def main(): # creating object of TwitterClient Class api = TwitterClient() # calling function to get tweets tweets = api.get_tweets(query = 'OnePlus', count = 200) # you can increase the count for more acurate result # picking positive tweets from tweets ptweets = [tweet for tweet in tweets if tweet['sentiment'] == 'positive'] # percentage of positive tweets print("Positive tweets percentage: {} %".format(100*len(ptweets)/len(tweets))) # picking negative tweets from tweets ntweets = [tweet for tweet in tweets if tweet['sentiment'] == 'negative'] # percentage of negative tweets print("Negative tweets percentage: {} %".format(100*len(ntweets)/len(tweets))) # percentage of neutral tweets nutweets = len(tweets) - len(ntweets) - len(ptweets) print("Neutral tweets percentage: {} %".format(100*nutweets/len(tweets))) ptweetper = 100*len(ptweets)/len(tweets) ntweetper = 100*len(ntweets)/len(tweets) nutweetper = 100*nutweets/len(tweets) labels = ['Positive_tweets','Negative_tweets','Neutral_Tweets'] values = [ptweetper,ntweetper,nutweetper] trace = go.Pie(labels=labels, values=values) py.iplot([trace], filename='sentiment_analysis_OnePlus')
def main(): idata_train, idata_test, itargets_train, itargets_test = load_iris() # Use my classifier on Iris num_loops = 100 classifier = NNClassifier(4, [10, 3]) list1 = classifier.fit(idata_train, itargets_train, idata_test, itargets_test, num_loops) # Create trace 1 trace1 = go.Scatter(x=np.arange(num_loops), y=np.asarray(list1), mode='lines', name='lines') data_train, data_test, targets_train, targets_test = load_pima_indian_diabetes( ) # Use my classifier on Pima Indian Diabetes num_loops = 50 classifier = NNClassifier(8, [7, 2]) list2 = classifier.fit(data_train, targets_train, data_test, targets_test, 50) # Create trace 2 trace1 = go.Scatter(x=np.arange(num_loops), y=np.asarray(list2), mode='lines', name='lines') data = [trace1, trace2] py.iplot(data, filename='line-mode')
def precipitacao(self): data = [ dict(type='scattergeo', lon=[], lat=[], mode='markers', marker=dict( size=8, opacity=0.8, reversescale=True, autocolorscale=False, line=dict(width=1, color='rgba(102, 102, 102)'), ), stream=stream_id, name="Plane") ] layout = dict( title='Busy Airplane Streaming', colorbar=False, geo=dict(scope='usa', projection=dict(type='albers usa'), showland=True, landcolor="rgb(250, 250, 250)", subunitcolor="rgb(217, 217, 217)", countrycolor="rgb(217, 217, 217)", countrywidth=0.5, subunitwidth=0.5), ) fig = dict(data=data, layout=layout) py.iplot(fig, validate=False, filename='geo-streaming2', auto_open=False, fileopt='extend')
data = TwitterData_Wordlist(data) data.build_wordlist() words = pd.read_csv(r"C:\Users\Caroline\Documents\twitractors\ml-twitter-sentiment-analysis-develop\data\wordlist.csv") x_words = list(words.loc[0:10,"word"]) x_words.reverse() y_occ = list(words.loc[0:10,"occurrences"]) y_occ.reverse() dist = [ graph_objs.Bar( x=y_occ, y=x_words, orientation="h" )] plotly.iplot({"data":dist, "layout":graph_objs.Layout(title="Top words in built wordlist")}) class TwitterData_BagOfWords(TwitterData_Wordlist): def __init__(self, previous): self.processed_data = previous.processed_data self.wordlist = previous.wordlist def build_data_model(self): label_column = [] if not self.is_testing: label_column = ["label"] columns = label_column + list( map(lambda w: w + "_bow", self.wordlist)) labels = []
import plotly as py py.tools.set_credentials_file(username='******', api_key='lr1c37zw81') import plotly.graph_objs as go import datetime def to_unix_time(dt): epoch = datetime.datetime.utcfromtimestamp(0) return (dt - epoch).total_seconds() * 1000 x = [ datetime.datetime(year=2013, month=10, day=4), datetime.datetime(year=2013, month=11, day=5), datetime.datetime(year=2013, month=12, day=6) ] data = [go.Scatter(x=x, y=[1, 3, 6])] layout = go.Layout(xaxis=dict(range=[ to_unix_time(datetime.datetime(2013, 10, 17)), to_unix_time(datetime.datetime(2013, 11, 20)) ])) fig = go.Figure(data=data, layout=layout) py.iplot(fig)
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) # 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)
