n", - "8 5\n", - "11 5\n", - "12 4\n", - "5 4\n", - "9 3\n", - "6 3\n", - "7 3\n", - "2 2\n", - "10 2\n", - "1 1\n", - "Name: ct_district, dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.ct_district.value_counts()" ] @@ -534,42 +268,20 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "1d832308-a425-404d-83a0-53ce8bfae279", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.project_name.nunique()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "55d2140f-feab-496b-b9b1-90bbe5701a9a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(44, 5)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.shape" ] @@ -579,30 +291,48 @@ "id": "7c0c499e-fa7b-4f01-a357-db7b0ec41416", "metadata": {}, "source": [ - "* You can preview a column with brackets [] as well with the column name encased in quotation marks." + "* You can preview a column with brackets [] as well with the column name encased in quotation marks.\n", + "* However, simply using a period . is much easier." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "4e232324-f75f-46a0-962d-76ed9273dac7", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[\"scope_of_work\"].nunique()" ] }, + { + "cell_type": "markdown", + "id": "cfbb9b16-b7cd-44d5-af92-6c6351a35022", + "metadata": {}, + "source": [ + "* Describe() gives you some descriptive statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "83536bb4-1939-4ceb-8d62-7aeab1473993", + "metadata": {}, + "outputs": [], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "497ae835-e7eb-45b8-b593-eaf44cb5858e", + "metadata": {}, + "outputs": [], + "source": [ + "df.project_cost.describe()" + ] + }, { "cell_type": "markdown", "id": "06ee15f6-ee2e-4e3e-91b2-115875292042", @@ -639,6 +369,7 @@ "id": "21a32ab4-bfb2-4e7a-b90a-6fa05b7ceb89", "metadata": {}, "source": [ + "### Application of Lists\n", "* I am placing all of the sheets in our Excel Workbook in a list.\n", "* Notice that the items in this list are strings. \n", " * Read about strings [here](https://www.practicalpythonfordatascience.com/00_python_crash_course_datatypes.html?highlight=dictionary#string).\n", @@ -649,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "02380fb6-c55b-477f-acfb-8b483e83beac", "metadata": {}, "outputs": [], @@ -660,68 +391,25 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "8a9a1a3e-e10d-4447-96dd-92ecb2fe6357", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(my_sheets)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "a3be037d-b21b-4192-9099-25bfcb660f01", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'projects_auto'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Index\n", + "# Index 0 is projects_auto\n", "my_sheets[0]" ] }, - { - "cell_type": "code", - "execution_count": 30, - "id": "ebf91535-a466-446a-9f7a-606503d78b6a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'overall_score'" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "my_sheets[1]" - ] - }, { "cell_type": "markdown", "id": "75df89d0-92fb-4e4e-aaa3-54f4944c55c3", @@ -733,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "2e2578bc-db1f-41f5-bc07-3cb82998420e", "metadata": {}, "outputs": [], @@ -752,27 +440,8 @@ "### Specificity is beautiful.\n", "* Grab out each individual sheet into its own dataframe using `df2.get(my_sheets[enter in the index number])`. \n", "* Make sure your `dataframe` is titled descriptively.\n", - "* `df` is not exactly very telling. " - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "4c6f8fdb-33d3-4c44-bb00-6d1447d49feb", - "metadata": {}, - "outputs": [], - "source": [ - "projects_df = to_snakecase(df2.get(my_sheets[0]))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "167af2f1-b09d-476d-87b4-b9374ad445c2", - "metadata": {}, - "outputs": [], - "source": [ - "scores_df = to_snakecase(df2.get(my_sheets[1]))" + "* `df` is not exactly very telling. \n", + "* Use the function `to_snakecase` to clean up your column names" ] }, { @@ -782,28 +451,27 @@ "source": [ "## Add a new column\n", "* Oops! Us analysts were so wrapped up in scoring, we forgot to to total up all the metrics to find the overall_score for the project. \n", - "* Sum up all the metric columns into a column called `overall_score`\n", + "* Using the dataframe you read in from the Excel sheet \"Overall Score\", sum up all the metric columns into a column called `overall_score`\n", "* There are a couple of ways to do this: experiment! \n", "* Here are some resources:\n", " * [Stackoverflow](https://stackoverflow.com/questions/22342285/summing-two-columns-in-a-pandas-dataframe)\n", " * [Statology](https://www.statology.org/pandas-sum-specific-columns/)\n", "* Food for thought:\n", + " * What happens when you create a new column with `scores_df.overall_score` instead of `scores_df[\"overall_score\"]`? \n", " * What does `axis = 1` mean?\n", " * What happens if you do `.sum(axis=0)`?\n", " * You don't always have to save everything into a dataframe. You can do something like `df.sum(axis=0)` just to see what happens. \n", " * Just make sure your dataframe isn't too large or else you will run out of memory!\n", - " * What happens when you create a new column with `scores_df.overall_score` instead of `scores_df[\"overall_score\"]`? " + " " ] }, { "cell_type": "code", - "execution_count": 34, - "id": "e9321f90-8c99-46fb-9d50-8571f3d94fc8", + "execution_count": null, + "id": "93085a1c-d479-424d-b2a1-d8e6cba150ab", "metadata": {}, "outputs": [], - "source": [ - "scores_df[\"overall_score\"] = scores_df.select_dtypes(include=['int64', 'float64']).sum(axis=1)" - ] + "source": [] }, { "cell_type": "markdown", @@ -813,27 +481,26 @@ }, "source": [ "## Subsetting\n", - "* Your manager asks for the `overall_score` for each project. \n", + "* Your manager asks for the `overall_score` for each project in Excel format. \n", "* They do not want to see the other metrics, only the project's name and its `overall_score`\n", - "* Subset the dataframe and save it into a new dataframe.\n", - "* Again, there are many ways to do the same thing in Python. \n", + "* Subset the dataframe and saveit into a new dataframe.\n", "* Method 1: Enter in all the columns you want to keep in a list and place the list in another set of brackets." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "4e6d8e70-ae57-46c5-a5aa-9972be77f415", "metadata": {}, "outputs": [], "source": [ "# Enter in the columns you want to keep\n", - "columns_to_keep = [\"project_name\",\"overall_score\"]" + "columns_to_keep = []" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "48ee899b-3db9-464f-802f-d431189176b7", "metadata": { "scrolled": true, @@ -854,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "2c64cdcf-9598-4f4a-b077-5caec0cfe264", "metadata": {}, "outputs": [], @@ -865,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "47a96b86-e5d1-4fcd-ba73-7db5badae28b", "metadata": { "scrolled": true, @@ -874,7 +541,7 @@ "outputs": [], "source": [ "\n", - "# subsetted_df2 = scores_df.drop(columns = columns_to_drop)" + "subsetted_df2 = scores_df.drop(columns = columns_to_drop)" ] }, { @@ -884,18 +551,19 @@ "source": [ "## F-Strings\n", "* Save your subsetted dataframe from above back into the `starter_kit` folder. \n", - " * The file path should be something like this `\"gs://calitp-analytics-data/data-analyses/starter_kit/aggregated_csis.xlsx\"`.\n", + "* The file path should be something like this `\"gs://calitp-analytics-data/data-analyses/starter_kit/your_file_name_here.xlsx\"`.\n", "* However, remember our original Excel workbook's file path? It was`\"gs://calitp-analytics-data/data-analyses/starter_kit/starter_kit_csis_scoring_workbook.xlsx\"`\n", - "* Essentially, the **only** difference between these two file paths are `aggregated_csis.xlsx` and `starter_kit_csis_scoring_workbook.xlsx` because the folder path `gs://calitp-analytics-data/data-analyses/starter_kit/` remains the same. \n", - "* This is where f-strings come in. Read more about them [here](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python).\n", + "* The **only** difference between these two file paths are `your_file_name_here.xlsx` and `starter_kit_csis_scoring_workbook.xlsx` because the folder path `gs://calitp-analytics-data/data-analyses/starter_kit/` remains the same. \n", + "* This is where f-strings come in. \n", "> Python f-strings provide a quick way to interpolate and format strings. They’re readable, concise, and less prone to error than traditional string interpolation and formatting tools...\n", - "* Let's practice !\n", - " * My file_path is always going to be `gs://calitp-analytics-data/data-analyses/starter_kit/`.\n" + " * Excerpt from [here](https://realpython.com/python-f-strings/#f-strings-a-new-and-improved-way-to-format-strings-in-python).\n", + "#### Application of F-Strings\n", + "* My file_path is always going to be `gs://calitp-analytics-data/data-analyses/starter_kit/` so I'll set that in its own variable.\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "4c9c53a5-dbf3-4dc0-aea0-832f3a91414d", "metadata": {}, "outputs": [], @@ -909,18 +577,18 @@ "metadata": {}, "source": [ "* However the file is going to change.\n", - "* Save the file name in a variable called `FILE`." + "* Save the file name in a new variable called `FILE`." ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "db111f34-08b8-42f9-96fe-6852c4af50ad", "metadata": {}, "outputs": [], "source": [ "\n", - "FILE = \"starter_kit_example_final_scores.xlsx\"" + "FILE = " ] }, { @@ -933,21 +601,10 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "edff403c-ef37-48d8-8c7a-60b388752a51", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'gs://calitp-analytics-data/data-analyses/starter_kit/starter_kit_example_final_scores.xlsx'" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Put them together using a f-string\n", "f\"{GCS_FILE_PATH}{FILE}\"" @@ -965,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "bf37fc2d-ac6c-4134-94de-79a9a4141ffc", "metadata": {}, "outputs": [], @@ -979,137 +636,13 @@ "id": "17c17adb-404e-4e54-bdb4-c3295e0e2be2", "metadata": {}, "source": [ + "### Parquets\n", "* Export the entire (not subsetted) dataframe with the new `overall_score` column using `df.to_parquet()`. \n", " * We typically prefer saving to `parquets`. Why? Read below. Text taken from [here](https://docs.calitp.org/data-infra/analytics_new_analysts/03-data-management.html#parquet).\n", " * Parquet is an “open source columnar storage format for use in data analysis systems.” Columnar storage is more efficient as it is easily compressed and the data is more homogenous. CSV files utilize a row-based storage format which is harder to compress, a reason why Parquets files are preferable for larger datasets. Parquet files are faster to read than CSVs, as they have a higher querying speed and preserve datatypes (i.e. Number, Timestamps, Points). They are best for intermediate data storage and large datasets (1GB+) on most any on-disk storage. This file format is also good for passing dataframes between Python and R. A similar option is feather.\n", "* Reference\n", - " * [DDS Docs: Saving Code](https://docs.calitp.org/data-infra/analytics_tools/saving_code.html)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "22562f2f-8359-4e44-951c-25e5ac033282", - "metadata": {}, - "outputs": [], - "source": [ - "scores_df.to_parquet(f\"{GCS_FILE_PATH}starter_kit_example_final_scores.parquet\")" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "9bc1a3cb-85e2-4203-bdd4-e45bb6c20ba4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " project_name accessibility_score dac_accessibility_score \\\n", - "0 Meadow Magic Multi-Use Path 2 8 \n", - "1 Bunny Hop Bike Boulevard 3 9 \n", - "\n", - " dac_traffic_impacts_score freight_efficiency_score \\\n", - "0 8 10 \n", - "1 7 6 \n", - "\n", - " freight_sustainability_score mode_shift_score lu_natural_resources_score \\\n", - "0 2 3 5 \n", - "1 7 6 3 \n", - "\n", - " safety_score vmt_score zev_score public_engagement_score \\\n", - "0 3 2 7 6 \n", - "1 2 2 10 2 \n", - "\n", - " climate_resilience_score program_fit_score overall_score \n", - "0 6 10 72 \n", - "1 6 5 68 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "overall_scores_df.head(2)" - ] - }, { "cell_type": "markdown", "id": "4c2dd160-ec10-41ce-b5c0-a9be5934d6ee", @@ -299,11 +65,11 @@ " * Min overall score\n", " * Number of unique projects\n", "* Annoyingly enough, the `overall_score` column and the `ct_district` are in two different dataframes. \n", - "* You'll have to merge it on the common column(s) the two dataframes share.\n", + "* You'll have to merge the dataframes on the common column(s) the two dataframes share.\n", "* Welcome to DDS! This will happen to you all the time starting now. \n", "\n", "### Relevant Resources\n", - "* Read about and practice merges before diving in. \n", + "* Read about and practice merges before continuing on the exercise. \n", " * [Resource #1 is a great tutorial for beginners](https://www.practicalpythonfordatascience.com/03_cleaning_data.html?highlight=merge#merging-dataframes-together).\n", " * [Resource #2 is written by our own Tiffany Ku, but it contains some geospatial references so it's a bit more to digest](https://docs.calitp.org/data-infra/analytics_new_analysts/01-data-analysis-intro.html#merge-tabular-and-geospatial-data-for-data-analysis).\n", " " @@ -311,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2d356494-a12a-4f67-beb3-b6ba92c8135f", "metadata": {}, "outputs": [], @@ -328,7 +94,7 @@ "**Food for Thought**\n", "* Which columns do the two dataframes have in common?\n", "* What type of merge will achieve my goal?\n", - " * Inner, outer, left, or right\n", + " * Inner, outer, left, or right?\n", "* What do I expect out of the merge?\n", " * Do I expect all the values of the merge keys to be 1:1? Or m:1? \n", " * Do I expect a project to correspond with multiple districts? Maybe, projects can and do cross multiple boundaries.\n", @@ -345,80 +111,7 @@ "### Double Checking\n", "* How many rows do you expect?\n", "* How many unique projects are there? \n", - "* Hint: check your original dataframes as well" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ad4962ca-ed83-48a3-b1e6-79e5d5b1042b", - "metadata": {}, - "outputs": [], - "source": [ - "m1 = pd.merge(projects_df, overall_scores_df, on=[\"project_name\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e820d7af-17d4-4b2a-8007-5d958a3f7d9e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(41, 19)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m1.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3642de14-3bf4-47c0-bd80-3502819ea14d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "41" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m1.project_name.nunique()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "4b5be67a-f579-4f22-97cb-b6b31d7b8433", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "projects_df.project_name.nunique()" + "* Hint: check the lengths of your original dataframes as well" ] }, { @@ -430,44 +123,17 @@ "* As you have noticed, we are missing a couple of projects.\n", "* This is where `outer` joins are very useful.\n", "* Merge your dataframes again using an `outer` join and with `indicator = True` on.\n", - "* Using `.value_counts()` check out how many rows are found in both dataframes, the left only, and the right only" + " * `m2 = pd.merge(df1, df2, on=[column], indicator=True, how=\"outer\")`\n", + "* Using `.value_counts()` on the column named `_merge` created by `indicator=True` to check out how many rows are found in both dataframes, the left only, and the right only" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "98e92c3f-ccd4-45f8-b6a6-523ddcb4a7ac", + "execution_count": null, + "id": "dcfb00b5-a08a-49b2-8978-e42abf4538fe", "metadata": {}, "outputs": [], - "source": [ - "m2 = pd.merge(\n", - " projects_df, overall_scores_df, on=[\"project_name\"], indicator=True, how=\"outer\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f134cddf-5220-44f9-9e15-1c5171cbedfd", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "both 41\n", - "left_only 3\n", - "right_only 3\n", - "Name: _merge, dtype: int64" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2._merge.value_counts()" - ] + "source": [] }, { "cell_type": "markdown", @@ -482,181 +148,27 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "4dd07bab-4d1b-41a0-954e-4c2d59584e57", + "execution_count": null, + "id": "97144120-29b6-4948-aa68-736ddbeb8d38", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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project_name_merge
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" - ], - "text/plain": [ - " project_name _merge\n", - "10 Rainbow Rush hot Lanes left_only\n", - "12 Bunny Lane HOV+2 heaven left_only\n", - "26 main street muffin top left_only\n", - "44 Rainbow Rush HOT Lanes right_only\n", - "45 Bunny Lane HOV+2 Haven right_only\n", - "46 Main Street Muffin Top Revitalization right_only" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2.loc[m2._merge != \"both\"][[\"project_name\", \"_merge\"]]" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", "id": "044330d9-8562-4510-ae62-268f240ec3bc", "metadata": {}, "source": [ - "* You could also use `isin([list of elements you want to keep])`" + "* You could also use `isin([list of elements you want to keep])` to retain multiple elements you want." ] }, { "cell_type": "code", - "execution_count": 18, - "id": "c47ef38d-6db5-4bf1-bd87-62b7d84943b6", + "execution_count": null, + "id": "121e64fa-cf65-4a3b-9a53-43994223d150", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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project_name_merge
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12Bunny Lane HOV+2 heavenleft_only
26main street muffin topleft_only
