a-comprehensive-seaborn-data-visualization-guide.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Table of Contents\n1. [Introduction to Seaborn](#1)\n   * [1.1. What is Seaborn?](#1.1)\n   * [1.2. Why use Seaborn?](#1.2)\n   * [1.3. Installing Seaborn](#1.3)\n   \n   \n2. [Data Visualization Basics](#2)\n   * [2.1. Importing Seaborn](#2.1)\n   * [2.2. Setting the Aesthetics](#2.2)\n   * [2.3. Loading Datasets](#2.3)\n   * [2.4. Plotting with Seaborn](#2.4)\n\n\n3. [Univariate Plots](#3)\n   * [3.1. Understanding Univariate Plots](#3.1)\n   * [3.2. Histogram](#3.2)\n   * [3.3. Kernel Density Estimation (KDE) Plot](#3.3)\n   * [3.4. Box Plot](#3.4)\n   * [3.5. Violin Plot](#3.5)\n\n\n4. [Bivariate Plots](#4)\n   * [4.1. Understanding Bivariate Plots](#4.1)\n   * [4.2. Scatter Plot](#4.2)\n   * [4.3. Line Plot](#4.3)\n   * [4.4. Pair Plot](#4.4)\n  \n  \n5. [Multivariate Plots](#5)\n   * [5.1. Understanding Multivariate Plots](#5.1)\n   * [5.2. Joint Plot](#5.2)\n   * [5.3. PairGrid](#5.3)\n\n\n6. [Customizing Plots](#6)\n   * [6.1. Colors and Palettes](#6.1)\n   * [6.2. Styling Plots](#6.2)\n   * [6.3. Axis Labels and Titles](#6.3)\n   * [6.4. Annotations and Text](#6.4)\n\n\n7. [Statistical Visualization](#7)\n   * [7.1. Understanding Statistical Visualization](#7.1)\n   * [7.2. Distribution Plot](#7.2)\n   * [7.3. Categorical Plot](#7.3)\n   * [7.4. Regression Plot](#7.4)\n\n\n8. [Advanced Seaborn Features](#8)\n   * [8.1. FacetGrid](#8.1)\n   * [8.2. Heatmap](#8.3)\n\n\n9. [Additional Learning Resources](#9)","metadata":{}},{"cell_type":"markdown","source":"<a id = \"1\"></a>\n# 1. Introduction to Seaborn\n\n<a id = \"1.1\"></a>\n### 1.1. What is Seaborn\nSeaborn is a popular data visualization library in python that helps data scientists and analysts create visually appealing and informative plots with ease. It is particularly well-suited for statistical data visualization.\n\n<a id = \"1.2\"></a>\n### 1.2. Why use Seaborn?\nSeaborn provides an elegant and user-friendly way to create visually appealing data visualizations. It offers a high-level interface, aesthetically pleasing default styles and built-in themes that make it a preferred choice for many data analysts and scientists.\n\n### 1.3. Installing Seaborn\nTo install Seaborn, you can use pip:","metadata":{}},{"cell_type":"code","source":"pip install seaborn","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"2\"></a>\n# 2. Data Visualization Basics\n\n<a id = \"2.1\"></a>\n### 2.1. Importing Seaborn\nBefore you can start using Seaborn, you need to import it into your python script or jupyter notebook.","metadata":{}},{"cell_type":"code","source":"import seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:29.971486Z","iopub.execute_input":"2023-10-15T06:47:29.971985Z","iopub.status.idle":"2023-10-15T06:47:30.905849Z","shell.execute_reply.started":"2023-10-15T06:47:29.971941Z","shell.execute_reply":"2023-10-15T06:47:30.904742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"2.2\"></a>\n### 2.2. Setting the Aesthetics\nSeaborn comes with built-in themes and color palettes to enhance the appearance of your plots. You can set the style and context to control the aesthetics of your visualizations.","metadata":{}},{"cell_type":"code","source":"sns.set_style(\"whitegrid\")\nsns.set_context(\"talk\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:30.907513Z","iopub.execute_input":"2023-10-15T06:47:30.908090Z","iopub.status.idle":"2023-10-15T06:47:30.914335Z","shell.execute_reply.started":"2023-10-15T06:47:30.908049Z","shell.execute_reply":"2023-10-15T06:47:30.912947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"2.3\"></a>\n### 2.3. Loading Datasets\nSeaborn includes several sample datasets for practice. You can load these datasets using the `load_dataset()` function.","metadata":{}},{"cell_type":"code","source":"tips = sns.load_dataset(\"tips\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:30.917768Z","iopub.execute_input":"2023-10-15T06:47:30.918664Z","iopub.status.idle":"2023-10-15T06:47:30.935937Z","shell.execute_reply.started":"2023-10-15T06:47:30.918621Z","shell.execute_reply":"2023-10-15T06:47:30.934753Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"2.4\"></a>\n### 2.4. Plotting with Seaborn\nSeaborn offers various functions for creating different types of plots. The fundamental plot types are univariate plots, bivariate plots and multivariate