import plotly.express as px
fig = px.parallel_coordinates(df2, color="mean_test_score",           labels=dict(zip(list(df2.columns),           list(['_'.join(i.split('_')[1:]) for i in df2.columns]))),          color_continuous_scale=px.colors.diverging.Tealrose,          color_continuous_midpoint=27)
fig.show()

pip install datawrapper

from datawrapper import Datawrapperdw = Datawrapper(access_token = "TODO")games_chart = dw.create_chart(title = "Most Frequent Game Publishers", chart_type = 'd3-bars', data = df)dw.update_description(    games_chart['id'],    source_name = 'Video Game Sales',    source_url = 'https://www.kaggle.com/gregorut/videogamesales',    byline = 'Pier Paolo Ippolito',)dw.publish_chart(games_chart['id'])

import chart_studio.plotly as pyimport plotly.graph_objs as gofrom plotly.offline import init_notebook_mode, iplotinit_notebook_mode(connected=True)import plotlydef predreport(y_pred, Y_Test):    diff = y_pred.flatten() - Y_Test.flatten()    perc = (abs(diff)/y_pred.flatten())*100    priority = []    for i in perc:        if i > 0.4:            priority.append(3)        elif i> 0.1:            priority.append(2)        else:            priority.append(1)    print("Error Importance 1 reported in ", priority.count(1),          "cases\n")    print("Error Importance 2 reported in", priority.count(2),           "cases\n")                                     print("Error Importance 3 reported in ", priority.count(3),              "cases\n")    colors = ['rgb(102, 153, 255)','rgb(0, 255, 0)',               'rgb(255, 153, 51)', 'rgb(255, 51, 0)']    fig = go.Figure(data=[go.Table(header=                    dict(                        values=['Actual Values', 'Predictions',                         '% Difference', "Error Importance"],                        line_color=[np.array(colors)[0]],                        fill_color=[np.array(colors)[0]],                                    align='left'),                    cells=dict(                       values=[y_pred.flatten(),Y_Test.flatten(),                               perc, priority],                        line_color=[np.array(colors)[priority]],                       fill_color=[np.array(colors)[priority]],                                      align='left'))])    init_notebook_mode(connected=False)    py.plot(fig, filename = 'Predictions_Table', auto_open=True)    fig.show()

Error Importance 1 reported in  34 cases 
Error Importance 2 reported in  13 cases 
Error Importance 3 reported in  53 cases

from dtreeviz.trees import *
viz = dtreeviz(clf,               X_train,               y_train.values,               target_name='Genre',               feature_names=list(X.columns),               class_names=list(labels.unique()),               histtype='bar',                orientation ='TD')
viz

from mlxtend.plotting import plot_decision_regions import matplotlib.pyplot as pltimport matplotlib.gridspec as gridspecimport itertoolsgs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10,8))
clf1 = LogisticRegression(random_state=1,                          solver='newton-cg',                          multi_class='multinomial')clf2 = RandomForestClassifier(random_state=1, n_estimators=100)clf3 = GaussianNB()clf4 = SVC(gamma='auto')
labels = ['Logistic Regression','Random Forest','Naive Bayes','SVM']for clf, lab, grd in zip([clf1, clf2, clf3, clf4],                         labels,                         itertools.product([0, 1], repeat=2)):
    clf.fit(X_Train, Y_Train)    ax = plt.subplot(gs[grd[0], grd[1]])    fig = plot_decision_regions(X_Train, Y_Train, clf=clf, legend=2)    plt.title(lab)
plt.show()

from keras.models import Sequentialfrom keras.layers import Densefrom ann_visualizer.visualize import ann_viz
model = Sequential()model.add(Dense(units=4,activation='relu',                  input_dim=7))model.add(Dense(units=4,activation='sigmoid'))model.add(Dense(units=2,activation='relu'))
ann_viz(model, view=True, filename="example", title="Example ANN")

from livelossplot import PlotLossesliveloss = PlotLosses()for epoch in range(epochs):    logs = {}    for phase in ['train', 'val']:        losses = []
        if phase == 'train':            model.train()        else:            model.eval()
        for i, (inp, _) in enumerate(dataloaders[phase]):            out, z_mu, z_var = model(inp)            rec=F.binary_cross_entropy(out,inp,reduction='sum')/                                       inp.shape[0]            kl=-0.5*torch.mean(1+z_var-z_mu.pow(2)-torch.exp(z_mu))            loss = rec + kl            losses.append(loss.item())
            if phase == 'train':                optimizer.zero_grad()                loss.backward()                optimizer.step()
        prefix = ''        if phase == 'val':            prefix = 'val_'        logs[prefix + 'loss'] = np.mean(losses)    liveloss.update(logs)    liveloss.send()

def latent_space(model, train_set, it=''):    x_latent = model.enc(train_set.data.float())    plt.figure(figsize=(10, 7))    plt.scatter(x_latent[0][:,0].detach().numpy(),                 x_latent[1][:,1].detach().numpy(),                 c=train_set.targets)    plt.colorbar()    plt.title("VAE Latent Space", fontsize=20)    plt.xlabel("X", fontsize=18)    plt.ylabel("Y", fontsize=18)    plt.savefig('VAE_space'+str(it)+'.png', format='png', dpi=200)    plt.show()

def manifold(model, it='', n=18, size=28):     result = torch.zeros((size * n, size * n))    # Defyining grid space    s, s2 = torch.linspace(-7, 7, n), torch.linspace(7, -7, n)    grid_x, grid_y = torch.std(s)*s, torch.std(s2)*s2    for i, y_ex in enumerate(grid_x):        for j, x_ex in enumerate(grid_y):            z_sample = torch.repeat_interleave(torch.tensor([                       [x_ex, y_ex]]),repeats=batch_size, dim=0)            x_dec = model.dec(z_sample)            element = x_dec[0].reshape(size, size).detach()            result[i * size: (i + 1) * size,                    j * size: (j + 1) * size] = element    plt.figure(figsize=(12, 12))    plt.title("VAE Samples", fontsize=20)    plt.xlabel("X", fontsize=18)    plt.ylabel("Y", fontsize=18)    plt.imshow(result, cmap='Greys')    plt.savefig('VAE'+str(it)+'.png', format='png', dpi=300)    plt.show()

import matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import axes3d, Axes3Dembedding_weights=pd.DataFrame(model.embed.weight.detach().numpy())embedding_weights.columns = ['X1','X2','X3']fig = plt.figure(num=None, figsize=(14, 12), dpi=80,                  facecolor='w', edgecolor='k')ax = plt.axes(projection='3d')for index, (x, y, z) in enumerate(zip(embedding_weights['X1'],                                       embedding_weights['X2'],                                       embedding_weights['X3'])):    ax.scatter(x, y, z, color='b', s=12)    ax.text(x, y, z, str(df.authors[index]), size=12,             zorder=2.5, color='k')ax.set_title("Word Embedding", fontsize=20)ax.set_xlabel("X1", fontsize=20)ax.set_ylabel("X2", fontsize=20)ax.set_zlabel("X3", fontsize=20)plt.show()

from wordcloud import WordCloudd = {}for x, a in zip(df.authors.value_counts(),                df.authors.value_counts().index):    d[a] = xwordcloud = WordCloud()wordcloud.generate_from_frequencies(frequencies=d)plt.figure(num=None, figsize=(12, 10), dpi=80, facecolor='w',           edgecolor='k')plt.imshow(wordcloud, interpolation="bilinear")plt.axis("off")plt.title("Word Cloud", fontsize=20)plt.show()