def get_market_data(self) -> pd.DataFrame:
"""Get all the base market information about given coin. [Source: CoinGecko]
Returns
-------
pandas.DataFrame
Base market information about coin
Metric,Value
"""
market_data = self.coin.get("market_data", {})
market_columns_denominated = [
"market_cap",
"fully_diluted_valuation",
"total_volume",
"high_24h",
"low_24h",
]
denominated_data = create_dictionary_with_prefixes(
market_columns_denominated, market_data, DENOMINATION)
market_single_columns = [
"market_cap_rank",
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
"price_change_percentage_60d",
"price_change_percentage_1y",
"market_cap_change_24h",
]
single_stats = {}
for col in market_single_columns:
single_stats[col] = market_data.get(col)
single_stats.update(denominated_data)
try:
single_stats["circulating_supply_to_total_supply_ratio"] = (
single_stats["circulating_supply"] /
single_stats["total_supply"])
except (ZeroDivisionError, TypeError) as e:
logger.exception(str(e))
console.print(e)
df = pd.Series(single_stats).to_frame().reset_index()
df.columns = ["Metric", "Value"]
df["Metric"] = df["Metric"].apply(
lambda x: lambda_replace_underscores_in_column_names(x)
if isinstance(x, str) else x)
return df[df["Value"].notna()]
def _get_base_market_data_info(self):
"""Helper method that fetches all the base market/price information about given coin
Returns
-------
dict
"""
market_dct = {}
market_data = self.coin.get("market_data")
for stat in [
"total_supply",
"max_supply",
"circulating_supply",
"price_change_percentage_24h",
"price_change_percentage_7d",
"price_change_percentage_30d",
]:
market_dct[stat] = market_data.get(stat)
prices = create_dictionary_with_prefixes(["current_price"],
market_data, DENOMINATION)
market_dct.update(prices)
return market_dct