Python - 在一个函数中处理多个数据帧

Question

I have a list like this:

symbols = ['AAPL', 'MSFT']

I want to use the values in the list to complete the same processing as below.

df_AAPL_income = get_annual_finData_by_symbol('income','AAPL','us')
df_AAPL_balancesheet = get_annual_finData_by_symbol('balancesheet','AAPL','us')

df_MSFT_income = get_annual_finData_by_symbol('income','MSFT','us')
df_MSFT_balancesheet = get_annual_finData_by_symbol('balancesheet','MSFT','us')

MSFT = calculateMetrics('df_'+'MSFT'+'_balancesheet','df_'+'MSFT'+'_income')
MSFT = MSFT.T
MSFT = MSFT.reset_index()
MSFT = MSFT.rename(columns={'breakdown': 'fiscal_year'})
MSFT.insert(0, 'Ticker', 'MSFT')
MSFT = MSFT.set_index(['Ticker','fiscal_year'])

AAPL = calculateMetrics(df_AAPL_balancesheet,df_AAPL_income)
AAPL = AAPL.T
AAPL = AAPL.reset_index()
AAPL = AAPL.rename(columns={'breakdown': 'fiscal_year'})
AAPL.insert(0, 'Ticker', 'AAPL')
AAPL = AAPL.set_index(['Ticker','fiscal_year'])

concate = pd.concat([AAPL, MSFT])

Is there a way that I can create a function to pull values from the list and process?

Thanks!

Answer 1

I would do it like this:

symbols = ['AAPL', 'MSFT']

def process_symbol(symbol: str):
  df_income= get_annual_finData_by_symbol('income',symbol,'us')
  df_balancesheet = get_annual_finData_by_symbol('balancesheet',symbol,'us')

  ret = calculateMetrics(df_balancesheet,df_income)
  ret = ret.T
  ret = ret.reset_index()
  ret = ret.rename(columns={'breakdown': 'fiscal_year'})
  ret.insert(0, 'Ticker', symbol)
  ret = ret.set_index(['Ticker','fiscal_year'])
  return ret

concate = pd.DataFrame(columns = ['Ticker','fiscal_year']) //define it with header you need
for symbol in symbols:
  what_i_need = process_symbol(symbol)
  concate = pd.concat([concate, what_i_need])

Looping over the list should allow you to process list with any length. If there are empty lists, you should take care of this, you might end up with empty variable concate otherwise.