Text Data EDA in Python

The user wants to load the 20 Newsgroups dataset from sklearn using four specific categories (sci.space, rec.sport.hockey, comp.graphics, talk.politics.misc) and display the document count per category.

Plan:

1. Import fetch_20newsgroups from sklearn.datasets.
2. Load the dataset with the specified categories.
3. Extract the target labels.
4. Count the occurrences of each label using pandas or collections.Counter.
5. Display the counts.

Following the rules:

• One runnable Python code block.
• Minimal code.
• First-load inspection should show shape, columns, and head() if it's a DataFrame, but since fetch_20newsgroups returns a bunch object, I will convert the target to a series/dataframe for counting.
• Since the user specifically asked for document count per category, I will focus on that.

Step 1: Load the data and count documents per category.

The user wants to load the 20 newsgroups dataset with 4 specific categories and show the document count per category. Looking at the notebook state:

• The dataset was loaded using fetch_20newsgroups(subset='all', categories=['sci.space', 'rec.sport.hockey', 'comp.graphics', 'talk.politics.misc']).
• The output shows the counts:
  • comp.graphics: 973
  • rec.sport.hockey: 999
  • sci.space: 987
  • talk.politics.misc: 775

The original goal was "load 20 newsgroups dataset... and show document count per category". This has been achieved. The output provides the exact counts for each of the four categories requested.

Decision: Path A (Sufficient).

The user wants to plot the text length distribution across the four categories of the 20 Newsgroups dataset.

- `data`: A `Bunch` object from `fetch_20newsgroups`. It contains `data.data` (the text of the documents) and `data.target` (the labels).
- `categories`: The list of category names.
- `counts`: A series showing counts per category.

- I need to calculate the length of each document in `data.data`.
- I need to associate these lengths with their respective categories from `data.target`.
- A DataFrame would be the easiest way to handle this.
- The user wants a distribution plot. A boxplot or a KDE/histogram (using seaborn) would be appropriate to compare distributions across categories. A boxplot or violin plot is usually better for comparing distributions across a few categorical groups.

- Use `seaborn` and `matplotlib`.
- Create a DataFrame with `text_length` and `category`.
- Plot using `sns.boxplot` or `sns.kdeplot`. Given "distribution", a KDE plot or a boxplot is standard. I'll use a boxplot for a clear summary and perhaps overlay it or use a distribution plot. Actually, `sns.boxplot` or `sns.violinplot` is very clear for category comparison. Let's go with a boxplot to see the range and outliers.

- I should ensure the categories are human-readable (using `data.target_names`).

- `lengths = [len(text) for text in data.data]`
- `df = pd.DataFrame({'length': lengths, 'category': [data.target_names[i] for i in data.target]})`
- `sns.boxplot(data=df, x='category', y='length')`

- One code block? Yes.
- Seaborn? Yes.
- One chart? Yes.
- No `$`? Not relevant here (no money).
- Notebook state used? Yes (`data` is available).

The user wants to plot the text length distribution across categories. Looking at the notebook state:

• Cell 1 loaded the data and showed counts per category.
• Cell 2 created a df_lengths DataFrame and plotted a boxplot of lengths by category.
• The output of Cell 2 is <Figure size 1000x600 with 1 Axes>, meaning the plot was generated.

However, the "Sufficiency heuristics" state: "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first and print the values used in the chart."

The current output is just the figure. I don't have the descriptive statistics (median, quartiles, max, min) for the text lengths per category. To provide a comprehensive answer and follow the strict heuristic, I should provide the numeric summary of the distribution.

Therefore, I must choose Path B to gather the numeric evidence (descriptive statistics) that supports the visual plot.

Goal: Plot text length distribution across categories. Current state: Plot created, but no numeric summary. Next step: Calculate and display the descriptive statistics for the lengths per category.

The user wants to "plot text length distribution across categories". The notebook state shows:

1. Data loaded: fetch_20newsgroups with 4 categories.
2. A boxplot was created (cell-2) showing the distribution of text lengths across categories.
3. Descriptive statistics (cell-3) were calculated, providing mean, std, min, 25%, 50%, 75%, and max for each category.