plots.","metadata":{}},{"cell_type":"markdown","source":"<a id = \"3\"></a>\n# 3. Univariate Plots\n\n<a id = \"3.1\"></a>\n### 3.1. Understanding Univariate Plots\nUnivariate plots visualize a single variable. Here are some common types:\n\n<a id = \"3.2\"></a>\n### 3.2. Histogram\nHistogram divides the data into discrete intervals. It counts the number of data points in each interval and represents this count with bars.","metadata":{}},{"cell_type":"code","source":"sns.histplot(data=tips, x=\"total_bill\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:30.937895Z","iopub.execute_input":"2023-10-15T06:47:30.938753Z","iopub.status.idle":"2023-10-15T06:47:31.379804Z","shell.execute_reply.started":"2023-10-15T06:47:30.938712Z","shell.execute_reply":"2023-10-15T06:47:31.378454Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"3.3\"></a>\n### 3.3. Kernel Density Estimation (KDE) Plot\nKDE plot provides a smooth and continuous estimate of the data distribution. It creates a continuous curve that represents the probability density of the data.","metadata":{}},{"cell_type":"code","source":"sns.kdeplot(data=tips, x=\"total_bill\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:31.381322Z","iopub.execute_input":"2023-10-15T06:47:31.381859Z","iopub.status.idle":"2023-10-15T06:47:31.733286Z","shell.execute_reply.started":"2023-10-15T06:47:31.381828Z","shell.execute_reply":"2023-10-15T06:47:31.732131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"3.4\"></a>\n### 3.4. Box Plot\nBox plot displays the distribution of a dataset. It shows the median (middle value), quartiles (dividing the data into quarters) and potential outliers.","metadata":{}},{"cell_type":"code","source":"sns.boxplot(data=tips, x=\"day\", y=\"total_bill\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:31.734633Z","iopub.execute_input":"2023-10-15T06:47:31.734938Z","iopub.status.idle":"2023-10-15T06:47:32.012285Z","shell.execute_reply.started":"2023-10-15T06:47:31.734910Z","shell.execute_reply":"2023-10-15T06:47:32.011236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"3.5\"></a>\n### 3.5. Violin Plot\nA violin plot combines key elements of a box plot and a kernel density plot. It displays the distribution of data, showing the range, median and quartiles like a box plot. Violin plot is useful for visualizing both summary statistics and the full data distribution simultaneously.","metadata":{}},{"cell_type":"code","source":"sns.violinplot(data=tips, x=\"day\", y=\"total_bill\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:32.013719Z","iopub.execute_input":"2023-10-15T06:47:32.014253Z","iopub.status.idle":"2023-10-15T06:47:32.402512Z","shell.execute_reply.started":"2023-10-15T06:47:32.014202Z","shell.execute_reply":"2023-10-15T06:47:32.401393Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"4\"></a>\n# 4. Bivariate Plots\n\n<a id = \"4.1\"></a>\n### 4.1. Understanding Bivariate Plots\nBivariate plots visualize relationships between two variables. Here are some common types:\n\n<a id = \"4.2\"></a>\n### 4.2. Scatter Plot\nScatter plot shows the relationship between two continuous variables. It shows individual data points as dots. Typically used for displaying unconnected data points.","metadata":{}},{"cell_type":"code","source":"sns.scatterplot(data=tips, x=\"total_bill\", y=\"tip\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:32.404058Z","iopub.execute_input":"2023-10-15T06:47:32.404606Z","iopub.status.idle":"2023-10-15T06:47:32.741685Z","shell.execute_reply.started":"2023-10-15T06:47:32.404576Z","shell.execute_reply":"2023-10-15T06:47:32.740623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"4.3\"></a>\n### 4.3. Line Plot\nLine plot is useful for visualizing trends over time or other continuous variables. It connects data points with lines and shows the trend or sequence of data over a continuous range.","metadata":{}},{"cell_type":"code","source":"sns.lineplot(data=tips, x=\"day\", y=\"total_bill\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:32.746546Z","iopub.execute_input":"2023-10-15T06:47:32.746875Z","iopub.status.idle":"2023-10-15T06:47:33.098265Z","shell.execute_reply.started":"2023-10-15T06:47:32.746848Z","shell.execute_reply":"2023-10-15T06:47:33.097361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"4.4\"></a>\n### 4.4. Pair Plot\nPair plot creates scatter plots for all combinations of variables in a dataset. It displays