38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
import os
|
|
import pandas as pd
|
|
import numpy as np
|
|
from scraper.top100_extractor import programming_crime_list
|
|
from typing import Optional
|
|
|
|
ROOT_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..')
|
|
COMPANIES_CSV_PATH: str = os.path.join('scraper', 'companies.csv')
|
|
COMPANY_DATA_CSV_PATH: str = os.path.join('Elaborated_Data', 'normalized_data.csv')
|
|
|
|
|
|
def non_nan(a: list[any]) -> list[any]:
|
|
return list(filter(lambda a: type(a) == str or not np.isnan(a), a))
|
|
|
|
|
|
def get_companies(tickers: Optional[list[str]] = None) -> list[dict]:
|
|
"""
|
|
reads the companies.csv file and returns it as a JSON-ifiable object
|
|
to return to the frontend.
|
|
"""
|
|
df = pd.read_csv(os.path.join(ROOT_PATH, COMPANIES_CSV_PATH), index_col='ticker')
|
|
tickers = pd.Series(programming_crime_list if tickers is None else tickers)
|
|
df = df.loc[df.index.isin(tickers), :]
|
|
df['tags'] = df[['tag 1', 'tag 2', 'tag 3']].values.tolist()
|
|
df['tags'] = df['tags'].apply(non_nan)
|
|
del df['tag 1']
|
|
del df['tag 2']
|
|
del df['tag 3']
|
|
|
|
# Include company metrics
|
|
df_data = pd.read_csv(os.path.join(ROOT_PATH, COMPANY_DATA_CSV_PATH), index_col='Ticker') \
|
|
.loc[:, ['Valuation', 'Financial Health', 'Estimated Growth', 'Past Performance']]
|
|
|
|
# Compute limits of metrics
|
|
# print(df_data.agg([min, max]).to_dict('records'))
|
|
|
|
df = df.join(df_data)
|
|
return df.reset_index().replace({ np.nan: None }).to_dict('records') |