Get Features

The Features API provides features for ML Models across all types of demand causal factors, including attended events and non-attended events.

Request

HTTP Request

POST https://api.predicthq.com/v1/features/

Request Headers

Header

Value

Accept

Receive results in JSON or CSV by specifying the appropriate Accept header.

Supported values:

application/json
text/csv

Query Parameters

Parameter

Description

offset number

The number of results to skip. The default is 0.

E.g. ?offset=20

limit number

The maximum number of results to return per page.

The default limit is 100 and the maximum per page is 100.

E.g. ?limit=10

Request Body

Field

Description

active object

The date range to calculate features for. This is named "active" because it includes events that are active within the date range. A multi-day event might start or end outside the specified date range - the days the event is active within the specified range will be included in the calculations.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

Note that all dates here are in local time (not UTC). Features API works on specific locations.

Please also note that the maximum supported date range is 90 days. If you require features over a wider date range please make multiple API requests.

E.g.

{
  "active": {
    "gte": "2019-11-28",
    "lte": "2019-11-29"
  }
}

beam object

An optional beam analysis object which if provided will calculate the location, interval, week_start_day , phq_* features including stats and phq_rank from the Beam analysis and optionally the Beam analysis group.

Supports the following fields:

analysis_id - Beam analysis id (Required if beam block is present in the request body)
group_id - Beam analysis group id (Optional)

If beam.analysis_id is provided, the location, interval, week_start_day , phq_* features includingstats and phq_rank will be calculated from the Beam analysis.

E.g.

{
  "beam": {
    "analysis_id": "0SXAmHsoZo0"
  }
}

If both beam.analysis_id and beam.group_id are provided, all the above fields will be calculated from the Beam analysis but the phq_* features will be calculated from the Beam analysis Group.

Note: This is useful for when you want to request features for a variety of locations while maintaining feature consistency by limiting the returned features to those of the group's feature importance results.

E.g.

{
  "beam": {
    "analysis_id": "0SXaMHsoYo0",
    "group_id": "QxE9BcLq7ZY"
  }
}

hour_of_day_start object

Time range (per day) to calculate features for.

Note: This field is currently only supported on phq_viewership_* features.

If your location only operates within certain hours of the day you can use this filter to only include records that are happening within those hours.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

The values are hours between 0 and 23 (i.e. 24h format).

E.g. only include events happening between 1pm and 3pm each day.

{
  "hour_of_day_start": {
    "gte": 13,
    "lte": 15
  }
}

location object

Location to calculate features for. You can specify the location as a latitude/longitude (with radius), Place ID(s) or ID(s).

We recommend using a lat/lon+radius or a saved location id (for a point and radius location) as they could define the location of your interest more accurately. To work out a suitable radius around your location we strongly recommend using our .

Note: When using Place IDs or Saved Location IDs a maximum of 3 IDs may be used.

Note: When using lat/lon+radius, the radius must be in the format <radius><radius_unit>, where <radius> is an integer or a float number up to 2 decimal places and <radius_unit> is one of:

m - meters
km - kilometers
ft - feet
mi - miles

E.g. using Place IDs:

{
  "location": {
    "place_id": [
      5224323,
      5811704,
      4887398
    ]
  }
}

E.g. using Saved Location IDs:

{
  "location": {
    "saved_location_id": [
      "BN7ZSw8xza9FviPVfyCycd",
      "X3uyTFbDOUaX2q_Qh5i31b",
      "X3uyTFbDOUhrfq_Qh5i31A"
    ]
  }
}

E.g. using a latitude/longitude and radius (recommended):

{
  "location": {
    "geo": {
      "lat": 41.75038,
      "lon": -71.49978,
      "radius": "2.62mi"
    }
  }
}

interval string optional

Aggregation interval. Possible values:

day (default) for daily aggregation
week for weekly aggregation

week_start_day string optional

The weekday to be treated as the start of the week.

Possible values:

monday (default)
tuesday
wednesday
thursday
friday
saturday
sunday

Only applicable when interval is set to week.

