data trove steam games catalog

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. Treatment: Drop before modelling or use as a row key only; do not use as a predictive feature. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

122,611

min

10

max

4.264e+06

mean

1.985e+06

median

1.907e+06

std

1.088e+06

q1

1.063e+06

q3

2.87e+06

iqr

1.806e+06

skew

0.1772

kurtosis

-1.05

n_outliers

0

outlier_rate

0

zero_rate

0

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. Treatment: Use as a descriptive label; deduplicate or flag the 1,156 repeated entries, and investigate the long-tail outliers (len_max 413) before any downstream grouping or embedding. medium · anthropic:default

n

122,611

nulls

1 (0.0%)

unique

121,454

len_min

1

len_max

413

len_mean

18.07

len_median

16

len_p95

37.55

word_mean

2.912

word_median

3

n_empty

0

n_duplicates

1,156

duplicate_rate

0.009428

vocab_size

18,813

readability_flesch_mean

52.87

emoji_rate

0.002585

url_rate

0

one_word_rate

0.1866

allcaps_rate

0.06731

boilerplate_rate

4.078e-05

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. Treatment: Parse to a native date type (e.g., datetime64) before any time-series analysis or feature engineering. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

5,081

len_min

11

len_max

12

len_mean

11.72

len_median

12

len_p95

12

word_mean

3

word_median

3

n_empty

0

n_duplicates

117,530

duplicate_rate

0.9586

vocab_size

68

readability_flesch_mean

98.6

emoji_rate

0

url_rate

0

one_word_rate

0

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Ordinal-encode using the natural bin order, or extract bin midpoints as a numeric approximation; investigate the '0 - 0' segment (21,641 rows) as a potential distinct class. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

14

top_value

0 - 20000

top_rate

0.615

cardinality

14

entropy

1.814

entropy_ratio

0.4764

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. Treatment: Consider a two-part model (zero-inflation indicator + log1p transform on non-zero values) or cap/winsorize at a high percentile before modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

1,110

min

0

max

1.014e+06

mean

54.59

median

0

std

3729

q1

0

q3

0

iqr

0

skew

210

kurtosis

5.145e+04

n_outliers

19,676

outlier_rate

0.1605

zero_rate

0.8395

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. Treatment: Treat as a sparse count; consider binarising (0 vs >0) or applying log1p transform, and flag the 1,272 non-zero rows as a minority sub-population for modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

15

min

0

max

21

mean

0.1676

median

0

std

1.654

q1

0

q3

0

iqr

0

skew

9.883

kurtosis

96.52

n_outliers

1,272

outlier_rate

0.01037

zero_rate

0.9896

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. Treatment: Model the zero-inflation separately (e.g., hurdle or Tweedie model), then log1p-transform the positive portion before regression or scaling. medium · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

941

min

0

max

1000

mean

4.765

median

2.24

std

12.53

q1

0.55

q3

5.24

iqr

4.69

skew

22.4

kurtosis

1135

n_outliers

9,297

outlier_rate

0.07583

zero_rate

0.2137

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. Treatment: Model with a two-part (hurdle/zero-inflated) approach, or apply an indicator for zero alongside the raw value; avoid log-transform without offset due to zero mass. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

88

min

0

max

100

mean

18.35

median

0

std

28.86

q1

0

q3

40

iqr

40

skew

1.22

kurtosis

-0.05072

n_outliers

0

outlier_rate

0

zero_rate

0.6682

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. Treatment: Apply log1p transform or use a zero-inflated / Poisson model; consider capping or winsorizing at a high quantile given the max of 3703. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

117

min

0

max

3,703

mean

0.5459

median

0

std

14.52

q1

0

q3

0

iqr

0

skew

171.8

kurtosis

3.836e+04

n_outliers

17,771

outlier_rate

0.1449

zero_rate

0.8551

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. Treatment: Tokenize and embed (e.g., sentence-transformers) before modelling; flag or truncate the extreme-length outliers above len_p95 of 2,966 characters. high · anthropic:default

n

122,611

nulls

8,449 (6.9%)

unique

113,556

len_min

1

len_max

89,665

len_mean

1297

len_median

1,064

len_p95

2,966

word_mean

214.3

word_median

177

n_empty

0

n_duplicates

606

duplicate_rate

0.005308

vocab_size

105,903

readability_flesch_mean

58.75

emoji_rate

0.04672

url_rate

0.0003504

one_word_rate

0.0004029

allcaps_rate

0.007559

boilerplate_rate

0.01517

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. Treatment: Parse the string-serialized lists into actual list structures, then multi-hot encode each language as a binary feature column. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