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" - ], - "text/plain": [ - " project_name _merge\n", - "10 Rainbow Rush hot Lanes left_only\n", - "12 Bunny Lane HOV+2 heaven left_only\n", - "26 main street muffin top left_only\n", - "44 Rainbow Rush HOT Lanes right_only\n", - "45 Bunny Lane HOV+2 Haven right_only\n", - "46 Main Street Muffin Top Revitalization right_only" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2.loc[m2._merge.isin([\"left_only\",\"right_only\"])][[\"project_name\", \"_merge\"]]" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -668,24 +180,11 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "09ff3055-29ee-4ea1-a164-5d4796aa1807", + "execution_count": null, + "id": "a1ffc2fe-e611-4cd1-8491-957a8f50b523", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(41, 20)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2.loc[~m2._merge.isin([\"left_only\",\"right_only\"])].shape" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -695,21 +194,21 @@ "### Dictionaries\n", "* String data is often entered in many different ways. \n", " * BART can be entered in as bart, Bay Area Rapid Transit, BaRT, and more. \n", - "* Often, strings are the reason why your dataframe is not merging properly.\n", + "* Often, differing strings between two dataframes are the reason why your dataframe is not merging properly.\n", "* In Excel, it's easy to go in and manually tweak everything. However, that is not reproducible and time consuming. \n", "* Luckily with Python we can automate this. \n", - "* Since there are a couple of names to replace, we can do it using a dictionary.\n", + "* Since there are onlh a couple of names to replace, we can do it using a dictionary.\n", "\n", "#### What is a dictionary?\n", "* Per Practical Python for Data Science, a dictionary is Dictionaries are used to store data values in key:value pairs. Similar to the list, a dictionary is a collection of objects. It is also mutable, meaning that you can add, remove, change values inside of it...With the list, we access elements using the index. With the dictionary, we access elements using keys..\n", - "* Dictionaries are very important. \n", + "* Dictionaries are very important.\n", "* Read more [here](https://www.practicalpythonfordatascience.com/00_python_crash_course_datatypes.html?highlight=dictionary#dictionary) and **follow its example in the cells below.**\n", " " ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "df6fa95e-cc25-4142-8c2b-ee254863e609", "metadata": {}, "outputs": [], @@ -722,7 +221,7 @@ "id": "76e42f11-fdcb-48f3-8951-2f2cea0384c0", "metadata": {}, "source": [ - "#### Replacing Values\n", + "#### Application of Dictionaries: Replacing Values\n", "* [Resource](https://www.practicalpythonfordatascience.com/03_cleaning_data#recoding-column-values)\n", "* **Step 1**: Filter out for the rows that didn't merge. Find the unique values of the `project_name` column using `.unique()`\n", "* Take a look at elements using \n", @@ -734,27 +233,11 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "5601fd36-d221-41da-ab76-b88c616e5e62", + "execution_count": null, + "id": "5cdc18c4-2312-41ae-a027-8c0fe164018b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Rainbow Rush hot Lanes', 'Bunny Lane HOV+2 heaven',\n", - " 'main street muffin top ', 'Rainbow Rush HOT Lanes',\n", - " 'Bunny Lane HOV+2 Haven', 'Main Street Muffin Top Revitalization'],\n", - " dtype=object)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m2.loc[m2._merge.isin([\"left_only\",\"right_only\"])].project_name.unique()" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -762,22 +245,18 @@ "metadata": {}, "source": [ "* **Step 2:** Decide whether you want to rename the values in the left dataframe or the right one. \n", - "* **Step 3:** The keys, are the values you want to replace. The values, are what you want to replace these values with. " + "* **Step 3:** The keys, are the values you want to replace. The values, are what you want to replace these values with. \n", + " * Let's say my left value is \"AC Transit\" but I want it to be \"Alameda Contra Costa County Transit\", my dictionary would be \n", + " * `my_dict = {\"AC Transit\":\"Alameda Contra Costa County Transit\"`" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "9dad92fe-87a6-434d-a62f-d269f3ad1054", "metadata": {}, "outputs": [], - "source": [ - "new_names = {\n", - " \"main street muffin top \": \"Main Street Muffin Top Revitalization\",\n", - " \"Bunny Lane HOV+2 heaven\": \"Bunny Lane HOV+2 Haven\",\n", - " \"Rainbow Rush hot Lanes\": \"Rainbow Rush HOT Lanes\",\n", - "}" - ] + "source": [] }, { "cell_type": "markdown", @@ -789,12 +268,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "d8532992-771e-446a-b419-55ad757ff45f", "metadata": {}, "outputs": [], "source": [ - "projects_df.project_name = projects_df.project_name.replace(new_names)" + "df.project_name = df.project_name.replace(your_dictionary)" ] }, { @@ -802,157 +281,34 @@ "id": "68562b10-b9bd-4892-8780-a66cad1a06d4", "metadata": {}, "source": [ - "#### Merge your dataframes again. This time the number of unique project names should match the rows of the merged dataframe perfectly." + "#### Merge your dataframes again. \n", + "* This time the number of unique project names should match the rows of the merged dataframe perfectly.\n", + "* Make sure to double check that!" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "db09aa04-7a94-4b94-9ade-10b1a987e006", "metadata": {}, "outputs": [], - "source": [ - "final_m = pd.merge(projects_df, overall_scores_df, how=\"inner\", on=\"project_name\")" - ] - }, - { - "cell_type": "markdown", - "id": "144aa8a8-df59-418a-a0c7-4dbc3537c68f", - "metadata": {}, - "source": [ - "* You can check if two values are equal using `==`." - ] + "source": [] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "04f4f6d8-55b6-460c-8a52-8626dcfd1cb9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(final_m) == final_m.project_name.nunique()" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "39d74f54-a72b-4acc-91b0-b3dcb4539a92", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ct_districtproject_namescope_of_workproject_costlead_agencyaccessibility_scoredac_accessibility_scoredac_traffic_impacts_scorefreight_efficiency_scorefreight_sustainability_scoremode_shift_scorelu_natural_resources_scoresafety_scorevmt_scorezev_scorepublic_engagement_scoreclimate_resilience_scoreprogram_fit_scoreoverall_score
01Meadow Magic Multi-Use PathA 2-mile Class I bike lane and multi-use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife.5245734Meadow Bunny Public Transportation (MBPT)28810235327661072
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" - ], - "text/plain": [ - " ct_district median_score min_score max_score n_projects\n", - "0 1 72.00 72 72 1\n", - "1 2 61.50 60 63 2\n", - "2 3 80.50 54 97 6\n", - "3 4 70.50 60 97 6\n", - "4 5 77.00 58 98 4\n", - "5 6 72.00 63 77 3\n", - "6 7 82.00 79 94 3\n", - "7 8 73.00 66 85 5\n", - "8 9 75.00 67 87 3\n", - "9 10 72.50 59 86 2\n", - "10 11 75.00 55 89 5\n", - "11 12 72.50 60 97 4" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg1" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1246,7 +396,7 @@ "* Some ideas:\n", " * Change the font\n", " * Turn off the index\n", - " * Use colors to indicate low-high values\n", + " * Use colors to code low-high values\n", " * Change the alignment of the values" ] }, @@ -1256,7 +406,9 @@ "id": "a0d8b97b-0f34-495a-8dfb-61642c44879a", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Practice here " + ] }, { "cell_type": "markdown", @@ -1266,7 +418,7 @@ "### Altair\n", "* While a table is great, sometimes a chart is a better way to display an insight.\n", "* Our preferred visualization library is `Altair`.\n", - " * Their docs page is [here](https://altair-viz.github.io/).\n", + " * Docs page is [here](https://altair-viz.github.io/).\n", "* The code to create a simple bar chart goes something like this. \n", " * `alt.Chart(source).mark_bar().encode(x='a',y='b')`\n", " * `source` is the dataframe you want to use for your chart.\n", @@ -1277,91 +429,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "fdcece32-d053-4b32-9e76-0f5ffed9ff52", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "alt.Chart(agg1).mark_bar().encode(x=\"ct_district\", y=\"n_projects\")" + "alt.Chart(agg1).mark_bar().encode(\n", + " x=\"ct_district\", y=\"n_projects\"\n", + ")" ] }, { @@ -1373,95 +448,17 @@ "* `altair` offers an endless ways to amp up the personality of your chart.\n", "* Additionally, the chart above without a title and legend is a data visualization \"taboo\" and the dull blue is uninspiring. \n", "\n", - "##### Add a title\n", - "* You can do so within `.Chart()`" + "#### Add a title\n", + "* You can do so within `.Chart()`\n", + "`alt.Chart(source, title=\"your_title_here\").mark_bar().encode(x='a',y='b')`" ] }, { "cell_type": "code", - "execution_count": 35, - "id": "88e1dff9-0188-49c9-b6cc-599610aca9a7", + "execution_count": null, + "id": "fc691315-e545-4120-971e-7a8f295a7c19", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alt.Chart(agg1, title=\"your_title_here\").mark_bar().encode(\n", " x=\"ct_district\", y=\"n_projects\"\n", @@ -1479,180 +476,10 @@ }, { "cell_type": "code", - "execution_count": 36, - "id": "d43cae4f-1faf-48fb-8c21-559feb5243b1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "alt.Chart(agg1, title=\"your_title_here\").mark_circle().encode(\n", - " x=\"ct_district\", y=\"n_projects\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "9b94c3f2-af01-43d4-9f17-98cd863511a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alt.Chart(agg1, title=\"your_title_here\").mark_line().encode(\n", " x=\"ct_district\", y=\"n_projects\"\n", @@ -1670,7 +497,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "cb8df2a3-bf37-4fe4-833e-1259a6ad7f15", "metadata": {}, "outputs": [], @@ -1690,66 +517,13 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "aa21d088-3360-4d3e-811c-8cc5bdb2d3a8", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mType:\u001b[0m module\n", - "\u001b[0;31mString form:\u001b[0m \n", - "\u001b[0;31mFile:\u001b[0m /opt/conda/lib/python3.9/site-packages/calitp_data_analysis/calitp_color_palette.py\n", - "\u001b[0;31mSource:\u001b[0m \n", - "\u001b[0;31m# --------------------------------------------------------------#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Cal-ITP style guide\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Google Drive > Cal-ITP Team > Project Resources >\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# Branded Resources and External Comms Guidelines > Branded Resources > Style Guide\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;31m# --------------------------------------------------------------#\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0mCALITP_CATEGORY_BRIGHT_COLORS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#2EA8CE\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# darker blue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#EB9F3C\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# orange\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#F4D837\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# yellow\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#51BF9D\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# green\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#8CBCCB\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# lighter blue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#9487C0\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# purple\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0mCALITP_CATEGORY_BOLD_COLORS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#136C97\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# darker blue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#E16B26\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# orange\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#F6BF16\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# yellow\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#00896B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# green\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#7790A3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# lighter blue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#5B559C\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# purple\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0mCALITP_DIVERGING_COLORS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#E16B26\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#EB9F3C\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# oranges\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#f6e7e1\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# linen\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#8CBCCB\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#2EA8CE\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#136C97\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# blues\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0mCALITP_SEQUENTIAL_COLORS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#B9D6DF\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# light blue (lightest)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#8CBCCB\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# lighter blue bright\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#2EA8CE\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# darker blue bright\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#136C97\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# darker blue bold\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"#0B405B\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# indigo dye (darkest)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "calitp_color_palette??" ] @@ -1759,95 +533,16 @@ "id": "f3971ba8-1c8f-4003-8e34-c3fd31f3f585", "metadata": {}, "source": [ - "* Place your color palette in the `scale` argument `scale=alt.Scale(range=your_color_palette)`.\n", - "* If I'm using a palette from `calitp_color_palette`, I would write `scale=alt.Scale(range=calitp_color_palette.CALITP_DIVERGING_COLORS)`." + "* Place the column you want the colors to be based on in `color=alt.Color(column)`\n", + "* Place your color palette in the `scale` argument `scale=alt.Scale(range=your_color_palette)`." ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "c629f242-9b1b-49d1-b4b0-1bb956782d69", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "alt.Chart(agg1, title=\"your_title_here\").mark_bar().encode(\n", " x=\"ct_district\",\n", @@ -1856,8 +551,8 @@ " \"n_projects\", # This is the column you want the color of your bar to be based on\n", " title=\"legend_title_here\", # This is the legend of your title\n", " scale=alt.Scale(\n", - " range=calitp_color_palette.CALITP_DIVERGING_COLORS # This is where you can customize the colors,\n", - " ), \n", + " range=calitp_color_palette.CALITP_DIVERGING_COLORS # This is where you can customize the colors,\n", + " ),\n", " ),\n", ")" ] @@ -1869,8 +564,7 @@ "source": [ "#### Adjusting the Axis\n", "* Sometimes, we want to adjust the axis to have a min and max value.\n", - "* You do so using the `scale=alt.Scale(domain=[min_value, max_value]))` argument behind the X and Y axis.\n", - "* `alt.X()` and `alt.Y` gives you many more customization options." + "* You do so using the `scale=alt.Scale(domain=[min_value, max_value]))` argument behind the X and Y axis." ] }, { @@ -1898,97 +592,18 @@ "source": [ "### Finishing Touches \n", "* `.properties(width=400, height=250)` adjusts the size of your chart. \n", - "* `tooltip=[columns you want]` gives you additional details on the columns you specify when you hover over each bar/circle/etc.\n", + "* `tooltip=[columns you want]` allows you to create a tooltip that pops up when you hover over each bar/circle/etc.\n", "* `.mark_bar(size=10)` adjusts the size of the bar/circle/etc." ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "8b85dd29-88cb-4b4b-b3b7-20ee1851335e", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "alt.Chart(agg1, title=\"your_title_here\").mark_bar(size = 10).encode(\n", + "alt.Chart(agg1, title=\"your_title_here\").mark_bar(size=10).encode(\n", " x=alt.X(\"ct_district\", scale=alt.Scale(domain=[1, 12])),\n", " y=alt.Y(\"n_projects\", scale=alt.Scale(domain=[0, 10])),\n", " color=alt.Color(\n", @@ -1996,14 +611,16 @@ " title=\"legend_title_here\",\n", " scale=alt.Scale(range=calitp_color_palette.CALITP_DIVERGING_COLORS),\n", " ),\n", - " tooltip=[\"ct_district\", \"n_projects\"]\n", + " tooltip=[\"ct_district\", \"n_projects\"],\n", ").properties(width=400, height=250)" ] }, { "cell_type": "markdown", "id": "281e37d9-8ece-471d-abc2-38e4ad9f9e83", - "metadata": {}, + "metadata": { + "tags": [] + }, "source": [ "### We have only visualized one column of data. \n", "* We have only visualized one column of data, but we have a couple of columns above. \n", @@ -2014,6 +631,14 @@ " * Altair's [gallery](https://altair-viz.github.io/gallery/index.html)\n", " * DDS's [portfolio](https://analysis.calitp.org/)\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffbfbafb-1055-448c-a889-18d1fe508cab", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/starter_kit/2024_basics_03.ipynb b/starter_kit/2024_basics_03.ipynb index 85796cd3d..58be931be 100644 --- a/starter_kit/2024_basics_03.ipynb +++ b/starter_kit/2024_basics_03.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ba8a0d90-9d57-4d01-9eb4-0b255970995e", "metadata": {}, "outputs": [], @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "ddcdbbc1-2e1b-4797-bd34-07d9a1999cb6", "metadata": {}, "outputs": [], @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "7c52b09e-90b5-4a5d-8fda-ca19cb8fe3cd", "metadata": {}, "outputs": [], @@ -54,133 +54,19 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "e0222b8c-0996-47bb-8639-fc703cfbd249", "metadata": {}, "outputs": [], - "source": [ - "FILE = \"starter_kit_example_merge.parquet\"" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "36bbc1d2-4285-4399-a0fd-1e02c5e5d5a1", "metadata": {}, "outputs": [], - "source": [ - "df = pd.read_parquet(f\"{GCS_FILE_PATH}{FILE}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c97f0ec6-bea0-401a-bb27-f37984a762eb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ct_districtproject_namescope_of_workproject_costlead_agencyaccessibility_scoredac_accessibility_scoredac_traffic_impacts_scorefreight_efficiency_scorefreight_sustainability_scoremode_shift_scorelu_natural_resources_scoresafety_scorevmt_scorezev_scorepublic_engagement_scoreclimate_resilience_scoreprogram_fit_scoreoverall_score
01Meadow Magic Multi-Use PathA 2-mile Class I bike lane and multi-use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife.5245734Meadow Bunny Public Transportation (MBPT)28810235327661072
