saturn·

data trove large meteorites 10kg

source /home/coolhand/html/datavis/data_trove/data/wild/nasa_meteorites.csv 45,716 rows 20 columns profiled 2026-06-22 raw JSON static .html .ipynb Report Notebook

Reading

dataset summary · high confidence anthropic:default

This dataset is a NASA meteorite landings catalogue covering 45,716 unique meteorite records with attributes including mass, classification, discovery year, and geographic coordinates. The most striking feature is the mass distribution: the median mass is just 32.6 g but the maximum reaches 60,000,000 g, producing extreme skew (skew=76.9) and over 7,000 statistical outliers — a handful of enormous meteorites are pulling the mean to 13,278 g. A second key finding is that 97.6% of records are classified as 'Found' rather than 'Fell', meaning nearly all entries are meteorites discovered on the ground rather than witnessed falling, which has strong implications for geographic and temporal bias in the data. The meteorite classification column (recclass) spans 466 types, dominated by ordinary chondrites (L6, H5, L5), and year of discovery shows a clear spike in the late 1990s–2000s likely tied to Antarctic collection campaigns.

citing: mass (g).stats.median · mass (g).stats.max · mass (g).stats.skew · mass (g).stats.mean · mass (g).stats.n_outliers · fall.top_values · fall.stats.top_rate · recclass.n_unique · recclass.top_values · year.top_values · row_count

Schema

20 columns
Per-column summary. Click column name to jump to its detail.
Alerts
sid text 0.0% 45,716
near_unique one_word short_text
id text 0.0% 45,716
near_unique one_word allcaps
position numeric 0.0% 1
constant
created_at numeric 0.0% 1
constant
created_meta unknown 0.0%
skipped
updated_at numeric 0.0% 1
constant
updated_meta unknown 0.0%
skipped
meta categorical 0.0% 1
imbalance
name text 0.0% 45,716
near_unique
id_1 numeric 0.0% 45,716
nametype categorical 0.0% 2
imbalance
recclass categorical 0.0% 466
mass (g) numeric 0.3% 12,576
high_skew outliers
fall categorical 0.0% 2
imbalance
year categorical 0.6% 266
reclat numeric 16.0% 12,738
reclong numeric 16.0% 14,640
GeoLocation text 16.0% 17,100
duplicates
States numeric 96.4% 45
null_rate
Counties numeric 96.4% 662
null_rate

sid

text identifier near_unique one_word short_text
This column is a Socrata-style row identifier, recognizable from the 'row-XXXX.XXXX-XXXX' format visible in all sampled values. Every value is exactly 18 characters long (len_min = len_max = len_mean = 18.0), and all 45,716 rows are unique with a duplicate_rate of 0.0 and null_rate of 0.0 — a perfect surrogate key. No surprises in the data; it is entirely consistent with an auto-generated system identifier from a Socrata open-data platform. Treatment: Drop before modelling; retain only if row-level traceability back to the source platform is needed. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
45,716
len_min
18
len_max
18
len_mean
18
len_median
18
len_p95
18
word_mean
1
word_median
1
n_empty
0
n_duplicates
0
duplicate_rate
0
vocab_size
20,000
readability_flesch_mean
-5.68
emoji_rate
0
url_rate
0
one_word_rate
1
allcaps_rate
0
boilerplate_rate
0

id

text identifier near_unique one_word allcaps
This column contains UUIDs (universally unique identifiers) serving as a primary key, with all 45,716 values being exactly 36 characters long and fully unique — zero duplicates, zero nulls. Notably, all sampled top values share the prefix '00000000-0000-0000-', suggesting the UUID version/variant fields are zeroed out, which is atypical of standard UUID v4 generation and may indicate a custom or synthetic ID scheme. The allcaps_rate of 1.0 is consistent with hex characters but worth noting if downstream systems are case-sensitive. Treatment: Use as primary key for joins; do not encode or transform for modelling — drop or pass through as-is. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
45,716
len_min
36
len_max
36
len_mean
36
len_median
36
len_p95
36
word_mean
1
word_median
1
n_empty
0
n_duplicates
0
duplicate_rate
0
vocab_size
20,000
readability_flesch_mean
65.38
emoji_rate
0
url_rate
0
one_word_rate
1
allcaps_rate
1
boilerplate_rate
0

position

numeric other constant
This column, named 'position', is a numeric field that is entirely constant — every one of its 45,716 non-null values is exactly 0.0 (zero_rate = 1.0, n_unique = 1). It carries zero information and would contribute nothing to any model or analysis. This is flagged as a constant alert, confirming it is safe to drop. Treatment: Drop immediately; constant value across all 45,716 rows provides no signal. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
1
min
0
max
0
mean
0
median
0
std
0
q1
0
q3
0
iqr
0
skew
0
kurtosis
0
n_outliers
0
outlier_rate
0
zero_rate
1

