Question 109472 in Json Question posted in
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Json – Update values of an array of dictionaries from a pandas dataframe

I have a this array of dictionaries:

jsn = [{'ID' : '01', 'Item' : 'Apple', 'salesman' :'Johnny'},
       {'ID': '02', 'Item': 'carrot', 'salesman':'Patricia'}, 
       {'ID': '04', 'Item' : 'airplane', 'salesman' : 'Eddgard'}, 
       {'ID' : '05', 'item' : 'iron', 'salesman' : 'Bettany'}]

and i also have this dataframe:

df1 = pd.DataFrame([["1", "apple"], ["2", "mango"], 
                    ["3", "melon"], ["4", "watermelon"], 
                    ["5", "strawberry"]],columns=["ID", "Item"])

i want to change all the "Item" Values from the jsn array based on the df1 "Item" values.

I’ve tried this approach

for k in df1:
   for i in jsn1:
        i.update({'Item': f"{k}"})

But it didn’t work as i’ve expected.

Tags:

4

Answers


  1. 0

    You can do this:

    for dict_ in jsn:
    dict_["Item"] = df1[df1.ID.astype(int) == int(dict_["ID"])].Item.values[0]

jsn

    Output:

    [{'ID': '01', 'Item': 'apple', 'salesman': 'Johnny'},
 {'ID': '02', 'Item': 'mango', 'salesman': 'Patricia'},
 {'ID': '04', 'Item': 'watermelon', 'salesman': 'Eddgard'},
 {'ID': '05', 'Item': 'strawberry', 'salesman': 'Bettany'}]
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  2. 0

    What about:

    ref = df1.set_index('ID')['Item']

for d in jsn:
    d['Item'] = ref.get(str(int(d['ID'])), d.setdefault('Item', None))
    # or
    # d['Item'] = ref.get(d['ID'].lstrip('0')), d.setdefault('Item', None))

    Updated jsn:

    [{'ID': '01', 'Item': 'apple', 'salesman': 'Johnny'},
 {'ID': '02', 'Item': 'mango', 'salesman': 'Patricia'},
 {'ID': '04', 'Item': 'watermelon', 'salesman': 'Eddgard'},
 {'ID': '05', 'Item': 'strawberry', 'salesman': 'Bettany'}]
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  3. 0

    You can create a dict base df1. then update each dict in jsn like the below:

    jsn = [{'ID' : '01', 'Item' : 'Apple', 'salesman' :'Johnny'},{'ID': '02', 'Item': 'carrot', 'salesman':'Patricia'}, {'ID': '04', 'Item' : 'airplane', 'salesman' : 'Eddgard'}, {'ID' : '05', 'Item' : 'iron', 'salesman' : 'Bettany'}]
df1 = pd.DataFrame( [["2", "mango"], ["3", "melon"], ["4", "watermelon"], ["5", "strawberry"]],columns=["ID", "Item"] )


dct = dict(zip(df1['ID'].astype(int), df1['Item']))
for j in jsn:
        j['Item'] = dct.get(int(j['ID']), j['Item'])
print(jsn)

    [{'ID': '01', 'Item': 'Apple', 'salesman': 'Johnny'},
 {'ID': '02', 'Item': 'mango', 'salesman': 'Patricia'},
 {'ID': '04', 'Item': 'watermelon', 'salesman': 'Eddgard'},
 {'ID': '05', 'Item': 'strawberry', 'salesman': 'Bettany'}]
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  4. 0

    Try using merge

    import pandas as pd

df1 = pd.DataFrame( [["1", "apple"], ["2", "mango"], ["3", "melon"], ["4", "watermelon"], ["5", "strawberry"]],columns=["ID", "Item"] )
jsn = [{'ID' : '01', 'Item' : 'Apple', 'salesman' :'Johnny'},{'ID': '02', 'Item': 'carrot', 'salesman':'Patricia'}, {'ID': '04', 'Item' : 'airplane', 'salesman' : 'Eddgard'}, {'ID' : '05', 'Item' : 'iron', 'salesman' : 'Bettany'}]
df2 = pd.DataFrame(jsn)

df1["Item"] = df1["Item"].str.lower()
df2["Item"] = df2["Item"].str.lower()

df1.merge(df2, how='left', on='Item')

    Output:

    ID_x Item ID_y salesman
    0 1 apple 01
    1 2 mango NaN
    2 3 melon NaN
    3 4 watermelon NaN
    4 5 strawberry NaN
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