sector_id int32 101 691 ⌀ | sector_name stringclasses 113
values | sector_ra int32 1 8 ⌀ | sector_geom unknown | sector_centroid unknown | sector_centroid_lng float64 -74.35 -73.13 ⌀ | sector_centroid_lat float64 45.2 46.1 ⌀ |
|---|---|---|---|---|---|---|
101 | Montreal : Centre-ville | 1 | [
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102 | Montreal : Centre-ville peripherique | 1 | [
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103 | Montreal : Sud-Ouest | 2 | [
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104 | Montreal : Notre-Dame-de-Grace | 2 | [
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105 | Montreal : Cote-des-Neiges | 2 | [
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106 | Montreal : Plateau Mont-Royal | 2 | [
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107 | Montreal : Villeray | 2 | [
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108 | Montreal : Ahuntsic | 2 | [
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109 | Montreal : Saint-Michel | 2 | [
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110 | Montreal : Rosemont | 2 | [
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111 | Montreal : Sud-Est | 2 | [
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112 | Montreal : Mercier | 2 | [
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113 | Montreal : Pointe-aux-Trembles | 3 | [
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114 | Montreal : Riviere-des-Prairies | 3 | [
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115 | Montreal-Est | 3 | [
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116 | Montreal : Anjou | 3 | [
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117 | Montreal : Saint-Leonard | 3 | [
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118 | Montreal : Montreal-Nord | 3 | [
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119 | Montreal : Saint-Laurent | 4 | [
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120 | Mont-Royal | 2 | [
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121 | Montreal : Outremont | 2 | [
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122 | Westmount | 2 | [
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123 | Hampstead | 2 | [
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124 | Cote-Saint-Luc | 4 | [
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125 | Montreal-Ouest | 4 | [
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End of preview. Expand in Data Studio
Bixi's OpenData Modelisation
Here is a huggingface dataset refined with AntoineGiraud/dbt_bixi_opendata dbt-core project
that loads & transform bixi OpenData thanks to DuckDB 🦆🚀
Viz' exploration ideas
I used Power BI to explore the transformed data offloaded to .parquet (~ 4.7 times lighter than .csv)
After the pandemic, Montrealers realy went back to bixi 🥳
Data sources
Bixi Rentals OpenData (link)
- 🚲 Rentals V1 : from 2014 to 2021
for station info, join to station yearly file with station_code
- ⛽ Stations V1 : from 2014 to 2021
1 station code per year
- 🚲 Rentals V2 : from 2022 to 2024+
start/end station info on each rentals
-> 2.7 times heavier.csv1.4Gb -> 0.5Gb
-> 2.3 times heavier.parquet250Mb -> 106Mb
GIS referential
- 🧠Municipal sectors : from the OD 2013 survey (cf. donnees quebec)
Schema/DB steps :
- raw : raw tables loaded as is from .csv
- stg : intermediate tables
- dtm : tables ready for analytics & reporting use
Loading
DuckDB realy shines by it's speed & local OLAP capabilities 😎
Here is 🚲 v1 rentals (2014 - 2021) load & offload to .parquet
.csvis 4.5 times heavier than.parquet.jsonis 2.7 times heavier than.csv
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