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" - ], - "text/plain": [ - " ct_district project_name \\\n", - "0 1 Meadow Magic Multi-Use Path \n", - "\n", - " scope_of_work \\\n", - "0 A 2-mile Class I bike lane and multi-use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife. \n", - "\n", - " project_cost lead_agency \\\n", - "0 5245734 Meadow Bunny Public Transportation (MBPT) \n", - "\n", - " accessibility_score dac_accessibility_score dac_traffic_impacts_score \\\n", - "0 2 8 8 \n", - "\n", - " freight_efficiency_score freight_sustainability_score mode_shift_score \\\n", - "0 10 2 3 \n", - "\n", - " lu_natural_resources_score safety_score vmt_score zev_score \\\n", - "0 5 3 2 7 \n", - "\n", - " public_engagement_score climate_resilience_score program_fit_score \\\n", - "0 6 6 10 \n", - "\n", - " overall_score \n", - "0 72 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(1)" - ] + "source": [] }, { "cell_type": "markdown", @@ -188,8 +74,8 @@ "metadata": {}, "source": [ "## Categorizing\n", - "* There are 40+ projects. They all vary in themes, some are transit oriented while others are focused on Active Transportation (ATP).\n", - "* Categorizing data is an important part of data cleaning and analyzing so we can present the data on a more succinct, broader level. \n", + "* There are 40+ projects. They all vary in themes, some contain transit elements while others contain Active Transportation (ATP) components. Some contain both! \n", + "* Categorizing data is an important part of data cleaning and analyzing so we can present the data on a more succinct level. \n", "* Let's organize projects into three categories.\n", " * ATP\n", " * Transit\n", @@ -203,13 +89,13 @@ "source": [ "### Task 1: Strings\n", "* Below are some of the common keywords that fall into the categories detailed above. They are held in a `list`.\n", - "* Feel free to add other terms you think are relevant. \n", + "* Add other terms you think are relevant. \n", "* We are going to search the `Scope of Work` column for these keywords. " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "6a6b817f-15e2-4d1c-aeae-5d7e9661a6f0", "metadata": {}, "outputs": [], @@ -228,29 +114,20 @@ "* Remember in Exercise 2 some of the project names didn't merge between the two dataframes?\n", "* In the real world, you won't have the bandwidth and time to replace each individual string value with a dictionary.\n", "* An easy way to clean most of the values up is by lowercasing, stripping the white spaces, and replacing characters.\n", - "* In our goal of categorizing values, we can search through it easier when we clean up the string values." + "* We can search through a string column easier when we simplify up the values." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "ea4a4df7-61ec-430b-a827-302704857318", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/3600759827.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", - " df.scope_of_work.str.lower()\n" - ] - } - ], + "outputs": [], "source": [ "df.scope_of_work = (\n", - " df.scope_of_work.str.lower()\n", - " .str.strip()\n", - " .str.replace(\"-\", \" \")\n", + " df.scope_of_work.str.lower() # Lowers the strings\n", + " .str.strip() # Strips trailing white spaces\n", + " .str.replace(\"-\", \" \") # Replaces hyphens with a space\n", " .str.replace(\"+\", \" \")\n", " .str.replace(\"_\", \" \")\n", ")" @@ -272,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "be843d6a-b751-4e9f-8820-b521089914d3", "metadata": {}, "outputs": [], @@ -286,36 +163,14 @@ "metadata": {}, "source": [ "* Let's see how many transit projects are in this dataset.\n", - "* Let's read through the Scope of Work to make sure it's what we expect.\n", "* Tip\n", - " * The data we work with tends to be pretty wide. Scrolling horizontally gets tiresome.\n", - " * Placing all the columns you want to temporarily work within a `list` like `preview_subset` below is a good idea. " + " * The data we typically work with tends to be wide (read about wide vs. long data [here](https://www.statology.org/long-vs-wide-data/)). Scrolling horizontally gets tiresome.\n", + " * Placing all the columns you want to temporarily work within a `list` like `preview_subset` below is a good idea to temporarily narrow down your dataframe while working. " ] }, { "cell_type": "code", - "execution_count": 10, - "id": "0d9a6259-8748-41fe-a549-01bdf0e9c273", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(transit_only_projects)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "315228d8-a72e-4f18-a0e7-2a254c87cc23", "metadata": {}, "outputs": [], @@ -325,100 +180,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "6789307c-5808-4501-a1a6-5a14a12b0219", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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project_namescope_of_work
11Greenway Gables Managed Lanesmanaged lanes prioritizing carpools, clean vehicles, and public transit, featuring real time traffic updates and incentives for sustainable transportation choices.
16Sparkle City Smart Streets Initiativean intelligent transportation system integrating traffic management, real time transit information, and smart parking solutions to enhance mobility and reduce congestion.
19Rolling Renaissance Rabbit Expressnew, eco friendly rolling stock for public transit, incorporating advanced propulsion systems, comfortable seating, and onboard amenities.
20Transit Treasure Transit Oasistransit supportive features, including shelters, wi fi, and real time information displays, prioritizing passenger convenience and accessibility.
25Trail of Treats and Transit Huba multi use path connecting to public transit, featuring public art installations, wayfinding signage, and amenities like bike storage and repair stations.
27Park and Ride Petal Paradisean attractive park and ride facility with amenities like ev charging, wi fi, and convenient access to nearby transit options.
43Brookside Bus Blossom Laneprioritize public transportation and enhance air quality by dedicating lanes to buses and hovs on brookside boulevard, integrating smart traffic signals and real time transit information inspired by the ancient elves.
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" - ], - "text/plain": [ - " project_name \\\n", - "11 Greenway Gables Managed Lanes \n", - "16 Sparkle City Smart Streets Initiative \n", - "19 Rolling Renaissance Rabbit Express \n", - "20 Transit Treasure Transit Oasis \n", - "25 Trail of Treats and Transit Hub \n", - "27 Park and Ride Petal Paradise \n", - "43 Brookside Bus Blossom Lane \n", - "\n", - " scope_of_work \n", - "11 managed lanes prioritizing carpools, clean vehicles, and public transit, featuring real time traffic updates and incentives for sustainable transportation choices. \n", - "16 an intelligent transportation system integrating traffic management, real time transit information, and smart parking solutions to enhance mobility and reduce congestion. \n", - "19 new, eco friendly rolling stock for public transit, incorporating advanced propulsion systems, comfortable seating, and onboard amenities. \n", - "20 transit supportive features, including shelters, wi fi, and real time information displays, prioritizing passenger convenience and accessibility. \n", - "25 a multi use path connecting to public transit, featuring public art installations, wayfinding signage, and amenities like bike storage and repair stations. \n", - "27 an attractive park and ride facility with amenities like ev charging, wi fi, and convenient access to nearby transit options. \n", - "43 prioritize public transportation and enhance air quality by dedicating lanes to buses and hovs on brookside boulevard, integrating smart traffic signals and real time transit information inspired by the ancient elves. " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "transit_only_projects[preview_subset]" ] @@ -430,16 +195,16 @@ "source": [ "#### Step 2: Filtering\n", "* We've found all the projects that says \"transit\" somewhere in its description. \n", - "* Now there are just many more elements to go. We forgot about bikes, bus, rail, so on and so forth.\n", + "* Now there are just many more transit related elements to go. We forgot about bikes, bus, rail, so on and so forth.\n", "* The method above leaves us with multiple dataframes. We actually just want our one original dataframe tagged with categories. \n", "* A faster way: join all the keywords you want into one large string.\n", " * | designates \"or\".\n", - " * You can read `transit_keywords` as \"I want projects that contain the word transit or passenger rai or bus or ferry\"" + " * You can read `transit_keywords` as \"I want projects that contain the word transit or passenger rail or bus or ferry\"" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c2575f75-44ac-46ba-a334-fdf984546cd3", "metadata": {}, "outputs": [], @@ -449,21 +214,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "f6a2a521-c0ae-4c2d-830d-4020a13855f2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'(transit|passenger rail|bus|ferry)'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print it out\n", "transit_keywords" @@ -479,122 +233,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "e5e23b6f-98b8-4219-bc52-d847ea39d121", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/1070197006.py:1: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " df.loc[df.scope_of_work.str.contains(transit_keywords)][preview_subset]\n" - ] - }, - { - "data": { - "text/html": [ - "
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project_namescope_of_work
11Greenway Gables Managed Lanesmanaged lanes prioritizing carpools, clean vehicles, and public transit, featuring real time traffic updates and incentives for sustainable transportation choices.
16Sparkle City Smart Streets Initiativean intelligent transportation system integrating traffic management, real time transit information, and smart parking solutions to enhance mobility and reduce congestion.
18Coastal Commuter Carousela 30 mile passenger rail line connecting coastal towns, featuring modern train sets, enhanced station amenities, and scenic viewing cars.
19Rolling Renaissance Rabbit Expressnew, eco friendly rolling stock for public transit, incorporating advanced propulsion systems, comfortable seating, and onboard amenities.
20Transit Treasure Transit Oasistransit supportive features, including shelters, wi fi, and real time information displays, prioritizing passenger convenience and accessibility.
21Berry Best Bus Rapid Transitdedicated bus lanes with comfortable stops, featuring off board fare payment, priority traffic signals, and enhanced passenger amenities.
25Trail of Treats and Transit Huba multi use path connecting to public transit, featuring public art installations, wayfinding signage, and amenities like bike storage and repair stations.
27Park and Ride Petal Paradisean attractive park and ride facility with amenities like ev charging, wi fi, and convenient access to nearby transit options.
43Brookside Bus Blossom Laneprioritize public transportation and enhance air quality by dedicating lanes to buses and hovs on brookside boulevard, integrating smart traffic signals and real time transit information inspired by the ancient elves.
\n", - "
" - ], - "text/plain": [ - " project_name \\\n", - "11 Greenway Gables Managed Lanes \n", - "16 Sparkle City Smart Streets Initiative \n", - "18 Coastal Commuter Carousel \n", - "19 Rolling Renaissance Rabbit Express \n", - "20 Transit Treasure Transit Oasis \n", - "21 Berry Best Bus Rapid Transit \n", - "25 Trail of Treats and Transit Hub \n", - "27 Park and Ride Petal Paradise \n", - "43 Brookside Bus Blossom Lane \n", - "\n", - " scope_of_work \n", - "11 managed lanes prioritizing carpools, clean vehicles, and public transit, featuring real time traffic updates and incentives for sustainable transportation choices. \n", - "16 an intelligent transportation system integrating traffic management, real time transit information, and smart parking solutions to enhance mobility and reduce congestion. \n", - "18 a 30 mile passenger rail line connecting coastal towns, featuring modern train sets, enhanced station amenities, and scenic viewing cars. \n", - "19 new, eco friendly rolling stock for public transit, incorporating advanced propulsion systems, comfortable seating, and onboard amenities. \n", - "20 transit supportive features, including shelters, wi fi, and real time information displays, prioritizing passenger convenience and accessibility. \n", - "21 dedicated bus lanes with comfortable stops, featuring off board fare payment, priority traffic signals, and enhanced passenger amenities. \n", - "25 a multi use path connecting to public transit, featuring public art installations, wayfinding signage, and amenities like bike storage and repair stations. \n", - "27 an attractive park and ride facility with amenities like ev charging, wi fi, and convenient access to nearby transit options. \n", - "43 prioritize public transportation and enhance air quality by dedicating lanes to buses and hovs on brookside boulevard, integrating smart traffic signals and real time transit information inspired by the ancient elves. " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[df.scope_of_work.str.contains(transit_keywords)][preview_subset]" ] @@ -604,36 +246,16 @@ "id": "c82ef0b7-d2c9-48d1-a53f-625fb083e196", "metadata": {}, "source": [ - "* Notice how many more projects appear when we filter for 3 additional transit related keywords, compared to only transit?" + "* Count how many more projects appear when we filter for 3 additional transit related keywords, compared to only transit below." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "7b62f28d-7b28-4258-8efa-74d1f9a41d04", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7\n", - "9\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/2770509021.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " print(len(df.loc[df.scope_of_work.str.contains(transit_keywords)]))\n" - ] - } - ], - "source": [ - "print(len(transit_only_projects))\n", - "print(len(df.loc[df.scope_of_work.str.contains(transit_keywords)]))" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -648,19 +270,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "47afb269-672f-44c1-8ab5-d70921c6e703", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/653877654.py:2: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " (df.scope_of_work.str.contains(transit_keywords)),\n" - ] - } - ], + "outputs": [], "source": [ "df[\"Transit\"] = np.where(\n", " (df.scope_of_work.str.contains(transit_keywords)),\n", @@ -679,26 +292,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "c63f2ff8-3d2f-41c6-96d1-36d35159aef8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "N 35\n", - "Y 9\n", - "Name: Transit, dtype: int64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.Transit.value_counts()" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -708,9 +306,9 @@ }, "source": [ "### Task 2: Functions \n", - "* It looks only the 9 transit projects were categorized.\n", - "* We are missing the 2 categories: ATP and General Lane related projects.\n", - "* We could repeat the steps above or we can use a function.\n", + "* It looks like there are only 9 transit projects.\n", + "* We are missing the 2 other categories: ATP and General Lane related projects.\n", + "* We could repeat the steps above or we can use a **function.**\n", " * You can think of a function as a piece of code you write only once but reuse more than once.\n", " * In the long run, functions save you work and look neater when you present your work.\n", "* You may not have realized this but you've been using functions this whole time.\n", @@ -719,21 +317,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "8c62fef2-8215-4983-a4e6-c671177b822f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "44" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(df)" ] @@ -748,63 +335,30 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "0659a036-76ad-4251-80a1-323a0a04c912", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "pandas.core.frame.DataFrame" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(df)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "2985ec16-35e1-4eae-b2c5-facb354ce4e5", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(GCS_FILE_PATH)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "65c6b0c7-a314-434f-8304-10afd6c84514", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "list" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(transit)" ] @@ -824,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "00ead246-8879-4075-a632-d0ded58df558", "metadata": {}, "outputs": [], @@ -840,13 +394,14 @@ }, "source": [ "#### Let's build a function together.\n", - "* This will be repetitive after the tutorials, but you will use functions all the time at DDS and this is a concept we would like to drive home.\n", - "* Start your function with `def():`` and the name you'd like." + "* This will be repetitive after the tutorials, but you will use functions all the time at DDS.\n", + "##### Step 1\n", + "* Start your function with `def` and the name you'd like. I'm calling it `categorize():`" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "97e597a2-8625-4f2b-8646-760c0c011208", "metadata": {}, "outputs": [], @@ -859,6 +414,7 @@ "id": "06ccd282-cf21-462b-8930-9a3148671ff1", "metadata": {}, "source": [ + "##### Step 2 \n", "* Now let's think of what are the two elements that we will repeat.\n", "* We merely want to substitute `transit_keywords` with ATP or General Lane related keywords.\n", "* Instead of the `df[\"Transit]\"`, we want to create two new columns called something like `df[\"ATP]\"` and `df[\"General_Lanes]\"` to hold our yes/no results.\n", @@ -869,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "61973dc6-d99b-48f0-842f-a3c8fe74f064", "metadata": {}, "outputs": [], @@ -882,13 +438,14 @@ "id": "ae178f6d-0f76-419c-aab2-9924ba294605", "metadata": {}, "source": [ - "* It's also a nice idea to document what your function will return.\n", + "##### Step 3\n", + "* It's also good to document what your function will return.