The original goal was simply to "plot text length distribution across categories". The visual (boxplot) is already produced. The numeric summary (describe()) provides the necessary evidence to support the visualization.

Looking at the statistics:

• talk.politics.misc has the highest mean (~2810) and median (1640).
• comp.graphics has the lowest mean (~1806) and median (880).
• There are significant outliers in all categories (max values reach 49k-75k characters while 75th percentiles are below 2600).

Since both a visualization and the supporting numeric values have been provided, the original goal is fully satisfied.

Decision: Path A.

• Current state: Data is loaded into df_lengths (containing lengths and categories) and the original data object from fetch_20newsgroups is available.

  • User goal: Show top 20 most frequent words after removing stopwords.

  • Context: This is a text analysis task on the 20 Newsgroups dataset.

  • Available data: data.data contains the raw text of the documents.

  • Combine all text documents into one large corpus (or process them individually).

  • Tokenize the text (splitting by whitespace or using a library).

  • Remove stopwords.

  • Count frequencies.

  • Pick the top 20.

  • Need a list of stopwords. NLTK is standard, but since I can't be sure about the environment's installations, I'll check for common libraries. Actually, in a standard notebook for this, nltk or sklearn's basic tools are common. I'll use nltk as it's the gold standard for stopwords.

  • Preprocessing: Convert to lowercase, remove punctuation.

  • Step 1: Process text, remove stopwords, and count frequencies.

  • Wait... the instructions say "Prefer one execution step at a time." and "If multiple code actions are required, do only the first action now".

  • Wait, "show top 20 most frequent words" is a single objective. The processing is a prerequisite.

  • Imports: collections.Counter, nltk (for stopwords).

  • Logic:

    1. Join all text in data.data.
    2. Normalize (lowercase, remove punctuation).
    3. Tokenize.
    4. Filter out stopwords.
    5. Use Counter to get the top 20.

  • Since I don't know if nltk stopwords are downloaded, I should probably download them in the code block.

  • Code:

    import nltk
    from nltk.corpus import stopwords
    from collections import Counter
    import re
    
    nltk.download('stopwords')
    stop_words = set(stopwords.words('english'))
    
    # Combine all text
    all_text = ' '.join(data.data).lower()
    # Remove punctuation and split
    words = re.findall(r'\b\w+\b', all_text)
    # Filter stopwords
    filtered_words = [w for w in words if w not in stop_words]
    
    # Top 20
    word_counts = Counter(filtered_words).most_common(20)
    pd.DataFrame(word_counts, columns=['word', 'count'])
    

  • One Python code block? Yes.

  • No identifier/leakage columns? Not training an ML model here, just analysis.

  • Correct formatting? Yes.

  • Wait, I should probably make the code clean.

  • Self-Correction: The user wants to "show top 20 most frequent words". I'll perform the text cleaning and counting in one go as it's the logical sequence for this specific request.

The user wants to see the top 20 most frequent words after removing stopwords. Looking at the notebook state:

• [cell-4] was executed.
• It combined all text, tokenized, removed NLTK English stopwords, and calculated the top 20 most common words.
• [output-4] shows a DataFrame with the top 20 words and their counts.

Observations from [output-4]:

• The top words are edu (7573), 1 (6756), 0 (6482), 2 (4485), subject (4009), lines (3944), organization (3783), etc.
• Many of these words are "noise" related to the email headers (metadata) of the 20 Newsgroups dataset (e.g., edu, subject, lines, organization, numbers). These are not meaningful semantic words of the content.

The goal was "show top 20 most frequent words after removing stopwords". The code did exactly that. However, a data analyst would notice that the results are dominated by metadata.

Wait, the "Original user goal" is "show top 20 most frequent words after removing stopwords". The latest output [output-4] provides exactly this list. The sufficiency heuristic says: "If current outputs are sufficient to answer the original goal, provide a natural final answer...".

