CODEWITHSUNDEEP
pythonjsonpandas
Ben
Ben

Reputation: 23

Changing json file format

The script scrapes prices, addresses, suburbs and postcodes of houses and then writes them to a csv file.

The csv file is imported into panda (only postcode and price) and groupby the mean price of the postcode. This groupby list is written to a json.

The csv file looks like this in excel

    ________________________
    |Postcode|Price        |
    ________________________
    |5061    | 205000      |
    ________________________
    |5063    | 930000      |
    ________________________

The code looks like this

import pandas as pd
from pandas import DataFrame
df = pd.read_csv('House_Prices.csv', usecols=[ 'Postcode' , ' Price' ], index_col=False)

grouped = df.groupby(['Postcode']).mean()

grouped.to_json('average_house_price.json')

The code above outputs the json file as

{" Price":{"5061":2025000.0,"5063":930000.0}}

I want the json file to be outputted like this

{"5061":2025000.0,"5063":930000.0}

Is there a way with the panda library (or other) to remove the starting Price index?

Upvotes: 2

Views: 42

Answers (3)

user7548672
user7548672

Reputation:

Try passing header=None

Look here:

df = pd.read_csv('t.csv', usecols=[ 'Postcode' , ' Price' ], index_col=False, header=None)

Upvotes: 0

vitkarpov
vitkarpov

Reputation: 409

Why don't take the field before saving as JSON, like this:

grouped[" Price"].to_json('average_house_price.json')

Upvotes: 0

jezrael
jezrael

Reputation: 863166

Add column name for aggregate "Price" or " Price" for Series:

grouped = df.groupby(['Postcode'])["Price"].mean()
#grouped = df.groupby(['Postcode'])[" Price"].mean()

grouped.to_json('average_house_price.json')

Upvotes: 1

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