{ "source_file": "0930", "source_platform": "Medium", "author": "Federico Trotta", "title_listed": "Building Interactive Data Visualizations in Python: An Introduction to Plotly", "keywords": "Plotly,交互式可视化", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/building-interactive-data-visualizations-in-python-an-introduction-to-plotly-3ffdd920fc63/", "publish_date": "2023-07", "corpus_stage": "预训练/SFT", "notes": "", "id": "6e958d4d4cda", "collected_at": "2026-10-02T00:40:12", "title": "Building Interactive Data Visualizations in Python: An Introduction to Plotly | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It helps us, in fact, understand the data and to ask more questions for further investigations.\nBut data visualization is not only a task we have to conclude in the Exploratory Data Analysis phase. We may also need to present the data, often to an audience to help it grab some conclusions.\nIn Python, we generally use `matplotlib` and `seaborn` as libraries to plot our graphs.\nHowever, sometimes we may need some interactive visualizations. In some cases, for a better understanding of the data. In some other cases, just to better present our solutions.\nIn this article, we'll talk about `plotly` which is a Python library for making interactive visualizations.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## What is Plotly?\nAs we can read [on their website](https://plotly.com/python/):\n> Plotly's Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.\n> Plotly.py is [free and open source](https://plotly.com/python/is-plotly-free) and you can [view the source, report issues or contribute on GitHub](https://github.com/plotly/plotly.py).\nSo, Plotly is a free and open-source Python library for making interactive visualizations.\nAs we can see on their website, it gives us the possibility to create plots for different scopes: AI/ML, statistical, scientific, financial, and much more.\nSince we're interested in Machine Learning and Data Science, we'll show some plots related to this field and how to create them in Python.\nFinally, to install it, we have to type:\ntext\n\n```\n$ pip install plotly\n```\n\n* * *\n## 1. Interactive Bubble charts\nOne interesting and useful feature of Plotly we can use is the possibility of interactive bubble plots.\nIn the case of bubble charts, in fact, sometimes the bubbles can intersect, making it difficult to read the data. If the graph is interactive, instead, we can read the data more easily.\nLet's see an example:\npython\n\n```\nimport plotly.express as px\nimport pandas as pd\nimport numpy as np\n\n# Generate random data\nnp.random.seed(42)\nn = 50\nx = np.random.rand(n)\ny = np.random.rand(n)\nz = np.random.rand(n) * 100 # Third variable for bubble size\n\n# Create a DataFrame\ndata = pd.DataFrame({'X': x, 'Y': y, 'Z': z})\n\n# Create the scatter plot with bubble size with Plotly\nfig = px.scatter(data, x='X', y='Y', size='Z',\n title='Interactive Scatter Plot with Bubble Plot')\n\n# Add labels to the bubbles\nfig.update_traces(textposition='top center', textfont=dict(size=11))\n\n# Update layout properties\nfig.update_layout(\n xaxis_title='X-axis',\n yaxis_title='Y-axis',\n showlegend=False\n)\n\n# Display the interactive plot\nfig.show()\n```\n\nAnd we get:\n![The interactive bubble chart we've coded. Image by Federico Trotta.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1E2voEmA614dZUm4AIkI0FA.gif)\nThe interactive bubble chart we've coded. Image by Federico Trotta.\nSo, we've created some data with `NumPy` and stored them in a `Pandas` data frame. Then, we've created the interactive plot with the method `px.scatter()` retrieving the data from the data frame, and specifying the title (as opposed to `Matplotlib` in which we insert the title outside the method used to create the plot itself).\n## 2. Interactive correlation matrices\nOne of the tasks I sometimes struggle with is a proper visualization of correlation matrices. These, in fact, can sometimes be challenging to read and visualize when we have a lot of data.\nOne way to solve the problem is to use Plotly to create an interactive visualization.