Convert Nested DateTime json to Pandas DataFrame-CodePudding

The problem here is different than those defined in the questions here, here, and here. Specifically, the transformation and form of the output I want is different than any specified in those questions, and I also want a DateTime index. These differences cause the answers on those pages fail.

I have data formatted as a json like this:

{
    "Meta Data": {
        "1. Information": "Daily Prices (open, high, low, close) and Volumes",
        "2. Symbol": "ABC",
    },
    "Time Series (Daily)": {
        "2001-06-31": {
            "1. open": "113.2000",
            "4. close": "113.8000",
        },
        "2001-07-01": {
            "1. open": "114.2000",
            "4. close": "114.2000",
        }
    }
}

I want the output to look be a pandas dataframe like this:

"Time Series (Daily)" | "1. open" | "2. close"
"2001-06-31"          | 113.2000  | 113.8000
"2001-07-01"          | 114.2000  | 114.2000

I wrote a function that works, but the for loop leaves performance wanting and I find it hard to read.

def convert_json_to_dataframe(all_json_data):
    json_data = all_json_data["Time Series (Daily)"]
    dates = []
    open = []
    close = []
    for key in json_data.keys():
        dates.append(key)
        open.append(json_data[key]["1. open"])
        close.append(json_data[key]["4. close"])
    df = pd.DataFrame(
        list(zip(open, close)),
        columns=["1. open", "2. close"],
        index=dates,
    )
    df = df.apply(pd.to_numeric, errors="ignore")
    return df

There's got to be a simpler, easier to read, higher-performing way to do this, maybe with json_normalize in pandas, but I can't figure it out.

Update after answers: All I had to do was:

df = pd.DataFrame(json_data["Time Series (Daily)"]).T

Transpose is my new favorite pandas method.

Pandas discovered the index and column names automatically, so I didn't need the reset_index approach. I got an unexpected keyword argument error when I tried the orient approach, though I expect that's because this environment is using pandas 0.22.

Thank you for the answers everyone.

CodePudding user response：

Why don't you just do this?

pd.DataFrame(data["Time Series (Daily)"]).T.reset_index().rename(columns = {"index":"Time Series (Daily)"})

Output -

	Time Series (Daily)	1. open	4. close
0	2001-06-31	113.2000	113.8000
1	2001-07-01	114.2000	114.2000

CodePudding user response：

It seems the relevant data is only under "Time Series (Daily)" key, so you could get that and construct a DataFrame (use the orient parameter to get it in the correct shape):

out = pd.DataFrame.from_dict(my_data['Time Series (Daily)'], orient='index')

Output:

            1. open  4. close
2001-06-31  113.2000  113.8000
2001-07-01  114.2000  114.2000

CodePudding user response：

Taking json from an URL is the easiest way:

import requests
url='url from json file'
r=requests.get(url)
data=r.json()
df=pd.DataFrame(data['Time Series (Daily)']).T
df=df.reset_index('Time Series (Daily)')

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