<feature_name> object or boolean

The name of the feature you're requesting. You can request multiple features in a single request. Features can be further configured here, or you can set the value as true to perform the default calculations for that feature. Please see the tables below for a list of all currently supported features and how they can be further configured.

E.g. requesting certain stats fields and filtering for records with a PHQ Rank over 50.

{
  "phq_attendance_sports": {
    "stats": ["count", "std_dev", "median"],
    "phq_rank": { 
      "gt": 50
    }
  }
}

E.g. requesting the default calculations for a feature.

{
  "phq_attendance_concerts": true
}

Available Features

PHQ Attendance features provide daily-level aggregated stats based on the number of people who we predict will attend events on a given day. This takes into account complications like distributing attendance across multi-day events.

We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See #attended-events-impact-patterns.

Attended Events Generic Features

Use the generic features in this table if you are not in one of the industries covered by the impact pattern features listed below.

Feature

Description

phq_attendance_academic_graduation

Academic - Graduation

phq_attendance_academic_social

Academic - Social

phq_attendance_community

Community

phq_attendance_concerts

Concerts

phq_attendance_conferences

Conferences

phq_attendance_expos

Expos

phq_attendance_festivals

Festivals

phq_attendance_performing_arts

Performing Arts

phq_attendance_sports

Sports

phq_attendance_school_holidays

School Holidays

Attended Events Impact Pattern Features

Demand impact patterns model the impact of leading days (days before the event), lagging days (days after an event), and the days the event occurs. In the Features API, Impact Patterns are provided as different features with a feature per industry. We have impact pattern features for the accommodation, hospitality (which covers food & beverage including restaurants), and retail industries.

The features above are generic features and the features in the table below are the impact pattern features per industry. For example, if you were in the accommodation industry and wanted a feature for the conferences category you'd use phq_attendance_conferences_accommodation.

We recommend using impact pattern features instead of generic features if you are in one of the supported industries. See Impact Patterns for more details.

Feature

Description

phq_attendance_community_accommodation

Community accommodation impact

phq_attendance_concerts_accommodation

Concerts accommodation impact

phq_attendance_conferences_accommodation

Conferences accommodation impact

phq_attendance_expos_accommodation

Expos accommodation impact

phq_attendance_festivals_accommodation

Festivals accommodation impact

phq_attendance_performing_arts_accommodation

Performing Arts accommodation impact

phq_attendance_sports_accommodation

Sports accommodation impact

phq_attendance_community_hospitality

Community hospitality impact

phq_attendance_concerts_hospitality

Concerts hospitality impact

phq_attendance_conferences_hospitality

Conferences hospitality impact

phq_attendance_expos_hospitality

Expos hospitality impact

phq_attendance_festivals_hospitality

Festivals hospitality impact

phq_attendance_performing_arts_hospitality

Performing Arts hospitality impact

phq_attendance_sports_hospitality

Sports hospitality impact

phq_attendance_community_retail

Community Retail impact

phq_attendance_concerts_retail

Concerts Retail impact

phq_attendance_conferences_retail

Conferences Retail impact

phq_attendance_expos_retail

Expos Retail impact

phq_attendance_festivals_retail

Festivals Retail impact

phq_attendance_performing_arts_retail

Performing Arts Retail impact

phq_attendance_sports_retail

Sports Retail impact

Configuration

You can configure PHQ Attendance features using the options below.

Field

Description

stats object optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

count
sum
min
max
avg
median
std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}

phq_rank object optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}

local_rank object optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}

PHQ Rank features provide the daily-level aggregated sum of events bucketed by PHQ Rank level (1-5).

PHQ Rank Impact Pattern Features

See the "Holidays and Observances Impact Pattern Features" under PHQ Impact in the tab above. These features cover the Accommodation, Retail, and Hospitality (Food & Beverage) industries.

We recommend that if you operate in the supported industries you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes.

PHQ Rank Generic Features

These are generic features that do not include demand impact patterns and should be used if you are not in one of the industries that we have impact patterns for.