19,113

len_min

2

len_max

1,216

len_mean

68.02

len_median

11

len_p95

224

word_mean

6.889

word_median

1

n_empty

0

n_duplicates

103,498

duplicate_rate

0.8441

vocab_size

217

readability_flesch_mean

14.07

emoji_rate

0

url_rate

0

one_word_rate

0.5333

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Parse the serialized list strings into proper multi-label indicators (one binary column per language) before modelling; treat '[]' as missing/unknown. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

3,710

len_min

2

len_max

1,216

len_mean

24.31

len_median

2

len_p95

46

word_mean

2.854

word_median

1

n_empty

0

n_duplicates

118,901

duplicate_rate

0.9697

vocab_size

194

readability_flesch_mean

8.003

emoji_rate

0

url_rate

0

one_word_rate

0.813

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Tokenize and embed (e.g., TF-IDF or sentence transformer) before modelling; impute or mask nulls explicitly given the 90.16% null rate. high · anthropic:default

n

122,611

nulls

110,541 (90.2%)

unique

11,884

len_min

3

len_max

2,912

len_mean

340.3

len_median

295

len_p95

763

word_mean

57.37

word_median

49

n_empty

0

n_duplicates

186

duplicate_rate

0.01541

vocab_size

61,840

readability_flesch_mean

57.83

emoji_rate

0.01649

url_rate

0

one_word_rate

0

allcaps_rate

0.008202

boilerplate_rate

0

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. Treatment: Extract Steam app ID from URL path for joining; drop raw URL before modelling or store as-is for image retrieval pipelines. high · anthropic:default

n

122,611

nulls

81 (0.1%)

unique

122,420

len_min

93

len_max

153

len_mean

104.6

len_median

98

len_p95

139

word_mean

1

word_median

1

n_empty

0

n_duplicates

110

duplicate_rate

0.0008977

vocab_size

19,992

readability_flesch_mean

-834.3

emoji_rate

0

url_rate

1

one_word_rate

1

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Extract domain as a categorical publisher identifier; flag or impute nulls; do not embed raw URL strings. high · anthropic:default

n

122,611

nulls

72,935 (59.5%)

unique

39,703

len_min

7

len_max

236

len_mean

32.57

len_median

29

len_p95

56

word_mean

1

word_median

1

n_empty

0

n_duplicates

9,973

duplicate_rate

0.2008

vocab_size

17,059

readability_flesch_mean

-260.3

emoji_rate

0

url_rate

0.9999

one_word_rate

0.9999

allcaps_rate

0

boilerplate_rate

0

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). Treatment: Extract domain as a categorical feature; treat raw URL as a grouping key rather than a text feature; impute or flag nulls separately given 55.8% null rate. high · anthropic:default

n

122,611

nulls

68,404 (55.8%)

unique

35,399

len_min

1

len_max

851

len_mean

31.19

len_median

29

len_p95

51

word_mean

1.002

word_median

1

n_empty

0

n_duplicates

18,808

duplicate_rate

0.347

vocab_size

14,875

readability_flesch_mean

-245.1

emoji_rate

0

url_rate

0.9559

one_word_rate

0.9993

allcaps_rate

0.0007933

boilerplate_rate

0

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. Treatment: Use as a grouping/join key on publisher or developer entity; normalize to lowercase and strip whitespace before joining. high · anthropic:default

n

122,611

nulls

22,243 (18.1%)

unique

60,519

len_min

1

len_max

169

len_mean

22.91

len_median

23

len_p95

31

word_mean

1.004

word_median

1

n_empty

0

n_duplicates

39,849

duplicate_rate

0.397

vocab_size

15,319

readability_flesch_mean

-223.7

emoji_rate

9.963e-06

url_rate

0.003906

one_word_rate

0.9986

allcaps_rate

0.001016

boilerplate_rate

0

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. Treatment: Investigate whether the 44 'False' rows are meaningful anomalies; otherwise drop as near-constant with no predictive variance. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

2

top_value

True

top_rate

0.9996

cardinality

2

entropy

0.004625

entropy_ratio

0.004625

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. Treatment: Cast to boolean/integer (0/1) and monitor class imbalance if used as a target or predictor. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

2

top_value

False

top_rate

0.8263

cardinality

2

entropy

0.666

entropy_ratio

0.666

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. Treatment: encode as binary integer (False=0, True=1) and address class imbalance (~87/13 split) before modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

