def get_stock_symbol_with_highest_cap():
"""Return the stock symbol (e.g. PACD) with the highest cap, use
the _cap_str_to_mln_float to parse the cap values"""
sorted_cap = sorted(data,
key=lambda data: _cap_str_to_mln_float(data["cap"]),
reverse=True)
return sorted_cap[0]["symbol"]
def get_industry_cap(industry):
"""Return the sum of all cap values for given industry, use
the _cap_str_to_mln_float to parse the cap values,
return a float with 2 digit precision"""
sum_cap_industry = sum(
_cap_str_to_mln_float(datum["cap"]) for datum in data
if industry == datum["industry"])
return round(sum_cap_industry, 2)
def get_industry_cap(industry):
"""Return the sum of all cap values for given industry, use
the _cap_str_to_mln_float to parse the cap values,
return a float with 2 digit precision"""
industry_cap = sum([
_cap_str_to_mln_float(item['cap']) for item in data
if item['industry'] == industry
])
return round(industry_cap, 2)
def get_sectors_with_max_and_min_stocks():
"""Return a tuple of the sectors with most and least stocks,
discard n/a"""
stocks = defaultdict(default_stock_cap)
for stock in data:
sector = stock['sector']
cap = stock['cap']
stocks[sector] += _cap_str_to_mln_float(cap)
min_ = min(stocks, key=stocks.get)
max_ = max(stocks, key=stocks.get)
return (max_, min_)
def test_cap_str_to_mln_float():
assert _cap_str_to_mln_float('n/a') == 0
assert _cap_str_to_mln_float('$100.45M') == 100.45
assert _cap_str_to_mln_float('$20.9B') == 20900
def test_cap_str_to_mln_float():
assert _cap_str_to_mln_float("n/a") == 0
assert _cap_str_to_mln_float("$100.45M") == 100.45
assert _cap_str_to_mln_float("$20.9B") == 20900
def get_stock_symbol_with_highest_cap():
"""Return the stock symbol (e.g. PACD) with the highest cap, use
the _cap_str_to_mln_float to parse the cap values"""
filtered_stock = max(data, key=lambda i: _cap_str_to_mln_float(i['cap']))
return filtered_stock['symbol']
