saturn

This dataset is a sample of 1,000 NYC 311 service requests, capturing complaints logged across the five boroughs with details on complaint type, location, agency, and resolution status. The dominant signal is complaint type: 'Noise - Residential' alone accounts for 39.3% of records, followed by 'Illegal Parking' (19.7%) and 'Noise - Commercial' (14.8%), pointing to a dataset heavily skewed toward NYPD-handled quality-of-life complaints (NYPD handles 88% of cases). A second area worth examining is resolution status — 61% of complaints are closed, 30.5% are still in progress, and 8.5% remain open, which raises questions about agency workload and response time. Many specialty columns (road_ramp, taxi_company_borough, bridge_highway fields) are nearly entirely null (99%+), indicating they apply only to rare complaint subtypes and can largely be ignored.

This column is a timestamp recording when a resolution action was last updated, stored as a categorical string in ISO 8601 format. The 30.6% null rate is a significant concern, indicating roughly one-third of records have no update date logged. Strikingly, one value — '2026-01-17T00:00:00.000' — accounts for 10.7% of all non-null records (74 occurrences) with a midnight-exactly timestamp, suggesting a bulk update or default-date assignment rather than genuine event timestamps. All remaining top values appear only twice, confirming a severe long-tail distribution (entropy ratio 0.94) consistent with mostly unique timestamps.

This column is a ticket or record closure timestamp, stored as a string rather than a parsed datetime type, with second-level precision. Two signals demand attention: 39% of rows are null, indicating a large share of records that have not yet been closed (open items); and with 585 unique values across 1,000 rows and an entropy ratio of 0.998, timestamps are nearly all distinct, which is expected for event times but confirms the column carries no categorical signal. All top values cluster on 2026-01-18, suggesting the snapshot or batch was captured on that single date.

This column is a secondary complaint descriptor, likely a sub-category or detail field attached to a service request or complaint record (consistent with NYC 311-style data). It is almost entirely empty — null_rate is 0.882, meaning only 118 of 1,000 rows carry a value. Among the 43 unique non-null values, 'NO HEAT' dominates at 27.97% of non-null entries (33 occurrences), but the spread across housing, noise, sanitation, and transportation categories (e.g., 'Cannabis Smoking or Vaping', 'Unsafe Driving - Non-Passenger') signals this field is populated inconsistently across complaint types, not a uniform taxonomy. The high entropy_ratio of 0.807 confirms the long-tail alert: values are broadly dispersed despite the sparse fill rate.

This column captures the named park facility associated with each record, but it is almost entirely uninformative: 999 of 1000 rows (99.9%) carry the placeholder value 'Unspecified', with only a single record attributed to Flushing Meadows Corona Park. The near-zero entropy (0.011) confirms the column is maximally imbalanced and conveys virtually no discriminative signal. This is likely a poorly populated administrative field rather than a reliable feature.

This column classifies the type of geographic address entry, with four categories: ADDRESS, INTERSECTION, BLOCKFACE, and PLACE. It is severely imbalanced — 'ADDRESS' dominates at 97.2% of valid records (970/1000), while the remaining three types collectively account for only 28 records. The entropy ratio of 0.108 confirms near-minimal informational diversity, meaning this field will contribute little discriminative power in most models without special handling.

This column contains standardized resolution outcome descriptions from NYC 311 service requests, drawn from a fixed vocabulary of only 16 distinct boilerplate phrases. Despite being a text column, it behaves as a low-cardinality categorical: the top value (NYPD 'no criminal violation, condition corrected') accounts for 35.4% of non-null rows. A 30.7% null rate is flagged as an alert, likely reflecting complaints that have not yet been resolved or closed.

This column is a creation timestamp, stored as a categorical string in ISO-8601 millisecond format (e.g., '2026-01-17T23:49:56.000'). With 939 unique values out of 1000 rows and an entropy ratio of 0.995, it behaves almost like a unique identifier. The long-tail alert and the fact that the most frequent value appears only 9 times (0.9% of rows) suggest occasional duplicate timestamps — likely batch inserts or rapid successive record creation within the same second.

This column is a unique row identifier, likely a primary key or transaction/record ID — every one of the 1000 rows has a distinct value, giving it perfect cardinality and a maximum entropy ratio of 1.0. Values appear to be numeric strings in a narrow range (~67519046–67525266), suggesting sequential or near-sequential ID assignment. There is nothing analytically surprising here beyond the expected long-tail alert, which is a trivial consequence of full uniqueness. The null rate is 0.0.

This column contains free-text street addresses of incident locations, likely from a New York City incident or complaints dataset given recognizable street names (Lenox Avenue, Avenue of the Americas, Bruckner Boulevard). With 776 unique values across 1,000 rows and an entropy ratio of 0.97, the distribution is highly dispersed — a classic long-tail pattern. Notably, '126 WEST 13 STREET' appears 31 times (3.1% of rows), a disproportionate spike that may indicate a shelter, institution, or high-incident venue worth investigating. Inconsistent spacing in values like '60 EAST 93 STREET' suggests formatting irregularities that will need normalization.

