def plot_koalas(data: Union["ks.DataFrame", "ks.Series"], kind: str, **kwargs): import plotly # Koalas specific plots if kind == "pie": return plot_pie(data, **kwargs) if kind == "hist": return plot_histogram(data, **kwargs) if kind == "box": return plot_box(data, **kwargs) # Other plots. return plotly.plot(KoalasPlotAccessor.pandas_plot_data_map[kind](data), kind, **kwargs)
def plot_pandas_on_spark(data: Union["ps.DataFrame", "ps.Series"], kind: str, **kwargs): import plotly # pandas-on-Spark specific plots if kind == "pie": return plot_pie(data, **kwargs) if kind == "hist": return plot_histogram(data, **kwargs) if kind == "box": return plot_box(data, **kwargs) if kind == "kde" or kind == "density": return plot_kde(data, **kwargs) # Other plots. return plotly.plot(PandasOnSparkPlotAccessor.pandas_plot_data_map[kind](data), kind, **kwargs)
else: dates = [q[0] for q in quotes] y = [q[1] for q in quotes] for date in dates: x.append(datetime.fromordinal(int(date))\ .strftime('%Y-%m-%d')) # Plotly timestamp format ma = moving_average(y, 10) # vvv clip first and last points of convolution mov_avg = go.Scatter( x=x[5:-4], y=ma[5:-4], \ line=dict(width=2,color='red',opacity=0.5), name='Moving average' ) data = [xy_data, mov_avg] py.iplot(data, filename='apple stock moving average') first_plot_url = py.plot(data, filename='apple stock moving average', auto_open=False,) print first_plot_url tickers = ['AAPL', 'GE', 'IBM', 'KO', 'MSFT', 'PEP'] prices = [] for ticker in tickers: quotes = quotes_historical_yahoo(ticker, date1, date2) prices.append( [q[1] for q in quotes] ) df = pd.DataFrame( prices ).transpose() df.columns = tickers df.head() fig = plotly_tools.get_subplots(rows=6, columns=6, print_grid=True, horizontal_spacing= 0.05, vertical_spacing= 0.05) """Kernel Density Estimation with Scipy"""
def send(): if request.method == 'POST': # just output to json file students = int(request.form['n-students']) layoutData = list(map(int, request.form['layout-data'].splitlines())) #layoutData = [int(i) for i in request.form['layout-data']] femaleRatio = float(request.form['female-ratio']) maleRatio = float(request.form['male-ratio']) otherRatio = float(request.form['other-ratio']) poorRatio = float(request.form['poor-ratio']) middleClassRatio = float(request.form['middle-class-ratio']) wealthyRatio = float(request.form['wealthy-ratio']) nativeRatio = float(request.form['native-ratio']) eslRatio = float(request.form['esl-ratio']) results = { "students":students, "layoutData":layoutData, "femaleRatio":femaleRatio, "maleRatio": maleRatio, "otherRatio": otherRatio, "poorRatio": poorRatio, "middleClassRatio": middleClassRatio, "wealthyRatio": wealthyRatio, "nativeRatio": nativeRatio, "eslRatio": eslRatio } f = open('in.json', 'w') f.write(json.dumps(results)) f.close() #retrieve input boundaries from user yearF = request.form['ctl_list_YearFrom'] yearFi = int(yearF) weekF = request.form['ctl_list_WeekFrom'] weekFi = int(weekF) yearT = request.form['ctl_list_YearTo'] yearTi = int(yearT) weekT = request.form['ctl_list_WeekTo'] weekTi = int(weekT) typeSelect = request.form['selectType'] if typeSelect == "Start End": df = time_filter_inclusive(yearFi, weekFi, yearTi, weekTi) dfWS = df #Week Fixer if yearFi != yearTi: i = 0 for index, row in df.iterrows(): if row['Year'] != yearFi: count = row['Year'] - yearFi dfWS.iat[i, 1] = row['Week'] + (count * 52) i += 1 #create graph A_H1 = Scatter(x = dfWS['Week'], y = dfWS['AH1'], mode = 'markers', name = 'A (H1)') A_H1N1 = Scatter(x = dfWS['Week'], y = dfWS['AH1N12009'], mode = 'markers', name = 'A (H1N1)') A_H3 = Scatter(x = dfWS['Week'], y = dfWS['AH3'], mode = 'markers', name = 'A (H3)') A_H5 = Scatter(x = dfWS['Week'], y = dfWS['AH5'], mode = 'markers', name = 'A (H5)') A_NoSub = Scatter(x = dfWS['Week'], y = dfWS['ANOTSUBTYPED'], mode = 'markers', name = 'A (No subtype)') A_Total = Scatter(x = dfWS['Week'], y = dfWS['INF_A'], mode = 'markers', name = 'A Total') B_Yamagata = Scatter(x = dfWS['Week'], y = dfWS['BYAMAGATA'], mode = 'markers', name = 'B (Yamagata)') B_Victoria = Scatter(x = dfWS['Week'], y = dfWS['BVICTORIA'], mode = 'markers', name = 'B (Victoria)') B_NoLineage = Scatter(x = dfWS['Week'], y = dfWS['BNOTDETERMINED'], mode = 'markers', name = 'B (No lineage)') B_Total = Scatter(x = dfWS['Week'], y = dfWS['INF_B'], mode = 'markers', name = 'B Total') data = [A_H1, A_H1N1, A_H3, A_H5, A_NoSub, A_Total, B_Yamagata, B_Victoria, B_NoLineage, B_Total] first_plot_url = py.plot(data, filename='Inclusive Graph', auto_open=False,) firsttPlotHTML = plotly_tools.get_embed(first_plot_url) else: df = time_filter_weekly(yearFi, weekFi, yearTi, weekTi) dfWS = df #Week Fixer if yearFi != yearTi: i = 0 for index, row in df.iterrows(): if row['Year'] != yearFi: count = row['Year'] - yearFi dfWS.iat[i, 1] = row['Week'] + (count * 52) i += 1 #create graph A_H1 = Scatter(x = dfWS['Week'], y = dfWS['AH1'], mode = 'markers', name = 'A (H1)') A_H1N1 = Scatter(x = dfWS['Week'], y = dfWS['AH1N12009'], mode = 'markers', name = 'A (H1N1)') A_H3 = Scatter(x = dfWS['Week'], y = dfWS['AH3'], mode = 'markers', name = 'A (H3)') A_H5 = Scatter(x = dfWS['Week'], y = dfWS['AH5'], mode = 'markers', name = 'A (H5)') A_NoSub = Scatter(x = dfWS['Week'], y = dfWS['ANOTSUBTYPED'], mode = 'markers', name = 'A (No subtype)') A_Total = Scatter(x = dfWS['Week'], y = dfWS['INF_A'], mode = 'markers', name = 'A Total') B_Yamagata = Scatter(x = dfWS['Week'], y = dfWS['BYAMAGATA'], mode = 'markers', name = 'B (Yamagata)') B_Victoria = Scatter(x = dfWS['Week'], y = dfWS['BVICTORIA'], mode = 'markers', name = 'B (Victoria)') B_NoLineage = Scatter(x = dfWS['Week'], y = dfWS['BNOTDETERMINED'], mode = 'markers', name = 'B (No lineage)') B_Total = Scatter(x = dfWS['Week'], y = dfWS['INF_B'], mode = 'markers', name = 'B Total') data = [A_H1, A_H1N1, A_H3, A_H5, A_NoSub, A_Total, B_Yamagata, B_Victoria, B_NoLineage, B_Total] first_plot_url = py.plot(data, filename='Inclusive Graph', auto_open=False,) firsttPlotHTML = plotly_tools.get_embed(first_plot_url) #create html raw data table dfRaw = df.to_html().replace('<table border="1" class="dataframe">','<table class="table table-striped">') #Table of raw data dfRaw = dfRaw.replace("<thead>",'<thead class="thead-dark">') #Run data analysis df.drop('Year', axis=1, inplace=True) df.drop('Week', axis=1, inplace=True) dfSum = df.describe() #create html summary table dfSum = dfSum.to_html().replace('<table border="1" class="dataframe">','<table class="table table-striped">') #Table of analyzed data dfSum = dfSum.replace("<thead>",'<thead class="thead-dark">') os.remove("/Users/jiabeiluo/Desktop/studentrngdemo/templates/result.html") #clear old results #HTML code for the results html_string = ''' <html> <head> <!