saturn

This is a Steam games catalogue with 122,611 rows and 40 columns, covering titles, publishers, developers, genres, pricing, review counts, and associated URLs. The most important thing to examine first is the extreme skew across nearly all numeric engagement columns (column23, column24, column27, column29, column31): medians sit at 0–5 while means run into the hundreds or thousands, meaning a tiny fraction of blockbuster titles account for the vast majority of reviews and activity. A second area worth attention is genre distribution (column36), where just a handful of Casual/Indie/Action combinations account for the bulk of the catalogue, and the estimated owner-count banding (column03) shows over 61% of games have fewer than 20,000 owners — pointing to a long-tail market dominated by low-visibility titles.

This column is a support/contact URL field — almost certainly a developer or publisher support link associated with game or software records. 95.6% of non-null values are URLs, and the one-word rate is 99.9%, consistent with bare URL strings. Two surprises stand out: the null rate is very high at 55.8%, meaning more than half of records lack this URL, and the duplicate rate is 34.7% (18,808 duplicate values out of ~54,200 non-null rows), reflecting that many games share the same support domain (e.g., Big Fish Games, EA, Facebook pages).

This column contains publisher or developer website URLs, almost certainly scraped from a Steam or similar games catalogue. Virtually every non-null value is a single URL (one_word_rate 0.9999, url_rate 0.9999), pointing to publisher homepages, Facebook pages, or Steam publisher/group pages. Two signals stand out: 59.48% of rows are null, meaning many game records carry no website; and 20.08% of non-null values are duplicates (9,973 repeated URLs), reflecting publishers with large catalogues who share one website across many titles.

This column contains serialized Python lists of language names, representing the supported or available languages for each record (likely a software product or game). The dominant value is `['English']` appearing 55,314 times, with `[]` (no languages listed) in 8,380 rows. The duplicate rate is extremely high at 84.4%, which is expected given the limited vocabulary of 217 unique tokens and only 19,113 unique values across 122,611 rows — the data is stored as raw string-serialized lists rather than a normalized structure, which is a notable preprocessing concern.

This column contains serialized Python lists of language names, representing the supported or available languages for each record (likely a software product or media item). The dominant value is '[]' (empty list) appearing 72,730 times — nearly 60% of rows — indicating most records have no language metadata populated. Despite 122,611 rows, only 3,710 unique values exist and the duplicate rate is 96.97%, which is expected for a categorical-list field, but the vocabulary is tiny at just 194 words, confirming a closed set of language names.

This column contains Steam CDN URLs pointing to game header images hosted on Akamai's steamstatic.com infrastructure — specifically `header.jpg` assets keyed by Steam app ID. With a url_rate of 1.0 and one_word_rate of 1.0, every single value is a single URL. The column is near-unique (122,420 distinct values out of 122,611 rows), with only 110 duplicates, suggesting these map closely to individual game or product records; the small number of repeated URLs (max frequency 5) likely reflects games appearing in multiple dataset rows.

This column contains comma-separated lists of Steam screenshot URLs (Akamai CDN), one packed string per row representing all screenshot images for a given Steam game entry. Every value is technically 'one word' (no spaces) because the URLs are concatenated without whitespace, explaining the paradoxical one_word_rate of 1.0 alongside a mean length of ~1319 characters and a max of 29132. With 116,483 unique values out of 122,611 rows and only 110 duplicates, this is near-unique; the small duplicate count likely reflects games with identical screenshot sets.

This column contains comma-separated game genre tags (e.g., 'Casual,Indie', 'Action,Adventure,Indie'), consistent with a Steam or similar game catalog dataset. The duplicate rate is extremely high at 97.5%, reflecting the natural cardinality collapse when games share genre combinations — only 2,894 unique tag-sets exist across 122,611 rows. The top words 'to', 'access', and 'play' suggest some rows contain free-text strings like 'Early Access' or 'Free to Play' mixed into the same field, indicating occasional value pollution worth investigating.

This column contains game developer or publisher names, evidenced by top values such as 'Choice of Games', 'KOEI TECMO GAMES CO., LTD.', and dominant vocabulary including 'games', 'studio', 'studios', 'interactive', and 'entertainment'. The duplicate rate of 37.98% (43,364 duplicates across 122,611 rows) is expected — publishers release multiple titles — but the 70,816 unique values and a max length of 584 characters suggest occasional free-text entries or combined multi-publisher strings. The one-word rate of 31.8% and mean word count of ~2 words are consistent with company name formats, though the wide length range (1–584 chars) warrants inspection for outliers.

