{ "cells": [ { "cell_type": "markdown", "id": "7537fe2a", "metadata": {}, "source": [ "# Visualization and Pandas\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "e7812349", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:43.395909Z", "iopub.status.busy": "2026-08-31T06:00:43.395660Z", "iopub.status.idle": "2026-08-31T06:00:43.787588Z", "shell.execute_reply": "2026-08-31T06:00:43.787307Z" }, "tags": [ "hide-input", "thebe-init", "runnable" ] }, "outputs": [], "source": [ "# %pip install pandas numpy ### ensure pandas and numpy are installed; uncomment when done\n", "# %pip install matplotlib ### ensure matplotlib is installed; uncomment when done\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt ### because pandas.plot is still matplotlib\n", "# %matplotlib inline ### uncomment if using Jupyter Notebook\n", " ### unless using Jupyter < 7\n", " ### use plt.show() to display plots in other environments " ] }, { "cell_type": "markdown", "id": "6eae939a", "metadata": {}, "source": [ "```{contents}\n", ":local:\n", ":depth: 2\n", "```\n" ] }, { "cell_type": "markdown", "id": "0c1909e9", "metadata": {}, "source": [ "\n", "\n", "## Visualization Orientation\n" ] }, { "cell_type": "markdown", "id": "23fb609a", "metadata": {}, "source": [ "Dr. Andrew Abela created this chart chooser visualization in 2013. The four-dimension chart categorization offer great insight when choosing visualizations. \n", "\n", "```{figure} ../../figures/andrew-abela-chart-chooser.jpg\n", ":name: andrew-abela-chart-chooser\n", ":width: 90%\n", ":align: center\n", "\n", "[Andrew Abela Chart Chooser](https://datavizblog.com/2013/04/29/andrew-abelas-chart-chooser/)\n", "```\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "9feb4359", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:43.788958Z", "iopub.status.busy": "2026-08-31T06:00:43.788863Z", "iopub.status.idle": "2026-08-31T06:00:44.784048Z", "shell.execute_reply": "2026-08-31T06:00:44.783776Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "id": "ce5f1e42", "metadata": {}, "source": [ "### Overview\n" ] }, { "cell_type": "markdown", "id": "9c507c08", "metadata": {}, "source": [ "**Data visualization** is the art and science of transforming raw numbers into compelling visual stories. While spreadsheets and statistical summaries provide precise information, visualizations reveal patterns, relationships, and insights that would remain hidden in tables of data. \n", "\n", "Imagine trying to understand weather patterns from thousands of temperature readings versus seeing them plotted as a line graph over time—the visual immediately shows trends, seasonal cycles, and anomalies that numbers alone cannot convey.\n", "\n", "\n", "\n", "Each tool has its strengths: Python excels in programmatic control and integration with data analysis workflows, while `R` provides statistical visualization excellence, while Tableau and Power BI offer user-friendly interfaces for business users.\n" ] }, { "cell_type": "markdown", "id": "baf169b8", "metadata": {}, "source": [ "**Why Visualization is Essential**\n", "\n", "Data visualization transforms numbers into understanding, serving three fundamental purposes:\n", "\n", "- **Exploration and Discovery**: Visualization reveals patterns, outliers, and relationships invisible in raw data, guiding initial analysis and data cleaning decisions.\n", "\n", "- **Communication and Persuasion**: Well-crafted visuals convey complex findings to diverse audiences, translating technical results into accessible insights.\n", "\n", "- **Decision Support and Action**: Visualization provides clarity for confident decision-making through dashboards, trend analysis, and comparative displays.\n", "\n", "Ultimately, visualization bridges the gap between data analysis and actionable insight. \n" ] }, { "cell_type": "markdown", "id": "fe999078", "metadata": {}, "source": [ "Effective data visualization follows four key principles:\n", "\n", "- Context is Key: Design for your specific audience and their decision-making needs.\n", "\n", "- Keep It Simple: Use clear labels and remove unnecessary elements that don't add value.