created_at

numeric timestamp constant
This column is a Unix timestamp named 'created_at', but every single one of its 45,716 non-null rows holds the identical value 1446143734 (approximately 2015-10-29 UTC), triggering a 'constant' alert. With n_unique of 1, std of 0.0, and IQR of 0.0, the column carries zero information variance — strongly suggesting a bulk-load default, a data pipeline bug, or a one-time snapshot import where timestamps were not properly captured. This is a critical data quality issue that renders the column useless as a temporal signal. Treatment: Drop or flag as corrupted; investigate ETL pipeline for the source of the constant value before using as a temporal feature. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
1
min
1.446e+09
max
1.446e+09
mean
1.446e+09
median
1.446e+09
std
0
q1
1.446e+09
q3
1.446e+09
iqr
0
skew
0
kurtosis
0
n_outliers
0
outlier_rate
0
zero_rate
0

created_meta

unknown metadata skipped
The column 'created_meta' is likely a creation timestamp or metadata field associated with record provenance, but saturn classified it as 'unknown' kind and skipped all profiling, yielding zero stats and no uniqueness count. With 45,716 rows, zero nulls, and no further signal available, its actual dtype, distribution, and content cannot be assessed from this evidence alone. Treatment: Inspect raw values to determine dtype (timestamp, JSON blob, string) before deciding on parsing, dropping, or feature extraction. low · anthropic:default
n
45,716
nulls
0 (0.0%)
unique

updated_at

numeric timestamp constant
This column is a Unix epoch timestamp named `updated_at`, representing a last-modified datetime for each row. Every single one of the 45,716 non-null records holds the identical value 1446143734 (approximately 2015-10-29 UTC), meaning the column is a constant — it carries zero information variance. This strongly suggests a bulk data load or migration event where all rows were stamped with the same timestamp rather than tracking real update times. Treatment: Drop from modelling; flag to data owner as a likely ETL artefact — all 45,716 rows share the single value 1446143734. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
1
min
1.446e+09
max
1.446e+09
mean
1.446e+09
median
1.446e+09
std
0
q1
1.446e+09
q3
1.446e+09
iqr
0
skew
0
kurtosis
0
n_outliers
0
outlier_rate
0
zero_rate
0

updated_meta

unknown other skipped
The column 'updated_meta' was skipped by the profiler, yielding no stats, no uniqueness count, and no type resolution beyond 'unknown'. With 45,716 non-null rows and a null rate of 0.0, the column is fully populated, but its content, structure, and distribution are entirely opaque from the available evidence. The name suggests it may hold metadata update timestamps or serialized metadata objects (e.g., JSON blobs), but nothing in the evidence confirms this. Treatment: Manually inspect raw values to determine type (timestamp, JSON, free text); re-profile after parsing or casting appropriately. low · anthropic:default
n
45,716
nulls
0 (0.0%)
unique

meta

categorical metadata imbalance
This column is a metadata field that contains exclusively the empty object literal '{ }' across all 45,716 rows, with zero nulls and a cardinality of 1. It carries no information whatsoever — entropy is 0.0 and top_rate is 1.0, meaning every single record is identical. This is almost certainly an unfilled placeholder or a defaulted JSON field that was never populated. Treatment: Drop before modelling; zero-variance column with no predictive or descriptive value. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
1
top_value
{ }
top_rate
1
cardinality
1
entropy
0
entropy_ratio
0

name

text label near_unique
This column contains proper names of geographic features — the top words ('range', 'hills', 'mountains', 'northwest', 'africa', 'grove', 'yamato', 'Queen Alexandra') are all typical components of named landforms or place names. Every one of the 45,716 rows has a distinct value (duplicate_rate 0.0, n_unique = 45,716) with zero nulls, making it a perfect natural identifier. The mean word count of 2.77 and median of 3.0 confirm multi-token names rather than single labels, while 'yamato' appearing 3,317 times as the top individual word suggests a large Antarctic or Japanese geographic sub-corpus driving partial lexical repetition even though full names are unique. Treatment: Use as a display label or natural key; tokenize on whitespace for gazetteer/NLP tasks, but drop before any ML feature matrix. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
45,716
len_min
2
len_max
28
len_mean
17.78
len_median
19
len_p95
27
word_mean
2.772
word_median
3
n_empty
0
n_duplicates
0
duplicate_rate
0
vocab_size
17,917
readability_flesch_mean
63.74
emoji_rate
0
url_rate
0
one_word_rate
0.04749
allcaps_rate
0
boilerplate_rate
0