This column contains street names, almost certainly from a New York City dataset given entries like 'WEST 13 STREET', 'LENOX AVENUE', 'BRUCKNER BOULEVARD', and 'JAMAICA AVENUE'. With 569 unique values across 1,000 rows and an entropy ratio of 0.955, the distribution is highly spread — the top value 'WEST 13 STREET' appears only 31 times (3.1% of rows), confirming the long-tail alert. The near-zero null rate (0.5%) is clean, but the high cardinality and long tail mean most street names are rare, which limits direct one-hot encoding utility.

This column contains geographic latitude coordinates stored as strings, with all top values clustering tightly around 40.6–40.8°N — consistent with New York City's latitude range. Despite being numeric in nature, it was ingested as categorical, yielding 770 unique values across 1,000 rows (entropy ratio 0.97), which is near-unique. The long-tail alert and the top value ('40.73706433046593') appearing 31 times (3.1% of rows) suggests either a default/fallback coordinate or a high-traffic location being logged repeatedly.

This column contains geographic longitude values, stored as strings (hence the categorical classification), representing locations clustered tightly around -73.8 to -74.0 — consistent with New York City. Despite 1,000 rows, there are 770 unique values, yet the top value '-73.99830041620608' appears 31 times (3.1% of rows), which is surprisingly high given the near-continuous nature of coordinate data and flags a long-tail concentration. Entropy ratio of 0.97 confirms very high diversity overall, but the repeated exact coordinate values suggest either data binning, snapping to a fixed point, or duplicated records at specific locations.

This column contains X-coordinates in a State Plane coordinate system, stored as categorical strings rather than numerics — values like '984721' and '1004501' are typical State Plane Easting values in feet. With 763 unique values out of 1000 rows (entropy ratio 0.97) and a long-tail alert, the distribution is nearly unique per record, which is expected for spatial point coordinates. The top value '984721' appearing 31 times (3.1% of rows) is surprisingly frequent for a coordinate and may indicate a default, imputed, or snapped location worth investigating.

This column represents Y-coordinates in a State Plane coordinate system, likely a geographic reference for spatial data (e.g., NYC or similar municipal dataset). Despite being numeric in nature, it is stored as a categorical type, which is unexpected and likely a data pipeline issue. With 768 unique values out of 1000 rows (entropy ratio 0.97) and a long-tail alert, the distribution is highly dispersed, though the top value '207809' appears 31 times — a modest but notable cluster suggesting a frequently referenced location. The near-unique cardinality makes this unsuitable for direct use as a categorical feature.

This column contains New York City Borough-Block-Lot (BBL) codes, a standard 10-digit property identifier encoding borough (leading digit 1–5), block, and lot numbers. With 719 unique values across 1,000 rows and an entropy ratio of 0.97, it is near-unique, but the top value '1006080026' appears 31 times (3.3% of non-null rows), indicating repeated records tied to a single property — flagged as a long tail. The 5.5% null rate and clustering of repeats suggest this may be a foreign key referencing a property registry rather than a row-level unique identifier.

This column contains the secondary cross-street name associated with a location record, likely from a NYC incident or address dataset. With 504 unique values across 1,000 rows and an entropy ratio of 0.948, the distribution is nearly flat — a strong long-tail signal where most street names appear very rarely. The top value '7 AVENUE' appears only 36 times (3.9% of non-null rows), and 'DEAD END' appearing 10 times is a notable data-quality flag indicating unresolved or terminus locations.

This column captures the first cross street in a NYC street-address intersection, most likely from a traffic incident, 311, or similar geospatial events dataset. With 511 unique values across 1,000 rows and an entropy ratio of 0.953, the distribution is nearly flat — a strong long-tail signal — meaning the vast majority of street names appear only once or twice. The top value 'AVENUE OF THE AMERICAS' appears just 35 times (3.8% of rows), and the 8% null rate suggests some records lack cross-street data entirely.

This column captures the secondary (cross) street name at an intersection, likely from a New York City incident or traffic dataset. With 499 unique values across 1,000 rows and an entropy ratio of 0.948, the distribution is nearly flat — a strong long-tail pattern where '7 AVENUE' is the modal value at only 3.9% frequency. The presence of 'DEAD END' (10 occurrences) as a top value is notable, indicating it is used as a literal street descriptor rather than a true street name, which may require cleaning. An 8.4% null rate suggests some incidents occurred at non-intersection locations (e.g., mid-block).