\n", "* In our case, it's a Pandas dataframe. " ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "a794693a-3bf2-48ba-b0a7-1ca3a41e03af", "metadata": {}, "outputs": [], @@ -901,6 +458,7 @@ "id": "be820c1a-a0d2-4b2f-bf01-70e753603291", "metadata": {}, "source": [ + "##### Step 4\n", "* Think about the steps we took to categorize transit only.\n", "* Add the sections of the code we will be reusing and sub in the original variables for the arguments.\n", " * First, we joined the keywords from a list into a big string.\n", @@ -910,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "4721b564-726a-4e05-9d27-8035609b5fcf", "metadata": {}, "outputs": [], @@ -935,110 +493,20 @@ "id": "81bbb109-beef-452c-b8d9-eb13e7b9ee03", "metadata": {}, "source": [ - "* Now let's use your function" + "#### Step 5 \n", + "* Now let's use your function: input the arguments in for each of the lists that hold the categorical keywords." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "23e31c98-17b3-41e2-883a-14dae9d6da7e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/2245515441.py:7: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " df[new_column] = np.where((df.scope_of_work.str.contains(joined_keywords)),\n" - ] - } - ], - "source": [ - "df = categorize(df, atp, \"ATP\")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d5ec64cf-432c-45e2-b14d-f4ea7ca3de2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "N 30\n", - "Y 14\n", - "Name: ATP, dtype: int64" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.ATP.value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "882a02a6-ce39-4da2-b2be-7e91322624e4", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/2245515441.py:7: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " df[new_column] = np.where((df.scope_of_work.str.contains(joined_keywords)),\n" - ] - } - ], - "source": [ - "df = categorize(df, transit, \"Transit\")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "ee56ee97-307c-44a4-a2d4-b02eff954f87", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2254/2245515441.py:7: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n", - " df[new_column] = np.where((df.scope_of_work.str.contains(joined_keywords)),\n" - ] - } - ], - "source": [ - "df = categorize(df, general_lanes, \"General_Lanes\")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "96f2efba-4179-4a8c-b969-fd2990f8a129", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "N 35\n", - "Y 9\n", - "Name: General_Lanes, dtype: int64" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df.General_Lanes.value_counts()" + "df = categorize(df = df, \n", + " keywords = atp, \n", + " new_column = \"ATP\")" ] }, { @@ -1054,111 +522,11 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "62115dcb-ea34-4bb1-9bd1-e678ec015b8c", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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General_LanesTransitATPproject_nameoverall_score
0NNN1573.00
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" - ], - "text/plain": [ - " General_Lanes Transit ATP project_name overall_score\n", - "0 N N N 15 73.00\n", - "1 N N Y 11 72.00\n", - "2 N Y N 8 75.00\n", - "3 N Y Y 1 75.00\n", - "4 Y N N 7 73.00\n", - "5 Y N Y 2 82.00" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.groupby([\"General_Lanes\", \"Transit\", \"ATP\"]).aggregate(\n", - " {\"project_name\": \"nunique\", \"overall_score\": \"median\"}\n", - ").reset_index()" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1177,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "6d824c18-4c2b-41c9-950b-866e567ab7f5", "metadata": {}, "outputs": [], @@ -1190,15 +558,13 @@ "id": "212570f5-e8ed-4151-be24-dd0994304334", "metadata": {}, "source": [ - "Goal: \n", + "### Practice #1: \n", "* We are going to write an If-Else function that categorizes projects by whether it scored low, medium, or high based on its `overall_score` and percentiles.\n", - "* For example, if a project scores below the 25% percentile, it is a \"low scoring project\". If a project scores above the 25% percentile but below the 75% percentile, it is a \"medium scoring project\". Anything above the 75% percentile is \"high scoring\".\n", - "* Use the values you find from .describe() as reference.\n", - "* You aren't limited to only the 25th, 50th, and 75th percentile. You can categorize low,medium, and high based on other percentile ranges. \n", - " * You can do so by specifying within `describe` like `.describe(percentiles=[0.05, 0.1, 0.9, 0.95])`.\n", + " * If a project scores below the 25% percentile, it is a \"low scoring project\". If a project scores above the 25% percentile but below the 75% percentile, it is a \"medium scoring project\". Anything above the 75% percentile is \"high scoring\".\n", "* In Data Science, we like to save our work into variables.\n", - " * If new projects are added, then what determines the different percentiles will likely switch.\n", - " * As such, you can save whatever percentile you like using `p75 = df.overall_score.quantile(0.75).astype(float)` which will change along with the dataset when you load in the new data." + " * If new projects are added, then different percentiles will likely switch.\n", + " * As such, you can save whatever percentile you like using `p75 = df.overall_score.quantile(0.75).astype(float)` which will change automatically when you load in the new data.\n", + "* Write an if-else and set the various percentiles using variables. " ] }, { @@ -1220,38 +586,19 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "d560dad0-de03-4469-99f8-5fadd9b198dc", "metadata": {}, "outputs": [], - "source": [ - "def categorize(row):\n", - " if (row.General_Lanes == \"N\") & (row.Transit == \"N\") & (row.ATP == \"N\"):\n", - " return \"Other\"\n", - " elif (row.General_Lanes == \"N\") & (row.Transit == \"N\") & (row.ATP == \"Y\"):\n", - " return \"ATP\"\n", - " elif (row.General_Lanes == \"N\") & (row.Transit == \"Y\") & (row.ATP == \"N\"):\n", - " return \"Transit\"\n", - " elif (row.General_Lanes == \"N\") & (row.Transit == \"Y\") & (row.ATP == \"Y\"):\n", - " return \"Transit and ATP\"\n", - " elif (row.General_Lanes == \"Y\") & (row.Transit == \"N\") & (row.ATP == \"N\"):\n", - " return \"General Lanes\"\n", - " elif (row.General_Lanes == \"Y\") & (row.Transit == \"N\") & (row.ATP == \"Y\"):\n", - " return \"General Lanes and ATP\"\n", - " else:\n", - " return \"Transit, General Lanes, and ATP\"" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "f8b7d946-c724-43cb-9a93-d1003f7f024f", "metadata": {}, "outputs": [], - "source": [ - "# Apply your function\n", - "df[\"category\"] = df.apply(categorize, axis=1)" - ] + "source": [] }, { "cell_type": "markdown", @@ -1263,132 +610,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "f18a7754-907c-46fa-ad77-4a09abb03206", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ct_districtproject_namescope_of_workproject_costlead_agencyaccessibility_scoredac_accessibility_scoredac_traffic_impacts_scorefreight_efficiency_scorefreight_sustainability_scoremode_shift_scorelu_natural_resources_scoresafety_scorevmt_scorezev_scorepublic_engagement_scoreclimate_resilience_scoreprogram_fit_scoreoverall_scoreTransitATPGeneral_Lanescategory
01Meadow Magic Multi-Use Patha 2 mile class i bike lane and multi use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife.5245734Meadow Bunny Public Transportation (MBPT)28810235327661072NYNATP
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" - ], - "text/plain": [ - " ct_district project_name \\\n", - "0 1 Meadow Magic Multi-Use Path \n", - "\n", - " scope_of_work \\\n", - "0 a 2 mile class i bike lane and multi use path through a scenic meadow, featuring wildflower plantings, public art installations, and educational signage highlighting local wildlife. \n", - "\n", - " project_cost lead_agency \\\n", - "0 5245734 Meadow Bunny Public Transportation (MBPT) \n", - "\n", - " accessibility_score dac_accessibility_score dac_traffic_impacts_score \\\n", - "0 2 8 8 \n", - "\n", - " freight_efficiency_score freight_sustainability_score mode_shift_score \\\n", - "0 10 2 3 \n", - "\n", - " lu_natural_resources_score safety_score vmt_score zev_score \\\n", - "0 5 3 2 7 \n", - "\n", - " public_engagement_score climate_resilience_score program_fit_score \\\n", - "0 6 6 10 \n", - "\n", - " overall_score Transit ATP General_Lanes category \n", - "0 72 N Y N ATP " - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(1)" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "2245ce0c-97fb-4f08-9791-9fb6b28b49c7", "metadata": {}, "outputs": [], - "source": [ - "\n", - "df.to_parquet(f\"{GCS_FILE_PATH}starter_kit_example_categorized.parquet\")" - ] + "source": [] }, { "cell_type": "markdown", @@ -1402,27 +636,10 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "48495a9f-e29c-41eb-b3e7-de6371fbd182", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "7\n", - "8\n", - "9\n" - ] - } - ], + "outputs": [], "source": [ "for i in range(10):\n", " print(i)" @@ -1440,89 +657,24 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "fca9e430-a906-4d0e-8046-36a0687b0636", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistics for zev_score\n" - ] - }, - { - "data": { - "text/plain": [ - "count 44.00\n", - "mean 6.00\n", - "std 2.96\n", - "min 1.00\n", - "25% 3.75\n", - "50% 6.50\n", - "75% 8.00\n", - "max 10.00\n", - "Name: zev_score, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistics for vmt_score\n" - ] - }, - { - "data": { - "text/plain": [ - "count 44.00\n", - "mean 4.52\n", - "std 2.73\n", - "min 1.00\n", - "25% 2.00\n", - "50% 4.00\n", - "75% 6.00\n", - "max 10.00\n", - "Name: vmt_score, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Statistics for accessibility_score\n" - ] - }, - { - "data": { - "text/plain": [ - "count 44.00\n", - "mean 5.14\n", - "std 2.66\n", - "min 1.00\n", - "25% 3.00\n", - "50% 5.00\n", - "75% 7.00\n", - "max 10.00\n", - "Name: accessibility_score, dtype: float64" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for column in [\"zev_score\", \"vmt_score\", \"accessibility_score\"]:\n", " print(f\"Statistics for {column}\")\n", " display(df[column].describe())" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "51cd25c3-2234-4d89-a715-4e5365d7c99a", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "ded54884-4bad-46ae-a82f-2a67936c57dd", @@ -1534,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "5b414d3f-71a4-4078-9d98-b9082114e2c5", "metadata": {}, "outputs": [], @@ -1557,99 +709,10 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "1698fe9c-6d1f-412b-a632-826aae1ffc65", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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categorymedian_scoremedian_project_costtotal_projects
0ATP72.004991255.0011
1General Lanes73.007487963.007
2General Lanes and ATP82.005672550.502
3Other73.003708858.0015
4Transit75.004399886.008
5Transit and ATP75.002069143.001
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" - ], - "text/plain": [ - " category median_score median_project_cost total_projects\n", - "0 ATP 72.00 4991255.00 11\n", - "1 General Lanes 73.00 7487963.00 7\n", - "2 General Lanes and ATP 82.00 5672550.50 2\n", - "3 Other 73.00 3708858.00 15\n", - "4 Transit 75.00 4399886.00 8\n", - "5 Transit and ATP 75.00 2069143.00 1" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "agg1" ] @@ -1664,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "320bd91e-b9ed-4423-80d4-c1a1aa5ba59f", "metadata": {}, "outputs": [], @@ -1700,92 +763,11 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "a6103703-8131-4ed8-9482-314c7895c279", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "create_chart(agg1, \"median_score\")" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1793,262 +775,18 @@ "metadata": {}, "source": [ "* We have a couple of other columns left that still need to be visualized. \n", - "* This is the perfect case for using a for loop, since we all we want to do is replace the column above with the two remainig columns. \n", + "* This is the perfect case for using a for loop, since all we want to do is replace the column above with the two remainig columns. \n", "* Try this below! \n", " * Hint: you'll have to wrap the function with `display()` to get your results." ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "ca8659f1-0842-4bb5-a544-9a2a5fb93c02", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for column in [\"median_score\", \"median_project_cost\", \"total_projects\"]:\n", - " display(create_chart(agg1, column))" - ] - }, - { - "cell_type": "markdown", - "id": "0f77dcf4-7b19-4e58-b20b-ae59721deb9c", - "metadata": {}, - "source": [ - "### Try it out yourself\n", - "* Think of some other use cases for a for loop and try them out here." - ] + "outputs": [], + "source": [] } ], "metadata": { diff --git a/starter_kit/2024_basics_04.ipynb b/starter_kit/2024_basics_04.ipynb index c36cc6a54..b63e78160 100644 --- a/starter_kit/2024_basics_04.ipynb +++ b/starter_kit/2024_basics_04.ipynb @@ -6,15 +6,15 @@ "metadata": {}, "source": [ "# Exercise 4: Python Scripts, Concept of Grains, Display, Markdown,\n", - "* Cleaning and analyzing data takes a lot of time, patience, and skill.\n", - "* However, presenting the data to stakeholders is also equaly important.\n", - "* At DDS, we often present our work in a Jupyter Notebook.\n", - "* This exercise will walk you through how we do so. " + "* After cleaning and analyzing data, it's time to present the data in a beautifl fashion.\n", + "* At DDS, we often present our work directly in a Jupyter Notebook, which has many benefits such as.\n", + " * We save the time it takes to copy and paste our graphs into a PowerPoint \n", + " * We ensure the accuracy of the data since we aren't manually retyping the data. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1d4e2cdf-a5b9-4ebb-aa2f-c7abe897a683", "metadata": {}, "outputs": [], @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a0403b50-d81c-4499-9b69-e164eb38f8cd", "metadata": {}, "outputs": [], @@ -47,15 +47,22 @@ "## Python Scripts\n", "* Up until now, we have been placing all of our code in the Jupyter Notebook.\n", "* While this is convenient, it's not the best practice. \n", - "* A notebook full of code isn't easy for viewers - it gets chaotic, quickly! \n", - "* Jupyter notebooks are also very difficult for Git to version control. \n", + "* A notebook full of code also isn't easy for viewers - it gets chaotic, quickly! \n", "* **The best solution is to move the bulk of your code when you have reached a stopping point to a Python Script.**\n", - " * Read all about the benefits of scripts [here in our DDS docs](https://docs.calitp.org/data-infra/analytics_tools/scripts.html).\n", - " * Summary points from the docs page above:\n", + "* Read all about the benefits of scripts [here in our DDS docs](https://docs.calitp.org/data-infra/analytics_tools/scripts.html). Summary points below: \n", + " * Summary points from the docs page above. What are Python scripts?\n", " * Python scripts (.py) are plain text files. Git tracks plain text changes easily.\n", " * Scripts are robust to scaling and reproducing work.\n", " * Break out scripts by concepts / stages\n", " * All functions used in scripts should have docstrings. Type hints are encouraged!\n", + " * Which components should a script contain?\n", + " * 1 script for importing external data and changing it from shapefile/geojson/csv to parquet/geoparquet\n", + " * If only using warehouse data or upstream warehouse data cached in GCS, can skip this first script\n", + " * At least 1 script for data processing to produce processed output for visualization\n", + " * Break out scripts by concepts / stages\n", + " * Include data catalog, README for the project\n", + " * All functions used in scripts should have docstrings. Type hints are encouraged!\n", + "### Sample Script \n", "* Making Python scripts is an art and not straight forward.\n", "* I have already populated a `.py` file called `_starterkit_utils` with some sample functions.\n", "* I imported my Python Script just like how I imported my other dependencies (Pandas, Altair, Numpy)." @@ -63,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "68d8980b-e857-491e-b03a-4648c5f4c5f3", "metadata": {}, "outputs": [], @@ -76,49 +83,19 @@ "id": "6f37fc46-a49e-45b4-92bf-d5b3910b2325", "metadata": {}, "source": [ - "### Breakdown of a Script.\n", + "### Breakdown of the Sample Script\n", "#### Function 1\n", "* You can also preview what a function does by writing `script_name.function_name??`\n", - "\n", "* Following what the DDS docs says, I am creating a new function every time I am processing the data in another stage.\n", "* I have one function that loads in my dataset." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "3454fecc-0b6b-4f1f-b74d-17792165f990", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0m_starterkit_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSource:\u001b[0m \n", - "\u001b[0;32mdef\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m->\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", - "\u001b[0;34m Load the final dataframe.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mGCS_FILE_PATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"gs://calitp-analytics-data/data-analyses/starter_kit/\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mFILE\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"starter_kit_example_categorized.parquet\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Read dataframe in\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_parquet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{GCS_FILE_PATH}{FILE}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Capitalize the Scope of Work column again since it is all lowercase\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope_of_work\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope_of_work\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapitalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Clean up the column names\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreverse_snakecase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFile:\u001b[0m ~/data-analyses/starter_kit/_starterkit_utils.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_starterkit_utils.load_dataset??" ] @@ -135,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "44467ccf-599f-4662-8164-8a58fac85711", "metadata": {}, "outputs": [], @@ -155,58 +132,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "0d82c825-d789-469e-8ae2-c69a94984511", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0m_starterkit_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate_by_category\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSource:\u001b[0m \n", - "\u001b[0;32mdef\u001b[0m \u001b[0maggregate_by_category\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", - "\u001b[0;34m Find the median overall score and project cost \u001b[0m\n", - "\u001b[0;34m and total unique projects by category.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0magg1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Category\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Overall Score\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"median\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Project Cost\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"median\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Project Name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"nunique\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Overall Score\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Median Score\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Project Cost\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Median Project Cost\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Project Name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Total Projects\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Format the Cost column properly\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0magg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Median Project Cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magg1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Median Project Cost'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'${:,.0f}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - 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CategoryMedian ScoreMedian Project CostTotal Projects