br02 = go.Scatter(y=origBR['Attention'], x=origBR['Time:512Hz'], mode='lines', name='Attention') br03 = go.Scatter(y=origBR['Meditation'], x=origBR['Time:512Hz'], mode='lines', name='Meditation') layout_br0 = go.Layout(title='Plot of Original Big Rigs CSV Data', plot_bgcolor='rgb(230, 230, 230)') fig_br0 = go.Figure(data=[br01, br02, br03], layout=layout_br0) # Plot data in the notebook py.iplot(fig_br0, filename='plot-from-orig-br0-csv') # #### Need for Speed: Hot Pursuit 2 (Average Rated Game) # In[6]: hp01 = go.Scatter( y=origHP['Electrode'], x=origHP['Time:512Hz'], # Data mode='lines', name='Electrode' # Additional options ) hp02 = go.Scatter(y=origHP['Attention'], x=origHP['Time:512Hz'], mode='lines', name='Attention')
trace2 = go.Sunburst(ids=df2.ids, labels=df2.labels, parents=df2.parents, domain=dict(column=1), maxdepth=2) layout = go.Layout(grid=go.layout.Grid(columns=2, rows=1), margin=go.layout.Margin(t=0, l=0, r=0, b=0), sunburstcolorway=[ "#636efa", "#EF553B", "#00cc96", "#ab63fa", "#19d3f3", "#e763fa", "#FECB52", "#FFA15A", "#FF6692", "#B6E880" ], extendsunburstcolors=True) data = [trace1, trace2] fig = go.Figure(data, layout) plotly.iplot(fig, filename='large_number_of_slices') @app.route('/sui_d3sunburst', methods=['GET', 'POST']) def sui_d3sunburst(): return render_template('sui-d3sunburst.html') if __name__ == '__main__': app.secret_key = 'secret123' app.run(debug=True)
from IPython.display import HTML x = [] y = [] ma = [] def moving_average(interval, window_size): 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) 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')
def create_total_line_graph(data_frame): colors = [ '#33CFA5', 'orange', '#F06A6A', 'blue', 'violet', 'yellowgreen', 'darkgrey', 'goldenrod' ] products = data_frame['product'].unique().tolist() month_year_list = data_frame['month-year'].unique().tolist() month_year_list = sorted(month_year_list) products = sorted(products) #print(products) data_list = [] for i in range(0, len(products)): x_list = [] y_list = [] temp_frame = data_frame[data_frame['product'] == products[i]] for key in month_year_list: temp_month_frame = temp_frame[temp_frame['month-year'] == key] x_list.append(key) y_list.append(get_total_sales(temp_month_frame)) #print(x_list, y_list) #print(colors[i]) temp_scatter = go.Scatter(x=x_list, y=y_list, name=products[i], line=dict(color=colors[i])) data_list.append(temp_scatter) updatemenus = list([ dict( type="buttons", active=-1, buttons=list([ dict(label='Brown Boots', method='update', args=[{ 'visible': [ True, False, False, False, False, False, False, False ] }, { 'title': 'Brown Boots Sales over Time ($)', 'annotations': [] }]), dict(label='Button-Down Shirt', method='update', args=[{ 'visible': [ False, True, False, False, False, False, False, False ] }, { 'title': 'Button-Down Shirt Sales over Time ($)', 'annotations': [] }]), dict(label='Khaki Pants', method='update', args=[{ 'visible': [ False, False, True, False, False, False, False, False ] }, { 'title': 'Khaki Pants Sales over Time ($)', 'annotations': [] }]), dict(label='Sticker Pack', method='update', args=[{ 'visible': [ False, False, False, True, False, False, False, False ] }, { 'title': 'Sticker Pack Sales over Time ($)', 'annotations': [] }]), dict(label='Super Soft Hoodie', method='update', args=[{ 'visible': [ False, False, False, False, True, False, False, False ] }, { 'title': 'Super Soft Hoodie Sales over Time ($)', 'annotations': [] }]), dict(label='Super Soft Sweater', method='update', args=[{ 'visible': [ False, False, False, False, False, True, False, False ] }, { 'title': 'Super Soft Sweater Sales over Time ($)', 'annotations': [] }]), dict(label='Vintage Logo Tee', method='update', args=[{ 'visible': [ False, False, False, False, False, False, True, False ] }, { 'title': 'Vintage Logo Tee Sales over Time ($)', 'annotations': [] }]), dict(label='Winter Hat', method='update', args=[{ 'visible': [ False, False, False, False, False, False, False, True ] }, { 'title': 'Winter Hat Sales over Time ($)', 'annotations': [] }]), dict(label='All', method='update', args=[{ 'visible': [True, True, True, True, True, True, True, True] }, { 'title': 'All Sales over Time ($)', 'annotations': [] }]) ]), ) ]) layout = dict(title='Total Sales vs Month Per Product', showlegend=False, xaxis=dict(title='Time (Months)'), yaxis=dict(title='Sales ($)'), updatemenus=updatemenus) fig = dict(data=data_list, layout=layout) py.iplot(fig, filename='sales-vs-month-int')
