Text Data EDA in Python

We need to load dataset with sklearn's fetch_20newsgroups with specified categories, then show document count per category. Provide code block that loads and then maybe use np.unique or pandas. Provide count per target. Use a dictionary. Should end with a bare expression that displays counts, maybe pd.Series(counts) or print? They said variables can be displayed directly; we can output a pandas Series. Let's write code.

We need to import from sklearn.datasets fetch_20newsgroups. Then load with categories list. Then count occurrences per target name. The dataset object has .target (ints) and .target_names list. We'll compute np.bincount or use pandas. Let's use pandas Series.

Provide code:

from sklearn.datasets import fetch_20newsgroups
import pandas as pd

categories = ['sci.space','rec.sport.hockey','comp.graphics','talk.politics.misc']
newsgroups = fetch_20newsgroups(subset='all', categories=categories, remove=('headers','footers','quotes'))

# count per category
counts = pd.Series(newsgroups.target).value_counts().rename_axis('category_index').reset_index(name='doc_count')
counts['category'] = counts['category_index'].map(dict(enumerate(newsgroups.target_names)))
counts = counts[['category','doc_count']]
counts

That ends with counts DataFrame displayed. That's fine.

We have loaded dataset and displayed counts per category. The original user goal: "load 20 newsgroups dataset (20 Newsgroups from sklearn) with 4 categories and show document count per category fetch_20newsgroups(categories=['sci.space','rec.sport.hockey','comp.graphics','talk.politics.misc'])". The output already shows category and doc count. That's sufficient. Need to answer with natural final answer and include <!--final answer-->. No code.

We need to plot text length distribution across categories. We have newsgroups dataset loaded with variable newsgroups (Bunch) containing data (list of strings) and target (ints). We need to compute length of each document (maybe number of words or characters). Then plot distribution per category. Use seaborn. We'll create a DataFrame with columns: text, length (maybe characters), category name. Then use seaborn boxplot or violin? Distribution: maybe histogram per category (facet). Provide code to compute length and plot.

We should not assume variable newsgroups is still available; but it's loaded earlier. In notebook state we have not listed newsgroups variable; but we can still refer to it; assume it's present. We'll create df_lengths = pd.DataFrame with columns 'category', 'length'. Use len of string (characters). Then seaborn histplot with hue or separate subplots.

Single chart: use seaborn.violinplot to show distribution of lengths across categories.

Proceed with code.

We need to decide A or B. Goal: plot text length distribution across categories. The user already plotted violin plot. Output includes a figure and also displayed df_len.head(). The figure is shown. That satisfies the goal: a plot of distribution across categories. The user wants plot, we have it. So Path A: provide final answer summarizing insights (distribution shape etc.) No code. Provide final answer.

We should include insights: maybe note median lengths, ranges observed from head and maybe summarize distribution. We can also compute some stats quickly but not required. But we can give insights from observed chart? Without numeric, we can mention distribution appears ... However we haven't numeric summary. But we can still describe based on visible head values: sci.space has longer documents (e.g., 4617 chars), rec.sport.hockey shorter (72). Violin plot shows ... Provide insights.

Thus Path A.

User wants top 20 most frequent words after removing stopwords. We have df_len with text column. Need to process text: tokenize, lower, remove stopwords (probably from nltk). Compute word frequencies across all documents (or maybe per category? Probably overall). Show top 20.

We need to import nltk stopwords; ensure they are downloaded. Use collections.Counter.

Provide code that creates tokens, filters stopwords, counts, and displays top 20 as a DataFrame.

We must follow notebook rules: one step at a time. Since we haven't yet computed frequencies, next step is to compute and display top 20.

We need to ensure stopwords downloaded: nltk.download('stopwords').

We'll create a new column maybe tokens, but not needed to store. Just compute.

We'll display as a DataFrame sorted descending.

Provide code block ending with the DataFrame.

We need to decide if current outputs answer original goal: "show top 20 most frequent words after removing stopwords". The latest output (output-3) is a table of top 20 words with frequencies. That directly answers the goal. So Path A: provide final answer using observed output, no code. Must include <!--final answer-->. Must not repeat output already visible. But we need to convey the top 20 words and frequencies. We can restate them. That's allowed, but we must not restate the entire output verbatim? The rule says "Do not restate output already visible in the latest output." That means we shouldn't just copy the same table verbatim, but we can summarize? Likely we should present the list in prose or a small table but maybe it's considered restating? It says "Do not restate output already visible". That suggests we shouldn't repeat the same table exactly. We can describe the top words and frequencies in a concise way.

We can say: The most frequent word after removing stopwords is "would" (2300 occurrences), followed by "one" (1820), etc. Provide the list maybe as bullet points. That is essentially restating but maybe acceptable as insight. It might count as restating. But we need to answer the goal: show top 20 most frequent words. We could present them in a formatted list.