This column contains game publisher or developer company names, as evidenced by top values like 'BFG Entertainment', 'Choice of Games', and 'Strategy First', and top words dominated by 'games', 'studio', 'studios', 'entertainment', and corporate suffixes ('llc', 'inc.', 'ltd.'). The duplicate rate is notably high at 44.9% (51,089 duplicates across 122,611 rows), which is expected since many games share the same publisher. The one-word rate of 31.8% reflects single-token studio names, and the 7.2% null rate warrants attention for records with unknown publishers.

This column contains email addresses for game developers or publishers, as evidenced by the top values (e.g., 'info@bigfishgames.com', 'support@quanticlab.com'). Nearly all values (99.86%) are single tokens, consistent with email format. The duplicate rate is high at 39.7% (39,849 duplicates out of 122,611 rows), indicating many records share a contact email — expected for a publisher-level field where one entity owns multiple titles. The null rate of 18.14% is notable and should be investigated for systematic missingness.

This column contains dates formatted as 'Mon DD, YYYY' (e.g., 'Oct 23, 2025'), stored as text rather than a native date type. The values span at least 2021–2025 based on top word frequencies, with a striking duplicate rate of 95.86% — 117,530 of 122,611 rows share one of only 5,081 distinct dates, meaning many records map to the same calendar day. The near-constant string length (median 12, min 11, max 12) and vocabulary of just 68 tokens confirm this is a tightly formatted date field with no free-text variation.

This column contains comma-separated genre/tag lists for software or game products (e.g., 'Adventure,Casual,Hidden Object', 'Action,Indie'), consistent with a Steam-style app catalog. The null rate of 32.02% is notably high and warrants investigation before modelling. A multilingual alert is raised, but the non-English content is negligible (26 records out of 3,376 detected), suggesting near-uniform English data with minor noise. The duplicate rate of 7.4% (6,167 duplicates) is expected given finite genre combinations across a large catalog.

This column contains free-text content warnings or age-rating disclosures for video games, with recurring phrases about mature content, nudity, sexual content, and violence. It is massively sparse — 81.68% of rows are null — meaning most games carry no such warning. The duplicate rate of 17.09% (3,839 duplicates across 18,620 unique values) reflects the use of templated boilerplate warning strings, while a small multilingual signal (2 Chinese, 1 Japanese entries) indicates some non-English publisher submissions. Flesch readability of 44.38 and a median length of 124 characters are consistent with dense legal/disclaimer prose.

This column contains substantial free-text descriptions or reviews, most likely about games — the word 'game' is the top non-stopword at 7,882 occurrences, average text length is ~340 characters (~57 words), and the vocabulary spans 61,840 unique tokens. The 90.16% null rate is a major alert: only about 12,000 of 122,611 rows carry any content, meaning this field is sparsely populated. An emoji_rate of ~1.6% and a median Flesch readability score of ~57.8 suggest informal, consumer-written prose. The near_unique flag is partially explained by the sparse population — 11,884 unique values among ~12,000 non-null rows confirms almost every entry is distinct.

This column is a heavily zero-inflated numeric field — likely a count, transaction amount, or event frequency — where 83.95% of values are exactly zero and the interquartile range is 0.0, meaning the entire middle half of the distribution is flat at zero. The remaining values are extremely right-skewed (skew = 209.95, kurtosis = 51452.44) with a max of 1,013,936 against a mean of only 54.59, indicating a small number of very large outliers; 16.05% of rows (19,676) are flagged as outliers. The 1,110 unique values and zero null rate suggest this may be a sparse activity or volume metric.

This column likely represents a monetary amount, duration, or rate — a continuous positive measure where most values are small. The distribution is extreme: the median is 2.24 and Q3 is only 5.24, yet the max reaches 999.98, producing a skew of 22.4 and a kurtosis of 1,135. Over 7.5% of rows (9,297) are flagged as outliers, and 21.4% of values are exactly zero, suggesting a two-part structure (zero-inflation plus a heavy-tailed positive component) that would violate standard regression assumptions.

This column is a sparse count or event-frequency field: 85.5% of its 122,611 rows are exactly zero, the median and IQR are both 0, yet the mean is 0.55 and the max reaches 3,703. The extreme concentration at zero combined with a skew of 171.8 and kurtosis of 38,359 indicates a heavy-tailed distribution driven by rare but very large values; 14.5% of rows (17,771) are flagged as outliers. Only 117 distinct values across 122,611 rows further suggests this is a discrete count, not a continuous measure.

This column is a numeric count or magnitude field — likely representing activity volume, transaction amount, or similar accumulation metric — with 122,611 non-null records and only 5,540 distinct values. The distribution is extraordinarily right-skewed (skew=177.84, kurtosis=45,295.94): the median is just 5.0 while the mean is 1,044.99, and the maximum reaches 7,642,084 — a value roughly 272x the standard deviation above the mean. About 34.5% of values are zero and 17.0% are flagged as outliers (20,797 rows), indicating a heavy zero-inflated tail with extreme rare events dominating the mean.