This column captures the name of the first cross-street at an intersection, consistent with NYC street-incident or infrastructure data. With 506 unique values across 1,000 rows (entropy ratio 0.95), the distribution is nearly flat — a long-tail alert confirms that the vast majority of street names appear only once or twice, while even the top value ('AVENUE OF THE AMERICAS') accounts for just 3.8% of non-null records. An 8.5% null rate suggests intersections are not always fully recorded, which may indicate missing location data rather than true absence of a street.

This column contains street or landmark names, likely representing the nearest notable street or geographic reference point for each record in a New York City–area dataset. With 515 unique values across 1,000 rows (51.5% uniqueness) and a 10.5% null rate, coverage is incomplete. The distribution is heavily long-tailed: the top value 'WEST 13 STREET' appears only 31 times (3.46%), and the entropy ratio of 0.955 indicates near-maximum disorder, meaning most landmark names are rare or unique — making this column unsuitable as a reliable grouping key without significant consolidation.

This column is named 'location' and contains 1,000 non-null values with a null rate of 0.0, but the profiler skipped it entirely, yielding no stats, no uniqueness count, and no type inference. Without distribution, cardinality, or sample values, it is impossible to determine whether this is a structured geographic field (e.g., city, coordinates, country code) or free-text. The 'skipped' alert is the dominant signal and warrants direct inspection of raw values before any downstream use.

This column identifies the New York City municipal agency associated with each record, with 11 distinct agency codes and no nulls across 1,000 rows. The distribution is severely skewed: NYPD alone accounts for 88% of all records (880 of 1,000), while the remaining 10 agencies collectively cover only 120 rows — several (DHS, OOS, DCWP, DPR) appear just twice each. The low entropy ratio of 0.223 confirms this near-monolithic concentration, which could bias any agency-level analysis or model trained on this sample.

This column identifies the New York City municipal agency responsible for each record, with 11 distinct agencies across 1,000 rows and no nulls. It is severely dominated by the New York City Police Department, which accounts for 88% of all records (880 of 1,000), producing a very low entropy ratio of 0.223. The remaining 10 agencies collectively cover only 120 records, with several (e.g., Department of Homeless Services, Office of the Sheriff) appearing just twice. This extreme class imbalance will distort any agency-level aggregation or model that uses this column as a feature.

This column represents the five New York City boroughs, functioning as a geographic label with complete coverage (null_rate 0.0) across all 1,000 rows. Four boroughs — Queens (274), Manhattan (258), Brooklyn (254), and Bronx (203) — are distributed with reasonable balance, but Staten Island is strikingly underrepresented at just 11 occurrences (~1.1%), compared to 20% expected under uniform distribution. The high entropy_ratio of 0.886 reflects the near-even spread among the four dominant boroughs, masking Staten Island's severe underrepresentation.

This column represents NYC Community Board designations, combining a numeric district ID with a borough name (e.g., '02 MANHATTAN'). With 65 unique values across 1,000 rows and zero nulls, coverage is complete. The distribution is notably flat — entropy ratio of 0.93 indicates near-uniform spread, with the most frequent value ('02 MANHATTAN') appearing only 5.6% of the time, suggesting no single board dominates the dataset. Manhattan and Queens boards appear disproportionately among the top 10, which may reflect a geographic sampling bias worth investigating.

This column contains the categorized type of civic complaint (likely NYC 311 service requests), with 45 distinct complaint categories across 1,000 records and zero nulls. The distribution is heavily skewed: 'Noise - Residential' alone accounts for 39.3% of all records, and the top three noise-related categories together represent roughly 60% of the dataset. The entropy ratio of 0.53 indicates moderate concentration — far from uniform — with a long tail of rare complaint types below the top 10.

This column contains descriptive sub-type labels for service requests or complaints — likely from a system such as NYC 311 — further specifying the nature of each complaint beyond a top-level category. 'Loud Music/Party' dominates with 42.7% of all 1,000 records, creating notable class imbalance; the top two values alone account for 54.1% of rows. With 71 unique values, entropy ratio of 0.576, and zero nulls, the column is moderately concentrated but still covers a meaningful range of complaint types including noise, parking, and building issues.

This column captures the channel through which a report or request was submitted, with four distinct values: ONLINE, MOBILE, PHONE, and UNKNOWN. ONLINE dominates at 46.6% of records, followed by MOBILE (28.8%) and PHONE (23.6%), leaving only 10 records (1%) tagged as UNKNOWN. The near-uniform distribution across the three known channels is notable, and the UNKNOWN category — while small — may warrant imputation or flagging depending on downstream use.

This column encodes the five NYC borough names associated with park locations, making it a low-cardinality geographic label with zero nulls across 1,000 rows. Four boroughs (Queens, Manhattan, Brooklyn, Bronx) are nearly evenly distributed (203–274 records each), but Staten Island is severely underrepresented at just 11 occurrences (1.1%), which would surprise an analyst expecting proportional borough coverage. Entropy ratio of 0.886 reflects the near-uniform spread across four classes, masking the sharp imbalance on the fifth.