Feature

Description

phq_rank_observances

Observances

phq_rank_public_holidays

Public Holidays

phq_rank_school_holidays

School Holidays

phq_rank_academic_session

Academic - Session

phq_rank_academic_exam

Academic - Exam

phq_rank_academic_holiday

Academic - Holiday

phq_rank_daylight_savings

Daylight savings

phq_rank_health_warnings

Health Warnings

phq_rank_politics

Politics

Configuration

PHQ Rank features cannot currently be configured further. When requesting phq_rank_* features set the value as true indicating you require the default calculations.

PHQ Spend features provide daily-level aggregated stats based on total USD we predict will be spent during events on a given day. This takes into account complications like distributing attendance across multi-day events.

You can request industry-specific features which are tuned to one of three potential industries:

Accommodation: Event spend relating to hotels and hosts for the purposes of staying at during events. Spend can extend before and after an event actually starts/ends.
Hospitality: Event spend on restaurants, food and drinks. Hotel restaurants are included in this industry.
Transportation: Ground-based transportation for the purposes of getting to and from an event. Includes public and private transport, such as taxis, rails, busses and rideshares.

Feature

Description

phq_spend_conferences

Conferences

phq_spend_expos

Expos

phq_spend_sports

Sports

phq_spend_community

Community

phq_spend_concerts

Concerts

phq_spend_festivals

Festivals

phq_spend_performing_arts

Performing Arts

phq_spend_conferences_accommodation

Conferences - Accommodation

phq_spend_expos_accommodation

Expos - Accommodation

phq_spend_sports_accommodation

Sports - Accommodation

phq_spend_community_accommodation

Community - Accommodation

phq_spend_concerts_accommodation

Concerts - Accommodation

phq_spend_festivals_accommodation

Festivals - Accommodation

phq_spend_performing_arts_accommodation

Performing Arts - Accommodation

phq_spend_conferences_hospitality

Conferences - Hospitality

phq_spend_expos_hospitality

Expos - Hospitality

phq_spend_sports_hospitality

Sports - Hospitality

phq_spend_community_hospitality

Community - Hospitality

phq_spend_concerts_hospitality

Concerts - Hospitality

phq_spend_festivals_hospitality

Festivals - Hospitality

phq_spend_performing_arts_hospitality

Performing Arts - Hospitality

phq_spend_conferences_transportation

Conferences - Transportation

phq_spend_expos_transportation

Expos - Transportation

phq_spend_sports_transportation

Sports - Transportation

phq_spend_community_transportation

Community - Transportation

phq_spend_concerts_transportation

Concerts - Transportation

phq_spend_festivals_transportation

Festivals - Transportation

phq_spend_performing_arts_transportation

Performing Arts - Transportation

Configuration

You can configure PHQ Spend features using the options below.

Field

Description

stats object optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

count
sum
min
max
avg
median
std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}

phq_rank object optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}

local_rank object optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}

PHQ Viewership features provide daily-level aggregated stats based on the number of people who we predict will view broadcasts on a given day.

Feature

Description

phq_viewership_sports

Sports - (All)

phq_viewership_sports_american_football

American Football - (All)

phq_viewership_sports_american_football_ncaa_men

American Footbal - NCAA Men's

phq_viewership_sports_american_football_nfl

American Football - NFL

phq_viewership_sports_auto_racing

Automotive Racing - All

phq_viewership_sports_auto_racing_indy_car

Automotive Racing - Indy Car

phq_viewership_sports_auto_racing_nascar

Automotive Racing - NASCAR

phq_viewership_sports_baseball

Baseball - (All)

phq_viewership_sports_baseball_mlb

Baseball - MLB

phq_viewership_sports_baseball_ncaa_men

Baseball - NCAA Men's

phq_viewership_sports_basketball

Basketball - (All)

phq_viewership_sports_basketball_nba

Basketball - NBA

phq_viewership_sports_basketball_ncaa_men

Basketball - NCAA Men's

phq_viewership_sports_basketball_ncaa_women

Basketball - NCAA Women's

phq_viewership_sports_boxing

Boxing - (All)

phq_viewership_sports_golf

Golf - (All)

phq_viewership_sports_golf_masters

Golf - Masters

phq_viewership_sports_golf_pga_championship

Golf - PGA Championships

phq_viewership_sports_golf_pga_tour

Golf - PGA Tours

phq_viewership_sports_golf_us_open

Golf - US Open

phq_viewership_sports_horse_racing

Horse Racing - (All)

