Handling photos and getting the actual locations for Foursquare location data from an export

Project Scope and ToDos

1. Create a new site
2. Process the Foursquare data to a set of locations with additional data
3. Set the data up so it can be put on a map (it needs lat and long)

• Can be searched
• Can show travel paths throughout a day

Day 2

I think I got it writing photos effectively, but I think I made a mistake trying to put them in the venue dataframe. I need a photo table.

git commit -am "Setting up a dataframe for individual photos"

Ok, I can see how to do it, but the sequence is getting complicated. I want to actually set up some functions and call them into the notebook I'm working on. Then I need to do the next thing, which is resolve venues to have lat and long values.

git commit -am "Processing sequence of files into dataframes now in functions"

Oh, I know some photos exist without a checkin, which means no venue entry. I'll have to add that.

git commit -am "Photos should always have venues associated with them."

Ok, so now we gotta look at hitting the Foursquare API to get the correct lat and long data.

Using the Foursquare API

Ok, I tried out the place details endpoint in the FourSquare API Explorer.

It returns an object like this:

Skip code block ▼

{
  "fsq_id": "59e63da08c35dc3e57ab5520",
  "categories": [
    {
      "id": 10032,
      "name": "Night Club",
      "short_name": "Night Club",
      "plural_name": "Night Clubs",
      "icon": {
        "prefix": "https://ss3.4sqi.net/img/categories_v2/nightlife/nightclub_",
        "suffix": ".png"
      }
    },
    {
      "id": 13009,
      "name": "Cocktail Bar",
      "short_name": "Cocktail",
      "plural_name": "Cocktail Bars",
      "icon": {
        "prefix": "https://ss3.4sqi.net/img/categories_v2/nightlife/cocktails_",
        "suffix": ".png"
      }
    },
    {
      "id": 13021,
      "name": "Speakeasy",
      "short_name": "Speakeasy",
      "plural_name": "Speakeasies",
      "icon": {
        "prefix": "https://ss3.4sqi.net/img/categories_v2/nightlife/secretbar_",
        "suffix": ".png"
      }
    }
  ],
  "chains": [],
  "closed_bucket": "Unsure",
  "geocodes": {
    "drop_off": {
      "latitude": 40.745138,
      "longitude": -73.990299
    },
    "main": {
      "latitude": 40.745324,
      "longitude": -73.990221
    },
    "roof": {
      "latitude": 40.745324,
      "longitude": -73.990221
    }
  },
  "link": "/v3/places/59e63da08c35dc3e57ab5520",
  "location": {
    "address": "49 W 27th St",
    "census_block": "360610058001001",
    "country": "US",
    "cross_street": "btwn Broadway & 6th Ave",
    "dma": "New York",
    "formatted_address": "49 W 27th St (btwn Broadway & 6th Ave), New York, NY 10001",
    "locality": "New York",
    "postcode": "10001",
    "region": "NY"
  },
  "name": "Patent Pending",
  "related_places": {
    "parent": {
      "fsq_id": "59e618dc4c9be64fbe509006",
      "categories": [
        {
          "id": 13035,
          "name": "Coffee Shop",
          "short_name": "Coffee Shop",
          "plural_name": "Coffee Shops",
          "icon": {
            "prefix": "https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_",
            "suffix": ".png"
          }
        },
        {
          "id": 13034,
          "name": "Café",
          "short_name": "Café",
          "plural_name": "Cafés",
          "icon": {
            "prefix": "https://ss3.4sqi.net/img/categories_v2/food/cafe_",
            "suffix": ".png"
          }
        }
      ],
      "name": "Patent Coffee"
    }
  },
  "timezone": "America/New_York"
}

Ok, that has all the information I need! And a lot more besides. I was thinking I could just pull the information in to the dataframe, and I think that is true, but also I think I want to just replicate the object into a physical file for now. I might want to do more with it later.

I want to play around with these new functions and test them in my Jupyter Notebook. Looks like the best way to do this is with autoreload.

Skip code block ▼

%autoreload 2
import data_processing

config = dotenv_values('../.env')
apiKey = config['FSQ_API_KEY']

data_processing.get_place_details("59e63da08c35dc3e57ab5520", apiKey)

git commit -am "Adding FSq API checks and file writing and new notebook for testing functions"