This column represents the police precinct assignment for each record, with 76 distinct precinct labels across 1,000 rows and zero nulls. The distribution is remarkably flat: the most common value, 'Precinct 6', appears only 50 times (5% of rows), and entropy ratio is 0.936 — nearly as uniform as a perfectly even distribution across all 76 precincts. No dominant precinct stands out, suggesting either a geographically broad dataset or deliberate sampling across jurisdictions.

This column is a workflow/lifecycle status field with exactly 3 states: Closed, In Progress, and Open. 'Closed' dominates at 61% (610/1000), while 'Open' is surprisingly rare at only 8.5% (85/1000), suggesting the dataset skews heavily toward resolved records — possibly a historical or archived snapshot rather than a live operational view. No nulls and perfect coverage across all 1000 rows.

This column contains US ZIP codes associated with incidents, all values appearing to be New York City ZIP codes (10xxx and 11xxx series, consistent with Manhattan, Queens, and the Bronx). With 146 unique values across 1,000 rows and a high entropy ratio of 0.931, the distribution is remarkably spread — the most frequent ZIP '10011' appears only 42 times (4.2%), indicating incidents are distributed broadly across many neighbourhoods rather than concentrated in a few. Null rate is negligible at 0.3%.

This column is a Socrata-generated computed region identifier, typically encoding a geographic zone or district (e.g., census tract, neighbourhood, or administrative boundary) as an opaque integer-like label. With 51 unique values across 1,000 rows and an entropy ratio of 0.948, the distribution is near-uniform — no single region dominates heavily, though region '10' leads with 6.4% of records (64 occurrences). The null rate of 0.7% is negligible, suggesting reliable spatial assignment.

This column is a Socrata-generated computed region identifier, assigning each row to one of 62 geographic zones (e.g., neighborhood, district, or census tract polygons). With an entropy ratio of 0.939 and 62 unique integer-like codes across 1,000 rows, values are distributed fairly evenly — the most frequent region ('57') accounts for only 5.6% of rows, suggesting no strong spatial concentration. The null rate is negligible at 0.7%. The numeric-looking codes are categorical labels, not ordinal or continuous values.

This column is a Socrata computed region identifier, automatically generated by the platform to assign rows to pre-defined geographic boundary zones (e.g., neighbourhoods, council districts, or census tracts). With 75 distinct integer-like codes across 1,000 rows and an entropy ratio of 0.94, the distribution is remarkably flat — no single zone dominates, with even the top value '3' appearing in only 5% of rows. The near-uniform spread across 75 regions and very low null rate (0.7%) suggest broad geographic coverage with no strong spatial concentration.

This column is a Socrata-generated computed region identifier (geo-zone lookup key), indicated by the ':@computed_region_' prefix — it maps each row to one of 5 predefined geographic regions. Values are nearly uniformly distributed across zones 2–4 (~254–269 rows each), but zone '1' is a stark outlier with only 11 occurrences (~1.1% of rows), which may signal a very small or edge-case geographic area. Null rate is negligible at 0.7%.

This column represents a council district code — a zero-padded numeric string identifier (e.g., '03', '32') used to assign records to geographic administrative units. With 51 distinct values across 1,000 rows and an entropy ratio of 0.954, the distribution is remarkably flat and near-uniform, meaning no single district dominates heavily; the most frequent value '03' appears only 56 times (5.65% of rows). This near-maximum entropy is unusual for a district field and suggests either broad geographic coverage or deliberate sampling across all districts. Null rate is negligible at 0.9%.

This column encodes the type of location where an incident or event occurred, with 20 distinct values across 1,000 rows. The dominant category is 'Residential Building/House' at 40.2% (396 occurrences), followed by 'Street/Sidewalk' at 324. A significant data quality issue is immediately apparent: the same real-world concept appears under multiple inconsistent labels — 'Residential Building/House' (396), 'RESIDENTIAL BUILDING' (74), and 'Residential Building' (7) are clearly duplicates, as are 'Street/Sidewalk' (324), 'Street' (8), and 'Sidewalk' (5) — meaning true cardinality is substantially lower than 20 and category frequencies are understated.

This column contains NYC neighborhood and borough city names, almost certainly a mailing-address city field from a dataset heavily concentrated in New York City. The top three values alone — BROOKLYN (250), NEW YORK (249), and BRONX (198) — account for roughly 70% of the 1,000 rows, while the remaining 33 values (e.g., WOODHAVEN, JAMAICA, RIDGEWOOD) are clearly Queens and Brooklyn sub-neighborhoods used as postal city names. The distribution is notably skewed (top_rate 0.255) but the entropy_ratio of 0.635 across 36 unique values suggests moderate spread beyond the top cluster; the 2.1% null rate is minor.