Revert "rename to remove quickstart"

This reverts commit ad8185ec5b.

quickstart_etl/assets/hackernews.py

@@ -0,0 +1,96 @@
+import base64
+import json
+import os
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+from dagster import AssetExecutionContext, MaterializeResult, MetadataValue, asset
+
+
+@asset(group_name="hackernews", compute_kind="HackerNews API")
+def topstory_ids() -> None:
+    """Get up to 100 top stories from the HackerNews topstories endpoint.
+
+    API Docs: https://github.com/HackerNews/API#new-top-and-best-stories
+    """
+    newstories_url = "https://hacker-news.firebaseio.com/v0/topstories.json"
+    top_new_story_ids = requests.get(newstories_url).json()[:100]
+
+    os.makedirs("data", exist_ok=True)
+    with open("data/topstory_ids.json", "w") as f:
+        json.dump(top_new_story_ids, f)
+
+
+@asset(deps=[topstory_ids], group_name="hackernews", compute_kind="HackerNews API")
+def topstories(context: AssetExecutionContext) -> MaterializeResult:
+    """Get items based on story ids from the HackerNews items endpoint. It may take 30 seconds to fetch all 100 items.
+
+    API Docs: https://github.com/HackerNews/API#items
+    """
+    with open("data/topstory_ids.json") as f:
+        topstory_ids = json.load(f)
+
+    results = []
+    for item_id in topstory_ids:
+        item = requests.get(f"https://hacker-news.firebaseio.com/v0/item/{item_id}.json").json()
+        results.append(item)
+
+        if len(results) % 20 == 0:
+            context.log.info(f"Got {len(results)} items so far.")
+
+    df = pd.DataFrame(results)
+    df.to_csv("data/topstories.csv")
+
+    return MaterializeResult(
+        metadata={
+            "num_records": len(df),  # Metadata can be any key-value pair
+            "preview": MetadataValue.md(df.head().to_markdown()),
+            # The `MetadataValue` class has useful static methods to build Metadata
+        }
+    )
+
+
+@asset(deps=[topstories], group_name="hackernews", compute_kind="Plot")
+def most_frequent_words(context: AssetExecutionContext) -> MaterializeResult:
+    """Get the top 25 most frequent words in the titles of the top 100 HackerNews stories."""
+    stopwords = ["a", "the", "an", "of", "to", "in", "for", "and", "with", "on", "is"]
+
+    topstories = pd.read_csv("data/topstories.csv")
+
+    # loop through the titles and count the frequency of each word
+    word_counts = {}
+    for raw_title in topstories["title"]:
+        title = raw_title.lower()
+        for word in title.split():
+            cleaned_word = word.strip(".,-!?:;()[]'\"-")
+            if cleaned_word not in stopwords and len(cleaned_word) > 0:
+                word_counts[cleaned_word] = word_counts.get(cleaned_word, 0) + 1
+
+    # Get the top 25 most frequent words
+    top_words = {
+        pair[0]: pair[1]
+        for pair in sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:25]
+    }
+
+    # Make a bar chart of the top 25 words
+    plt.figure(figsize=(10, 6))
+    plt.bar(list(top_words.keys()), list(top_words.values()))
+    plt.xticks(rotation=45, ha="right")
+    plt.title("Top 25 Words in Hacker News Titles")
+    plt.tight_layout()
+
+    # Convert the image to a saveable format
+    buffer = BytesIO()
+    plt.savefig(buffer, format="png")
+    image_data = base64.b64encode(buffer.getvalue())
+
+    # Convert the image to Markdown to preview it within Dagster
+    md_content = f"![img](data:image/png;base64,{image_data.decode()})"
+
+    with open("data/most_frequent_words.json", "w") as f:
+        json.dump(top_words, f)
+
+    # Attach the Markdown content as metadata to the asset
+    return MaterializeResult(metadata={"plot": MetadataValue.md(md_content)})