Probably okay. Let's produce answer summarizing table.

User wants to extract top TF-IDF keywords per category. We have df_len with text and category. Need to compute TF-IDF per document, then aggregate per category (e.g., compute average TF-IDF score for each term within category, then take top N per category). We'll use sklearn's TfidfVectorizer, possibly with stopwords removal (already have). Use sublinear_tf maybe. Then compute per category by grouping the document-term matrix rows. Approach: fit vectorizer on all texts, get sparse matrix X. Then for each category, get rows indices, compute mean TF-IDF across rows (or sum). Then extract top terms per category, e.g., top 10. Show as DataFrame per category. Provide code. As per guidelines, one step per response: we are to propose next step and give code. Since we haven't yet computed TF-IDF, next step is to compute and display top TF-IDF terms per category. Provide code that computes and displays top 10 per category. This is one step. Provide the code block.

We need to decide if current outputs answer original goal: "extract top TF-IDF keywords per category". The output from cell-4 shows top terms for comp.graphics and rec.sport.hockey, and started sci.space but not displayed fully. The user likely wants the list for each category. The outputs partially show only two categories fully, and sci.space incomplete. Also need to show for talk.politics.misc? Actually categories are sci.space, rec.sport.hockey, comp.graphics, talk.politics.misc. The output only shows three categories (comp.graphics, rec.sport.hockey, sci.space). The sci.space table is not displayed; perhaps truncated. So we need to show the missing table for sci.space and also for talk.politics.misc. Also maybe present the final list of top TF-IDF keywords per category in concise form.

Thus current outputs insufficient. Provide brief insights: we have top TF-IDF terms for comp.graphics and rec.sport.hockey; need to retrieve for sci.space and talk.politics.misc. So Path B: we need code to display the remaining categories.

We should produce code that iterates through results dict and displays all categories (including those not shown). But they already looped through results and displayed each. Maybe the notebook truncated output after sci.space due to long output. Possibly we need to explicitly display the sci.space and talk.politics.misc tables.

Simplest: display results for missing categories specifically. Provide code:

for cat in ['sci.space','talk.politics.misc']: display(df) etc.

But must be minimal.

Will give code to print the tables for all categories sorted.

Provide:

for cat, df in results.items(): display(f"Top TF‑IDF terms for {cat}") display(df)

But that's same as before; perhaps the notebook didn't show due to truncation. To ensure we get them, we can request results dict and print.

We'll just output code to display results dict items again.

Given guidelines: Only one code block.

Proceed.

We need to decide if we have sufficient output to answer original goal: extract top TF-IDF keywords per category.

We have outputs for comp.graphics and rec.sport.hockey, but sci.space output is empty (no table displayed). Also talk.politics.misc not shown.

Thus not sufficient. Need to get missing top terms for sci.space and talk.politics.misc. Provide minimal code to display those results. Could just display results dict again but we already did. The issue is that the DataFrames for those categories are empty? Likely because there are no terms? Actually likely because top_terms_for_category returned empty DataFrame due to something? idx mask works, but perhaps there are terms but stopwords removed? Wait TF-IDF computed across all, but maybe for sci.space, the top terms maybe appear but due to sorting, they exist. The display shows empty DF with just header? Actually output shows just the title '**Top TF‑IDF terms for sci.space**' but no table afterward, meaning the DataFrame is empty (maybe zero rows). That indicates something wrong: maybe average tfidf computed but all zeros? Could be because tfidf_mat[idx] returns a sparse matrix of shape (n_docs, n_terms) but mean across rows returns zeros if no docs? idx not empty. Could be that tfidf_mat[idx] returns a compressed row, but mean returns something else? Actually tfidf_mat[idx] works, returns submatrix. Then .mean(axis=0).A1 gives average tfidf per term across selected docs. That should have non-zero values where terms appear. Then top_idx = mean_tfidf.argsort()[::-1][:n] yields indices; if all zeros, argsort still returns sorted indices, and top_idx gives first n indices, so DataFrame not empty. But observed empty indicates maybe idx.sum()==0? That would be false. Or maybe function returned empty because idx.sum()==0? No.

Maybe the display suppressed empty DataFrames because they had no rows? Not sure. Anyway need to compute again for those categories and ensure display. Could also simply print results for those categories directly: results['sci.space'] and results['talk.politics.misc'].

Thus Path B: minimal code to display these two DataFrames.

We have outputs showing top TF-IDF terms for all four categories. They are displayed as tables. So original goal "extract top TF-IDF keywords per category" is fulfilled. Need to provide final answer summarizing top keywords per category. Must include <!--final answer-->. No code. Provide concise summary.