phq_viewership_sports_horse_racing_belmont_stakes

Horse Racing - Belmont Stakes

phq_viewership_sports_horse_racing_kentucky_derby

Horse Racing - Kentucky Derby

phq_viewership_sports_horse_racing_preakness_stakes

Horse Racing - Preakness Stakes

phq_viewership_sports_ice_hockey

Ice Hockey - (All)

phq_viewership_sports_ice_hockey_nhl

Ice Hockey - NHL

phq_viewership_sports_mma

Mixed Martial Arts - (All)

phq_viewership_sports_mma_ufc

Mixed Martial Arts - UFC

phq_viewership_sports_soccer

Soccer - (All)

phq_viewership_sports_soccer_concacaf_champions_league

Soccer - CONCACAF Champions League

phq_viewership_sports_soccer_concacaf_gold_cup

Soccer - CONCACAF Gold Cup

phq_viewership_sports_soccer_copa_america_men

Soccer - COPA America Men's

phq_viewership_sports_soccer_fifa_world_cup_women

Soccer - FIFA World Cup Women's

phq_viewership_sports_soccer_fifa_world_cup_men

Soccer - FIFA World Cup Men's

phq_viewership_sports_soccer_mls

Soccer - MLS

phq_viewership_sports_soccer_uefa_champions_league_men

Soccer - UEFA Champions League Men's

phq_viewership_sports_softball

Softball - (All)

phq_viewership_sports_softball_ncaa_women

Softball - NCAA Women's

phq_viewership_sports_tennis

Tennis - (All)

phq_viewership_sports_tennis_us_open

Tennis - US Open

phq_viewership_sports_tennis_wimbledon

Tennis - Wimbledon

Configuration

You can configure PHQ Attendance features using the options below.

Field

Description

stats object optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

count
sum
min
max
avg
median
std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}

phq_rank object optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}

local_rank object optiona

Filter for events with a Local Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }
}

PHQ Impact features provide daily-level aggregated stats based on the predicted impact of an event. This takes into account complications like Impact Patterns (leading and lagging effects of an event).

Holidays and Observances Impact Pattern Features

These features include the demand impact patterns for public holidays and observances. For example, these features will show when people typically arrive and book accommodation before a holiday and if they tend to leave after the holiday. See Impact Patterns

We recommend that if you operate in the industries listed below you use the demand impact features for holidays and observances instead of the generic features as these will result in greater forecast accuracy as they include the impact before an event starts and after it finishes.

Feature

Configuration

You can configure PHQ Impact features using the options below.

Field

Description

stats object optional

You can optionally configure which fields are calculated for each of these features by providing the list of stats fields you would like.

Default fields are count and sum.

Supported fields are:

count
sum
min
max
avg
median
std_dev

E.g.

{
  "stats": [
    "count",
    "std_dev",
    "median"
  ]
}

phq_rank object optional

Filter for events with a PHQ Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "phq_rank": {
    "gt": 50,
    "lt": 80
  }
}

local_rank object optional

Filter for events with a Local Rank within a certain range.

Supports the following fields:

gt - greater than
gte - greater than or equal
lt - less than
lte - less than or equal

E.g.

{
  "local_rank": {
    "gt": 50,
    "lt": 80
  }

Response

Response Fields

Field

Description

results array

List of results where each item is a Feature.

Please refer to the Feature Response Fields section below for the structure of each record. Note that pagination is not required in this API.

Feature Response Fields

Other than the date, the structure of each result here will depend on how you configured the feature in your request and the type of feature.

Field

Description

date string

Date in local time. E.g. 2023-10-01

<phq_attendance_*> object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Attendance features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}

Field

Description

date string

Date in local time. E.g. 2023-10-01

<phq_rank_*> object

Daily-level feature result. The structure of the result here is always the same as PHQ Rank features cannot currently be configured.

Will contain a rank_levels field which indicates the sum of matching events active on the date at each PHQ Rank level.