-- Required meta tags --> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <!-- Bootstrap CSS --> <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous"> <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js"></script> <script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js"></script> <title>Results</title> <style> body { position: relative; } </style> </head> <body data-spy="scroll" data-target=".navbar" data-offset="50"> <!--NarBar--> <nav class="navbar navbar-expand-sm bg-dark navbar-dark fixed-top"> <ul class="navbar-nav"> <li class="nav-item"> <a class="nav-link" href="#section1">Data Selection Graph</a> </li> <li class="nav-item"> <a class="nav-link" href="#section2">Full Data Selection Results</a> </li> <li class="nav-item"> <a class="nav-link" href="#section3">Summary table</a> </li> </ul> </nav> <div id="section1" class="container-fluid" style="padding-top:70px;padding-bottom:70px"> <div class="container"> <div class="pb2 mt-4 mb-2 border-bottom"> <h2>Data Selection Graph</h2> </div> </div> ''' + firsttPlotHTML + ''' </div> <div id="section2" class="container-fluid" style="padding-top:70px;padding-bottom:70px"> <div class="container"> <div class="pb2 mt-4 mb-2 border-bottom"> <h3>Full Data Selection Results</h3> </div> </div> ''' + dfRaw + ''' </div> <div id="section3" class="container-fluid" style="padding-top:70px;padding-bottom:70px"> <div class="container"> <div class="pb2 mt-4 mb-2 border-bottom"> <h3>Summary table: Data Selection Results</h3> </div> </div> ''' + dfSum + ''' </div> </body> </html>''' #create the HTML file f = open('/Users/jiabeiluo/Desktop/studentrngdemo/templates/result.html','w') f.write(html_string) f.close() #Return results to user return render_template('result.html') return render_template('my-form.html')
N = 40 x = np.linspace(0, 1, N) y = np.random.randn(N) df = pd.DataFrame({'x': x, 'y': y}) df.head() data = [ go.Bar( x=df['x'], # assign x as the dataframe column 'x' y=df['y'] ) ] # IPython notebook # py.iplot(data, filename='pandas-bar-chart') url = py.plot(data, filename='pandas-bar-chart') # In[197]: data=db['mc_tweets_to_plot'] data_df =pd.DataFrame(list(data.find({}))) #tweets_to_plot_json = tweets_to_plot_df.to_json(orient='index') # with open('data.txt', 'w') as outfile: # json.dump(tweets_to_plot_json , outfile) data_df.to_csv('twitterdata.csv')
# -*- coding: utf-8 -*- """ Created on Thu Aug 1 10:26:23 2019 @author: STEM """ import pandas as pd import plotly import plotly.offline as plot import plotly.graph_objs as go wodf = pd.read_excel("GISdata.xlsx", sheet_name="womenOccupation") wodf womenoccupation = go.Bar(x=wodf["occupation"], y=wodf["women"], marker={ "color": wodf["women"], "colorscale": "Jet" }) plot([womenoccupation])
window = np.ones(int(window_size))/float(window_size) return np.convolve(interval, window, 'same') date1 = dt_date( 2014, 1, 1 ) date2 = dt_date( 2014, 12, 12 ) quotes = quotes_historical_yahoo('AAPL', date1, date2) if len(quotes) == 0: print "Couldn't connect to yahoo trading database" else: dates = [q[0] for q in quotes] y = [q[1] for q in quotes] for date in dates: x.append(datetime.fromordinal(int(date))\ .strftime('%Y-%m-%d')) # Plotly timestamp format ma = moving_average(y, 10) #Now graph the data with Plotly. See here for Plotly's line plot #syntax and here for getting started with the Plotly Python client. xy_data = Scatter( x=x, y=y, mode='markers', marker=Marker(size=4), name='AAPL' ) # vvv clip first and last points of convolution mov_avg = Scatter( x=x[5:-4], y=ma[5:-4], \ line=Line(width=2,color='red',opacity=0.5), name='Moving average' ) data = Data([xy_data, mov_avg]) py.iplot(data, filename='apple stock moving average') #Save the plot URL - we'll use it when generating the report later. first_plot_url = py.plot(data, filename='apple stock moving average', auto_open=False,) print first_plot_url
