ml 32m movies
Reading
This dataset is a movie catalogue of 87,585 rows with three columns: a unique movieId, a title, and a pipe-delimited genres string. The genres column is the most analytically interesting: only 1,798 unique combinations exist, and Drama, Documentary, and Comedy dominate, while 7,080 rows are tagged '(no genres listed)' — a sizeable gap worth flagging. Titles are nearly unique (87,382 distinct of 87,585), and the frequent '(2014)'–'(2019)' tokens in titles suggest the catalogue skews toward recent years. movieId spans 1 to 292,757 with no outliers, indicating a sparse identifier range rather than a clean sequence. Start with the genre distribution and the missing-genre share before any deeper modelling.
citing: row_count · columns.genres.n_unique · columns.genres.top_values · columns.genres.stats.one_word_rate · columns.title.n_unique · columns.title.top_words · columns.title.stats.len_mean · columns.movieId.stats.min · columns.movieId.stats.max · columns.movieId.stats.median
Charts the summary said to look at first
Show data table
| chars | count |
|---|---|
| 3 – 5 | 125 |
| 5 – 7 | 24325 |
| 7 – 9 | 3474 |
| 9 – 10 | 2417 |
| 10 – 12 | 14431 |
| 12 – 14 | 11262 |
| 14 – 16 | 3313 |
| 16 – 18 | 2692 |
| 18 – 20 | 9626 |
| 20 – 22 | 5612 |
| 22 – 23 | 3690 |
| 23 – 25 | 1617 |
| 25 – 27 | 1066 |
| 27 – 29 | 758 |
| 29 – 31 | 1008 |
| 31 – 33 | 640 |
| 33 – 34 | 366 |
| 34 – 36 | 430 |
| 36 – 38 | 193 |
| 38 – 40 | 76 |
| 40 – 42 | 135 |
| 42 – 44 | 159 |
| 44 – 46 | 42 |
| 46 – 47 | 45 |
| 47 – 49 | 25 |
| 49 – 51 | 30 |
| 51 – 53 | 5 |
| 53 – 55 | 9 |
| 55 – 57 | 6 |
| 57 – 58 | 3 |
| 58 – 60 | 3 |
| 60 – 62 | 0 |
| 62 – 64 | 0 |
| 64 – 66 | 1 |
| 66 – 68 | 0 |
| 68 – 70 | 0 |
| 70 – 71 | 0 |
| 71 – 73 | 0 |
| 73 – 75 | 0 |
| 75 – 77 | 1 |
Show data table
| chars | count |
|---|---|
| 3 – 5 | 125 |
| 5 – 7 | 24325 |
| 7 – 9 | 3474 |
| 9 – 10 | 2417 |
| 10 – 12 | 14431 |
| 12 – 14 | 11262 |
| 14 – 16 | 3313 |
| 16 – 18 | 2692 |
| 18 – 20 | 9626 |
| 20 – 22 | 5612 |
| 22 – 23 | 3690 |
| 23 – 25 | 1617 |
| 25 – 27 | 1066 |
| 27 – 29 | 758 |
| 29 – 31 | 1008 |
| 31 – 33 | 640 |
| 33 – 34 | 366 |
| 34 – 36 | 430 |
| 36 – 38 | 193 |
| 38 – 40 | 76 |
| 40 – 42 | 135 |
| 42 – 44 | 159 |
| 44 – 46 | 42 |
| 46 – 47 | 45 |
| 47 – 49 | 25 |
| 49 – 51 | 30 |
| 51 – 53 | 5 |
| 53 – 55 | 9 |
| 55 – 57 | 6 |
| 57 – 58 | 3 |
| 58 – 60 | 3 |
| 60 – 62 | 0 |
| 62 – 64 | 0 |
| 64 – 66 | 1 |
| 66 – 68 | 0 |
| 68 – 70 | 0 |
| 70 – 71 | 0 |
| 71 – 73 | 0 |
| 73 – 75 | 0 |
| 75 – 77 | 1 |
Show data table
| chars | count |
|---|---|
| 2 – 7 | 44 |
| 7 – 11 | 1905 |
| 11 – 16 | 14749 |
| 16 – 21 | 17609 |
| 21 – 26 | 20947 |
| 26 – 30 | 12610 |
| 30 – 35 | 7127 |
| 35 – 40 | 3670 |
| 40 – 45 | 3134 |
| 45 – 49 | 2086 |
| 49 – 54 | 1102 |
| 54 – 59 | 932 |
| 59 – 63 | 548 |
| 63 – 68 | 381 |
| 68 – 73 | 213 |
| 73 – 78 | 180 |
| 78 – 82 | 123 |
| 82 – 87 | 81 |
| 87 – 92 | 41 |
| 92 – 96 | 33 |
| 96 – 101 | 23 |
| 101 – 106 | 13 |
| 106 – 111 | 11 |
| 111 – 115 | 8 |
| 115 – 120 | 3 |
| 120 – 125 | 1 |
| 125 – 130 | 2 |
| 130 – 134 | 4 |
| 134 – 139 | 0 |
| 139 – 144 | 2 |
| 144 – 148 | 1 |
| 148 – 153 | 0 |
| 153 – 158 | 0 |
| 158 – 163 | 1 |
| 163 – 167 | 0 |
| 167 – 172 | 0 |
| 172 – 177 | 0 |
| 177 – 182 | 0 |
| 182 – 186 | 0 |
| 186 – 191 | 1 |
Show data table
| bin | count |
|---|---|
| 1 – 7320 | 7196 |
| 7320 – 1.464e+04 | 1111 |
| 1.464e+04 – 2.196e+04 | 0 |
| 2.196e+04 – 2.928e+04 | 1114 |
| 2.928e+04 – 3.66e+04 | 796 |
| 3.66e+04 – 4.391e+04 | 435 |
| 4.391e+04 – 5.123e+04 | 754 |
| 5.123e+04 – 5.855e+04 | 816 |
| 5.855e+04 – 6.587e+04 | 789 |
| 6.587e+04 – 7.319e+04 | 1132 |
| 7.319e+04 – 8.051e+04 | 1107 |