\n", "\n", "- Choose the Right Chart Type: Match chart types to data structure: \n", " - bars for categories, \n", " - lines for trends, \n", " - scatter plots for relationships.\n", "\n", "- Tell a Story: Structure visualizations to guide the audience through a logical narrative flow.\n", " \n" ] }, { "cell_type": "markdown", "id": "a1c9fc28", "metadata": {}, "source": [ "### Common Tools and Libraries\n", "\n", "The data visualization landscape offers tools ranging from point-and-click platforms to code-based libraries. Understanding this ecosystem helps you choose the right tool for your needs.\n", "\n", "Business Intelligence Platforms:\n", "- **Tableau**: Industry-leading dashboard creation with drag-and-drop interface\n", "- **Power BI**: Microsoft's analytics platform with strong Excel and cloud integration\n", "\n", "Python Visualization Libraries:\n", "- **Matplotlib**: The foundational library providing complete control over plot elements\n", "- **Seaborn**: Statistical graphics with simplified syntax, built on Matplotlib. Seaborn is a higher-level library specifically designed for creating more visually appealing and informative statistical graphics with less code. It's excellent for exploring relationships between variables. Seaborn also requires Matplotlib as a dependency. \n", "- **Plotly**: Interactive, web-ready visualizations including 3D and geographic plots\n", "- **Pandas**: Quick plotting directly from DataFrames for exploratory analysis\n", "\n", "\n", "While business tools excel at dashboard creation and user-friendly interfaces, Python libraries offer several advantages for data scientists:\n", "- **Integration**: Seamless workflow from data analysis to visualization\n", "- **Reproducibility**: Code-based plots can be version controlled and automated\n", "- **Customization**: Complete control over every visual element\n", "- **Cost**: Open-source tools reduce licensing expenses\n", "\n", "This chapter focuses on **Pandas visualization**—the fastest way to create exploratory plots directly from your data. \n" ] }, { "cell_type": "markdown", "id": "adfa1baa", "metadata": {}, "source": [ "### Which Library/Plot to Use?\n", "\n", "It is essential to know which plots to use when the need for data visualization arises. **Matplotlib** (`plt/ax`), **Pandas** (`df.plot`), and **Seaborn** (`.`) each have a set of functions and methods for data visualization. You will learn about the functions and methods later, but here is a summary of them. Pay attention to what they are used for for now and use this table as a reference.\n", "\n", "| Plot Type | Pandas (`.plot`) | Matplotlib (`plt`) | Seaborn (`sns`)| Uses |\n", "| -------------------- | ---------------- | ---------------------| ------------ | ---------------------- |\n", "| **Line** | `.line()`/`.plot()` | `.plot()` | `.lineplot()` | Time series, trends |\n", "| **Scatter** | `.scatter(x, y)` | `.scatter()` | `.scatterplot()` | Correlations, relationships | \n", "| **Bar (vert.)** | `.bar()` | `.bar()` | `.barplot()` | Category comparisons |\n", "| **Bar (horiz.)** | `.barh()` | `.barh()` | `.barplot(y, x)` | Long category labels |\n", "| **Histogram** | `.hist()` | `.hist()` | `.histplot()` | Distribution of numeric data |\n", "| **Box plot** | `.box()` | `.boxplot()` | `.boxplot()` | Distribution + outliers |\n", "| **KDE/
Density** | `.kde()`/
`.density()` | — | `.kdeplot()` | Smoothed distribution |\n", "| **ECDF** | — | — `.ecdf()` (v3.8+) | `.ecdfplot()` | cumulative distribution |\n", "| **Area** | `.area()` | `.fill_between()` | — | Cumulative trends |\n", "| **Stacked area** | `.area(`
`stacked=True)` | `.stackplot()` | — | Composition over time |\n", "| **Pie** | `.pie()`*(ser)* | `.pie()` | — | Part-to-whole (use sparingly) |\n", "| **Hexbin** | `.hexbin(x, y)`*(df)* | `.hexbin()` | `.jointplot(`
`kind=\"hex\")`*(fig.)* | Dense scatter (binning) |\n", "| **Violin** | — | `.violinplot()` | `.violinplot()` | Distribution shape by category |\n", "| **Heatmap** | — | `.imshow()`/