0ATP72.00$4,991,25511
1General Lanes73.00$7,487,9637
2General Lanes and ATP82.00$5,672,5502
3Other73.00$3,708,85815
4Transit75.00$4,399,8868
5Transit and ATP75.00$2,069,1431
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" - ], - "text/plain": [ - " Category Median Score Median Project Cost Total Projects\n", - "0 ATP 72.00 $4,991,255 11\n", - "1 General Lanes 73.00 $7,487,963 7\n", - "2 General Lanes and ATP 82.00 $5,672,550 2\n", - "3 Other 73.00 $3,708,858 15\n", - "4 Transit 75.00 $4,399,886 8\n", - "5 Transit and ATP 75.00 $2,069,143 1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "aggregated_df" ] @@ -319,64 +166,24 @@ "metadata": {}, "source": [ "#### Function 3\n", - "* I want to swap my dataframe from wide to long. \n", + "* I want process my data a second way by changing it from wide to long. \n", "* [Read about wide to long.](https://www.statology.org/long-vs-wide-data/)\n", "* [Pandas doc on melt](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "2b0231f0-eb97-46d4-9541-aee43b138755", "metadata": {}, - 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"\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", - "\u001b[0;34m Change the dataframe from wide to long based on the project name and\u001b[0m\n", - "\u001b[0;34m Caltrans District.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmelt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mid_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CalTrans District\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Project Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mvalue_vars\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"Accessibility Score\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - 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"\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFile:\u001b[0m ~/data-analyses/starter_kit/_starterkit_utils.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_starterkit_utils.wide_to_long??" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "82172952-3d59-436e-b08c-7096454b6e04", "metadata": {}, "outputs": [], @@ -386,67 +193,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "1bcac91b-b0a1-4efd-8a73-f019c376d030", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CalTrans DistrictProject NameMetricScore
01Meadow Magic Multi-Use PathAccessibility Score2
14Bunny Hop Bike BoulevardAccessibility Score3
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" - ], - "text/plain": [ - " CalTrans District Project Name Metric Score\n", - "0 1 Meadow Magic Multi-Use Path Accessibility Score 2\n", - "1 4 Bunny Hop Bike Boulevard Accessibility Score 3" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df2.head(2)" ] @@ -463,76 +213,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "d69a4f91-4e37-4207-93e0-2eaa18f998ff", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CategoryMedian ScoreMedian Project CostTotal Projects
ATP72$4,991,25511
General Lanes73$7,487,9637
General Lanes and ATP82$5,672,5502
Other73$3,708,85815
Transit75$4,399,8868
Transit and ATP75$2,069,1431
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_starterkit_utils.style_df(aggregated_df)" ] @@ -543,154 +227,26 @@ "metadata": {}, "source": [ "#### Function 5 \n", + "* After aggregating and reshaping the data, the next function presents the data.\n", "* This is function that creates a chart that shows the scores by metric for each project." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1e2ec6b7-b494-4db5-a863-91882c77a7a8", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0m_starterkit_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_metric_chart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0maltair\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvegalite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv5\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmetrics_dropdown\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinding_select\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Metrics: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Column that controls the bar charts\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mxcol_param\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselection_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Metric\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetrics_dropdown\u001b[0m\u001b[0;34m\u001b[0m\n", - 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"\u001b[0;34m\u001b[0m \u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mchart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFile:\u001b[0m ~/data-analyses/starter_kit/_starterkit_utils.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_starterkit_utils.create_metric_chart??" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "7f39ca4e-9fb9-497d-bee6-22be385a9d34", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "_starterkit_utils.create_metric_chart(df2)" ] @@ -703,85 +259,16 @@ "## Grains\n", "* This is a light introduction to the concept of grains.\n", "* Grain means the level your dataset is presented at.\n", - "* You can think of it as: what does each row represent?\n", - "* The original dataset is presented on the project-level grain because each row represents a unique project. \n" + "* Basically, what does each row represent?\n", + "* The original dataset is presented on the project-level grain because each row represents a unique project. " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "bb228391-f907-4d76-a2b5-45b7fd188d21", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Project NameOverall Score
0Meadow Magic Multi-Use Path72
1Bunny Hop Bike Boulevard68
2Strawberry Shortcake Sidewalks87
3River Ramble Rabbit Trail75
4Lilac Lane Dream Complete Street72
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" - ], - "text/plain": [ - " Project Name Overall Score\n", - "0 Meadow Magic Multi-Use Path 72\n", - "1 Bunny Hop Bike Boulevard 68\n", - "2 Strawberry Shortcake Sidewalks 87\n", - "3 River Ramble Rabbit Trail 75\n", - "4 Lilac Lane Dream Complete Street 72" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df[[\"Project Name\", \"Overall Score\"]].head()" ] @@ -796,121 +283,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "9a332009-4ac5-4eca-9632-6d45c03765a4", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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CalTrans DistrictTotal Projects
011
122
236
346
454
563
673
785
893
9102
10115
11124
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" - ], - "text/plain": [ - " CalTrans District Total Projects\n", - "0 1 1\n", - "1 2 2\n", - "2 3 6\n", - "3 4 6\n", - "4 5 4\n", - "5 6 3\n", - "6 7 3\n", - "7 8 5\n", - "8 9 3\n", - "9 10 2\n", - "10 11 5\n", - "11 12 4" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.groupby([\"CalTrans District\"]).agg({\"Project Name\": \"nunique\"}).reset_index().rename(\n", " columns={\"Project Name\": \"Total Projects\"}\n", @@ -927,127 +303,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "c0f00432-60ca-4e8a-9c2a-45bba234dbd7", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Lead AgencyTotal Projects
0Bunny's Meadow Hop Transportation (BMHT)3
1Cherry Metro Services (CMS)1
2Dewdrop Ride Transit2
3Elf's Efficient Transportation (EET)3
4Fairy Creek Public Transit (FCPT)5
5Gnome Valley Rail Link (GVRL)3
6Meadow Bunny Public Transportation (MBPT)4
7Morning Dewdrop Transit (MDT)4
8Mushroom Metro Transit Agency (MMTA)5
9Rainbow Mushroom Transportation Corporation (RMTC)5
10Shining Sparkle Transit Systems (SSTS)4
11Strawberry Rainbow Transit Systems (SRTS)4
12Unicorn Fairy Express Bus (UFX)1
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" - ], - "text/plain": [ - " Lead Agency Total Projects\n", - "0 Bunny's Meadow Hop Transportation (BMHT) 3\n", - "1 Cherry Metro Services (CMS) 1\n", - "2 Dewdrop Ride Transit 2\n", - "3 Elf's Efficient Transportation (EET) 3\n", - "4 Fairy Creek Public Transit (FCPT) 5\n", - "5 Gnome Valley Rail Link (GVRL) 3\n", - "6 Meadow Bunny Public Transportation (MBPT) 4\n", - "7 Morning Dewdrop Transit (MDT) 4\n", - "8 Mushroom Metro Transit Agency (MMTA) 5\n", - "9 Rainbow Mushroom Transportation Corporation (RMTC) 5\n", - "10 Shining Sparkle Transit Systems (SSTS) 4\n", - "11 Strawberry Rainbow Transit Systems (SRTS) 4\n", - "12 Unicorn Fairy Express Bus (UFX) 1" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.groupby([\"Lead Agency\"]).agg({\"Project Name\": \"nunique\"}).reset_index().rename(\n", " columns={\"Project Name\": \"Total Projects\"}\n", @@ -1059,349 +318,19 @@ "id": "55137a34-1624-4d8a-8ee8-33c773868cde", "metadata": {}, "source": [ - "* Grains can get very complicated. The one below is Lead Agency and Category Grain. " + "* Grains can get very minute. The one below is Lead Agency and Category Grain. " ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "7892454f-5f70-4237-9f04-560405cf1775", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Lead AgencyCategoryTotal Projects
0Bunny's Meadow Hop Transportation (BMHT)Other2
1Bunny's Meadow Hop Transportation (BMHT)Transit1
2Cherry Metro Services (CMS)Other1
3Dewdrop Ride TransitATP1
4Dewdrop Ride TransitOther1
5Elf's Efficient Transportation (EET)ATP1
6Elf's Efficient Transportation (EET)General Lanes1
7Elf's Efficient Transportation (EET)Transit1
8Fairy Creek Public Transit (FCPT)ATP1
9Fairy Creek Public Transit (FCPT)Other2
10Fairy Creek Public Transit (FCPT)Transit2
11Gnome Valley Rail Link (GVRL)ATP1
12Gnome Valley Rail Link (GVRL)Other1
13Gnome Valley Rail Link (GVRL)Transit and ATP1
14Meadow Bunny Public Transportation (MBPT)ATP1
15Meadow Bunny Public Transportation (MBPT)General Lanes and ATP1
16Meadow Bunny Public Transportation (MBPT)Other1
17Meadow Bunny Public Transportation (MBPT)Transit1
18Morning Dewdrop Transit (MDT)General Lanes2
19Morning Dewdrop Transit (MDT)Other1
20Morning Dewdrop Transit (MDT)Transit1
21Mushroom Metro Transit Agency (MMTA)General Lanes1
22Mushroom Metro Transit Agency (MMTA)Other3
23Mushroom Metro Transit Agency (MMTA)Transit1
24Rainbow Mushroom Transportation Corporation (RMTC)ATP2
25Rainbow Mushroom Transportation Corporation (RMTC)General Lanes1
26Rainbow Mushroom Transportation Corporation (RMTC)Other1
27Rainbow Mushroom Transportation Corporation (RMTC)Transit1
28Shining Sparkle Transit Systems (SSTS)ATP1
29Shining Sparkle Transit Systems (SSTS)General Lanes1
30Shining Sparkle Transit Systems (SSTS)General Lanes and ATP1
31Shining Sparkle Transit Systems (SSTS)Other1
32Strawberry Rainbow Transit Systems (SRTS)ATP2
33Strawberry Rainbow Transit Systems (SRTS)General Lanes1
34Strawberry Rainbow Transit Systems (SRTS)Other1
35Unicorn Fairy Express Bus (UFX)ATP1
\n", - "
" - ], - "text/plain": [ - " Lead Agency Category \\\n", - "0 Bunny's Meadow Hop Transportation (BMHT) Other \n", - "1 Bunny's Meadow Hop Transportation (BMHT) Transit \n", - "2 Cherry Metro Services (CMS) Other \n", - "3 Dewdrop Ride Transit ATP \n", - "4 Dewdrop Ride Transit Other \n", - "5 Elf's Efficient Transportation (EET) ATP \n", - "6 Elf's Efficient Transportation (EET) General Lanes \n", - "7 Elf's Efficient Transportation (EET) Transit \n", - "8 Fairy Creek Public Transit (FCPT) ATP \n", - "9 Fairy Creek Public Transit (FCPT) Other \n", - "10 Fairy Creek Public Transit (FCPT) Transit \n", - "11 Gnome Valley Rail Link (GVRL) ATP \n", - "12 Gnome Valley Rail Link (GVRL) Other \n", - "13 Gnome Valley Rail Link (GVRL) Transit and ATP \n", - "14 Meadow Bunny Public Transportation (MBPT) ATP \n", - "15 Meadow Bunny Public Transportation (MBPT) General Lanes and ATP \n", - "16 Meadow Bunny Public Transportation (MBPT) Other \n", - "17 Meadow Bunny Public Transportation (MBPT) Transit \n", - "18 Morning Dewdrop Transit (MDT) General Lanes \n", - "19 Morning Dewdrop Transit (MDT) Other \n", - "20 Morning Dewdrop Transit (MDT) Transit \n", - "21 Mushroom Metro Transit Agency (MMTA) General Lanes \n", - "22 Mushroom Metro Transit Agency (MMTA) Other \n", - "23 Mushroom Metro Transit Agency (MMTA) Transit \n", - "24 Rainbow Mushroom Transportation Corporation (RMTC) ATP \n", - "25 Rainbow Mushroom Transportation Corporation (RMTC) General Lanes \n", - "26 Rainbow Mushroom Transportation Corporation (RMTC) Other \n", - "27 Rainbow Mushroom Transportation Corporation (RMTC) Transit \n", - "28 Shining Sparkle Transit Systems (SSTS) ATP \n", - "29 Shining Sparkle Transit Systems (SSTS) General Lanes \n", - "30 Shining Sparkle Transit Systems (SSTS) General Lanes and ATP \n", - "31 Shining Sparkle Transit Systems (SSTS) Other \n", - "32 Strawberry Rainbow Transit Systems (SRTS) ATP \n", - "33 Strawberry Rainbow Transit Systems (SRTS) General Lanes \n", - "34 Strawberry Rainbow Transit Systems (SRTS) Other \n", - "35 Unicorn Fairy Express Bus (UFX) ATP \n", - "\n", - " Total Projects \n", - "0 2 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - "5 1 \n", - "6 1 \n", - "7 1 \n", - "8 1 \n", - "9 2 \n", - "10 2 \n", - "11 1 \n", - "12 1 \n", - "13 1 \n", - "14 1 \n", - "15 1 \n", - "16 1 \n", - "17 1 \n", - "18 2 \n", - "19 1 \n", - "20 1 \n", - "21 1 \n", - "22 3 \n", - "23 1 \n", - "24 2 \n", - "25 1 \n", - "26 1 \n", - "27 1 \n", - "28 1 \n", - "29 1 \n", - "30 1 \n", - "31 1 \n", - "32 2 \n", - "33 1 \n", - "34 1 \n", - "35 1 " - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df.groupby([\"Lead Agency\", \"Category\"]).agg({\"Project Name\": \"nunique\"}).reset_index().rename(\n", - " columns={\"Project Name\": \"Total Projects\"}\n", - ")" + "df.groupby([\"Lead Agency\", \"Category\"]).agg(\n", + " {\"Project Name\": \"nunique\"}\n", + ").reset_index().rename(columns={\"Project Name\": \"Total Projects\"})" ] }, { @@ -1410,9 +339,8 @@ "metadata": {}, "source": [ "## Create your own Script\n", - "* **Make sure your functions make sense for the district grain.**\n", - "* You will be using these functions for Exercise 5. \n", - "* Make sure to separate out functions by theme. \n", + "* **Make sure your functions make sense for the district grain. You will be using these functions for Exercise 5.**\n", + "* In your script, separate out functions by step like above. \n", " * One function that loads the dataset and does some light cleaning.\n", " * One (or more) functions that transform your dataframe.\n", " * `melt()`, `.T`, `.groupby()` are just some of the many options available through `pandas`. \n", @@ -1421,10 +349,10 @@ "* Other things to consider\n", " * Our [DDS Docs](https://docs.calitp.org/data-infra/publishing/sections/4_notebooks_styling.html#narrative) has a great guide on what \"checkboxes\" need to be \"checked\" when presenting data. The first 3 sections are the most relevant.\n", " * To summarize the docs, double check:\n", - " * Are the currency columns formatted with $ and commas?\n", + " * Are currency columns formatted with $ and commas?\n", " * Are all the scores formatted with the same number of decimals?\n", " * Are the string columns formatted with the right punctuation and capitalization?\n", - " * Are the column names formatted properly? While `snake_case` is very handy when we are analyzing the dataframe, it is not slightly when presenting the data. We typically reverse the `snake_case` back to something like `Project Name`.\n", + " * Are the column names formatted properly? While `snake_case` is very handy when we are analyzing the dataframe, it is not very nice when presenting the data. We typically reverse the `snake_case` back to something like `Project Name`.\n", " * [CalTrans Districts are currently integers, but they have actual names that can be mapped.](https://cwwp2.dot.ca.gov/documentation/district-map-county-chart.htm) \n", " " ] @@ -1437,24 +365,19 @@ "## Markdown/Display\n", "* Although our code is now neatly stored in a Python script, a Jupyter Notebook on its own is a bit plain, even when we have beautiful charts. \n", "* There are many ways to jazz it up.\n", - "* **Resource**: [Data Camp](https://www.datacamp.com/tutorial/markdown-in-jupyter-notebook)" - ] - }, - { - "cell_type": "markdown", - "id": "ed396a2f-c3f1-40be-aad2-64835be8431b", - "metadata": {}, - "source": [ - "#### Images\n", + "* **Resource**: [Data Camp's Markdown Tutorial](https://www.datacamp.com/tutorial/markdown-in-jupyter-notebook)\n", + "### Images\n", + "#### In a Markdown Cell\n", "* You can add an image in a markdown cell\n", "``

