From 6cff388bdf2492bef5f9b80f8a89dd461c4c1929 Mon Sep 17 00:00:00 2001 From: Carl Cervone <42869436+ccerv1@users.noreply.github.com> Date: Fri, 8 Mar 2024 11:08:50 -0500 Subject: [PATCH] docs: add data science tutorial (#988) --- docs/docs/integrate/data-science.md | 302 +++++++++++++++++-- docs/docs/integrate/gcp_add_key.png | Bin 0 -> 122296 bytes docs/docs/integrate/gcp_apis.png | Bin 0 -> 127249 bytes docs/docs/integrate/gcp_credentials.png | Bin 0 -> 119482 bytes docs/docs/integrate/gcp_credentials_keys.png | Bin 0 -> 106914 bytes docs/docs/integrate/gcp_playground.png | Bin 0 -> 150880 bytes docs/docs/integrate/gcp_service_account.png | Bin 0 -> 160909 bytes docs/docs/integrate/histogram.png | Bin 0 -> 27731 bytes docs/docs/integrate/stars_vs_forks.png | Bin 0 -> 158374 bytes 9 files changed, 281 insertions(+), 21 deletions(-) create mode 100644 docs/docs/integrate/gcp_add_key.png create mode 100644 docs/docs/integrate/gcp_apis.png create mode 100644 docs/docs/integrate/gcp_credentials.png create mode 100644 docs/docs/integrate/gcp_credentials_keys.png create mode 100644 docs/docs/integrate/gcp_playground.png create mode 100644 docs/docs/integrate/gcp_service_account.png create mode 100644 docs/docs/integrate/histogram.png create mode 100644 docs/docs/integrate/stars_vs_forks.png diff --git a/docs/docs/integrate/data-science.md b/docs/docs/integrate/data-science.md index fa80efc7c..c71e7ca0e 100644 --- a/docs/docs/integrate/data-science.md +++ b/docs/docs/integrate/data-science.md @@ -4,31 +4,170 @@ sidebar_position: 4 --- :::info -Jupyter notebooks are a great way for data scientists to explore data, organize ad-hoc analysis, and share insights. We've included several template notebooks to help you get started working with OSO data. You can find these in the [community directory](https://github.com/opensource-observer/insights/tree/main/community/notebook_templates) of our insights repo. We encourage you to share your analysis and visualizations with the OSO community. +Notebooks are a great way for data scientists to explore data, organize ad-hoc analysis, and share insights. We've included several template notebooks to help you get started working with OSO data. You can find these in the [community directory](https://github.com/opensource-observer/insights/tree/main/community/notebook_templates) of our insights repo. We encourage you to share your analysis and visualizations with the OSO community. ::: -## Getting Started +## Setting Up Your Environment --- -We will assume you have some familiarity with setting up a local Python environment and running Jupyter notebooks. +We will assume you have some familiarity with setting up a local Python environment and running [Jupyter notebooks](https://jupyter.org/). We strongly recommend using Python >= 3.11. However, this guide should work for Python >= 3.7. -In order to run the notebooks, you should have the following standard dependencies installed in your local environment: +:::tip +If this is your first time setting up a data science workstation, we recommend [downloading Anaconda](https://www.anaconda.com/download) and following their instructions for installation. Then, check out the [Jupyter docs](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/) to learn how to write your first notebooks. +::: + +### Install Standard Dependencies + +You should have the following standard dependencies installed in your local environment. It is a best practice to use a Python virtual environment tool such as [virtualenv](https://virtualenv.pypa.io/en/latest/) to manage dependencies. + +#### For working with dataframes and vector operations - [pandas](https://pandas.pydata.org/) +- [numpy](https://numpy.org/) + +#### For graph and statistical analysis + - [networkx](https://networkx.org/) +- [scikit-learn](https://scikit-learn.org/stable/) +- [scipy](https://www.scipy.org/) + +#### For charting and data visualization + - [matplotlib](https://matplotlib.org/) - [seaborn](https://seaborn.pydata.org/) - [plotly](https://plotly.com/python/) -- [numpy](https://numpy.org/) -- [scipy](https://www.scipy.org/) -- [scikit-learn](https://scikit-learn.org/stable/) -:::tip -If you need help getting started, check out the [Jupyter docs](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/). -::: +### Install the BigQuery Python Client Library + +From the command line, install **google-cloud-bigquery** either directly on your machine or in a new virtual environment: + +```bash +$ pip install google-cloud-bigquery +``` + +## Connecting to GCP + +--- + +This section will walk you