`.pcolormesh()` | `.heatmap()` | Correlation matrices, grids |\n", "| **Count plot** | — | — | `.countplot()` | Frequency of categories |\n", "| **Pair plot** | — | — | `.pairplot()`*(fig.)* | Multivariate relationships |\n", "| **Joint plot** | — | — | `.jointplot()`*(fig.)* | Bivariate + marginals |\n", "| **Regression** | — | — | `.regplot()`(ax)/
`.lmplot()`*(fig.)* | Linear relationships + CI |\n", "| **Contour** | — | `.contour()`/
`.contourf()` | `.kdeplot(x, y)`
*(2D density)* | Continuous 2D fields/level sets |\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "84601497", "metadata": {}, "source": [ "### Seaborn Datasets\n", "\n" ] }, { "cell_type": "markdown", "id": "f069d5f2", "metadata": {}, "source": [ "Some of the Seaborn built-in datasets are commonly used for learning data science and machine learning.\n" ] }, { "cell_type": "markdown", "id": "424610be", "metadata": {}, "source": [ "| Name | Rows × Cols | What it’s about | Common uses |\n", "| ---------- | ----------: | ---------------------------------------- | --------------------------------------- |\n", "| `tips` | 244 × 7 | Restaurant bills & tips | categorical plots, regression, grouping |\n", "| `iris` | 150 × 5 | Iris flower measurements | pairplots, clustering, basics |\n", "| `diamonds` | 53,940 × 10 | Diamond prices & attributes | regression, categorical + numeric |\n", "| `flights` | 144 × 3 | Monthly air passengers (’49–’60) | heatmaps, time series |\n", "| `titanic` | 891 × 15 | Titanic passengers | categorical analysis, missing data |\n", "| `planets` | 1,035 × 6 | Exoplanet discoveries | distributions, facet grids |\n", "| `fmri` | 1,064 × 4 | fMRI signal over time | lineplots with CIs |\n" ] }, { "cell_type": "markdown", "id": "96c2103e", "metadata": {}, "source": [ "To load the datasets, use the `load_dataset` function with syntax:\n", "\n", "``` python\n", "[name] = sns.load_dataset(\"[dataset]\")\n", "```\n", "\n", "Here we would take a quick look at the popular ones.\n" ] }, { "cell_type": "markdown", "id": "5b3f496c", "metadata": {}, "source": [ "#### Iris\n", "\n", "The **Iris dataset**, introduced by Ronald A. Fisher in 1936, is one of the most well-known datasets in statistics and machine learning. It is often used for testing classification and visualization techniques. It contains measurements of three species of iris flowers: *Setosa*, *Versicolor*, and *Virginica*. The features of the iris dataset include:\n", "\n", "| Feature | Description | Units |\n", "|----------|--------------|--------|\n", "| sepal_length | Length of the outer part of the flower | cm |\n", "| sepal_width | Width of the outer part | cm |\n", "| petal_length | Length of the inner petal | cm |\n", "| petal_width | Width of the inner petal | cm |\n", "| species | Type of iris flower (*setosa*, *versicolor*, *virginica*) | categorical |\n" ] }, { "cell_type": "code", "execution_count": null, "id": "48af12e7", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.785668Z", "iopub.status.busy": "2026-08-31T06:00:44.785578Z", "iopub.status.idle": "2026-08-31T06:00:44.794613Z", "shell.execute_reply": "2026-08-31T06:00:44.794416Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width species\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### load dataset\n", "\n", "# !pip install seaborn ### ensure seaborn is installed; uncomment when done\n", "# import seaborn as sns ### uncomment if seaborn is not already imported\n", "\n", "iris = sns.load_dataset(\"iris\")\n", "iris.head(5)" ] }, { "cell_type": "markdown", "id": "bd2e75c1", "metadata": {}, "source": [ "To ensure a dataset is imported for your analysis, you may use the following code.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "189492bf", "metadata": {}, "outputs": [], "source": [ "if \"sns\" not in globals():\n", " import seaborn as sns\n", "\n", "if \"titanic\" not in globals():\n", " titanic = sns.load_dataset(\"titanic\")" ] }, { "cell_type": "markdown", "id": "57e0d1c7", "metadata": {}, "source": [ "#### Tips\n", "\n", "The **tips** dataset records restaurant bills and gratuities along with simple demographics, making it useful for practicing categorical comparisons and relationships between numeric variables.