pairwise relationships between variables in a dataset and helps identify patterns and correlations between different features.","metadata":{}},{"cell_type":"code","source":"sns.pairplot(data=tips)","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-10-15T06:47:33.099494Z","iopub.execute_input":"2023-10-15T06:47:33.100477Z","iopub.status.idle":"2023-10-15T06:47:36.009600Z","shell.execute_reply.started":"2023-10-15T06:47:33.100448Z","shell.execute_reply":"2023-10-15T06:47:36.008310Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"5\"></a>\n# 5. Multivariate Plots\n\n\n<a id = \"5.1\"></a>\n### 5.1. Understanding Multivariate Plots\nMultivariate plots involve multiple variables. Here are some common types:\n\n<a id = \"5.2\"></a>\n### 5.2. Joint Plot\nJoint plot combines multiple univariate and bivariate plots in a single figure. It often includes a scatter plot with marginal histograms or density plots.","metadata":{}},{"cell_type":"code","source":"sns.jointplot(data=tips, x=\"total_bill\", y=\"tip\", kind=\"reg\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:36.011577Z","iopub.execute_input":"2023-10-15T06:47:36.011973Z","iopub.status.idle":"2023-10-15T06:47:36.940674Z","shell.execute_reply.started":"2023-10-15T06:47:36.011938Z","shell.execute_reply":"2023-10-15T06:47:36.939557Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"5.3\"></a>\n### 5.3. PairGrid\nPairGrid allows you to create customized pair plots for exploring relationships between multiple variables. It shows scatter plots, histograms, or other plots for pairs of variables. It is useful for visualizing relationships between multiple variables at once.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ng = sns.PairGrid(data=tips, vars=[\"total_bill\", \"tip\", \"size\"])\ng.map(plt.scatter)","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:36.942277Z","iopub.execute_input":"2023-10-15T06:47:36.943133Z","iopub.status.idle":"2023-10-15T06:47:39.060398Z","shell.execute_reply.started":"2023-10-15T06:47:36.943090Z","shell.execute_reply":"2023-10-15T06:47:39.059299Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"6\"></a>\n# 6. Customizing Plots\n\n<a id = \"6.1\"></a>\n### 6.1. Colors and Palettes\nSeaborn provides various color palettes to customize your plots. You can set a palette using `sns.set_palette()`.","metadata":{}},{"cell_type":"code","source":"sns.set_palette(\"Set2\")   # \"Set2\" refers to one of the predefined color palettes in Seaborn","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:39.061878Z","iopub.execute_input":"2023-10-15T06:47:39.062163Z","iopub.status.idle":"2023-10-15T06:47:39.068409Z","shell.execute_reply.started":"2023-10-15T06:47:39.062137Z","shell.execute_reply":"2023-10-15T06:47:39.067439Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"6.2\"></a>\n### 6.2. Styling Plots\nYou can customize plot styles with `sns.set()`","metadata":{}},{"cell_type":"code","source":"sns.set(style=\"whitegrid\", rc={\"grid.linewidth\": 1.5}) ","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:39.069749Z","iopub.execute_input":"2023-10-15T06:47:39.070018Z","iopub.status.idle":"2023-10-15T06:47:39.084686Z","shell.execute_reply.started":"2023-10-15T06:47:39.069994Z","shell.execute_reply":"2023-10-15T06:47:39.083915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"6.3\"></a>\n### 6.3. Axis Labels and Titles\nAdding labels and titles to your plots is crucial for clarity. Use `plt.xlabel()` and `plt.title()`","metadata":{}},{"cell_type":"code","source":"plt.xlabel(\"X-axis label\")\nplt.title(\"Plot Title\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:39.085568Z","iopub.execute_input":"2023-10-15T06:47:39.085874Z","iopub.status.idle":"2023-10-15T06:47:39.405559Z","shell.execute_reply.started":"2023-10-15T06:47:39.085844Z","shell.execute_reply":"2023-10-15T06:47:39.404312Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"6.4\"></a>\n### 6.4. Annotations and Text\nYou can annotate your plots with text using `plt.text()`","metadata":{}},{"cell_type":"code","source":"plt.text(2, 1, \"Important point\", fontsize=12, color='red')","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:39.407322Z","iopub.execute_input":"2023-10-15T06:47:39.407781Z","iopub.status.idle":"2023-10-15T06:47:39.676915Z","shell.execute_reply.started":"2023-10-15T06:47:39.407740Z","shell.execute_reply":"2023-10-15T06:47:39.675773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"7\"></a>\n# 7. Statistical Visualization\n\n<a id = \"7.1\"></a>\n### 7.1. Understanding Statistical Visualization\nSeaborn's statistical plots are designed to reveal relationships in data. Let's explore some types.