Get Data
Country
import pandas as pd
import pandas_datareader as pdr
from pandas_datareader import wb
countries = pdr.wb.get_countries()
countries
iso3c iso2c name region \
0 ABW AW Aruba Latin America & Caribbean
1 AFG AF Afghanistan South Asia
2 AFR A9 Africa Aggregates
3 AGO AO Angola Sub-Saharan Africa
4 ALB AL Albania Europe & Central Asia
adminregion incomeLevel \
0 High income
1 South Asia Low income
2 Aggregates
3 Sub-Saharan Africa (excluding high income) Lower middle income
4 Europe & Central Asia (excluding high income) Upper middle income
lendingType capitalCity longitude latitude
0 Not classified Oranjestad -70.0167 12.51670
1 IDA Kabul 69.1761 34.52280
2 Aggregates NaN NaN
3 IBRD Luanda 13.2420 -8.81155
4 IBRD Tirane 19.8172 41.33170
Ticker lists
import yahoo_fin.stock_info as si
# get list of Down Jones Stocks
dow_list = tickers_dow()
dow_list
# Output
['AAPL', 'AXP', 'BA', 'CAT', 'CSCO', 'CVX', 'DIS', 'DOW', 'GS',
'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK',
'MSFT', 'NKE', 'PFE', 'PG', 'RTX', 'TRV', 'UNH', 'V', 'VZ',
'WBA', 'WMT', 'XOM']
# get list of NASDAQ stocks
nasdaq_list = tickers_nasdaq()
nasdaq_list
# Output (Example)
['AACQU', 'AAL', 'AAME', 'AAO', 'AAOI', 'AAPL', 'AAWW',
'AAX', 'AAXJ', 'ABEO', 'ABIO', 'ABMD', 'ABTX']
Stock Info & Stat
import yahoo_fin.stock_info as si
get_quote_table('ptt.bk')
# Output
{'1y Target Est': 57.51,
'52 Week Range': '23.60 - 47.75',
'Ask': '38.75 x 0',
'Avg. Volume': 63144106.0,
'Beta (5Y Monthly)': 1.01,
'Bid': '38.50 x 0',
"Day's Range": '38.50 - 39.25',
'EPS (TTM)': 2.13,
'Earnings Date': 'Aug 11, 2020',
'Ex-Dividend Date': 'Mar 04, 2020',
'Forward Dividend & Yield': '2.00 (5.19%)',
'Market Cap': '1.1T',
'Open': 39.0,
'PE Ratio (TTM)': 18.08,
'Previous Close': 39.75,
'Quote Price': 38.5,
'Volume': 42731400.0}
# yfinance
import yfinance as yf
data = yf.Ticker("ptt.bk")
data.info
# Output
{'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'currency': 'THB',
'market': 'th_market',
'financialCurrency': 'THB',
'averageDailyVolume3Month': 48305487,
'averageDailyVolume10Day': 29637716,
'fiftyTwoWeekLowChange': 3.75,
'fiftyTwoWeekLowChangePercent': 0.09202454,
'fiftyTwoWeekRange': '40.75 - 52.25',
'fiftyTwoWeekHighChange': -7.75,
'fiftyTwoWeekHighChangePercent': -0.14832535,
'fiftyTwoWeekLow': 40.75,
'fiftyTwoWeekHigh': 52.25,
'earningsTimestamp': 1573480800,
'earningsTimestampStart': 1550480400,
'earningsTimestampEnd': 1550826000,
'trailingAnnualDividendRate': 2.1,
'trailingPE': 12.228635,
'trailingAnnualDividendYield': 0.047457624,
'epsTrailingTwelveMonths': 3.639,
'epsForward': 4.93,
'bookValue': 30.561,
'priceHint': 2,
'sharesOutstanding': 28562999296,
'regularMarketChangePercent': 0.56497175,
'regularMarketDayRange': '44.0 - 44.75',
'regularMarketPreviousClose': 44.25,
'bid': 44.25,
'ask': 44.5,
'bidSize': 0,
'askSize': 0,
'messageBoardId': 'finmb_5469268',
'fullExchangeName': 'Thailand',
'longName': 'PTT Public Company Limited',
'fiftyDayAverage': 45.40625,
'fiftyDayAverageChange': -0.90625,
'fiftyDayAverageChangePercent': -0.019958707,
'twoHundredDayAverage': 46.589287,
'twoHundredDayAverageChange': -2.0892868,
'twoHundredDayAverageChangePercent': -0.04484479,
'marketCap': 1271053418496,
'forwardPE': 9.026369,
'priceToBook': 1.4561042,
'sourceInterval': 15,
'exchangeTimezoneName': 'Asia/Bangkok',
'exchangeTimezoneShortName': 'ICT',
'gmtOffSetMilliseconds': 25200000,
'esgPopulated': False,
'tradeable': False,
'triggerable': False,
'regularMarketPrice': 44.5,
'regularMarketTime': 1571391547,
'regularMarketChange': 0.25,
'regularMarketOpen': 44.25,
'regularMarketDayHigh': 44.75,
'regularMarketDayLow': 44.0,
'regularMarketVolume': 25782300,
'marketState': 'CLOSED',
'shortName': 'PTT PUBLIC COMPANY LIMITED',
'exchangeDataDelayedBy': 15,
'exchange': 'SET',
'symbol': 'PTT.BK'}
Stock US. Large Cap
import pandas as pd
n = ['ticker','last','cht_percent','change','rating','vol','mkt_cap','p_e','eps','a','sector']
data = pd.read_html('https://www.tradingview.com/markets/stocks-usa/market-movers-large-cap/')
df = data[0].copy()
df.columns = n
# āļāļģāļāđāļāļĄāļđāļĨ ticker āļĄāļē split āļāļģ
df.ticker
a = df.ticker
# split āđāļāļĩāļĒāļāļĢāļēāļĒāļāļēāļĢāđāļĢāļ (āļāļąāļ§āļāļ·āđāļāđāļĄāđāļāđāļāļāļāļģāļāļ°āđāļĢ)
s = a.str.split(" ",expand=True, n=1)
s
ID | Ticeer | Company |
0 | AAPL | Apple Inc |
1 | MSFT | Microsoft Corp. |
2 | AMZN | AMAZON COM INC |
3 | GOOG | Alphabet Inc (Google) Class C |
4 | GOOGL | Alphabet Inc (Google) Class A |
... | ... | ... |
95 | SPGI | S&P GLOBAL INC |
96 | UN | UNILEVER NV EUR0.16(NEW YORK SHARES) |
97 | ISRG | INTUITIVE SURGICAL INC |
98 | Z | ZM ZOOM VIDEO COMMUNICATIONS INC |
99 | MS | MORGAN STANLEY |
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