\n", "\n", "The **tips** dataset has features such as:\n", "\n", "| Feature | Description | Type |\n", "|----------|--------------|------|\n", "| **total_bill** | Total bill amount (including tax) | float |\n", "| **tip** | Tip amount given by the customer | float |\n", "| **sex** | Gender of the server’s customer (*Male*, *Female*) | categorical |\n", "| **smoker** | Whether the party included smokers (*Yes*, *No*) | categorical |\n", "| **day** | Day of the week (*Thur*, *Fri*, *Sat*, *Sun*) | categorical |\n", "| **time** | Meal type (*Lunch*, *Dinner*) | categorical |\n", "| **size** | Number of people in the dining party | integer |\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "162d6d84", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.795670Z", "iopub.status.busy": "2026-08-31T06:00:44.795612Z", "iopub.status.idle": "2026-08-31T06:00:44.799936Z", "shell.execute_reply": "2026-08-31T06:00:44.799767Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tips = sns.load_dataset('tips')\n", "tips.head(3)" ] }, { "cell_type": "markdown", "id": "d07e4462", "metadata": {}, "source": [ "#### Titanic\n", "\n", "The **titanic** dataset includes passenger demographics, ticket class, fares, and survival outcome, making it a classic dataset for classification and categorical analysis of the catastrophic event in 1912.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "06a5aa07", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.800925Z", "iopub.status.busy": "2026-08-31T06:00:44.800872Z", "iopub.status.idle": "2026-08-31T06:00:44.805321Z", "shell.execute_reply": "2026-08-31T06:00:44.805164Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " survived pclass sex age sibsp parch fare embarked class \\\n", "0 0 3 male 22.0 1 0 7.2500 S Third \n", "1 1 1 female 38.0 1 0 71.2833 C First \n", "2 1 3 female 26.0 0 0 7.9250 S Third \n", "\n", " who adult_male deck embark_town alive alone \n", "0 man True NaN Southampton no False \n", "1 woman False C Cherbourg yes False \n", "2 woman False NaN Southampton yes True " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = sns.load_dataset('titanic')\n", "titanic.head(3)" ] }, { "cell_type": "markdown", "id": "4b05ca94", "metadata": {}, "source": [ "### Previewing Data\n", "\n", "After you load a new dataset, always use the following methods/property to explore the data:\n", "\n", "- `head()` (how the dataset looks like),\n", "- `describe()` (descriptive statistics), \n", "- `info()`, and\n", "- `shape` (get dimension information; or just evaluate the dataframe)\n" ] }, { "cell_type": "markdown", "id": "950fb1bd", "metadata": {}, "source": [ "Here below is how you can **preview** a dataset before starting your analysis.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "0263f10d", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.808461Z", "iopub.status.busy": "2026-08-31T06:00:44.808392Z", "iopub.status.idle": "2026-08-31T06:00:44.816269Z", "shell.execute_reply": "2026-08-31T06:00:44.816097Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4 \n", "\n", "\n", "RangeIndex: 244 entries, 0 to 243\n", "Data columns (total 7 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 total_bill 244 non-null float64 \n", " 1 tip 244 non-null float64 \n", " 2 sex 244 non-null category\n", " 3 smoker 244 non-null category\n", " 4 day 244 non-null category\n", " 5 time 244 non-null category\n", " 6 size 244 non-null int64 \n", "dtypes: category(4), float64(2), int64(1)\n", "memory usage: 7.4 KB\n", "None \n", "\n", " total_bill tip size\n", "count 244.000000 244.000000 244.000000\n", "mean 19.785943 2.998279 2.569672\n", "std 8.902412 1.383638 0.951100\n", "min 3.070000 1.000000 1.000000\n", "25% 13.347500 2.000000 2.000000\n", "50% 17.795000 2.900000 2.000000\n", "75% 24.127500 3.562500 3.000000\n", "max 50.810000 10.000000 6.000000 \n", "\n", "(244, 7) \n", "\n" ] } ], "source": [ "print(tips.head(), \"\\n\")\n", "print(tips.info(), \"\\n\")\n", "print(tips.describe(), \"\\n\")\n", "print(tips.shape, \"\\n\")\n" ] }, { "cell_type": "markdown", "id": "c8f4e96b", "metadata": {}, "source": [ "### Style Sheets\n", "\n", "The style sheet, or themes, basically creates a set of style rules that your plots follow. The use of a stylesheet gives your plots a unified look and feel, making them more professional. You can even create your own stylesheet.