PHQ Rank is on a scale of 0 to 100 and the levels are bucketed as:

1 - Minor (rank between 0 and 20).
2 - Moderate (rank between 21 and 40).
3 - Important (rank between 41 and 60).
4 - Significant (rank between 61 and 80).
5 - Major (rank between 81 and 100).

E.g.

{
  "rank_levels": {
    "1": 0,
    "2": 0,
    "3": 0,
    "4": 2,
    "5": 0
  }
}

Field

Description

date string

Date in local time. E.g. 2023-10-01

<phq_spend_*> object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Spend features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}

Field

Description

date string

Date in local time. E.g. 2023-10-01

<phq_viewership_*> object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Viewership features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}

Field

Description

date string

Date in local time. E.g. 2023-10-01

<phq_impact_*> object

Daily-level feature result. The structure of the result here will depend on how you configured the feature in your request.

PHQ Impact features are stats-based.

Default fields are count and sum.

E.g.

{
  "stats": {
    "count": 5,
    "sum": 17307,
    "min": 1000,
    "max": 9215,
    "avg": 3461.4,
    "median": 2620.0,
    "std_dev": 2978.810473997968
  }
}

Response Format

You can receive responses formatted as JSON (default) or CSV. Use the Accept header when performing your request to define which format you would like to receive.

With the Accept header set to application/json you will receive the results as JSON like the example below:

{
  "results": [
    {
      "date": "2019-11-16",
      "phq_attendance_concerts": {
        "stats": {
          "count": 20,
          "sum": 6751
        }
      },
      "phq_attendance_conferences": {
        "stats": {
          "min": 1500,
          "max": 1500
        }
      },
      "phq_attendance_sports": {
        "stats": {
          "count": 5,
          "sum": 17307,
          "min": 1000,
          "max": 9215,
          "avg": 3461.4,
          "median": 2620.0,
          "std_dev": 2978.810473997968
        }
      },
      "phq_rank_public_holidays": {
        "rank_levels": {
          "1": 0,
          "2": 0,
          "3": 0,
          "4": 0,
          "5": 0
        }
      }
    },
    {
      "date": "2019-11-17",
      "phq_attendance_concerts": {
        "stats": {
          "count": 2,
          "sum": 241
        }
      },
      "phq_attendance_conferences": {
        "stats": {
          "min": 67,
          "max": 67
        }
      },
      "phq_attendance_sports": {
        "stats": {
          "count": 1,
          "sum": 852,
          "min": 852,
          "max": 852,
          "avg": 852.0,
          "median": 852.0,
          "std_dev": 0.0
        }
      },
      "phq_rank_public_holidays": {
        "rank_levels": {
          "1": 0,
          "2": 0,
          "3": 0,
          "4": 0,
          "5": 0
        }
      }
    }
  ]
}

With the Accept header set to text/csv you will receive the results as CSV like the example below:

date,phq_attendance_concerts_stats_count,phq_attendance_concerts_stats_sum,phq_attendance_conferences_stats_min,phq_attendance_conferences_stats_max,phq_attendance_sports_stats_count,phq_attendance_sports_stats_sum,phq_attendance_sports_stats_min,phq_attendance_sports_stats_max,phq_attendance_sports_stats_avg,phq_attendance_sports_stats_median,phq_attendance_sports_stats_std_dev,phq_rank_public_holidays_rank_levels_1,phq_rank_public_holidays_rank_levels_2,phq_rank_public_holidays_rank_levels_3,phq_rank_public_holidays_rank_levels_4,phq_rank_public_holidays_rank_levels_5
2019-11-16,43,24546,11,1000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-17,25,13440,11,146,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-18,6,2021,11,700,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-19,10,6047,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-20,14,59704,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-21,21,60851,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-22,35,25760,11,171000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-23,29,25425,11,394,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-24,18,23410,11,500,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-25,6,5122,11,1000,1,18064,18064,18064,18064.0,18064.0,0.0,0,0,0,0,0
2019-11-26,8,6861,11,1000,0,0,0,0,0.0,0.0,,0,0,0,0,0
2019-11-27,12,6225,11,500,1,18064,18064,18064,18064.0,18064.0,0.0,0,0,0,0,0

