JP Hwang databyjp
databyjp
/ tidy_your_data_melt1
Created
March 31, 2020 12:28
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| >>> messy_df.melt(var_list) | |
| Country Name Country Code Indicator Name Indicator Code variable value | |
| 0 Aruba ABW Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 1960 NaN | |
| 1 Afghanistan AFG Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 1960 0.170627 | |
| 2 Angola AGO Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 1960 2.061462 | |
| 3 Albania ALB Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 1960 5.102676 | |
| 4 Andorra AND Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 1960 NaN | |
| ... ... ... ... ... ... | |
| 15835 Kosovo XKX Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 2019 NaN | |
| 15836 Yemen, Rep. YEM Hospital beds (per 1,000 people) SH.MED.BEDS.ZS 2019 NaN |
databyjp
/ bezos_tracker_get_data.py
Created
April 19, 2020 09:43
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| # ========== LOAD TIINGO TOKEN ========== | |
| with open('../../tokens/tiingo_token.txt', 'r') as f: | |
| tiingo_token = f.read().strip() | |
| # ========== GET STOCK DATA ========== | |
| def get_stock_data(tkn, sym='amzn', start_date='2020-01-01'): | |
| headers = {'Content-Type': 'application/json'} | |
| requestResponse = requests.get("https://api.tiingo.com/tiingo/daily/" + sym + "/prices?startDate=" + start_date + "&token=" + tkn, headers=headers) |
databyjp
/ bezos_tracker_df_head.py
Created
April 19, 2020 09:44
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| >>> amzn_df.head() | |
| date close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor | |
| 0 2020-01-02T00:00:00.000Z 1898.01 1898.01 1864.15 1875.00 4035910 1898.01 1898.01 1864.15 1875.00 4035910 0.0 1.0 | |
| 1 2020-01-03T00:00:00.000Z 1874.97 1886.20 1864.50 1864.50 3766604 1874.97 1886.20 1864.50 1864.50 3766604 0.0 1.0 | |
| 2 2020-01-06T00:00:00.000Z 1902.88 1903.69 1860.00 1860.00 4065698 1902.88 1903.69 1860.00 1860.00 4065698 0.0 1.0 | |
| 3 2020-01-07T00:00:00.000Z 1906.86 1913.89 1892.04 1904.50 4134010 1906.86 1913.89 1892.04 1904.50 4134010 0.0 1.0 | |
| 4 2020-01-08T00:00:00.000Z 1891.97 1911.00 1886.44 1898.04 3511966 1891.97 1911.00 1886.44 1898.04 3511966 0.0 1.0 |
databyjp
/ bezos_tracker_gain_cals.py
Created
April 19, 2020 10:43
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| amzn_df = amzn_df.assign(bezos_year=(amzn_df.close - amzn_df.iloc[0].close) * 498 * 10**6 * 0.112) | |
| amzn_df = amzn_df.assign(bezos_week=[(row['close'] - amzn_df.iloc[max(0, i-6)].close) * 498 * 10**6 * 0.112 for i, row in amzn_df.iterrows()]) |
databyjp
/ bezos_tracker_interval_example.py
Last active
April 20, 2020 10:17
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| app.layout = html.Div([ | |
| html.Div([ | |
| dcc.Graph(id='graph_id'), | |
| dcc.Interval( | |
| id='interval_id', | |
| interval=1 * 1000, # in milliseconds | |
| n_intervals=0 | |
| ) | |
| ]), | |
| ]) |
databyjp
/ bezos_tracker_update_graph.py
Last active
April 20, 2020 10:38
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| def get_bezos_data(): | |
| amzn_data = get_stock_data(tiingo_token).json() | |
| amzn_df = pd.DataFrame(amzn_data) | |
| amzn_df = amzn_df.assign(bezos_year=(amzn_df.close - amzn_df.iloc[0].close) * 498 * 10**6 * 0.112) | |
| return amzn_df | |
| app.layout = dbc.Container([ | |
| html.Div([ | |
| dcc.Graph( | |
| id='bezos-worth-graph', |
databyjp
/ bezos_trackers_card_example.py
Last active
April 20, 2020 13:17
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| card_content_worth = [ | |
| dbc.CardHeader(f"Bezos' net worth has increased by: "), | |
| dbc.CardBody([ | |
| html.H3([dbc.Badge(f"US${int(bezos_secondly * n)}", color='success')], className="card-title"), | |
| html.P([ | |
| f"That is ", dbc.Badge(str(round(bezos_tot/median_us_income, 1)), color='primary'), | |
| " typical U.S. incomes' worth of money."], | |
| className="card-text", | |
| ), | |
| ])] |
databyjp
/ get_stock_data_function.py
Last active
May 6, 2020 18:57
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| # ========== FUNCTION TO GET STOCK DATA ========== | |
| def get_stock_data(tkn, sym='amzn', start_date='2020-01-01'): | |
| headers = {'Content-Type': 'application/json'} | |
| requestResponse = requests.get("https://api.tiingo.com/tiingo/daily/" + sym + "/prices?startDate=" + start_date + "&token=" + tkn, headers=headers) | |
| if requestResponse.status_code == 200: | |
| logger.info(f'Success fetching {sym} data from {start_date} to today') | |
| else: | |
| logger.warning(f'Something looks wrong - status code {requestResponse.status_code}') |
databyjp
/ get_stock_data_plot1.py
Last active
May 7, 2020 09:04
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| fig = px.scatter(total_ticker_df, | |
| x='date', y='close', color='sym', template='plotly_white', title='Sample Stock Price Graphs', | |
| facet_col='sym', facet_col_wrap=3 | |
| ) | |
| fig.update_traces(mode='lines') | |
| fig.show() |
databyjp
/ get_stock_data_corr_matrix_simple.py
Created
May 7, 2020 09:38
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| # Build a correlation (and p-value) matrix | |
| r_array = np.zeros([len(symbols_ser), len(symbols_ser)]) | |
| p_array = np.zeros([len(symbols_ser), len(symbols_ser)]) | |
| for i in range(len(symbols_ser)): | |
| for j in range(len(symbols_ser)): | |
| ser_i = total_ticker_df[total_ticker_df.sym == symbols_ser[i]]['close'].values | |
| ser_j = total_ticker_df[total_ticker_df.sym == symbols_ser[j]]['close'].values | |
| r_ij, p_ij = scipy.stats.pearsonr(ser_i, ser_j) | |
| r_array[i, j] = r_ij | |
| p_array[i, j] = p_ij |