brent-daily.csv

brent-monthly.csv

brent-weekly.csv

brent-year.csv

wti-daily.csv

wti-monthly.csv

wti-weekly.csv

wti-year.csv

The Unlicense

oil-prices

Europe Brent and WTI (Western Texas Intermediate) Spot Prices (Annual/ Monthly/ Weekly/ Daily) from EIA U.S. (Energy Information Administration).

## Data

This series is available through the EIA open data API

- [Europe Brent Spot Price FOB (Dollars per Barrel)](https://www.eia.gov/dnav/pet/hist/RBRTEd.htm) - From 20 May 1987 till today
- [Cushing, OK WTI Spot Price FOB (Dollars per Barrel)](https://www.eia.gov/dnav/pet/hist/RWTCD.htm) - From 01 February 1986 till today

### Definitions

#### Brent
> A blended crude stream produced in the North Sea region which serves as a reference or "marker" for pricing a number of other crude streams.
[source](https://www.eia.gov/dnav/pet/TblDefs/pet_pri_spt_tbldef2.asp)

#### West Texas Intermediate (WTI - Cushing)
> A crude stream produced in Texas and southern Oklahoma which serves as a reference or "marker" for pricing a number of other crude streams and which is traded in the domestic spot market at Cushing, Oklahoma.
[source](https://www.eia.gov/dnav/pet/TblDefs/pet_pri_spt_tbldef2.asp)

## Preparation

You will need Python 3.6 or greater and dataflows library to run the script

To update the data run the process script locally:

```
# Install dataflows
pip install dataflows

# Run the script
python oil_prices_flow.py
```

## License

> U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."

You may find further information [here](https://www.eia.gov/about/copyrights_reuse.cfm)

### Additional work

> All the additional work made to build this Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/


https://api.github.com/repos/luccasmmg/test-data-repo-1/contents/{+path}

data_1.csv

data_2.csv

MIT License

test-data-repo-1

https://api.github.com/repos/datahubio/test-data-repo-1/contents/{+path}

# Data

This is the README.md this project.

## Table 

<FlatUiTable url="https://raw.githubusercontent.com/luccasmmg/test-data-repo-1/main/data_2.csv" />
 
## LineChart from URL 

<Vega
  data={{
    table: [
      {
        x: 1850,
        y: -0.418
      },
      {
        x: 2020,
        y: 0.923
      }
    ]
  }}
  spec={{
    $schema: 'https://vega.github.io/schema/vega-lite/v4.json',
    data: {
      name: 'table'
    },
    encoding: {
      x: {
        field: 'x',
        type: 'ordinal'
      },
      y: {
        field: 'y',
        type: 'quantitative'
      }
    },
    mark: 'bar'
  }}
/>


https://api.github.com/repos/datasets/investor-flow-of-funds-us/contents/{+path}

Monthly net new cash flow into various mutual fund investment classes (equities, bonds etc).

monthly.csv

weekly.csv

investor-flow-of-funds-us

Monthly net new cash flow by US investors into various mutual fund investment
classes (equities, bonds etc). Statistics come from the Investment Company
Institute (ICI).

## Data

Data comes from the data provided on the [ICI Statistics pages][ici], in
particular:

* Summary: Estimated Long-Term Mutual Fund Flows Data (xls)

[ici]: http://www.ici.org/research/stats

Notes for Long-Term Mutual Fund Flows Data:

* All figures are (nominal) millions of US dollars (USD)
* Weekly cash flows are estimates based on reporting covering 98 percent of
  industry assets, while monthly flows are actual numbers as reported in ICI's
  "Trends in Mutual Fund Investing."

## Preparation

Run the python script:

Install the requirements   
```  
pip install -r scripts/requirements.txt
```
Now run the script    
```
python scripts/process.py
```    
## License

This Data Package is licensed by its maintainers under the [Public Domain Dedication and License (PDDL)](http://opendatacommons.org/licenses/pddl/1.0/).

https://api.github.com/repos/fivethirtyeight/data/contents/{+path}

Data and code behind the articles and graphics at FiveThirtyEight

bad-drivers.csv

Creative Commons Attribution 4.0 International

data

# Bad Drivers

This folder contains data behind the story [Dear Mona, Which State Has The Worst Drivers?](http://fivethirtyeight.com/datalab/which-state-has-the-worst-drivers/)

Variable | Source
---|---------
`State` | N/A
`Number of drivers involved in fatal collisions per billion miles` | National Highway Traffic Safety Administration, 2012
`Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding` | National Highway Traffic Safety Administration, 2009
`Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired` | National Highway Traffic Safety Administration, 2012
`Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted`	 | National Highway Traffic Safety Administration, 2012
`Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents` | National Highway Traffic Safety Administration, 2012
`Car Insurance Premiums ($)` | National Association of Insurance Commissioners, 2011
`Losses incurred by insurance companies for collisions per insured driver ($)` | National Association of Insurance Commissioners, 2010


