如何制作 Pandas 数据框 for 循环（用于股票市场 API）

Question

I'm trying to get a stock data metric from an API into a Pandas Dataframe (Debt/Equity ratio for a company).

I've been successful in getting the data for a single company, but would like to do it with several companies at a time.

The code I used for a single company is:

# Variables
ticker  = "AAPL" 
FMP_API = "<api_key_here>"
data    = "balance-sheet-statement"

def get_jsonparsed_data(url):
    response = urlopen(url)
    data = response.read().decode("utf-8")
    return json.loads(data)


# Download info from API
url = "https://financialmodelingprep.com/api/v3/"+data+"/"+ticker+"?limit=120&apikey="+FMP_API
results = get_jsonparsed_data(url)
df = json_normalize(results)

# Calculate  Debt/Equity Ratio
df[ticker] = df.totalLiabilities / df.totalStockholdersEquity
df = df[["date", ticker]].round(2)

# Convert the column Date, now in string type to a datetime type
# Make the Date Column the Index
# Creating a new dataframe with the new index and add the Date column name
# Dropp the extra Date Column
datetime_series = pd.to_datetime(df['date'])
datetime_index = pd.DatetimeIndex(datetime_series.values)
df = df.set_index(datetime_index).rename_axis('date', axis=1)
df.drop('date',axis=1,inplace=True)

df.head()

The result I get is:

date        AAPL
2020-09-26  3.96
2019-09-28  2.74
2018-09-29  2.41
2017-09-30  1.80
2016-09-24  1.51

date
AAPL    float64
dtype: object

What I would like to get is:

ticker  = ["AAPL", "FB", "GOOG", "AMZN"]

date        AAPL   FB   GOOG  AMZN
2020-09-26  3.96  0.24  0.44  2.44
2019-09-28  2.74  0.32  0.37  2.63
2018-09-29  2.41  0.16  0.31  2.73
2017-09-30  1.80  0.14  0.29  3.74
2016-09-24  1.51  0.10  0.20  3.32

date
AAPL    float64
FB      float64
GOOG    float64
AMZN    float64
dtype: object

I tried using a for loop, but i keep writing over the same dataframe and can only get the values for the last ticker in the list.

Answer 1

Use pandas merge to merge two of the same dataframes with different tickers by the same index.

You can also simplify your code where you set the date as the index:

df.date = pd.to_datetime(df['date'])
df = df.set_index(date)

Answer 2

Found the anwser.

The idea is to create an empty list and a for loop.

Each loop gets the data from one ticker and in the end we make a dataframe by concatenating the list.

Here is the code:

FMP_API = "<api_code_here>"

data    = "balance-sheet-statement"
tickers = ['AAPL', 'MSFT']
     
df_list = []
     
for ticker in tickers:
    url = "https://financialmodelingprep.com/api/v3/" + data + "/" + ticker + "?limit=120&apikey=" + FMP_API
    df = pd.read_json(url)
    df.set_index('date', inplace=True)
     
    df[ticker] = df.totalLiabilities / df.totalStockholdersEquity
    df_list.append(df[ticker])
     
df_final = pd.concat(df_list, axis=1)
print(df_final)