\n", "\n", "Matplotlib has [style sheets](https://matplotlib.org/stable/gallery/style_sheets/index.html) (or *themes*) you can use to make your plots look a little nicer. Popular stylesheets include:\n", "- **bmh** (Bayesian Methods for Hackers)\n", "- **fivethirtyeight** (FiveThirtyEight is a news site)\n", "- **ggplot** (R’s ggplot2 default theme)\n", "- **dark_background**\n", "\n", "The syntax for using stylesheets in matplotlib is:\n", "\n", "`plt.style.use(style_name)`\n", "\n", "To see all the stylesheets available, use:\n", "\n", "`plt.style.available`\n", "\n", "Note that:\n", "- we use `plt.style`, which means we are using matplotlib here.\n", "- Pandas by default pulls colors from Matplotlib's `axes.prop_cycle`, a Matplotlib `rcParam` (runtime configuration parameter), which is a color iterator (cycler) that cycles through a list of 10 predefined colors. That's why you see different colors, e.g., when plotting multiple columns from a `df`, because Matplotlib cycles through 10 colors before repeating.\n" ] }, { "cell_type": "markdown", "id": "17ea5742", "metadata": {}, "source": [ "Comment/uncomment out the following lines of plt.style.use() to see how the example histogram change looks.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c3f6b06a", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.817221Z", "iopub.status.busy": "2026-08-31T06:00:44.817163Z", "iopub.status.idle": "2026-08-31T06:00:44.818428Z", "shell.execute_reply": "2026-08-31T06:00:44.818266Z" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# plt.style.use('default')\n", "# plt.style.use('ggplot')\n", "# plt.style.use('bmh')\n", "plt.style.use('fivethirtyeight')\n", "# plt.style.use('dark_background')\n", "# plt.style.use('fast')" ] }, { "cell_type": "code", "execution_count": 9, "id": "29f78473", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2026-08-31T06:00:44.819238Z", "iopub.status.busy": "2026-08-31T06:00:44.819188Z", "iopub.status.idle": "2026-08-31T06:00:44.868291Z", "shell.execute_reply": "2026-08-31T06:00:44.868103Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "titanic['age'].hist(figsize=(4, 3), bins=30, grid=False, edgecolor='white')" ] }, { "cell_type": "markdown", "id": "5b967f81", "metadata": {}, "source": [ "To see all the available styles/themes in the environment, use the syntax:\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "e5e746bc", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.869327Z", "iopub.status.busy": "2026-08-31T06:00:44.869276Z", "iopub.status.idle": "2026-08-31T06:00:44.870927Z", "shell.execute_reply": "2026-08-31T06:00:44.870765Z" } }, "outputs": [ { "data": { "text/plain": [ "['Solarize_Light2',\n", " '_classic_test_patch',\n", " '_mpl-gallery',\n", " '_mpl-gallery-nogrid',\n", " 'bmh',\n", " 'classic',\n", " 'dark_background',\n", " 'fast',\n", " 'fivethirtyeight',\n", " 'ggplot',\n", " 'grayscale',\n", " 'petroff10',\n", " 'seaborn-v0_8',\n", " 'seaborn-v0_8-bright',\n", " 'seaborn-v0_8-colorblind',\n", " 'seaborn-v0_8-dark',\n", " 'seaborn-v0_8-dark-palette',\n", " 'seaborn-v0_8-darkgrid',\n", " 'seaborn-v0_8-deep',\n", " 'seaborn-v0_8-muted',\n", " 'seaborn-v0_8-notebook',\n", " 'seaborn-v0_8-paper',\n", " 'seaborn-v0_8-pastel',\n", " 'seaborn-v0_8-poster',\n", " 'seaborn-v0_8-talk',\n", " 'seaborn-v0_8-ticks',\n", " 'seaborn-v0_8-white',\n", " 'seaborn-v0_8-whitegrid',\n", " 'tableau-colorblind10']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.style.available" ] }, { "cell_type": "markdown", "id": "6fd17b5e", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "id": "f14d74f5", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "d756dd99", "metadata": {}, "source": [ "## Pandas Overview" ] }, { "cell_type": "markdown", "id": "cf8af583", "metadata": {}, "source": [ "Pandas offers a simple, high-level interface for creating plots directly from Series and DataFrame objects. It builds on Matplotlib behind the scenes, allowing you to make quick, readable plots with calls like `df.plot()` or `series.plot()`. You can easily produce line, bar/stacked bar, area, scatter, box, histogram, KDE, hexbin, and pie charts while using your **index** (including datetime) and **column labels**. " ] }, { "cell_type": "markdown", "id": "21c3fb68", "metadata": {}, "source": [ "### The `.plot(kind=)` Method" ] }, { "cell_type": "markdown", "id": "cd281ec3", "metadata": {}, "source": [ "`.plot()`, a wrapper around Matplotlib's `plt.plot()`, is the primary top-level plotting method in pandas, which uses the `kind` **keyword argument** to access a variety of plotting methods including:\n", "\n", "- `bar` or `barh` for bar plots\n", "- `hist` for histogram\n", "- `box` for boxplot\n", "- `kde’ or `density` for density plots\n", "- `area` for area plots\n", "- `scatter` for scatter plots\n", "- `hexbin` for hexagonal bin plots\n", "- `pie` for pie plots" ] }, { "cell_type": "markdown", "id": "d72d1254", "metadata": {}, "source": [ "For example, you may want to see the histogram of the age of the titanic passengers." ] }, { "cell_type": "code", "execution_count": 2, "id": "bceedcf3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "titanic = sns.load_dataset('titanic')\n", "titanic[\"age\"].plot(kind='hist', bins=30)\n" ] }, { "cell_type": "markdown", "id": "9fa24be2", "metadata": {}, "source": [ "### The Plotting Methods (**`df.plot.`**)\n", "\n", "Instead of the `kind=` arguments, your can use the plotting methods directly by using the **`df.plot.`** syntax, which makes it easier to find other plot methods. " ] }, { "cell_type": "code", "execution_count": 9, "id": "f05685b9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "titanic = sns.load_dataset('titanic')\n", "titanic.head()\n", "titanic[\"age\"].plot.hist(bins=30, alpha=0.5, grid=False, edgecolor='white', figsize=(4, 3))" ] }, { "cell_type": "markdown", "id": "56d102a1", "metadata": {}, "source": [ "In the Python shell, you hit the tab key after df.plot., and in Jupyter, you just type til the dot and the menu with the methods will show up." ] }, { "cell_type": "markdown", "id": "da5e8773", "metadata": {}, "source": [ "```bash\n", "(.venv) tcn85✪mstː~/workspace/dsm$ python\n", "Python 3.13.7 (main, Aug 14 2025, 11:12:11) [Clang 17.0.0 (clang-1700.0.13.3)] on darwin\n", "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n", "```\n", "```python\n", ">>> import pandas as pd\n", ">>> df = pd.DataFrame()\n", ">>> df.plot.\n", "df.plot.area( df.plot.box( df.plot.hist( df.plot.pie( \n", "df.plot.bar( df.plot.density( df.plot.kde( df.plot.scatter( \n", "df.plot.barh( df.plot.hexbin( df.plot.line( \n", "```" ] }, { "cell_type": "markdown", "id": "b642743e", "metadata": {}, "source": [ "### Sample Plots\n", "\n", "For example, here we have a piece of randomly generated time series data to plot using `.plot()`, which by default gives you a **line plot**. Because the index consists of dates, `.plot()` calls `gcf().autofmt_xdate()` to try to format the x-axis. " ] }, { "cell_type": "code", "execution_count": 28, "id": "f18aac17", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.872176Z", "iopub.status.busy": "2026-08-31T06:00:44.872111Z", "iopub.status.idle": "2026-08-31T06:00:44.922162Z", "shell.execute_reply": "2026-08-31T06:00:44.921972Z" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Random Walk Simulation')" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "np.random.seed(42) ### for reproducibility\n", "n = 1000 ### number of time points\n", "ts = pd.Series(np.random.randn(n), index=pd.date_range(\"2023-01-01\", periods=n))\n", "ts = ts.cumsum() ### cumulative sum to create a random walk\n", "\n", "ax = ts.plot(kind=\"line\", figsize=(4, 3), fontsize=8) ### plot the entire time series\n", "ax.set_title(\"Random Walk Simulation\", fontsize=10)" ] }, { "cell_type": "markdown", "id": "463949bb", "metadata": {}, "source": [ "For DataFrames, `.plot()` plots