This column is likely a count or frequency measure (e.g., event occurrences, transaction counts, or interaction tallies) given its non-negative integer-like range and high zero rate. The distribution is extraordinarily right-skewed: the median is 1.0 and Q3 is only 10.0, yet the maximum reaches 1,173,003 — a difference of over six orders of magnitude. With 45% zeros, ~16.9% flagged outliers (20,696 rows), a skew of 156.86, and kurtosis exceeding 30,000, the bulk of records cluster near zero while a small number of extreme values dominate the mean (169.20 vs. median 1.0). This is a severe long-tail distribution that will distort any linear model if used as-is.

This column is likely a count or frequency metric (e.g., event occurrences, transaction counts, or tenure in days/months), given its non-negative integer values with only 448 distinct values across 122,611 rows. The distribution is severely right-skewed (skew=32.63, kurtosis=1192.15): the median is just 2.0 while the mean is 18.09, Q1 is 0.0, and the maximum reaches 9,821—an extreme outlier relative to the IQR of 19. Nearly half the rows (48.6%) are zero, and 6.9% are flagged as outliers, signaling a heavy zero-inflated tail that will distort any linear model trained on raw values.

This column is a sparse, heavily right-skewed numeric count or amount field — likely representing an event frequency, transaction volume, or similar quantity that is zero for the vast majority of records. 82.9% of the 122,611 rows are exactly zero, the median is 0.0, and the IQR is 0.0, yet the mean is 961.8 and the maximum reaches 4,830,455 — indicating a tiny fraction of extreme values driving nearly all the variance. The skew of 113.9 and kurtosis of 20,874.5 are extraordinary, and 17.1% of rows are flagged as outliers, confirming that the non-zero tail is severely extreme relative to the bulk of the distribution.

This column is a heavily zero-inflated count or amount field — 78.7% of its 122,611 rows are exactly zero, and the interquartile range is 0.0, meaning the entire middle 50% of the distribution is zero. Despite a median of 0 and mean of only 208, the max reaches 3,429,544, producing extreme skew (262.89) and kurtosis (75,698), with 21.3% of rows flagged as outliers. This pattern is consistent with a sparse event-count, transaction amount, or usage metric where most entities are inactive but a small tail drives enormous values.

This column is a sparse count or activity metric where the overwhelming majority of records (78.7%) are zero, producing a median of 0.0 and an IQR of exactly 0.0. The distribution is extraordinarily right-skewed (skew = 263.99, kurtosis = 76112.44), driven by extreme outliers reaching a max of 3,429,544 against a mean of only 173.57 — indicating a tiny fraction of records carry massive values. Roughly 21.3% of rows (26,119) are flagged as outliers, which is an unusually high outlier rate and signals a power-law or heavy-tailed phenomenon rather than a simple data error.

This column contains a comma-delimited list of Steam game features/categories (e.g., 'Single-player', 'Steam Achievements', 'Family Sharing', 'Full controller support'), typical of the Steam store's supported features field per game. The extreme duplicate rate (88.3%, 100,367 of 122,611 rows) is expected because many games share identical feature sets, and the tiny vocabulary size of 589 words confirms a finite, enumerated tag system. The 'da' language detection on 12 rows is almost certainly a false positive from short comma-separated tokens, not actual Danish text. With only 13,291 unique combinations out of 122,611 rows, this column is highly suitable for multi-label binarization.

This column is a boolean flag stored as string values ('True'/'False'), covering 122,611 rows with no nulls. It is severely imbalanced: 'True' accounts for 99.964% of rows (122,567 occurrences) while 'False' appears only 44 times. The near-zero entropy (0.0046) confirms the column carries almost no information, making it nearly constant.

This column contains short, near-unique text strings averaging ~3 words and 18 characters, consistent with game or software session/product titles. The dominant top words — 'playtest', 'vr', 'simulator' — strongly suggest these are names of VR game playtesting sessions or titles. Surprising signals include 1,156 duplicates (~0.94% duplicate rate) despite the near-unique alert, a small emoji presence (0.26%), and a maximum length of 413 characters which is anomalously long relative to the median of 16.

This column contains long-form natural language text, likely user-generated content such as reviews, product descriptions, or messages — with a mean of 1,297 characters and 214 words per entry, and a vocabulary of 105,903 unique terms. The near-unique alert (113,556 unique values out of 122,611 rows) confirms these are essentially free-text narratives rather than categorical labels. Notably, 4.7% of entries contain emojis, suggesting informal or consumer-facing content, and the max length of 89,665 characters indicates some extreme outliers well beyond the 95th-percentile length of 2,966 characters. Flesch readability mean of 58.7 places the text in a 'fairly easy' register, consistent with consumer writing.

