Parsing data from JSON API in Pandas I made a little program to parse data from an API.
I do not have experience with Pandas. It is working but I would like to know how to do that better and more efficiently.

class Albion_data():
    def __init__(self,item):
        self.location = f'Bridgewatch,Thetford,FortSterling,Martlock,Bridgewatch,Lymhurst'
        self.item = item
    @property
    def get_data(self):
        url = f"https://www.albion-online-data.com/api/v1/stats/Prices/{self.item}?locations={self.location}"
        response = (requests.get(url).text)
        response_json = json.loads(response)
        if response_json != []:
            response_json = pd.DataFrame(response_json)[["item_id", "city", "sell_price_min"]]
            max = response_json.loc[response_json["sell_price_min"].idxmax()]
            min = response_json.loc[response_json["sell_price_min"].idxmin()]
            gain = max[2] - min[2]
            data = pd.DataFrame(
                [[max[0], max[1], min[1], min[2], max[2], gain]],
                columns=["ITEM", "CITY max ", "CITY min", "MIN_PRICE", "MAX PRICE", "GAIN"])

            return data

def get_data_frame():
    item_list = pd.read_csv('items.txt', sep=':', names=['nuber', 'item']['item']
    item_price = pd.DataFrame(columns=["ITEM", "CITY max ", "CITY min","MIN_PRICE", "MAX PRICE", "GAIN"])
    for item in item_list:
        item_price = item_price.append(Albion_data(item).get_data)
        item_price.to_csv('data.csv')
    return item_price
```
Do you need a class?

Right now, I'm not sure you need a class. Your class has two methods, one of which is __init__, so this could be refactored to be a function

def get_data(item):
    location = f'Bridgewatch,Thetford,FortSterling,Martlock,Bridgewatch,Lymhurst'
    url = f"https://www.albion-online-data.com/api/v1/stats/Prices/{item}?locations={location}"
    # you also don't need these parentheses
    response = requests.get(url).text
    response_json = json.loads(response)
    if response_json != []:
        response_json = pd.DataFrame(response_json)[["item_id", "city", "sell_price_min"]]
        max = response_json.loc[response_json["sell_price_min"].idxmax()]
        min = response_json.loc[response_json["sell_price_min"].idxmin()]
        gain = max[2] - min[2]
        data = pd.DataFrame(
                [[max[0], max[1], min[1], min[2], max[2], gain]],
                columns=["ITEM", "CITY max ", "CITY min", "MIN_PRICE", "MAX PRICE", "GAIN"])

        return data


This minimizes the cost of constructing the class just to get a dataframe.

Checking the Value of json.loads

Now, I'm not completely sure what response.text will look like every time, but reading your code, it will always be a list. The fact that there's no else means that if your code doesn't satisfy the if statement, it will return None, which could lead to TypeErrors in your dataframe construction. It might be easier to make a generator, that way you can easily skip return values:

# set the argument to be an iterable

def get_data(items):
    columns=["ITEM", "CITY max ", "CITY min", "MIN_PRICE", "MAX PRICE", "GAIN"])
    location = f'Bridgewatch,Thetford,FortSterling,Martlock,Bridgewatch,Lymhurst'

    for item in items:
        url = f"https://www.albion-online-data.com/api/v1/stats/Prices/{item}?locations={location}"
        response = requests.get(url).text
        response_json = json.loads(response)

        # rely on the falsiness of empty lists and enforce that it will indeed be
        # a list type using isinstance
        if response_json and isinstance(response_json, list):
            # since response_json is a list, don't create a dataframe,
            # you can instead provide a key to the max and min functions
            maxval = max(response_json, key=lambda x: x['sell_price_min'])
            minval = min(response_json, key=lambda x: x['sell_price_min'])

            # Now you don't need to rely on an index
            gain = maxval['sell_price_min'] - minval['sell_price_min']
            vals = [maxval.pop('item_id')]
            for k in ('city','sell_price_min'):
                vals.append(maxval[k])
                vals.append(minval[k])
            vals.append(gain)
            yield dict(zip(vals, columns))


Now, in case the if statement isn't True, you won't get None from the function:

def func(a):
   if a&lt;2:
       return [1,2]

df = pd.DataFrame([func(x) for x in range(4)], columns = ['a', 'b'])
# TypeError: object of type 'NoneType' has no len()


The benefit here is that now your get_data_frame function can be a lot easier to leverage:


def get_data_frame():
    item_list = pd.read_csv('items.txt', sep=':', names=['nuber', 'item']['item']

    # You also don't need to initialize the item_price dataframe

    # You can use a list comprehension, and because your items are
    # dictionaries, you don't need to specify the columns
    item_price = pd.DataFrame([a for a in get_data(item_list)])
    item_price.to_csv('data.csv')
    return item_price


read_csv

Now, the only other issue I can see is that you are collecting everything into memory multiple times with pd.read_csv. You really only want the item_id's, so just iterate over a file handle directly:

def file_iterate():
    with open('items.txt') as fh:
        for line in fh:
            # item is the second entry, so split on ':' and grab index 1
            yield line.split(':')[1].strip()

def get_data_frame():
    items = file_iterate()
    item_price = pd.DataFrame([a for a in get_data(items)])
    item_price.to_csv('data.csv')
    return item_price


The generators will allow you to keep one dataframe in-memory, and produces values one at a time.

try/except

The last thing I'd do is to ensure the program doesn't crash on a bad web request or attempt at json.loads:

from requests.exceptions import HTTPError

def get_data(items):
    columns=["ITEM", "CITY max ", "CITY min", "MIN_PRICE", "MAX PRICE", "GAIN"])
    location = f'Bridgewatch,Thetford,FortSterling,Martlock,Bridgewatch,Lymhurst'

    for item in items:
        url = f"https://www.albion-online-data.com/api/v1/stats/Prices/{item}?locations={location}"
        # wrap in try/except here
        try:
            response = requests.get(url).text
            response_json = json.loads(response)
        except JSONDecodeError as e:
            print(f'bad json for {item}')
            continue # skip it so it doesn't ruin your process, or you can raise it
        except HTTPError as e:
            print(f'HTTPError on {url}'
            continue # skip or you can raise
        except Exception: # Unexpected error
            raise
        # rest of function