who_is_hiring_comments_analysis_2

hello

Hello! What would you like to analyze?

A local file has been selected for analysis:
"/home/piotr/Downloads/who_is_hiring_comments.zip"

Load and inspect this file now.

Thinking

Inspecting zip files for data

I need to figure out how to handle zip archives properly. Firstly, I want to inspect the archive to identify its contents, which are likely in CSV format but could also be JSON. I could list the files in the zip and choose the first tabular file to load, but I must be cautious since I can't assume what the contents are. Using Python will help me list the files and determine which one to load, ensuring a resilient approach.Returning a single code for file handling

I want to create a code that uses zipfile and io to support various file formats like CSV, JSON, Parquet, and Excel. The logic may get a bit lengthy, so I might first list the filenames and ensure there's only one to load. If the archive contains multiple files, it’s likely to have CSV comments. I could focus on reading the largest CSV file, possibly named who_is_hiring_comments.csv, but I need to inspect and ensure that I select only supported formats when there’s exactly one.

I’ll load the archive’s tabular file and show a quick preview.

load data

Thinking

Inspecting code for zip files

I’m thinking about how we need code to work with zip files, possibly containing CSVs. I should focus on loading and inspecting these files first. To do this, I plan to use the zipfile module to find potential candidates. After that, I can utilize pandas to read the CSV files in order to inspect their content. My main goal is to ensure that I'm following the developer pattern correctly and efficiently.

</>Load dataset from archive

import zipfile
from pathlib import Path
import pandas as pd

file_path = Path("/home/piotr/Downloads/who_is_hiring_comments.zip")

with zipfile.ZipFile(file_path) as z:
    data_files = [
        name for name in z.namelist()
        if Path(name).suffix.lower() in {".csv", ".tsv", ".json", ".parquet"}
    ]
    if not data_files:
        raise ValueError("No supported tabular data file found in the ZIP archive.")
    data_file = data_files[0]
    
    with z.open(data_file) as f:
        if data_file.lower().endswith(".csv"):
            df = pd.read_csv(f)
        elif data_file.lower().endswith(".tsv"):
            df = pd.read_csv(f, sep="\t")
        elif data_file.lower().endswith(".json"):
            df = pd.read_json(f)
        else:
            df = pd.read_parquet(f)

print(f"Loaded: {data_file}")
print(f"Shape: {df.shape}")
columns = df.columns.tolist()
print(f"Columns ({len(columns)}):", columns if len(columns) <= 100 else columns[:100] + ["..."])
df.head()

Loaded: who_is_hiring_comments.csv
Shape: (88975, 7)
Columns (7): ['thread_id', 'thread_title', 'thread_timestamp', 'comment_id', 'timestamp', 'author', 'comment']

	thread_id	thread_title	thread_timestamp	comment_id	timestamp	author	comment
0	4857714	Ask HN: Who is hiring? (December 2012)	2012-12-01 14:37:05+00:00	4857738	2012-12-01 14:47:23+00:00	ryporter	Palo Alto, CA. Full time. H1B okay. AMA Capita...
1	4857714	Ask HN: Who is hiring? (December 2012)	2012-12-01 14:37:05+00:00	4857758	2012-12-01 14:51:44+00:00	btfh	London, Full-time Chaffinch is hiring a junior...
2	4857714	Ask HN: Who is hiring? (December 2012)	2012-12-01 14:37:05+00:00	4857765	2012-12-01 14:53:51+00:00	orta	Art.sy - New York City - iOS Developers and We...
3	4857714	Ask HN: Who is hiring? (December 2012)	2012-12-01 14:37:05+00:00	4857771	2012-12-01 14:55:09+00:00	akshxy	Rails Hacker/ Chief Technical Officer Mumbai, ...
4	4857714	Ask HN: Who is hiring? (December 2012)	2012-12-01 14:37:05+00:00	4857779	2012-12-01 14:57:56+00:00	akshxy	User Experience Designer/ Chief Architect Mumb...

The dataset contains 88,975 Hacker News “Who is hiring?” comments across seven fields. It includes thread metadata, comment and thread timestamps, author names, and the full comment text, enabling analysis by posting period, thread, author, or job-post content.

show number of counts per month, and plot interactive plot

Thinking

Preparing date visualizations

I need to parse dates and then create monthly counts with an interactive Altair chart, but I only want one chart. I should consider using date strings and possibly pd.to_datetime for better manipulation. When I think about counts, I might mean comments, so I’ll need some code for that. Maybe I’ll use an existing DataFrame to aggregate the data without showing a table—just focusing on the final chart. This should be fun!

This counts comments by their posting month.

</>Plot monthly comments

import pandas as pd
import altair as alt

monthly_counts = (
    df.assign(month=pd.to_datetime(df["timestamp"], utc=True).dt.to_period("M").dt.to_timestamp())
      .groupby("month", as_index=False)
      .size()
      .rename(columns={"size": "comment_count"})
)

chart = (
    alt.Chart(monthly_counts)
    .mark_line(point=True)
    .encode(
        x=alt.X("month:T", title="Posting month"),
        y=alt.Y("comment_count:Q", title="Number of comments"),
        tooltip=[
            alt.Tooltip("month:T", title="Month", format="%B %Y"),
            alt.Tooltip("comment_count:Q", title="Comments", format=","),
        ],
    )
    .properties(
        title="Hacker News “Who is Hiring?” Comments per Month",
        width="container",
        height=360,
    )
    .interactive()
)

chart

/tmp/ipykernel_1326899/3468138492.py:5: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df.assign(month=pd.to_datetime(df["timestamp"], utc=True).dt.to_period("M").dt.to_timestamp())

Thinking

Considering chart details

I need to focus on creating a B due chart that strictly shows number counts. I think using a code display for monthly counts might be the way to go. With about 12 years and 140 rows of data, I'll aim to present it in a clear table format. There's really no need to ask for further clarification since the user seems specific about what they want. Let's make it simple and straightforward!

The interactive chart spans the full comment history and uses each comment’s timestamp aggregated by calendar month. The numeric monthly totals are needed alongside the chart to show the exact counts.

</>Format monthly comment counts

display(
    monthly_counts.assign(
        month=monthly_counts["month"].dt.strftime("%Y-%m")
    ).rename(columns={"month": "month", "comment_count": "comments"})
)

	month	comments
0	2012-12	220
1	2013-01	186
2	2013-02	211
3	2013-03	277
4	2013-04	330
...	...	...
158	2026-04	333
159	2026-05	324
160	2026-06	318
161	2026-07	267
162	2026-08	240

163 rows × 2 columns

Monthly comment counts are available for 163 months, from December 2012 through August 2026, and the interactive line chart supports hover-based inspection of each month’s exact total. Counts begin at 220 in December 2012, rise to 330 by April 2013, and the latest displayed months range from 240 to 333 comments.