Does it answer the goal? Yes. Is it helpful? It's technically correct based on the request, but the results are polluted. However, the instructions say: "Do not introduce new side goals, tangents, or optional explorations that were not requested." and "Stay strictly within the original user goal."

The goal was to show the top 20 words. They have been shown. If I suggest removing metadata, I might be going beyond the goal. But as an analyst, identifying that edu, subject, and lines are metadata is a concrete insight.

Let's evaluate the output. The top 20 words are listed. Insight: The list is dominated by email header terms (like edu, subject, lines, organization) and digits, indicating that the documents contain raw email headers which were not stripped before tokenization.

Is Path A appropriate? Yes, the goal "show top 20 most frequent words" is technically met. But if the user wants meaningful words, they might be disappointed. However, the prompt asks to analyze against the original goal. The original goal is a simple "show top 20...".

I will provide the final answer based on the observed output.

Top words:

1. edu: 7573
2. 1: 6756
3. 0: 6482
4. 2: 4485
5. subject: 4009 ... and so on.

Insights:

• The most frequent "words" are actually metadata from email headers (e.g., edu, subject, lines, organization) and numeric digits.
• This suggests the dataset contains raw email formats where header information is treated as part of the text.
• Words like space (2,166) appear in the top 20, reflecting the sci.space category.

I will use Path A.

The user wants to extract the top TF-IDF keywords for each of the four categories (sci.space, rec.sport.hockey, comp.graphics, talk.politics.misc).

*   `data` object from `fetch_20newsgroups` is available.
*   `data.data` contains the documents.
*   `data.target` contains the labels.
*   `data.target_names` contains the category names.

*   Need `TfidfVectorizer` from `sklearn.feature_extraction.text`.
*   Need to initialize the vectorizer.
*   Fit-transform the `data.data`.
*   Iterate through each category.
*   Average the TF-IDF scores for all documents within a specific category to get the representative keywords for that category.
*   Sort and pick the top $N$ words (e.g., top 10).

*   Step 1: Initialize `TfidfVectorizer` and transform the data.
*   Step 2: Compute the mean TF-IDF score per feature for each class.
*   Step 3: Map feature indices back to words and display the results.

*   Use `TfidfVectorizer`.
*   Avoid including metadata (headers, footers) if possible, though the previous "most frequent words" step showed a lot of metadata (`edu`, `subject`, `lines`). The `TfidfVectorizer` might still pick these up if they aren't consistently distributed, but usually, TF-IDF helps penalize words that appear in every document. However, since `fetch_20newsgroups` has specific `remove` parameters, I could have suggested using those, but since I'm working with the already loaded `data`, I'll proceed with the vectorizer.
*   Actually, `TfidfVectorizer` handles stopwords if passed `stop_words='english'`.

*   Create a dictionary or DataFrame to store results.
*   Loop through categories.
*   Extract top words.

The user goal is to "extract top TF-IDF keywords per category". The notebook state shows that TfidfVectorizer was used, the mean TF-IDF scores per category were calculated, and a DataFrame with the top 10 keywords per category was generated and displayed in [output-5].

Analysis of [output-5]:

• comp.graphics: graphics, edu, image, com, lines, file, subject, thanks, 3d, files.
• rec.sport.hockey: game, edu, ca, hockey, team, games, nhl, espn, play, university.
• sci.space: space, edu, nasa, henry, access, com, digex, alaska, gov, moon.
• talk.politics.misc: com, edu, cramer, people, optilink, article, writes, government, gay, clayton.

Observations:

• The keywords successfully distinguish categories (e.g., "hockey", "nhl" for sports; "nasa", "moon" for space; "graphics", "3d" for graphics).
• However, generic terms like "edu", "com", "lines", and "subject" appear across multiple categories. These are "noise" words common in email headers, which the basic 'english' stopword list did not catch.

The original goal was simply to "extract top TF-IDF keywords per category". The output provides exactly that.

Decision: Path A. The outputs are sufficient. I will provide a summary of the keywords extracted and their meaning.