id_1

numeric identifier
This column is almost certainly a row or entity identifier: it has 45,716 unique values across 45,716 rows with zero nulls and zero duplicates, indicating a perfect 1-to-1 mapping. Values run from 1 to 57,458, suggesting either a sparse sequential ID (gaps exist since max > n) or a pre-filtered subset of a larger table. The near-uniform distribution (kurtosis −1.16, skew 0.27, zero outliers) is consistent with a sequential or pseudo-random integer key rather than a meaningful numeric feature. Treatment: Retain as a join/lookup key; exclude from any modelling feature set. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
45,716
min
1
max
57,458
mean
2.689e+04
median
2.426e+04
std
1.686e+04
q1
1.269e+04
q3
4.066e+04
iqr
27,968
skew
0.2665
kurtosis
-1.16
n_outliers
0
outlier_rate
0
zero_rate
0

nametype

categorical label imbalance
This column is a meteorite name-type classification flag, distinguishing between currently valid meteorite names ('Valid') and relict/superseded ones ('Relict'). The distribution is extremely imbalanced: 45,641 of 45,716 records (99.84%) are 'Valid', with only 75 'Relict' entries. The near-zero entropy (0.018) confirms this column carries almost no information variance, which triggered the imbalance alert. Treatment: Exclude from predictive features due to near-zero variance; retain only as a filter to subset valid records if analysis should exclude relict entries. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
2
top_value
Valid
top_rate
0.9984
cardinality
2
entropy
0.01754
entropy_ratio
0.01754

recclass

categorical label
This column contains meteorite classification codes, identifying the mineralogical and petrologic type of each recovered meteorite specimen. The top 7 values (L6, H5, L5, H6, H4, LL5, LL6) are all ordinary chondrite classes and together account for the vast majority of records, with L6 alone representing 18.1% of the 45,716 rows. Despite 466 unique classes, the entropy ratio of 0.51 indicates moderate concentration — the long tail of rare classes (e.g., CM2 with only 416 occurrences) will create sparse dummy variables if one-hot encoded naively. Treatment: Group rare classes (below a frequency threshold) into an 'Other' bucket before encoding, or use target/frequency encoding to handle the 466-cardinality tail. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
466
top_value
L6
top_rate
0.1812
cardinality
466
entropy
4.548
entropy_ratio
0.5131

mass (g)

numeric feature high_skew outliers
This column records the physical mass of objects in grams, almost certainly meteorite or asteroid specimen weights given the scale and distribution. The median is just 32.6 g while the mean explodes to 13,278 g and the maximum reaches 60,000,000 g (60 tonnes), indicating a tiny fraction of massive outliers dragging the distribution — skew of 76.9 and kurtosis of 6,796 confirm an extreme long tail. Fully 15.5% of rows (7,086) are flagged as outliers, meaning the bulk of specimens are small rocks while a handful of giants dominate the aggregate statistics. Treatment: log-transform (log1p) before modelling to compress the extreme right tail; consider capping or flagging the ~7,086 outliers separately. high · anthropic:default
n
45,716
nulls
131 (0.3%)
unique
12,576
min
0
max
6e+07
mean
1.328e+04
median
32.6
std
5.75e+05
q1
7.2
q3
202.6
iqr
195.4
skew
76.91
kurtosis
6796
n_outliers
7,086
outlier_rate
0.1554
zero_rate
0.0004168

fall

categorical label imbalance
This column captures whether a meteorite was discovered on the ground ('Found') versus observed falling ('Fell'), making it a binary classification label for meteorite recovery type. The distribution is severely imbalanced: 'Found' accounts for 97.6% of 45,716 records (44,609), while 'Fell' represents only 2.4% (1,107). The entropy ratio of 0.164 confirms near-minimum uncertainty, flagged explicitly as an imbalance alert. Any model using this as a target will require class-balancing techniques. Treatment: Apply class-balancing (e.g., SMOTE or class weights) before using as a classification target. high · anthropic:default
n
45,716
nulls
0 (0.0%)
unique
2
top_value
Found
top_rate
0.9758
cardinality
2
entropy
0.1645
entropy_ratio
0.1645