\n", "\n", + "#### In a Code Cell\n", "* You can add an image in a code cell if you import the packages below." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "4ec41786-491f-46ad-963e-f380d8095ade", "metadata": {}, "outputs": [], @@ -1464,26 +387,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "8d0a6849-f178-46fc-919a-f45b5436c423", "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "text/plain": [ - "" - ] - }, - "metadata": { - "image/jpeg": { - "height": 600, - "width": 960 - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(Image(filename=\"./19319_en_1.jpg\", retina=True))" ] @@ -1496,7 +403,7 @@ "### Display\n", "* Of course, you can write your narratives in a Markdown cell like what I'm doing right now.\n", "* However, what if you want to incorporate values from your dataframe into the narrative?\n", - "* Writing out the values manually in markdown locks you in. If the values change, you'll have to rewrite your narrative.\n", + "* Writing out the values manually in markdown locks you in. If the values change, you'll have to rewrite your narrative which is timely and prone to inaccuracy.\n", "* The best way is to use `display` and `markdown` from `from IPython.display`\n", "* We are using District 3 as an example" ] @@ -1507,12 +414,12 @@ "metadata": {}, "source": [ "#### No hard coding\n", - "* Save out your desired value into a new variable if you are manipulating it." + "* Save out your desired value into a new variable whenever you want to reference it in a narrative." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "caecab58-2d26-4604-a3f1-ab4a11400038", "metadata": {}, "outputs": [], @@ -1523,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "995eb899-0397-4f60-b587-18fcf8a4cb0e", "metadata": {}, "outputs": [], @@ -1534,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "bc73cc66-911a-44bf-9710-920328b40609", "metadata": {}, "outputs": [], @@ -1545,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "8a5dafba-c901-412c-bf53-e418dc558787", "metadata": {}, "outputs": [], @@ -1556,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "e183e629-7f79-45e3-810b-294851ca9abf", "metadata": {}, "outputs": [], @@ -1571,43 +478,27 @@ "metadata": {}, "source": [ "#### Long F-String + Headers\n", - "* The f-string has multiple quotation marks. This allows you to write a f-string that goes over multiple lines.\n", - "*