x = subset.loc[subset['kmeans_0']==n, DADcolsscaled[0]] y = subset.loc[subset['kmeans_0']==n, DADcolsscaled[1]] # Calculate the point density xy = np.vstack([x,y]) z1 = gaussian_kde(xy)(xy) cb1 = ax.scatter(x, y, c=z1, cmap=cmaps[n], s=50, edgecolor=None,edgecolors=None,alpha=1) plt.colorbar(cb1, ax=ax) ax.set_xlim([-1, 2]) ax.set_ylim([-1, 2]) ax.set_xlabel(DADcolsscaled[0]) ax.set_ylabel(DADcolsscaled[1]) ax.set_axis_bgcolor('white') ax.grid(b=True, which='both', color="gray",linestyle='-',alpha=.1) plt.tight_layout(pad=0.8, w_pad=0.8, h_pad=1.0) fig = plt.gcf() py.plot_mpl(fig, filename="mpl-colormaps-simple") plt.show() layout = dict(title = 'Styled Scatter', yaxis = dict(zeroline = False), xaxis = dict(zeroline = False) ) fig = dict(data=data, layout=layout) py.iplot(fig, filename='styled-scatter', colorscale = cmaps)
y=df2['AvgPoints'], name='Average Points per Game' ) trace2 = go.Bar( #The second column - No Dependent Status x=df2['Team'], y=df2['AvgAssist'], name='Average Assists per Game' ) data4 = [trace1, trace2] #read four columns into one data layout4 = go.Layout( #starting to plot a grouped bar chart title='Team performance', barmode='group' ) fig = go.Figure(data=data4, layout=layout4) py.iplot(fig, filename='grouped-bar') # In[14]: """look at shooting performance by team""" trace1 = go.Bar( #The first column - Level III Salary x=df2['Team'], y=df2['FGPct'], name='Average Field Goal Percentage' ) trace2 = go.Bar( #The second column - No Dependent Status x=df2['Team'], y=df2['ThreePct'], name='Average Three Pointer Percentage'
#Simples chuvas_smm.plot(x=chuvas_smm.index, y=['Total', 'Maxima']) #Boxplot (Gráficos interativos via Plotly) total1= go.Box( y=chuvas_smm["Total"] ) maxima1 = go.Box( y=chuvas_smm["Maxima"] ) smm_box_mt= [total1, maxima1] py.offline.plot(smm_box_mt, filename="chuvas_smm_box.html") #Visualização de Série Temporal (GI) #Maxima Mensal chuvas_dg_max= [go.Scatter(x=chuvas_smm.index, y=chuvas_smm['Maxima'] )] py.iplot(chuvas_dg_max, filename='chuvas_dg_max.html') #Dias de chuva chuvas_smm_dias= [go.Scatter(x=chuvas_smm.index, y=chuvas_smm['NumDiasDeChuva'] )] py.offline.plot(chuvas_smm_dias, filename='chuvas_smm_dias.html') #Gantt (GI) #Delmiro Gouveia #Simples chuvas_dg.plot(x='Data', y=['Total', 'Maxima']) #Boxplot (Graficos interativos via Plotly) total2= go.Box( y=chuvas_dg["Total"] ) maxima2 = go.Box( y=chuvas_dg["Maxima"]
separator = ',' for item in data.cuisines: item_list = item.split(',') new_list = [] for it in item_list: str_content = it.strip() new_list.append(str_content) main_list.append(separator.join(sorted(new_list))) data['new_cuisines'] = main_list data['new_cuisines'][0] # Visualization location_count = data.groupby(['place']).size().reset_index(name="count") location_count = location_count.sort_values('count', ascending=True) location_count.head() inputdata = [go.Bar(x=location_count['place'], y=location_count['count'])] py.iplot(inputdata, filename='basic-bar') plt.show() rate_group_count = data.groupby(['rate']).size().reset_index(name="count") rate_group_count = rate_group_count.sort_values('count', ascending=True) rate_group_count.head() inputdata = [go.Bar(x=rate_group_count['rate'], y=rate_group_count['count'])] py.iplot(inputdata, filename='basic-bar') plt.show() name_group_count = data.groupby(['name']).size().reset_index(name="count") name_group_count = name_group_count.sort_values('count', ascending=False) #name_group_count.head() top20 = name_group_count[0:20] top20 inputdata = [go.Bar(x=top20['name'], y=top20['count'])] py.iplot(inputdata, filename='basic-bar') plt.show()
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
print(str(count) + '. ' + item + ' ' + '${0:,.2f}'.format(temp_float)) count = count + 1 if count > 3: break #from https://github.com/madelinenlee/OPIM-243-chart-exercise x = [] y = [] for item in product_subtotals: y.append(item) x.append('$' + str(product_subtotals[item])) x = list(reversed(x)) data = [go.Bar(x=x, y=y, orientation='h')] margin = go.Margin(l=200, r=50) layout = go.Layout(title='Top Selling Products (' + month + ' ' + year + ')', xaxis=dict(title='USD ($)'), yaxis=dict(title='Product'), margin=margin) figure = go.Figure(data=data, layout=layout) py.iplot(figure, filename='horizontal-bar.html') print( 'see your top selling products for this month at https://plot.ly/~madelinelee/75' ) #https://plot.ly/~madelinelee/75