through the process of obtaining a GCP service account key and connecting to BigQuery from a Jupyter notebook. If you don't have a GCP account, you will need to create one (see [here](../getting-started/first-queries) for instructions). + +### Obtain a GCP Service Account Key + +From the [GCP Console](https://console.cloud.google.com/), navigate to the BigQuery API page by clicking **API & Services** > **Enabled APIs & services** > **BigQuery API**. + +You can also go there directly by following [this link](https://console.cloud.google.com/apis/api/bigquery.googleapis.com/). + +![GCP APIs](./gcp_apis.png) + +--- + +Click the **Create Credentials** button. + +![GCP Credentials](./gcp_credentials.png) + +--- + +You will prompted to configure your credentials: + +- **Select an API**: BigQuery API +- **What data will you be accessing**: Application data (Note: this will create a service account) + +Click **Next**. + +--- + +You will be prompted to create a service account: + +- **Service account name**: Add whatever name you want (eg, playground-service-account) +- **Service account ID**: This will autopopulate based on the name you entered and give you a service account email +- **Service account description**: Optional: describe the purpose of this service account + +Click **Create and continue**. + +--- + +You will be prompted to grant your service account access to your project. + +- **Select a role**: BigQuery > BigQuery Admin + +![GCP Service Account](./gcp_service_account.png) + +Click **Continue**. + +--- + +You can skip the final step by clicking **Done**. Or, you may grant additional users access to your service account by adding their emails (this is not required). + +You should now see the new service account under the **Credentials** screen. + +![GCP Credentials Keys](./gcp_credentials_keys.png) + +--- + +Click the pencil icon under **Actions** in the **Service Accounts** table. + +Then navigate to the **Keys** tab and click **Add Key** > **Create new key**. + +![GCP Add Key](./gcp_add_key.png) + +--- + +Choose **JSON** and click **Create**. + +It will download the JSON file with your private key info. You should be able to find the file in your downloads folder. + +Now you're ready to authenticate with BigQuery using your service account key. + +### Connect to BigQuery from a Jupyter Notebook + +From the command line, open a Jupyter notebook: -## Structuring Your Analysis +```bash +$ jupyter notebook +``` + +A Jupyter directory will open in your browser. Navigate to the directory where you want to store your notebook. + +Click **New** > **Python 3** to open a new notebook. (Use your virtual environment if you have one.) + +--- + +You should have a blank notebook open. + +Import the BigQuery client library and authenticate with your service account key. + +```python + +from google.cloud import bigquery +import os + +os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '' # path to your service account key in your downloads folder +client = bigquery.Client() +``` + +Try a sample query to test your connection: + +```python +query = """ + SELECT * + FROM `opensource-observer.oso_playground.collections` +""" +results = client.query(query) +results.to_dataframe() +``` + +If everything is working, you should see a dataframe with the results of your query. + +### Safekeeping Your Service Account Key + +You should never commit your service account key to a public repository. Instead, you can store it in a secure location on your local machine and reference it in your code using an environment variable. + +If you plan on sharing your notebook with others, you can use a package like [python-dotenv](https://pypi.org/project/python-dotenv/) to load your environment variables from a `.env` file. + +Always remember to add your `.env` or `credentials.json` file to your `.gitignore` file to prevent it from being committed to your repository. + +## Running Your Own Analysis --- @@ -42,24 +181,145 @@ These notebooks typically have the following structure: - **Analyze**: Perform analysis and generate visualizations. - **Export**: Export the results to a CSV or JSON file. -## Fetching Data +This next section will help you create a notebook from scratch, performing each of these steps using the OSO playground dataset. + +The example below fetches the latest code metrics for all projects in the OSO data warehouse and generates a scatter plot of the number of forks vs the number of stars for each project. + +You can find the full notebook [here](https://github.com/opensource-observer/insights/blob/main/community/notebooks/oso_starter_tutorial.ipynb). + +### Setup + +From the command line, create a new Jupyter notebook: + +```bash +$ jupyter notebook +``` + +A Jupyter directory will open in your browser. Navigate to the directory where you want to store your notebook. Create a new notebook. + +Import the following dependencies: + +```python +from google.cloud import bigquery +import os +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +``` + +Authenticate with your service account key: + +```python +os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '' # path to your service account key in your downloads folder +client = bigquery.Client() +``` + +### Query + +In this example, we will fetch the latest code metrics for all projects in the OSO data warehouse. + +```python +query = """ + SELECT * + FROM `opensource-observer.oso_playground.code_metrics_by_project` + ORDER BY last_commit_date DESC +""" +results = client.query(query) +``` + +We recommend exploring the data in [the BigQuery console](https://console.cloud.google.com/bigquery) before running your query in your notebook. This will help you understand the structure of the data and the fields you want to fetch. + +[![GCP Playground](./gcp_playground.png)](https://console.cloud.google.com/bigquery) --- -In order to access OSO data directly, you will need access to BigQuery. See our guide for writing your first queries [here](../getting-started/first-queries). +### Transform + +Once you have fetched your data, you can transform it into a format that is ready for analysis. + +Store the results of your query in a dataframe and preview the first few rows: + +```python +df = results.to_dataframe() +df.head() +``` + +Next, we will apply some basic cleaning and transformation to the data: + +- Remove any rows where the number of forks or stars is 0; copy to a new dataframe +- Create a new column to indicate whether the project has had recent activity (commits in the last 6 months) + +```python +dff = df[(df['forks']>0) & (df['stars']>0)].copy() +dff['recent_activity'] = dff['commits_6_months'] > 0 +``` + +### Analyze + +Now that we have our data in a format that is ready for analysis, we can perform some basic analysis and generate visualizations. -Here's a sample BigQuery query that fetches the latest GitHub metrics for all projects in the OSO data warehouse: +We'll start by creating a logscale scatter plot of the number of forks vs the number of stars for each project. -```sql -SELECT * -FROM `opensource-observer.oso.github_metrics_by_project` +```python +fig, ax = plt.subplots(figsize=(10,10)) +sns.scatterplot( + data=dff, + x='stars', + y='forks', + hue='recent_activity', + alpha=.5, + ax=ax +) +ax.set( + xscale='log', + yscale='log', + xlim=(.9,10_000), + ylim=(.9,10_000) +) +ax.set_title("Ratio of stars to forks by project", loc='left') ``` -Here's a more complex query that fetches onchain user data: +Here's a preview of the scatter plot: + +![Stars vs Forks](./stars_vs_forks.png) + +We can continue the analysis by differentiating between projects that have a high ratio of stars to forks and those that have a low ratio. We'll borrow [Nadia Asparouhova](https://nadia.xyz/oss/)'s term "stadium" and simplistically apply it to any project that has a higher than average ratio of stars to forks. + +```python +dff['stars_to_forks_ratio'] = dff['stars'] / dff['forks'] +avg = dff['stars_to_forks_ratio'].mean() +dff['stadium_projects'] = dff['stars_to_forks_ratio'] >= avg +print(avg) +``` + +We can now perform further analysis to see how the distribution of stars to forks ratios is spread across the dataset, with a vertical line indicating the average ratio. + +```python +fig, ax = plt.subplots(figsize=(15,5)) +sns.histplot(dff['stars_to_forks_ratio'], ax=ax) +ax.axvline(avg, color='red') +``` + +Here's a preview of the histogram: + +![Stars to Forks Ratio](./histogram.png) + +Finally, we'll make a crosstab to see how many projects are classified as "stadium" and how many are not. + +```python +pd.crosstab(dff['recent_activity'], dff['stadium_projects']) +``` + +At the time of writing, the crosstab shows 110 "stadium" projects with recent activity versus 829 non-stadium projects. + +Some of the top projects in the OSO dataset by this categorization include [IPFS](https://github.com/ipfs), [Trail of Bits](https://github.com/trailofbits), and [Solidity](https://github.com/ethereum/solidity). + +### Export + +When working with smaller datasets like this one, it's helpful to export the results of your analysis to a CSV or JSON file. 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