layout = dict(title='Explained variance by different principal components', yaxis=dict(title='Explained variance in percent'), annotations=list([ dict( x=1.16, y=1.05, xref='paper', yref='paper', text='Explained Variance', showarrow=False, ) ])) fig = dict(data=data, layout=layout) py.plot(fig) #得到前四个的因子的projection结果 matrix_w = np.hstack( (eig_pairs[0][1].reshape(13, 1), eig_pairs[1][1].reshape(13, 1), eig_pairs[2][1].reshape(13, 1), eig_pairs[3][1].reshape(13, 1))) matrix_w.index = factor_list ''' 0 1 2 3 downside_risk_2 -0.478293 -0.137210 0.384094 0.069760 status_dsr -0.061976 -0.062509 0.211771 0.309597 illiq_2_2 0.249994 0.119395 0.377741 -0.505791 returns_p -0.004464 -0.017830 -0.031858 0.013654 creditRate -0.170669 0.571809 0.004226 -0.081692 default_spread 0.000000 0.000000 0.000000 0.000000 log_trade_value 0.264545 0.140962 0.461324 -0.405704
def plotly_map_r_e_locations(): try: trace1 = dict(type='scattergeo', locationmode='USA-states', lon=event_lon_vals, lat=event_lat_vals, text=event_text_vals, mode='markers', marker=dict(size=20, symbol='star', color='red')) trace2 = dict(type='scattergeo', locationmode='USA-states', lon=restaurant_lon_vals, lat=restaurant_lat_vals, text=restaurant_text_vals, mode='markers', marker=dict(size=8, symbol='circle', color='blue')) data = [trace1, trace2] min_lat = 10000 max_lat = -10000 min_lon = 10000 max_lon = -10000 lat_vals = event_lat_vals + restaurant_lat_vals lon_vals = event_lon_vals + restaurant_lon_vals for str_v in lat_vals: v = float(str_v) if v < min_lat: min_lat = v if v > max_lat: max_lat = v for str_v in lon_vals: v = float(str_v) if v < min_lon: min_lon = v if v > max_lon: max_lon = v center_lat = (max_lat + min_lat) / 2 center_lon = (max_lon + min_lon) / 2 max_range = max(abs(max_lat - min_lat), abs(max_lon - min_lon)) padding = max_range * .10 lat_axis = [min_lat - padding, max_lat + padding] lon_axis = [min_lon - padding, max_lon + padding] layout = dict( title='Local Restaurants and Events<br>(Hover for site names)', geo=dict(scope='usa', projection=dict(type='albers usa'), showland=True, landcolor="rgb(250, 250, 250)", subunitcolor="rgb(100, 217, 217)", countrycolor="rgb(217, 100, 217)", lataxis={'range': lat_axis}, lonaxis={'range': lon_axis}, center={ 'lat': center_lat, 'lon': center_lon }, countrywidth=3, subunitwidth=3), ) fig = dict(data=data, layout=layout) py.plot(fig, filename='restaurants_and_local_events') except ValueError: pass
def do_display_graph(self, graph): plt.plot(self.graph_data)
else: dates = [q[0] for q in quotes] y = [q[1] for q in quotes] for date in dates: x.append(datetime.fromordinal(int(date))\ .strftime('%Y-%m-%d')) # Plotly timestamp format ma = moving_average(y, 10) # ************************* END importing AAPL INFO # Graph Creation xy_data = go.Scatter(x=x, y=y, mode='markers', marker=dict(size=4), name='AAPL') # vvv clip first and last points of convolution mov_avg = go.Scatter( x=x[5:-4], y=ma[5:-4], \ line=dict(width=2,color='red',opacity=0.5), name='Moving average' ) data = [xy_data, mov_avg] py.iplot(data, filename='apple stock moving average') # Graph URL first_plot_url = py.plot( data, filename='apple stock moving average', auto_open=False, ) print(first_plot_url)