\n\n<a id = \"7.2\"></a>\n### 7.2. Distribution Plot\nDistribution plot displays the distribution of a single variable. It is used for continuous data. Histograms, KDE plots, box plots, etc., are common types.","metadata":{}},{"cell_type":"code","source":"sns.displot(data=tips, x=\"total_bill\", kde=True)","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:39.678351Z","iopub.execute_input":"2023-10-15T06:47:39.678658Z","iopub.status.idle":"2023-10-15T06:47:40.236209Z","shell.execute_reply.started":"2023-10-15T06:47:39.678631Z","shell.execute_reply":"2023-10-15T06:47:40.235102Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"7.3\"></a>\n### 7.3. Categorical Plot\nCategorical plot displays the distribution of data across categories. It shows how data is distributed within different categories or how one categorical variable relates to another. Bar plots, count plots, point plots, etc. are common types.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ntips = sns.load_dataset(\"tips\") # Load a sample dataset from Seaborn\nsns.catplot(data=tips, x=\"day\", kind=\"count\") # Create a categorical plot using sns.catplot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:40.237997Z","iopub.execute_input":"2023-10-15T06:47:40.238354Z","iopub.status.idle":"2023-10-15T06:47:40.513000Z","shell.execute_reply.started":"2023-10-15T06:47:40.238324Z","shell.execute_reply":"2023-10-15T06:47:40.511930Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"7.4\"></a>\n### 7.4. Regression Plot\nRegression plot helps you explore relationships between variables and fit regression models to your data.","metadata":{}},{"cell_type":"code","source":"sns.lmplot(x=\"total_bill\", y=\"tip\", data=tips)","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:40.514378Z","iopub.execute_input":"2023-10-15T06:47:40.515404Z","iopub.status.idle":"2023-10-15T06:47:41.179579Z","shell.execute_reply.started":"2023-10-15T06:47:40.515366Z","shell.execute_reply":"2023-10-15T06:47:41.178578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"8\"></a>\n# 8. Advanced Seaborn Features\n\n<a id = \"8.1\"></a>\n### 8.1. FacetGrid\nFacetGrid is a grid of subplots that allows you to create multiple plots for subsets of your data. It is useful for visualizing relationships within different categories or subsets of your data.","metadata":{}},{"cell_type":"code","source":"g = sns.FacetGrid(tips, col=\"time\", row=\"smoker\")\ng.map(sns.scatterplot, \"total_bill\", \"tip\")","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:41.180899Z","iopub.execute_input":"2023-10-15T06:47:41.181213Z","iopub.status.idle":"2023-10-15T06:47:42.695522Z","shell.execute_reply.started":"2023-10-15T06:47:41.181186Z","shell.execute_reply":"2023-10-15T06:47:42.694293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"8.3\"></a>\n### 8.2. Heatmap\nHeatmap is used to visualize matrix-like data, often showing correlations between variables. It is a specific type of plot that displays data values using a color scale.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport pandas as pd\n\n# A DataFrame \ndata = {\n    'total_bill': [16.99, 10.34, 21.01, 23.68, 24.59],\n    'tip': [1.01, 1.66, 3.50, 3.31, 3.61],\n    'size': [2, 3, 3, 2, 4]\n}\n\ntips = pd.DataFrame(data)\n\ncorrelation_matrix = tips.corr() \n\nplt.figure(figsize=(8, 6))  # Adjust the figure size if needed\nsns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", linewidths=0.5)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-10-15T06:47:42.696717Z","iopub.execute_input":"2023-10-15T06:47:42.697003Z","iopub.status.idle":"2023-10-15T06:47:43.023968Z","shell.execute_reply.started":"2023-10-15T06:47:42.696978Z","shell.execute_reply":"2023-10-15T06:47:43.022847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id = \"9\"></a>\n# 9. Additional Learning Resources\n\n[Seaborn Official Documentation](https://seaborn.pydata.org/)\n\n[Data Visualization with Seaborn (Kaggle Tutorial)](https://www.kaggle.com/learn/data-visualization-with-seaborn)\n\n[Python for Data Science Handbook - Chapter 9](https://jakevdp.github.io/PythonDataScienceHandbook/04.14-visualization-with-seaborn.html)","metadata":{}}]}