\nFor the scope, let's create a Pandas data frame with 10 columns and create an interactive correlation matrix with Plotly:\npython\n\n```\nimport pandas as pd\nimport numpy as np\nimport plotly.figure_factory as ff\n\n# Create random data\nnp.random.seed(42)\ndata = np.random.rand(100, 10)\n\n# Create DataFrame\ncolumns = ['Column' + str(i+1) for i in range(10)]\ndf = pd.DataFrame(data, columns=columns)\n\n# Round values to 2 decimals\ncorrelation_matrix = df.corr().round(2) \n\n# Create interactive correlation matrix with Plotly\nfigure = ff.create_annotated_heatmap(\n z=correlation_matrix.values,\n x=list(correlation_matrix.columns),\n y=list(correlation_matrix.index),\n colorscale='Viridis',\n showscale=True\n)\n\n# Set axis labels\nfigure.update_layout(\n title='Correlation Matrix',\n xaxis=dict(title='Columns'),\n yaxis=dict(title='Columns')\n)\n\n# Display the interactive correlation matrix\nfigure.show()\n```\n\nAnd we get:\n![The interactive correlation matrix we created. Image by Federico Trotta.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1_sEvH3UryBfS5WxDHi7pJQ.gif)\nThe interactive correlation matrix we created. Image by Federico Trotta.\nSo, in a very simple way, we can create an interactive correlation matrix with the method `ff.create_annotated_map()`.\n## 3. Interactive ML plots\nIn Machine Learning, we sometimes need to compare quantities graphically. And, in these cases, sometimes it's hard to read our plots.\nThe typical case is a ROC/AUC curve where we compare the performance of different ML models. Sometimes, in fact, the curves intercepts and we're not able to properly visualize them.\nTo improve our visualizations, we can use Plotly to create a ROC/AUC curve like so:\npython\n\n```\nimport pandas as pd\nimport numpy as np\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import roc_curve, auc\nimport plotly.graph_objects as go\n\n# Create synthetic binary classification data\nX, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n\n# Scale the data using StandardScaler\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(X)\n\n# Split the data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n\n# Initialize the models\nknn = KNeighborsClassifier()\nrf = RandomForestClassifier()\ndt = DecisionTreeClassifier()\nsvm = SVC(probability=True)\n\n# Fit the models on the train set\nknn.fit(X_train, y_train)\nrf.fit(X_train, y_train)\ndt.fit(X_train, y_train)\nsvm.fit(X_train, y_train)\n\n# Predict probabilities on the test set\nknn_probs = knn.predict_proba(X_test)[:, 1]\nrf_probs = rf.predict_proba(X_test)[:, 1]\ndt_probs = dt.predict_proba(X_test)[:, 1]\nsvm_probs = svm.predict_proba(X_test)[:, 1]\n\n# Calculate the false positive rate (FPR) and true positive rate (TPR) for ROC curve\nknn_fpr, knn_tpr, _ = roc_curve(y_test, knn_probs)\nrf_fpr, rf_tpr, _ = roc_curve(y_test, rf_probs)\ndt_fpr, dt_tpr, _ = roc_curve(y_test, dt_probs)\nsvm_fpr, svm_tpr, _ = roc_curve(y_test, svm_probs)\n\n# Calculate the AUC (Area Under the Curve) for ROC curve\nknn_auc = auc(knn_fpr, knn_tpr)\nrf_auc = auc(rf_fpr, rf_tpr)\ndt_auc = auc(dt_fpr, dt_tpr)\nsvm_auc = auc(svm_fpr, svm_tpr)\n\n# Create an interactive AUC/ROC curve using Plotly\nfig = go.Figure()\nfig.add_trace(go.Scatter(x=knn_fpr, y=knn_tpr, name='KNN (AUC = {:.2f})'.format(knn_auc)))\nfig.add_trace(go.Scatter(x=rf_fpr, y=rf_tpr, name='Random Forest (AUC = {:.2f})'.format(rf_auc)))\nfig.add_trace(go.Scatter(x=dt_fpr, y=dt_tpr, name='Decision Tree (AUC = {:.2f})'.format(dt_auc)))\nfig.add_trace(go.Scatter(x=svm_fpr, y=svm_tpr, name='SVM (AUC = {:.2f})'.format(svm_auc)))\nfig.update_layout(title='AUC/ROC Curve',\n xaxis=dict(title='False Positive Rate'),\n yaxis=dict(title='True Positive Rate'),\n legend=dict(x=0.7, y=0.2))\n# Show plot\nfig.show()\n```\n\nAnd we get:\n![The AUC/ROC curve we've created. Image by Federico Trotta.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1bQNUbqb_D1sx-IuKo0T6Eg.gif)\nThe AUC/ROC curve we've created. Image by Federico Trotta.\nSo, with the method `add.trace(go.Scatter)` we've created scatterplots for each ML model we've used (KNN, SVM, Decision Tree, and Random Forest).\nThis way, it's easy to display the details and the values of the zones where the curves intercept themselves.\n* * *\n## Conclusions\nIn this article, we've shown a quick introduction to Plotly and we've seen how we can use it for better visualizations in data analysis and Machine Learning.\nAs we can see, this is a low-code library that, indeed, helps us better visualize our data, improving our results.\n* * *\n![Federico Trotta](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1Qgmrx70AYppweXztN8b0VA.png)\nFederico Trotta\nHi, I'm Federico Trotta and I'm a freelance Technical Writer.\nWant to collaborate with me? 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