# table_ekf_output import plotly.offline as py from plotly.graph_objs import * # estimations trace1 = Scatter(x=table_ekf_output['px_est'], y=table_ekf_output['py_est'], xaxis='x2', yaxis='y2', name='KF- Estimate', fillcolor='rgb(255,0,0)', mode='markers') # Measurements trace2 = Scatter(x=table_ekf_output['px_meas'], y=table_ekf_output['py_meas'], xaxis='x2', yaxis='y2', name='Measurements', fillcolor='rgb(255,255,255)', mode='markers') data = [trace1, trace2] layout = Layout(xaxis2=dict(anchor='x2', title='px'), yaxis2=dict(anchor='y2', title='py')) fig = Figure(data=data, layout=layout) py.plot(fig, filename='EKF')
def plot_data(): DATA_FILE_PATH = 'flightComputer.csv' DATA_LABELS = { 'time': 0, 'acceleration_x': 1, 'acceleration_y': 2, 'acceleration_z': 3, 'rotation_x': 4, 'rotation_y': 5, 'rotation_z': 6, 'pressure': 7, 'temperature': 8, 'altitude': 9, 'events': 10 } ###LIST FOR TRACES ##NOTE: CHANGE THE COLUMNS ACCORDING TO THE XLS DATA FROM FLIGHT COMP time = [] #column 1 i.e 0 WILL BE THE X FOR ALL VALUES acceleration = [] acceleration_x = [] #column 6 in xls, 5 in py acceleration_y = [] #column 6 in xls, 5 in py acceleration_z = [] #column 6 in xls, 5 in py rotation_x = [] rotation_y = [] rotation_z = [] air_pressure = [] #col 51 in xls, 50 in py temperature = [] #col 50 in xls, 49 in py velocity = [] #col 5 in xls, 4 in py altitude = [] #col 2 in xls, 1 in py inertia = [] #col 23 in xls, 22 in py thrust = [] #col 29 in xls, 28 in py data = [] stop_time = 0 with open(DATA_FILE_PATH, newline='') as csvfile: data_reader = csv.reader(csvfile, delimiter=',', quotechar='|') for n, row in enumerate(data_reader): if n == 0: print(len(row)) for i in range(len(row)): data.append([]) for i in range(len(row)): try: data[i].append(int(row[i])) except: data[i].append(float(row[i])) data = np.array(data) data = data.astype('float64') print(data[DATA_LABELS['pressure']]) endIndex = 0 diffs = np.diff(data[DATA_LABELS['time']]) print(diffs) for p, val in enumerate(diffs): print(val) if val > 50.0: endIndex = p - 1 print("endIndex: {}".format(endIndex)) break #Data conditioning for key in DATA_LABELS.keys(): print(key) if 'acceleration' in key: print(key) data[DATA_LABELS[key]] = data[DATA_LABELS[key]] / (2**15) * 12 print(data[DATA_LABELS[key]][1]) if 'temperature' in key: print(key) data[DATA_LABELS[key]] = data[DATA_LABELS[key]] / 100 print(data[DATA_LABELS[key]][1]) if 'pressure' in key: print(key) data[DATA_LABELS[key]] = data[DATA_LABELS[key]] / 100000 print(data[DATA_LABELS[key]][1]) if 'time' in key: print(key) data[DATA_LABELS[key]] = data[DATA_LABELS[key]] / 1000 print(data[DATA_LABELS[key]][1]) # data[DATA_LABELS[key]] = data[DATA_LABELS[key]][:endIndex] ##Accleration trace trace1 = { 'x': data[DATA_LABELS['time']] [:endIndex], #put the x values here, they are same for all i.e time 'y': data[DATA_LABELS['acceleration_x']] [:endIndex], #put the y values i want, i.e this is acceleration column 'mode': 'lines', #want a line curve 'name': 'ACCELERATION X (m/s^2)', #name of the trace i want i.e name of y 'type': 'scatter', #want to create scatter plots