Bad Drivers

historical_RAPTOR_by_player.csv

historical_RAPTOR_by_team.csv

---
files:
  - https://projects.fivethirtyeight.com/nba-model/2023/latest_RAPTOR_by_team.csv
  - https://projects.fivethirtyeight.com/nba-model/2023/latest_RAPTOR_by_player.csv
---
# nba-raptor

This folder contains data behind the story [Introducing RAPTOR, Our New Metric For The Modern NBA](https://fivethirtyeight.com/features/introducing-raptor-our-new-metric-for-the-modern-nba) and the interactive [The Best NBA Players, According To RAPTOR](https://projects.fivethirtyeight.com/nba-player-ratings/).


`modern_RAPTOR_by_player.csv` contains RAPTOR data for every player broken out by season since 2014, when NBA player-tracking data first became available.

`modern_RAPTOR_by_team.csv` contains RAPTOR data for every player broken out by team, season and season_type since 2014, when NBA player-tracking data first became available.

These "modern" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.


`historical_RAPTOR_by_player.csv` contains RAPTOR data for every player broken out by season since the 1976 ABA-NBA merger.


`historical_RAPTOR_by_team.csv` contains RAPTOR data for every player broken out by team, season and season_type since the 1976 ABA-NBA merger.

These "historical" data files use full player-tracking RAPTOR for seasons since 2014, a version of RAPTOR that mixes box score value estimates with single-year regularized plus-minus data for seasons from 2001 through 2013, and a version of RAPTOR that only uses a box score estimate of value for the seasons from 1977 through 2000. In each era, the RAPTOR version with the highest level of detail is the version used.


The linked file `latest_RAPTOR_by_player.csv` contains RAPTOR data for every player in the latest season.

The linked file `latest_RAPTOR_by_team.csv` contains RAPTOR data for every player broken out by team, season and season_type for the latest season.

These "latest" data files contain the box score and on/off plus-minus components of RAPTOR, which are then combined into a total RAPTOR rating.



Column | Description
-------|---------------
`player_name` |	Player name
`player_id` |	Basketball-Reference.com player ID
`season` |	Season
`season_type` |	Regular season (RS) or playoff (PO)
`team` |	Basketball-Reference ID of team
`poss` |	Possessions played
`mp` |	Minutes played
`raptor_box_offense` |	Points above average per 100 possessions added by player on offense, based only on box score estimate
`raptor_box_defense` |	Points above average per 100 possessions added by player on defense, based only on box score estimate
`raptor_box_total` |	Points above average per 100 possessions added by player, based only on box score estimate
`raptor_onoff_offense` |	Points above average per 100 possessions added by player on offense, based only on plus-minus data
`raptor_onoff_defense` |	Points above average per 100 possessions added by player on defense, based only on plus-minus data
`raptor_onoff_total` |	Points above average per 100 possessions added by player, based only on plus-minus data
`raptor_offense` |	Points above average per 100 possessions added by player on offense, using both box and on-off components
`raptor_defense` |	Points above average per 100 possessions added by player on defense, using both box and on-off components
`raptor_total` |	Points above average per 100 possessions added by player on both offense and defense, using both box and on-off components
`war_total` |	Wins Above Replacement between regular season and playoffs
`war_reg_season` |	Wins Above Replacement for regular season
`war_playoffs` |	Wins Above Replacement for playoffs
`predator_offense` |	Predictive points above average per 100 possessions added by player on offense
`predator_defense` |	Predictive points above average per 100 possessions added by player on defense
`predator_total` |	Predictive points above average per 100 possessions added by player on both offense and defense
`pace_impact` |	Player impact on team possessions per 48 minutes


Name	Repository	Description	Last updated
datasets/oil-prices	@datasets/oil-prices	Brent crude and WTI oil prices from US EIA	16 hours ago	info
luccasmmg/test-data-repo-1	@luccasmmg/test-data-repo-1		37 minutes ago	info
datasets/investor-flow-of-funds-us	@datasets/investor-flow-of-funds-us	Monthly net new cash flow into various mutual fund investment classes (equities, bonds etc).	5 years ago	info
Bad Drivers	@fivethirtyeight/data	Data about bad drivers	2 months ago	info
fivethirtyeight/data/nba-raptor	@fivethirtyeight/data	Data and code behind the articles and graphics at FiveThirtyEight	2 months ago	info

GitHub Datasets