2

top_value

False

top_rate

0.8719

cardinality

2

entropy

0.5522

entropy_ratio

0.5522

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. Treatment: Apply log1p transform or binarise (zero vs. non-zero) before modelling; consider separating the zero-inflated mass from the active tail for two-part modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

73

min

0

max

97

mean

2.565

median

0

std

13.66

q1

0

q3

0

iqr

0

skew

5.227

kurtosis

25.75

n_outliers

4,256

outlier_rate

0.03471

zero_rate

0.9653

n

122,611

nulls

118,355 (96.5%)

unique

4,160

len_min

42

len_max

142

len_mean

72.43

len_median

70

len_p95

91

word_mean

1

word_median

1

n_empty

0

n_duplicates

96

duplicate_rate

0.02256

vocab_size

4,160

readability_flesch_mean

-704.1

emoji_rate

0

url_rate

1

one_word_rate

1

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Binarise (zero vs. non-zero) or treat as a rare-event indicator; if the raw magnitude matters, cap at a sensible percentile and log1p-transform before modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

31

min

0

max

100

mean

0.02455

median

0

std

1.395

q1

0

q3

0

iqr

0

skew

59.25

kurtosis

3628

n_outliers

40

outlier_rate

0.0003262

zero_rate

0.9997

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. Treatment: Apply log1p-transform (to handle zeros) before modelling, and consider capping or winsorizing at a high percentile to suppress the extreme outliers up to 7,642,084. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

5,540

min

0

max

7.642e+06

mean

1045

median

5

std

2.809e+04

q1

0

q3

37

iqr

37

skew

177.8

kurtosis

4.53e+04

n_outliers

20,797

outlier_rate

0.1696

zero_rate

0.3448

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. Treatment: Apply log1p-transform (or cap at a high percentile) before modelling to reduce extreme skew. medium · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

2,725

min

0

max

1.173e+06

mean

169.2

median

1

std

5375

q1

0

q3

10

iqr

10

skew

156.9

kurtosis

3.063e+04

n_outliers

20,696

outlier_rate

0.1688

zero_rate

0.4502

n

122,611

nulls

122,571 (100.0%)

unique

3

min

98

max

100

mean

99.17

median

99

std

0.6751

q1

99

q3

100

iqr

1

skew

-0.2149

kurtosis

-0.7872

n_outliers

0

outlier_rate

0

zero_rate

0

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. Treatment: Apply log1p-transform (or a zero-inflated model) to compress the extreme right tail before modelling. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

448

min

0

max

9,821

mean

18.09

median

2

std

141.5

q1

0

q3

19

iqr

19

skew

32.63

kurtosis

1192

n_outliers

8,433

outlier_rate

0.06878

zero_rate

0.4859

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. Treatment: Apply log1p-transform (or treat as two-part: zero/non-zero indicator + log-transformed non-zero value) before modelling to handle extreme skew and outliers. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

5,332

min

0

max

4.83e+06

mean

961.8

median

0

std

2.188e+04

q1

0

q3

0

iqr

0

skew

113.9

kurtosis

2.087e+04

n_outliers

20,906

outlier_rate

0.1705

zero_rate

0.8295

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. Treatment: Encode as binary 'has_warning' flag and/or extract categorical warning types (violence, nudity, sexual content) via keyword/regex before modelling; drop raw text. high · anthropic:default

n

122,611

nulls

100,152 (81.7%)

unique

18,620

len_min

2

len_max

2,020

len_mean

164.1

len_median

124

len_p95

445

word_mean

25.74

word_median

20

n_empty

0

n_duplicates

3,839

duplicate_rate

0.1709

vocab_size

23,061

readability_flesch_mean

44.38

emoji_rate

0.0007124

url_rate

8.905e-05

one_word_rate

0.009039

allcaps_rate

0.008193

boilerplate_rate

0.009484

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. Treatment: Apply log1p-transform or treat as two-part model (zero vs. non-zero) before regression or ML use. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

3,037

min

0

max

3.43e+06

mean

208

median

0

std

1.122e+04

q1

0

q3

0

iqr

0

skew

262.9

kurtosis

7.57e+04

n_outliers

26,119

outlier_rate

0.213

zero_rate

0.787

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. Treatment: Apply zero-inflated modelling or split into a binary indicator plus a log-transformed positive-value sub-model before regression. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

993

min

0

max

20,088

mean

13.79

median

0

std

270.4

q1

0

q3

0

iqr

0

skew

51.68

kurtosis

3253

n_outliers

3,898

outlier_rate

0.03179

zero_rate

0.9682

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. Treatment: Apply log1p-transform (or a zero-inflated model) before regression; consider capping at a high percentile to manage extreme outliers. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