of these values 'xaxis': 'x', 'yaxis': 'y' } trace2 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['acceleration_y']][:endIndex], 'mode': 'lines', 'name': 'ACCELERATION Y (m/s)', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } trace3 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['acceleration_z']][:endIndex], 'mode': 'lines', 'name': 'ACCELERATION Z', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } trace4 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['altitude']][:endIndex], 'mode': 'lines', 'name': 'Altitude (m)', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } trace5 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['events']][:endIndex], 'mode': 'lines', 'name': 'Events', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } trace6 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['pressure']][:endIndex], 'mode': 'lines', 'name': 'AIR PRESSURE (kPa)', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } trace7 = { 'x': data[DATA_LABELS['time']][:endIndex], 'y': data[DATA_LABELS['temperature']][:endIndex], 'mode': 'lines', 'name': 'TEMPERATURE (C)', 'type': 'scatter', 'xaxis': 'x', 'yaxis': 'y' } #do same for all the components i want data = [trace1, trace2, trace3, trace4, trace5, trace6, trace7] #put the name of all the traces # layout = { "autosize": True, "dragmode": "pan", "showlegend": True, "title": { # "x": 0.48, "font": { "size": 20 }, "text": "FLIGHT DATA ANALYSIS (UMSATS ROCKETRY)" }, "xaxis": { "autorange": True, "range": [0, 180.91], "rangeslider": { "autorange": True, "range": [0, 180.91], "visible": False }, "title": { "text": "Time (s)" }, "type": "linear" }, "yaxis": { "autorange": True, "range": [-173.70122222222219, 3205.563222222222], "showspikes": False, "title": { "text": "Flight data" }, "type": "linear" } } fig = dict(data=data, layout=layout) ply.plot(fig, filename='app/templates/Plot.html', auto_open=False)
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri May 11 17:34:30 2018 @author: batman """ print_imports() import plotly as py py.plotly.plotly.tools.set_credentials_file(username='******', api_key='1hy2cho61mYO4ly5R9Za') import plotly.graph_objs as pg trace0 = pg.Scatter( x=[1, 2, 3, 4], y=[10, 15, 13, 17] ) trace1 = pg.Scatter( x=[1, 2, 3, 4], y=[16, 5, 11, 9] ) data = pg.Data([trace0, trace1]) py.plot(data, filename = 'basic-line') py.offline.plot({ "data": [pg.Scatter(x=[1, 2, 3, 4], y=[4, 3, 2, 1])], "layout": pg.Layout(title="hello world") })
def plotClusters(data): ''' Use the plotly API to plot data from clusters. Gets a plot URL from plotly and then uses subprocess to 'open' that URL from the command line. This should open your default web browser. ''' # List of symbols each cluster will be displayed using symbols = ['circle', 'cross', 'triangle-up', 'square'] # Convert data into plotly format. traceList = [] for i, c in enumerate(data): data = [] for p in c.points: data.append(p.coords) # Data trace = {} trace['x'], trace['y'] = zip(*data) trace['marker'] = {} trace['marker']['symbol'] = symbols[i] trace['name'] = "Cluster " + str(i) traceList.append(trace) # Centroid (A trace of length 1) centroid = {} centroid['x'] = [c.centroid.coords[0]] centroid['y'] = [c.centroid.coords[1]] centroid['marker'] = {} centroid['marker']['symbol'] = symbols[i] centroid['marker']['color'] = 'rgb(200,10,10)' centroid['name'] = "Centroid " + str(i) traceList.append(centroid) # Log in to plotly #py = plotly(username=PLOTLY_USERNAME, key=PLOTLY_KEY) # Style the chart ''' datastyle = {'mode': 'markers', 'type': 'scatter', 'marker': {'line': {'width': 0}, 'size': 12, 'opacity': 0.6, 'color': 'rgb(74, 134, 232)'}} resp = py.plot(*traceList, style=datastyle) # Display that plot in a browser cmd = "open " + resp['url'] subprocess.call(cmd, shell=True)''' # Style the chart layout = dict( title='Plot', xaxis=dict(title='X axis'), yaxis=dict(title='Y axis'), plot_bgcolor='lightblue', ) fig = dict(data=traceList, layout=layout) py.offline.iplot(fig) resp = py.plot(*traceList, style=layout) # Display that plot in a browser cmd = "open " + resp['url'] subprocess.call(cmd, shell=True)
x=table_ekf_output['px_gt'], y=table_ekf_output['py_gt'], xaxis='x2', yaxis='y2', name='Ground Truth', mode='markers' ) data = [trace1, trace2, trace3] layout = Layout( xaxis2=dict( anchor='x2', title='px' ), yaxis2=dict( anchor='y2', title='py' ) ) fig = Figure(data=data, layout=layout) py.plot(fig, filename='EKF') x=table_ekf_output['px_est'], y=table_ekf_output['py_est'], py.plot(x,y,color='blue',linestyle='solid',marker='o')
plt.hist(cell['Age'], color='green', edgecolor='black') plt.xlabel('Age') plt.ylabel('Smoke') #correct the label 'smoke' isnt right plt.title('Figure 7: for Age Levels') plt.show() #Boxplot plt.boxplot(cell['Calories']) plt.xlabel('Number of Calories') plt.title('Figure 8: Boxplot of Caloric Intake') plt.show() plt.boxplot(cell['Fat']) plt.xlabel('Fat Levels') plt.title('Figure 9: Boxplot of Fat Levels') plt.show() plt.plot(cell['Age']) plt.xlabel('Age of Smokers') plt.title('Figure 10: Boxplot of Fat Levels') plt.show() #Tables Rel. Freq. print(cell['Calories'].value_counts()) a = pd.crosstable(cell.Calories, columns='Calories') print(s) s_rel = a / a.sum() print(s_rel)
return '重要保持客户' elif x == '低低高': return '重要挽留客户' elif x == '高高低': return '一般价值客户' elif x == '高低低': return '一般发展客户' elif x == '低高低': return '一般保持客户' else: return '一般挽留客户' rfm['用户等级'] = rfm['value'].apply(trans_value) rfm['用户等级'].value_counts() trade_basic = [ go.Bar(x=rfm['用户等级'].value_counts().index, y=rfm['用户等级'].value_counts().values, marker=dict(color='orange'), opacity=0.50) ] layout = go.Layout(title='用户等级情况', xaxis=dict(title='用户重要度')) figure_basic = go.Figure(data=trade_basic, layout=layout) py.plot(figure_basic) # trace = [go.Pie(labels= rfm['用户等级'].value_counts().index, values=rfm['用户等级'].value_counts().values,textfont=dict(size=12,color='white'))] # layout2 = go.Layout(title='用户等级比例') # figure_basic2 = go.Figure(data= trace,layout=layout2) # py.plot(figure_basic2) #结论和建议
paso = paso + 1 else: paso = paso + np.random.randint(1,7) if np.random.rand() <= 0.001: paso = 0 caminata_aleatoria.append(paso) #Agregar el numero que se creo Todas.append(caminata_aleatoria) #Al terminar de correr obtenemos una caminata aleatoria el cual nombraremos Todas #Graficaremos los resultados obtenidos. np_Todas = np.array(Todas) #Convertimos en array el objeto Todas plt.plot(np_Todas) plt.show() plt.clf() np_Todas_transpuesta = np.transpose(np_Todas) plt.plot(np_Todas_transpuesta) plt.show() Final = np_Todas[-1,:] plt.hist(Final) plt.show() np.mean(Final[Final > 30]) #Calculo de la media de datos que se ecuentran en la cola de datos aleatorios con un valor mayor a 30 ################################ #Definicion simple de funciones#