This column is a low-cardinality integer count (only 15 distinct values, range 0–21) where 98.96% of rows are exactly zero, making it an extreme sparse count feature — likely recording rare events or occurrences per record. The distribution is severely right-skewed (skew 9.88, kurtosis 96.52) with only 1,272 outlier rows (1.04%) carrying any non-zero signal; the IQR is zero because all three quartiles collapse to 0.

This column is a sparse numeric count or score with only 73 distinct values across 122,611 rows, almost certainly representing an event count, frequency, or discrete rating. The distribution is extraordinarily concentrated at zero — 96.5% of values are exactly 0 — with IQR of 0.0 and a median of 0.0, yet the max reaches 97.0, producing extreme positive skew (5.23) and kurtosis (25.75). The 4,256 outlier rows (3.47%) carrying non-zero values likely represent a small active or engaged sub-population, which is the analytically interesting segment.

This column is almost certainly a sparse indicator or rare-event count: 99.97% of its 122,611 values are exactly zero, with only 40 flagged outliers and a maximum of 100.0. The 31 unique values and an IQR of 0.0 confirm that the vast majority of rows carry no signal at all. The extreme skew (59.25) and kurtosis (3,627.8) are a direct consequence of this near-total zero mass, making standard continuous modelling inappropriate without transformation or binarisation.

This column is a heavily zero-inflated count or amount field: 96.8% of its 122,611 rows are exactly zero, driving a median of 0.0 and an IQR of 0.0. The remaining values are extremely skewed (skew = 51.68, kurtosis = 3252.96), with a mean of 13.79 pulled far right by a maximum of 20,088 — likely representing rare but large events such as transaction amounts, error counts, or penalty values. The 3,898 outliers (3.2% of rows) account for virtually all non-zero variance, which is the defining surprise here.

This column is almost certainly a sparse count or occurrence field — likely an event frequency, error count, or similar rare-event tally. The zero_rate of 96.8% means the vast majority of rows have no event, while the remaining ~3.2% drive an extreme right tail (skew=48.9, kurtosis=2848.5) reaching a maximum of 20,088 against a median of 0 and mean of 14.7. The IQR of 0.0 confirms the middle 50% of the distribution is entirely flat at zero, with 3,898 flagged outliers carrying virtually all the variance.

This column was skipped by the profiler, so its content and type are entirely unknown. With 122,611 rows, zero nulls, and no computed statistics or uniqueness information, no data-driven characterisation is possible. The 'skipped' alert is the only signal available.

This column contains 122,611 numeric values that are all unique, null-free, and span from 10 to 4,264,350 — strongly suggesting it is a unique numeric identifier (e.g., a record ID or transaction number). The distribution is remarkably flat and near-uniform: kurtosis of -1.05, negligible skew of 0.18, and zero detected outliers, which is highly unusual for a natural measurement or feature and is consistent with a sequentially or pseudo-randomly assigned integer key. The IQR of 1,806,385 is close to half the full range, further supporting a uniform spread across the ID space.

This column is a bounded numeric score or percentage, ranging from 0 to 100 with only 88 distinct values, suggesting a discretized or rounded measurement (e.g., a completion rate, satisfaction score, or grade). The most striking feature is that 66.8% of values are exactly zero, making the distribution heavily zero-inflated; the median is 0.0 while the mean is 18.35 and Q3 is only 40.0, confirming the mass is concentrated at the floor. Despite the zero inflation, kurtosis is near zero (−0.05), meaning the non-zero portion is roughly flat or uniform across the 0–100 range. Analysts should treat this as a zero-inflated bounded variable rather than a standard continuous feature.

This column encodes a numeric quantity as binned range labels — almost certainly an income, revenue, or financial amount bracket given the scale (0 to 10,000,000+) and logarithmically spaced bin edges. The distribution is heavily right-skewed: 61.5% of rows fall in the '0 - 20000' bucket alone, and a notable 21,641 rows sit in '0 - 0', suggesting a zero-value spike that may warrant separate treatment. With only 14 distinct values and zero nulls across 122,611 rows, the encoding is clean but lossy.

This column is a binary boolean flag stored as string literals 'True'/'False', with zero nulls across 122,611 rows. The dominant value is 'False' at 82.6% (101,319 occurrences), leaving 'True' at roughly 17.4% (21,292) — a moderately imbalanced split that may matter for classification tasks. The entropy ratio of 0.666 confirms meaningful but uneven information content.

This column is a boolean flag stored as string literals 'True'/'False', covering all 122,611 rows with zero nulls. The distribution is heavily skewed: 'False' dominates at 87.2% (106,905 rows) versus 'True' at only 12.8% (15,706 rows). The low entropy of 0.552 confirms the imbalance. An analyst building a classifier on this as a target should anticipate class imbalance requiring resampling or adjusted class weights.