def print_results(self): # Print every players top 10 most common words for speaker, count in self.speakers.items(): wordFreqPairs = count.most_common(10) table = generate_table(wordFreqPairs) print(f"\n{speaker}:") print(table) totalWords = PrettyTable() totalWords.field_names = ["Words", "Player"] totalWords.sortby = "Words" totalWords.reversesort = True for player, count in self.totalWords.items(): totalWords.add_row([count, player]) print(f"\nTotal Words by Player") print(totalWords) table = generate_table(self.allSpeakers.most_common(20)) print(f"\nMost Common Words:") print(table)
def create_layout(app): return html.Div( [ Header(app), # page 2 html.Div( [ # Row html.Div( [ html.Div( [ html.H6(["Team Location"], className="subtitle padded"), # scatter geo plot of nba teams' location dcc.Graph( id='scatter_geo', className='my_graph', figure={ 'data': [{ # Setting coordinate and description for each team 'lon': [ -98.37, -97.52, -87.90, -75.16, -118.24, -122.27, -79.38, -112.07, -90.07, -122.675, -80.84, -77.04, -83.05, -93.265, -71, -111.89, -87.63 ], 'lat': [ 29.76, 35.47, 43, 39.95, 34.05, 37.8, 43.65, 33.45, 29.95, 45.5, 35.23, 38.91, 42.33, 44.98, 42.37, 40.76, 41.88 ], 'text': [ 'HOU, Houston Rocket', 'OKC, Oklahoma Thunder', 'MIL, Milwaukee Bucks,', 'PHI, Philadelphia 76ers', 'LAL, Los Angeles Lakers', 'GSW, Golden State Warriors', 'TOR, Toronto Raptors', 'PHX, Phoenix Suns', 'NOP, New Orleans Pelicans', 'POR, Portland Trail Blazers', 'CHA, Charlotte Hornets', 'WAS, Washington Wizards', 'DET, Detroit Pistons', 'MIN, Minnesota Timberwolves', 'BOS, Boston Celtics', 'UTA, Utah Jazz', 'CHI, Chicago Bulls' ], 'type': 'scattergeo', 'mode': 'markers', "marker": { "size": 10, "opacity": 10.0, }, }], 'layout': { 'title': 'NBA teams location', 'height': 600, 'yaxis': { 'hoverformat': '.2%' }, 'margin': { 'l': 35, 'r': 35, 't': 50, 'b': 80 }, "geo": { "scope": "usa", 'showframe': True, 'showcoastlines': True, }, "colorbar": True, } }, config={'displayModeBar': True}) ], className="six columns", ), html.Div( [ html.H6( ["Salary Distribution"], className="subtitle padded", ), # Scatter plot of player's salaries dcc.Graph( id='graph-scatter', className='my_graph', figure={ 'data': [{ # Name of each player on x axis and their corresponding salary on y axis 'x': df['Name'], 'y': df['Salary'], 'type': 'scatter', 'mode': 'markers', }], # Description of the plot 'layout': { 'title': 'Distribution of NBA players salaries', 'height': 600, 'yaxis': { 'hoverformat': '.2%' }, 'margin': { 'l': 35, 'r': 35, 't': 50, 'b': 80 }, } }, config={'displayModeBar': False}) ], className="six columns", ), ], className="row ", ), # Row 2 html.Div( [ html.Div( [ html.H6("LeBron James choose Lakers", className="subtitle padded"), # Plot image from web for a overall sleek design html.Img( src= 'https://i.pinimg.com/originals/9f/15/98/9f15989577e13555c75031ee72d9c9a5.jpg', style={ 'height': '100%', 'width': '100%', 'float': 'right', 'position': 'relative', 'padding-top': 0, 'padding-right': 0 }) ], className="twelve columns", ) ], className="row ", ), # Row 3 html.Div( [ html.Div( [ html.H6( ["plot4"], className="subtitle padded", ), html.Div( [generate_table(df)], style={"overflow-x": "auto"}, ), ], className="twelve columns", ) ], className="row ", ), #end ], className="sub_page", ), ], className="page", )
def show_table(input_value): if input_value: return generate_table(query_db(get_query(input_value)))
def quick_info_update(sym): '''Updates tables on dashboards. Section 4 requirement queries. args: sym: Stock symbol for "any" argument in requirements. returns: list: HTML headers and DCC tables ''' # Really lazy hack to prevent SQL injection valid_syms = [val for val, _ in GetStockSymbols()] if sym not in valid_syms: return "ERROR: Invalid symbol selection" # Query 4.1 in requirements qh1 = html.H3("Company Prices") header = 'Price' df = pd.read_sql( ''' SELECT stock_price_minute.sym AS "Company", close AS "{}" FROM stock_price_minute JOIN ( SELECT sym, MAX(dateid) as mdate FROM stock_price_minute GROUP BY sym ) as sub ON stock_price_minute.dateid = sub.mdate AND stock_price_minute.sym = sub.sym ORDER BY dateid DESC, stock_price_minute.sym ASC '''.format(header), db.engine) df[header] = df[header].apply(money_format) sym_table = generate_table(df, 10) # Query 4.2 in requirements qh2 = html.H3("Highest Price in 10 Days") header = 'Highest Price' df = pd.read_sql( ''' SELECT sym AS "Company", MAX(close) AS "{}" FROM stock_price_minute WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY) AND sym = '{}'; '''.format(header, sym), db.engine) df[header] = df[header].apply(money_format) max_table = generate_table(df, 10) # Query 4.3 in requirements qh3 = html.H3("Year Average Price") header = 'Average Price' df = pd.read_sql( ''' SELECT sym AS "Company", AVG(close) AS "{}" FROM stock_price_minute WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY) AND sym = '{}'; '''.format(header, sym), db.engine) df[header] = df[header].apply(money_format) avg_table = generate_table(df, 1) # Query 4.4 in requirements qh4 = html.H3("Year Lowest Price") header = 'Lowest Price' df = pd.read_sql( ''' SELECT sym AS "Company", MIN(close) AS "{}" FROM stock_price_minute WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY) AND sym = '{}'; '''.format(header, sym), db.engine) df[header] = df[header].apply(money_format) low_table = generate_table(df, 1) # Query 4.5 in requirements qh5 = html.H3("Average Less Than Lowest of {}".format(sym.upper())) header = 'Average Price' df = pd.read_sql( ''' SELECT sym AS "Company", sub1.close AS "{}" FROM ( SELECT sym, AVG(close) AS "close" FROM stock_price_minute WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY) GROUP BY sym ) as sub1 LEFT JOIN ( SELECT MIN(close) AS criteria FROM stock_price_minute WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY) AND sym = '{}' ) AS sub2 ON 1 = 1 GROUP BY sym, criteria HAVING sub1.close < criteria ORDER BY sym ASC;'''.format(header, sym), db.engine) df[header] = df[header].apply(money_format) avglow_table = generate_table(df, 100) if len(dash_data['y_preds']['_all']) <= 0: display_val = 'Loading...' else: display_val = ' ${0:.2f}'.format(dash_data['y_preds']['_all'][-1]) display_val = ' ${0:.2f}'.format(dash_data['y_preds']['_all'][-1]) # Buy sell summary pred_list = [html.H3("Predicted Value")] if len(dash_data['y_preds']['_all']) <= 0: pred_list.append( html.Img(src=load_img_url, style={'height': '50px'})) elif dash_data['y_preds']['_all'][-1] > dash_data['closes'][-1]: pred_list.append( html.Img(src=buy_img_url, style={'height': '50px'})) else: pred_list.append( html.Img(src=sell_img_url, style={'height': '50px'})) pred_list.append(display_val) pred_data = html.Div(pred_list) return [ pred_data, qh1, sym_table, qh2, max_table, qh3, avg_table, qh4, low_table, qh5, avglow_table ]
# 'title': 'Comments are clustered into 10 topics', # 'xaxis':{'title':'x-axis label'}, 'yaxis': { 'title': 'Number of comments per topic' }, } }, ), ], className="six columns"), # View individual comments html.Div([ html.H6('Top 5 words in each topic:'), html.Br(), generate_table(df.drop(['Comments'], axis=1)) ], className="six columns"), ], className="Row"), # Pick a selected comment to view highlighting html.Div([ html.H6('Input a comment number to view highlighting by topic:'), dcc.Input(id='number-in', value=1, style={'fontSize': 20}), html.Button(id='submit-button', n_clicks=0, children='Submit', style={'fontSize': 20}), html.H4('Highlighting indicates topic area:'), html.Iframe(
import utils import numpy as np import matplotlib.pyplot as plt country = 'France' sol1 = "Data/sol_jan (2).xls" sol2 = "Data/sol_feb (2).xls" sol3 = "Data/sol_march (2).xls" win1 = "Data/win_jan (2).xls" win2 = "Data/win_feb (2).xls" win3 = "Data/win_march (2).xls" price1 = "Data/price_jan.xlsx" price2 = "Data/price_feb.xlsx" price3 = "Data/price_march.xlsx" #t = read_generated_energy(sol1,win1); tab1 = utils.generate_table(sol1, win1, price1, country) tab2 = utils.generate_table(sol2, win2, price2, country) tab3 = utils.generate_table(sol3, win3, price3, country) tab = tab1 + tab2 + tab3 sol, win, price = utils.sort_by_hour(tab) plt.hist(sol[10]) #histogramme de la production solaire à 10h
figure={ 'data': get_data_of_count_words(), 'layout': { 'title': 'Dash Data Visualization' } }), ], className="six columns", style={ 'width': '49%', 'display': 'inline-block', 'vertical-align': 'middle' }), ], className="row"), html.Div([generate_table(user_df)]) ]) @app.callback(dash.dependencies.Output('user-indicator-scatter', 'figure'), [ dash.dependencies.Input('user-dropdown', 'value'), ]) def update_graph(owner_id): print('owner_id %s' % str(owner_id)) owner_id = int(owner_id) user_df_updated = user_df[user_df['ownerId'] == owner_id] return { 'data': [ go.Scatter(