| 8.051e+04 – 8.783e+04 | 1398 |
| 8.783e+04 – 9.515e+04 | 1556 |
| 9.515e+04 – 1.025e+05 | 1541 |
| 1.025e+05 – 1.098e+05 | 1535 |
| 1.098e+05 – 1.171e+05 | 1847 |
| 1.171e+05 – 1.244e+05 | 2673 |
| 1.244e+05 – 1.317e+05 | 2763 |
| 1.317e+05 – 1.391e+05 | 3280 |
| 1.391e+05 – 1.464e+05 | 3282 |
| 1.464e+05 – 1.537e+05 | 3146 |
| 1.537e+05 – 1.61e+05 | 3258 |
| 1.61e+05 – 1.683e+05 | 3464 |
| 1.683e+05 – 1.757e+05 | 3492 |
| 1.757e+05 – 1.83e+05 | 3484 |
| 1.83e+05 – 1.903e+05 | 3486 |
| 1.903e+05 – 1.976e+05 | 3401 |
| 1.976e+05 – 2.049e+05 | 3369 |
| 2.049e+05 – 2.122e+05 | 3050 |
| 2.122e+05 – 2.196e+05 | 2907 |
| 2.196e+05 – 2.269e+05 | 2325 |
| 2.269e+05 – 2.342e+05 | 1839 |
| 2.342e+05 – 2.415e+05 | 1672 |
| 2.415e+05 – 2.488e+05 | 1531 |
| 2.488e+05 – 2.562e+05 | 1566 |
| 2.562e+05 – 2.635e+05 | 1679 |
| 2.635e+05 – 2.708e+05 | 1911 |
| 2.708e+05 – 2.781e+05 | 2168 |
| 2.781e+05 – 2.854e+05 | 2580 |
| 2.854e+05 – 2.928e+05 | 2132 |
Schema
3 columns| Alerts | ||||
|---|---|---|---|---|
| movieId | numeric | 0.0% | 87,585 |
|
| title | text | 0.0% | 87,382 |
near_unique
|
| genres | text | 0.0% | 1,798 |
one_word
duplicates
|
movieId
numeric identifiermovieId is fully unique across all 87585 rows with zero nulls, spanning 1 to 292757 — classic surrogate key behaviour rather than a measurable quantity. The wide range with a mean of 157651 and median of 165741 suggests non-contiguous IDs (gaps in the sequence), but there is no statistical signal to extract from it. Treatment: use as a join key; exclude from modelling features.
- n
- 87,585
- nulls
- 0 (0.0%)
- unique
- 87,585
- min
- 1
- max
- 292,757
- mean
- 1.577e+05
- median
- 165,741
- std
- 7.901e+04
- q1
- 112,657
- q3
- 213,203
- iqr
- 100,546
- skew
- -0.3914
- kurtosis
- -0.5775
- n_outliers
- 0
- outlier_rate
- 0
- zero_rate
- 0
title
text free_text near_uniqueShort free-text titles, averaging 4.2 words and 25 characters, with 87,382 unique values across 87,585 rows. The frequent year tokens like (2018), (2016), (2017), (2019) suggest these are titled works (likely films or publications) with release years appended. Near-unique with only 203 duplicates and a high Flesch readability of 81.2; emoji and URL rates are effectively zero. Treatment: Tokenize and embed (or strip the trailing year into a separate feature) before modelling; do not use as a key.
- n
- 87,585
- nulls
- 0 (0.0%)
- unique
- 87,382
- len_min
- 2
- len_max
- 191
- len_mean
- 25.28
- len_median
- 23
- len_p95
- 48
- word_mean
- 4.226
- word_median
- 4
- n_empty
- 0
- n_duplicates
- 203
- duplicate_rate
- 0.002318
- vocab_size
- 19,981
- readability_flesch_mean
- 81.2
- emoji_rate
- 3.425e-05
- url_rate
- 0
- one_word_rate
- 0.001279
- allcaps_rate
- 0.005526
- boilerplate_rate
- 9.134e-05
genres
text feature one_word duplicatesPipe-delimited movie genre tags, with 1,798 distinct combinations across 87,585 rows and a 97.9% duplicate rate. Drama (12,443), Documentary (8,132), and Comedy (7,761) dominate, while 7,080 rows carry the literal placeholder '(no genres listed)' that should be treated as missing. 91.9% of values are single tokens (word_mean 1.16), so multi-genre entries like 'Comedy|Drama' are the minority. Treatment: split on '|' and one-hot or multi-hot encode; recode '(no genres listed)' as null.
- n
- 87,585
- nulls
- 0 (0.0%)
- unique
- 1,798
- len_min
- 3
- len_max
- 77
- len_mean
- 13.24
- len_median
- 12
- len_p95
- 27
- word_mean
- 1.162
- word_median
- 1
- n_empty
- 0
- n_duplicates
- 85,787
- duplicate_rate
- 0.9795
- vocab_size
- 909
- readability_flesch_mean
- -117.7
- emoji_rate
- 0
- url_rate
- 0
- one_word_rate
- 0.9192
- allcaps_rate
- 1.142e-05
- boilerplate_rate
- 0