2,511

min

0

max

3.43e+06

mean

173.6

median

0

std

1.12e+04

q1

0

q3

0

iqr

0

skew

264

kurtosis

7.611e+04

n_outliers

26,119

outlier_rate

0.213

zero_rate

0.787

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. Treatment: Apply log1p transform or treat as binary (zero vs. non-zero) flag before modelling; consider capping at a high percentile to suppress the extreme tail. high · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

993

min

0

max

20,088

mean

14.72

median

0

std

294.5

q1

0

q3

0

iqr

0

skew

48.91

kurtosis

2848

n_outliers

3,898

outlier_rate

0.03179

zero_rate

0.9682

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. Treatment: Normalize casing and strip punctuation variants before grouping; use as a categorical grouping key or encode as a feature via target/frequency encoding. high · anthropic:default

n

122,611

nulls

8,431 (6.9%)

unique

70,816

len_min

1

len_max

584

len_mean

14.37

len_median

13

len_p95

27

word_mean

2.019

word_median

2

n_empty

0

n_duplicates

43,364

duplicate_rate

0.3798

vocab_size

18,429

readability_flesch_mean

38.73

emoji_rate

0.0008933

url_rate

0.000219

one_word_rate

0.3181

allcaps_rate

0.07974

boilerplate_rate

0

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. Treatment: Encode as a categorical feature (e.g. frequency or target encoding); investigate nulls at 7.2% before modelling. high · anthropic:default

n

122,611

nulls

8,833 (7.2%)

unique

62,689

len_min

1

len_max

164

len_mean

13.82

len_median

13

len_p95

26

word_mean

1.988

word_median

2

n_empty

0

n_duplicates

51,089

duplicate_rate

0.449

vocab_size

15,765

readability_flesch_mean

40.22

emoji_rate

0.0009141

url_rate

0.0002285

one_word_rate

0.3178

allcaps_rate

0.0817

boilerplate_rate

0

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. Treatment: Split on commas and one-hot encode each feature tag for modelling. high · anthropic:default

n

122,611

nulls

8,953 (7.3%)

unique

13,291

len_min

3

len_max

534

len_mean

71.58

len_median

59

len_p95

178

word_mean

5.089

word_median

4

n_empty

0

n_duplicates

100,367

duplicate_rate

0.8831

vocab_size

589

readability_flesch_mean

-105.9

emoji_rate

0

url_rate

0

one_word_rate

0.04047

allcaps_rate

8.798e-06

boilerplate_rate

0

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. Treatment: Split on comma to multi-hot encode genre tags before modelling; flag rows where values contain free-text phrases ('to', 'access', 'play') for cleansing. high · anthropic:default

n

122,611

nulls

8,413 (6.9%)

unique

2,894

len_min

3

len_max

236

len_mean

22.21

len_median

21

len_p95

45

word_mean

1.364

word_median

1

n_empty

0

n_duplicates

111,304

duplicate_rate

0.9747

vocab_size

940

readability_flesch_mean

-206.1

emoji_rate

0

url_rate

0

one_word_rate

0.7892

allcaps_rate

0.009781

boilerplate_rate

0

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. Treatment: Split on commas to multi-hot encode genre tags; investigate and decide on imputation strategy for the 32.02% null rows before modelling. high · anthropic:default

n

122,611

nulls

39,265 (32.0%)

unique

77,179

len_min

3

len_max

295

len_mean

141.3

len_median

163

len_p95

228

word_mean

4.923

word_median

5

n_empty

0

n_duplicates

6,167

duplicate_rate

0.07399

vocab_size

57,260

readability_flesch_mean

-449.7

emoji_rate

0

url_rate

0

one_word_rate

0.1233

allcaps_rate

4.799e-05

boilerplate_rate

0

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. Treatment: Split on commas to extract individual screenshot URLs per game; store as a list-type column or explode into a separate screenshots table keyed by game id. high · anthropic:default

n

122,611

nulls

6,018 (4.9%)

unique

116,483

len_min

144

len_max

29,132

len_mean

1319

len_median

1,039

len_p95

2,773

word_mean

1

word_median

1

n_empty

0

n_duplicates

110

duplicate_rate

0.0009435

vocab_size

19,994

readability_flesch_mean

-5099

emoji_rate

0

url_rate

1

one_word_rate

1

allcaps_rate

0

boilerplate_rate

0

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. Treatment: Manually inspect raw values to determine type and role before any further processing. low · anthropic:default

n

122,611

nulls

0 (0.0%)

unique

—