API Fruityvice - JSON to df

Currently the webservice consists of two functions:

• Receiving data for a specific fruit or all fruit, and
• A function to add your own data.
• An example of what the response body would look like

• To receive the shown data, you have to make a HTTP GET call on the resource /api/fruit/{ID} or /api/fruit/{name} of this website’s IP.
• To add data, make a HTTP PUT call on the resource /api/fruit with the data of a fruit in JSON format in the request body.

Case Study

• Make an HTTP get request call
• Data returned will be in json format, so convert the response data to pandas df
• Normalize/flatten the results
• Find out how many calories in a banana

Setup

import pandas as pd
import requests
import json

Send Get Request

# Make the get request - it appears fruityvice is temporarily out of order
data = requests.get("https://fruityvice.com/api/fruit/all")

# The text data type returned is json so use json.loads to load it
results = json.loads(data.text)

# View the first two elements
results[:2]

Convert json to DF

df3 = pd.DataFrame(results)
df3.head(3)

Normalize/Flatten DF

• As you can see above, the nutritions column contains multiple subcolumns
• We need to flatten or normalize it

# Flaten or Normalize the df
df3 = pd.json_normalize((results))
df3.head(5)

Extract Information

Genus & Family of a Cherry

• Filter out the Cherry row

# create a cherry var
#cherry = df3["name"] == "Cherry" this gives us a column of False & True

# this will give us the row from the df that has a value of True
cherry = df3[df3["name"] == "Cherry"]
cherry

• Extract the family using the label method. Since we know we only have one row we can use [ :, ccc]

cherry.loc[:,['family']]

• Let’s extract genus using the location method iloc

cherry.iloc[0]['genus']

Calories in Banana

banana = df3[df3["name"] == "Banana"]
banana.loc[:,['nutritions.calories']]