and

displays District 3 in a header. Headers vary in size, 1 being the largest. \n", + "* F-strings can have multiple quotation marks. This allows you to write a f-string that goes over multiple lines.\n", + "* `

` and `

` displays District 3 in a header. \n", + " * Headers vary in size, 1 being the largest. \n", "* `` bolds the text. \n", - " * ` italicizes the text.\n", - "* Notice that you always have to **close** your HTML with `` strikes the text.\n", + "* Notice that you always have to **close** your HTML with ``" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "8d7e68ad-79dd-48b6-8e17-90496d470b69", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "

District 3

\n", - " The median score for projects in District 3 is 80.5
\n", - " The total number of projects is 6
\n", - " The most expensive project costs $9,448,022.00\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(\n", " Markdown(\n", " f\"\"\"

District 3

\n", " The median score for projects in District 3 is {d3_median_score}
\n", " The total number of projects is {d3_total_projects}
\n", - " The most expensive project costs {d3_max_project}\n", + " The most expensive project costs {d3_max_project}\n", " \"\"\"\n", " )\n", ")" @@ -1618,111 +509,20 @@ "id": "4aa41900-7dbf-4c61-b927-4f1e42f6b8da", "metadata": {}, "source": [ - "* You can code in this cell. I'm filtering out for district 3 values.\n", + "#### You can code in this cell. I'm filtering out for district 3 values.\n", "* Notice the header went from `

` to `

`. " ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "38d84ed3-9626-4f91-9aea-e2449aef4cf8", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "

Metric Scores

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(\n", " Markdown(\n", - " f\"\"\"