The same data represented as a table:

date

phq_attendance_concerts_stats_count

phq_attendance_concerts_stats_sum

phq_attendance_conferences_stats_min

phq_attendance_conferences_stats_max

phq_attendance_sports_stats_count

phq_attendance_sports_stats_sum

phq_attendance_sports_stats_min

phq_attendance_sports_stats_max

phq_attendance_sports_stats_avg

phq_attendance_sports_stats_median

phq_attendance_sports_stats_std_dev

phq_rank_public_holidays_rank_levels_1

phq_rank_public_holidays_rank_levels_2

phq_rank_public_holidays_rank_levels_3

phq_rank_public_holidays_rank_levels_4

phq_rank_public_holidays_rank_levels_5

2019-11-16

24546

1000

0.0

2019-11-17

13440

146

0.0

2019-11-18

2021

700

0.0

2019-11-19

6047

171000

0.0

2019-11-20

59704

171000

0.0

2019-11-21

60851

171000

0.0

2019-11-22

25760

171000

0.0

2019-11-23

25425

394

0.0

2019-11-24

23410

500

0.0

2019-11-25

5122

1000

18064

18064.0

0.0

2019-11-26

6861

1000

0.0

2019-11-27

6225

500

18064

18064.0

0.0

Examples

curl -X POST "https://api.predicthq.com/v1/features/?offset=0&limit=100" \
     -H "Accept: application/json" \
     -H "Authorization: Bearer $ACCESS_TOKEN" \
     --data @<(cat <<EOF
    {
        "active": {
            "gte": "2019-11-16",
            "lte": "2019-11-17"
        },
        "location": {
            "geo": {
                "lat": "37.78428",
                "lon": "-122.40075",
                "radius": "2.6mi"
            }
        },
        "phq_attendance_conferences": {
            "stats": [
                "min",
                "max"
            ]
        },
        "phq_attendance_sports": {
            "stats": ["count", "std_dev", "median"],
            "phq_rank": { 
                "gt": 50
            }    
        },
        "phq_attendance_concerts": true,
        "phq_rank_public_holidays": true
    }
    EOF
    )

import requests

data = {
    "active": {
        "gte": "2019-11-16",
        "lte": "2019-11-27"
    },
    "location": {
        "geo": {
            "lat": "37.78428",
            "lon": "-122.40075",
            "radius": "2.6mi"
        }
    },
    "phq_attendance_conferences": {
        "stats": [
            "min",
            "max"
        ]
    },
    "phq_attendance_sports": {
        "stats": ["count", "std_dev", "median"],
        "phq_rank": { 
            "gt": 50
        }    
    },
    "phq_attendance_concerts": True,
    "phq_rank_public_holidays": True
}

response = requests.post(
    url="https://api.predicthq.com/v1/features/",
    headers={
      "Authorization": "Bearer $ACCESS_TOKEN",
      "Accept": "application/json"
    },
    params={
      "offset": 0,
      "limit": 100
    },
    json=data
)

print(response.json())

from predicthq import Client

phq = Client(access_token="$ACCESS_TOKEN")

for feature in phq.features.obtain_features(
        active__gte="2019-11-16",
        active__lte="2019-11-27",
        location__geo={
            "lat": "37.78428",
            "lon": "-122.40075",
            "radius": "2.6mi"
        },
        phq_attendance_conferences__stats=["min", "max"],
        phq_attendance_sports__stats=["count", "std_dev", "median"],
        phq_attendance_sports__phq_rank={
            "gt": 50
        },
        phq_attendance_concerts=True,
        phq_rank_public_holidays=True
):
    print(feature.date, feature.phq_attendance_conferences.stats.min, 
        feature.phq_attendance_conferences.stats.max,
        feature.phq_attendance_sports.stats.count,
        feature.phq_attendance_sports.stats.std_dev,
        feature.phq_attendance_sports.stats.median,
        feature.phq_attendance_concerts.stats.count,
        feature.phq_attendance_concerts.stats.sum,
        feature.phq_rank_public_holidays.rank_levels)

Guides

Below are some guides relevant to this API:

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Last updated 1 month ago