year

categorical timestamp
This column represents a calendar year, stored as full ISO-8601 timestamps normalised to January 1st of each year (e.g. '2003-01-01T00:00:00'), confirming the time component carries no information. Despite being profiled as categorical, it is effectively an annual time dimension spanning at least the range visible in the top values (1979–2006). Surprising: cardinality is 266 distinct values against only ~45 years visible in top values, suggesting either a much wider date range or some malformed/unexpected entries worth inspecting. The top year (2003) accounts for just 7.3% of rows, indicating a reasonably spread distribution rather than heavy concentration. Treatment: Parse to date/integer year, investigate the 266 distinct values vs expected ~45-year span for anomalies, then use as a time feature or group-by dimension. high · anthropic:default
n
45,716
nulls
291 (0.6%)
unique
266
top_value
2003-01-01T00:00:00
top_rate
0.07315
cardinality
266
entropy
5.299
entropy_ratio
0.6578

reclat

numeric feature
This column represents the recorded latitude of meteorite find/fall locations, with values ranging from -87.37° to +81.17° consistent with geographic latitude bounds. Surprising signals: the median of -71.5° indicates the majority of records are concentrated in high southern latitudes (likely Antarctic recovery sites), yet the Q3 is exactly 0.0°, suggesting a notable cluster at the equator or a placeholder zero — reinforced by a zero_rate of 16.8% that almost exactly matches the null_rate of 16%, implying zeros may be encoding missing coordinates rather than true equatorial finds. Kurtosis of -1.48 confirms a flat, bimodal-like distribution rather than a normal one. Treatment: Treat zero values as missing (mask alongside existing nulls before modelling); use as-is for geospatial analysis or pair with longitude for coordinate-based features. high · anthropic:default
n
45,716
nulls
7,315 (16.0%)
unique
12,738
min
-87.37
max
81.17
mean
-39.12
median
-71.5
std
46.38
q1
-76.71
q3
0
iqr
76.71
skew
0.4916
kurtosis
-1.477
n_outliers
0
outlier_rate
0
zero_rate
0.1677

reclong

numeric feature
This column represents the recorded longitude of meteorite landing or find locations, covering a range from -165.43° to 354.47°. The maximum value of 354.47 is surprising — valid WGS84 longitude should cap at 180°, suggesting some records use a 0–360° convention rather than the standard -180 to 180° range, which will cause mapping errors if not normalised. The zero_rate of ~16% mirrors the null_rate of 16%, strongly implying that zero-filled values are placeholder/missing entries rather than genuine equatorial coordinates at the prime meridian. Distribution is near-symmetric (skew -0.17, kurtosis -0.73) with a large IQR of 157.17, consistent with a globally spread geographic variable. Treatment: Normalise values > 180 to the -180–180 range (subtract 360), then treat zero values matching the null_rate as missing and impute or exclude before spatial analysis. high · anthropic:default
n
45,716
nulls
7,315 (16.0%)
unique
14,640
min
-165.4
max
354.5
mean
61.07
median
35.67
std
80.65
q1
0
q3
157.2
iqr
157.2
skew
-0.1745
kurtosis
-0.7312
n_outliers
0
outlier_rate
0
zero_rate
0.1618

GeoLocation

text feature duplicates
GeoLocation stores geographic coordinates as serialized Python list strings in the format [None, '', '', None, False], representing what appears to be a structured geo-point object flattened to text. The most common value — '[None, '0.0', '0.0', None, False]' appearing 6,214 times — is almost certainly a null/unknown sentinel rather than a genuine equatorial location, masking true missingness beyond the 16% null rate. Duplicate rate is high at 55.5% (21,301 duplicates across 17,100 unique values), consistent with many records sharing the same geographic coordinates. The column should be parsed to extract numeric longitude and latitude fields rather than used as raw text. Treatment: Parse the serialized list string to extract longitude (index 1) and latitude (index 2) as separate numeric columns, treating '0.0', '0.0' as missing. high · anthropic:default
n
45,716
nulls
7,315 (16.0%)
unique
17,100
len_min
33
len_max
47
len_mean
40.3
len_median
41
len_p95
45
word_mean
5
word_median
5
n_empty
0
n_duplicates
21,301
duplicate_rate
0.5547
vocab_size
15,461
readability_flesch_mean
117.2
emoji_rate
0
url_rate
0
one_word_rate
0
allcaps_rate
0
boilerplate_rate
0

States

numeric null_rate
n
45,716
nulls
44,057 (96.4%)
unique
45
min
1
max
51
mean
17.34
median
15
std
10.41
q1
9
q3
23
iqr
14
skew
1.115
kurtosis
0.6891
n_outliers
40
outlier_rate
0.02411
zero_rate
0

Counties

numeric null_rate
n
45,716
nulls
44,057 (96.4%)
unique
662
min
5
max
3,210
mean
1353
median
1,195
std
994.1
q1
482
q3
2,113
iqr
1,631
skew
0.2374
kurtosis
-1.19
n_outliers
0
outlier_rate
0
zero_rate
0