Metric Scores

\n", + " f\"\"\"
Metric Scores
\n", " \"\"\"\n", " )\n", ")\n", @@ -1734,549 +534,27 @@ "id": "fe45d252-1d46-4d34-98f4-7118afd96406", "metadata": {}, "source": [ - "### This can be a function too\n", - "* What if I wanted to generate these narratives for every district?\n", - "* I can simply turn this into a function.\n", - "* I only want to print out a couple of districts or else this notebook will become too large" + "### `Markdown` and `Display` can be worked into functions \n", + "* What if I wanted to generate these reports for every district?\n", + "* I can simply turn this into a function." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "8875a82f-2df3-4777-a115-87ba84ea96a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0m_starterkit_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_district_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mcaltrans_district\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mSource:\u001b[0m \n", - "\u001b[0;32mdef\u001b[0m \u001b[0mcreate_district_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaltrans_district\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n", - "\u001b[0;34m Create a summary of CSIS metrics for one Caltrans District.\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfiltered_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CalTrans District\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mcaltrans_district\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Finding the values referenced in the narrative\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmedian_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Overall Score\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mtotal_projects\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Project Name\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmax_project\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Project Cost\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmax_project\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"${max_project:,.2f}\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Aggregate the dataframe\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0maggregated_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate_by_category\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Change the dataframe from wide to long\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwide_to_long\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;31m# Create narrative\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mMarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34mf\"\"\"The median score for projects in District {caltrans_district} is {median_score}
\u001b[0m\n", - "\u001b[0;34m The total number of projects is {total_projects}
\u001b[0m\n", - "\u001b[0;34m The most expensive project costs {max_project}\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mMarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34mf\"\"\"

Metrics aggregated by Categories

\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mstyle_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggregated_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mMarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34mf\"\"\"

Overview of Projects

\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mstyle_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Project Name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Overall Score\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Scope Of Work\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mMarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34mf\"\"\"

Metric Scores by Project

\u001b[0m\n", - "\u001b[0;34m \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcreate_metric_chart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFile:\u001b[0m ~/data-analyses/starter_kit/_starterkit_utils.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_starterkit_utils.create_district_summary??" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "6d6f524a-d49b-4729-801f-ccc4bd800149", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "The median score for projects in District 10 is 72.5
\n", - " The total number of projects is 2
\n", - " The most expensive project costs $7,160,933.00\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Metrics aggregated by Categories

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CategoryMedian ScoreMedian Project CostTotal Projects
Other59$816,5691
Transit86$7,160,9331
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Overview of Projects

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Project NameOverall ScoreScope Of Work
Countryside Clover Rail Connector59A 20 mile rail improvement project for freight transportation, upgrading track infrastructure, and implementing advanced safety features to reduce derailment risk.
Brookside Bus Blossom Lane86Prioritize public transportation and enhance air quality by dedicating lanes to buses and hovs on brookside boulevard, integrating smart traffic signals and real time transit information inspired by the ancient elves.
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Metric Scores by Project

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "The median score for projects in District 11 is 75.0
\n", - " The total number of projects is 5
\n", - " The most expensive project costs $8,956,026.00\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Metrics aggregated by Categories

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CategoryMedian ScoreMedian Project CostTotal Projects
ATP79$8,956,0261
General Lanes89$1,557,7511
Other75$5,796,4771
Transit55$5,425,7841
Transit and ATP75$2,069,1431
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Overview of Projects

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Project NameOverall ScoreScope Of Work
Berry Best Bus Rapid Transit55Dedicated bus lanes with comfortable stops, featuring off board fare payment, priority traffic signals, and enhanced passenger amenities.
Trail of Treats and Transit Hub75A multi use path connecting to public transit, featuring public art installations, wayfinding signage, and amenities like bike storage and repair stations.
Fairy Glen Boulevard79Welcome travelers to our enchanted town with a refreshed fairy glen boulevard, featuring sparkling streetlights, lush wildflower medians, and meandering pedestrian paths
Parkside Pixie Carpool Lane75Encourage sustainable transportation and reduce traffic congestion by constructing high occupancy vehicle (hov) lanes along parkside drive, adorned with fairy inspired artwork.
Ridgewood Ride-Share Rainbow Lane89Support environmentally friendly commuting options by building hov lanes on ridgewood highway, featuring designated ride share pickup and drop off zones, and a touch of magic from the meadow.
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "

Metric Scores by Project

\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "for district in range(10, 12):\n", " _starterkit_utils.create_district_summary(df, district)" @@ -2289,7 +567,7 @@ "source": [ "## Your turn to combine all your functions into one function\n", "* Take some inspiration from ` _starterkit_utils.create_district_summary(df, district).`\n", - "* Incorporate concepts from `markdown` and `display`. " + "* Incorporate concepts from `markdown` and `display` to create a polished report. " ] } ], diff --git a/starter_kit/2024_basics_05.ipynb b/starter_kit/2024_basics_05.ipynb index 40e31e42d..f53ee454f 100644 --- a/starter_kit/2024_basics_05.ipynb +++ b/starter_kit/2024_basics_05.ipynb @@ -14,8 +14,8 @@ "* Spend some time exploring our portfolio above. \n", "\n", "**How does the portfolio work?**\n", - "* For the majority of the sites on the portfolio are using a single notebook essentially as a template that is looped one or more variables. \n", - " * This [National Transit Dataset Monthly Ridership by Regional Transit Planning Authority (RTPA) portfolio](https://ntd-monthly-ridership--cal-itp-data-analyses.netlify.app/readme) takes [this notebook](https://github.com/cal-itp/data-analyses/blob/main/ntd/monthly_ridership_report.ipynb) and reruns it for every \n", + "* The majority of the sites on the portfolio use a single notebook essentially as a template that is looped one or more variables. \n", + " * This [National Transit Dataset Monthly Ridership by Regional Transit Planning Authority (RTPA) portfolio](https://ntd-monthly-ridership--cal-itp-data-analyses.netlify.app/readme) is generated from [this notebook](https://github.com/cal-itp/data-analyses/blob/main/ntd/monthly_ridership_report.ipynb) which is reran over every \n", "RTPA in this [yml file](https://github.com/cal-itp/data-analyses/blob/main/portfolio/sites/ntd_monthly_ridership.yml). \n", " * This process of looping over variables to generate new notebooks is called parameterizing a notebook.\n", " \n", @@ -24,7 +24,7 @@ " * [Publishing to the portfolio](https://docs.calitp.org/data-infra/publishing/sections/5_analytics_portfolio_site.html)\n", "\n", "**Let's make a portfolio**\n", - "* Feel free to delete all the instructions off once you're done. \n", + "* Feel free to delete all the instruction markdown cells (including this one) off once you're done. \n", "* Spoiler alert! Your end result will look something like [this](https://ha-starterkit-district--cal-itp-data-analyses.netlify.app/readme)." ] }, @@ -90,7 +90,7 @@ "* For every notebook you make, **you must copy and paste this block of code below in this exact order.** Otherwise, your notebook won't work.\n", "* What am I importing?\n", " * `%%capture`: Captures the parameter/yml parts.\n", - " * `import warnings warnings.filterwarnings('ignore')`: Sometimes when you are analyzing data, warnings pop up. These warnings are quite unattractive and we don't want them to be displayed in a portfolio so we turn off these warnings. You don't want to turn off the warnings if you are still analyzing your data! \n", + " * `import warnings warnings.filterwarnings('ignore')`: Sometimes when you are analyzing data, harmless warnings pop up. These warnings are quite unattractive and we don't want them to be displayed in a portfolio so we turn off these warnings.\n", " * `import calitp_data_analysis.magics`: the library that makes the parameterization magic happen.\n", "* **Resource**: [DDS Getting Notebooks Ready for Parameterization](https://docs.calitp.org/data-infra/publishing/sections/4_notebooks_styling.html#getting-ready-for-parameterization)" ] @@ -110,7 +110,7 @@ "import calitp_data_analysis.magics\n", "\n", "# All your other packages go here\n", - "# Here I just want pandas and my own utils.\n", + "# Here I only need pandas and my own utils.\n", "import pandas as pd\n", "import _starterkit_utils " ] @@ -136,8 +136,9 @@ "**Step 7: Setting your parameters**\n", "* While these steps have already been done for you, it would still benefit you to re-do these steps and refer to the resource below. \n", "* **Resource**: [DDS Docs Capturing Parameters](https://docs.calitp.org/data-infra/publishing/sections/4_notebooks_styling.html#capturing-parameters).\n", - "* **Parameter #1:** Set a cell that is commented out with your parameter. Turn on the parameter tag.\n", - " * To turn on the parameter tag: go to the code cell go to the upper right hand corner -> click on the gears -> go to “Cell Tags” -> Add Tag + -> add a tag called “parameters” -> click on the new “parameters” tag to ensure a checkmark shows up and it turns dark gray" + "* **Parameter #1:** Set a cell that is commented out with your parameter. \n", + " * Our parameter is every district in the `yml` file. \n", + " * Turn on the parameter tag: go to the code cell go to the upper right hand corner -> click on the gears -> go to “Cell Tags” -> Add Tag + -> add a tag called “parameters” -> click on the new “parameters” tag to ensure a checkmark shows up and it turns dark gray" ] }, { @@ -179,7 +180,9 @@ "id": "f9285e93-924d-4681-b526-97b8e46643b1", "metadata": {}, "source": [ - "* **Parameter #3:** The first markdown cell must include parameters to inject. This line below generates the title District 1 Analysis when it is creating the notebook for District 1. Likewise, it'll say District 2 Analysis for District 2's page. \n", + "* **Parameter #3:** \n", + "* The first markdown cell must include parameters to inject. \n", + "* This line below generates the title District 1 Analysis when it is creating the notebook for District 1. Likewise, it'll say District 2 Analysis for District 2's page. \n", "* Feel free to change this to anything you wish, but make sure this stays a markdown cell.\n", "* This cell is extremely important and read why [here](https://docs.calitp.org/data-infra/publishing/sections/4_notebooks_styling.html#header)." ] @@ -197,7 +200,7 @@ "id": "64615b63-3848-45b7-af2e-19c7b7346997", "metadata": {}, "source": [ - "**Step 8: Input your functions**\n", + "**Step 8: Input your functions that will create your report**\n", "* I am loading my dataset first.\n", "* Then I am adding in my dataset and the district parameter into `_starterkit_utils.create_district_summary`." ] @@ -239,10 +242,10 @@ "metadata": {}, "source": [ "**Step 10: Build your portfolio**\n", - "* Double check you are at the root of your repo.\n", + "* Double check you that are at the root of your repo.\n", "* Replace `REPLACE_YML_NAME` with just the name of your `yml` file without the `.yml` extension into the command below.\n", "* Run `python portfolio/portfolio.py build REPLACE_YML_NAME --deploy` to build your portfolio.\n", - " * Example: My yml is called `ha_starterkit_district.yml` so I would run `python portfolio/portfolio.py build ha_starterkit_district --deploy`." + "* Example: My yml is called `ha_starterkit_district.yml` so I would run `python portfolio/portfolio.py build ha_starterkit_district --deploy`." ] }, { @@ -264,9 +267,10 @@ "metadata": {}, "source": [ "**Step 12: Something not right?**\n", - "* What if something is a little off? After updating your code, rerun this line of code to redo your portfolio. You must always `clean` your portfolio before regenerating new notebooks. \n", + "* What if something is a little off? After updating your code, rerun this line of code to redo your portfolio. \n", + " * You must always `clean` your portfolio before regenerating new notebooks. \n", "` python portfolio/portfolio.py clean REPLACE_YML_NAME && python portfolio/portfolio.py build REPLACE_YML_NAME --deploy`\n", - "* There are many other specifications you can add to `python portfolio/portfolio.py build` and they are all detailed on [DDS Other Specifications](https://docs.calitp.org/data-infra/publishing/sections/5_analytics_portfolio_site.html#other-specifications). " + "* There are many other specifications you can add to `python portfolio/portfolio.py build`, detailed on [DDS Other Specifications](https://docs.calitp.org/data-infra/publishing/sections/5_analytics_portfolio_site.html#other-specifications). " ] }, { @@ -276,12 +280,11 @@ "source": [ "**Step 13: Run a Makefile**\n", "* You can generate all 12 of your notebooks in one swift line of code instead of running the same couple of lines over and over again using a `Makefile`. \n", - "* You can think of a `Makefile` as a coffee machine that does the same thing day in and day out. \n", + "* You can think of a `Makefile` as a machine that does the same thing day in and day out. \n", " * You always install the same packages.\n", " * You always clean out the repo.\n", " * You generally will rerun the notebook in its entirety.\n", " * You always add the `md,yml,ipynb` and other files that the parameterization process creates.\n", - "* Makefiles are great for automating tasks and saving time. \n", "\n", "**Instructions** \n", "* Make sure you are still at the root of our repo `~/data-analyses`.\n",