all of the columns with labels." ] }, { "cell_type": "code", "execution_count": 30, "id": "e969f745", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.923213Z", "iopub.status.busy": "2026-08-31T06:00:44.923151Z", "iopub.status.idle": "2026-08-31T06:00:44.984228Z", "shell.execute_reply": "2026-08-31T06:00:44.984042Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C D\n", "2023-01-01 1.399355 0.924634 0.059630 -0.646937\n", "2023-01-02 2.097579 1.318119 0.954824 -0.011765\n", "2023-01-03 3.147131 0.782884 2.272218 0.185835\n", "2023-01-04 5.222392 0.093696 4.008181 0.383745\n", "2023-01-05 4.570974 -0.390190 3.687834 0.807911\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(42) ### for reproducibility\n", "n = 1000 ### number of time points\n", "ts = pd.Series(data = np.random.randn(n), index=pd.date_range(\"2023-01-01\", periods=n))\n", "ts = ts.cumsum() ### cumulative sum to create a random walk\n", "df = pd.DataFrame(data = np.random.randn(1000, 4), index=ts.index, columns=list(\"ABCD\"))\n", "df = df.cumsum()\n", "print(df.head())\n", "plt.figure();\n", "df.plot(kind=\"line\", figsize=(4, 3), lw=1, fontsize=8); ### plot all columns together\n", "plt.title(\"Random Walks for A, B, C, D\", fontsize=10);" ] }, { "cell_type": "markdown", "id": "862c5d8c", "metadata": {}, "source": [ "Or you can pick columns of your choice to plot." ] }, { "cell_type": "code", "execution_count": 22, "id": "4b801004", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:44.985272Z", "iopub.status.busy": "2026-08-31T06:00:44.985215Z", "iopub.status.idle": "2026-08-31T06:00:45.021489Z", "shell.execute_reply": "2026-08-31T06:00:45.021295Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " B C A\n", "0 0.785185 -1.777681 0\n", "1 1.499931 -2.011405 1\n", "2 2.207388 -1.234473 2\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 = pd.DataFrame(np.random.randn(1000, 2), columns=[\"B\", \"C\"]).cumsum()\n", "df1[\"A\"] = pd.Series(data = list(range(len(df1))))\n", "print(df1.head(3))\n", "df1.plot(x=\"A\", y=\"B\", figsize=(4, 3), fontsize=8, title=\"B (line plot)\");" ] }, { "cell_type": "markdown", "id": "03312444", "metadata": {}, "source": [ "Or, similarly," ] }, { "cell_type": "code", "execution_count": 33, "id": "634b410b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " B C\n", "0 0.765402 1.073413\n", "1 1.264092 -0.869085\n", "2 1.108670 -2.024793\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 = pd.DataFrame(\n", " data=np.random.randn(1000, 2),\n", " columns=[\"B\", \"C\"]\n", ").cumsum()\n", "\n", "print(df1.head(3))\n", "\n", "df1.plot(y=\"B\", figsize=(4, 3), fontsize=8, title=\"B (line plot)\");\n", "# df1.plot(y=\"B\", figsize=(4, 3), fontsize=8, title=\"B (line plot)\", xlabel=\"Index\", ylabel=\"Value\");" ] }, { "cell_type": "markdown", "id": "08c0d65f", "metadata": {}, "source": [ "So far we have been seeing the default `line` plots. A histogram can be create with `pd.plot(kind=\"hist\")`. Here we use the **`kind=`** keyword argument to specify the type of plot." ] }, { "cell_type": "code", "execution_count": null, "id": "537793c4", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:45.022536Z", "iopub.status.busy": "2026-08-31T06:00:45.022476Z", "iopub.status.idle": "2026-08-31T06:00:45.047664Z", "shell.execute_reply": "2026-08-31T06:00:45.047496Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " A B C D\n", "2023-01-01 1.399355 0.924634 0.059630 -0.646937\n", "2023-01-02 2.097579 1.318119 0.954824 -0.011765\n", "2023-01-03 3.147131 0.782884 2.272218 0.185835\n", "2023-01-04 5.222392 0.093696 4.008181 0.383745\n", "2023-01-05 4.570974 -0.390190 3.687834 0.807911\n", "A 3.147131\n", "B 0.782884\n", "C 2.272218\n", "D 0.185835\n", "Name: 2023-01-03 00:00:00, dtype: float64\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(df.head()) ### df is a DataFrame with 4 columns (A, B, C, D) and 1000 rows\n", "print(df.iloc[2]) ### df.iloc[2] is a Series with 4 values (one for each column)\n", "# plt.figure();\n", "df.iloc[2].plot(kind=\"bar\", figsize=(4, 3), ### plot the 3rd row of df as a bar plot\n", " title=\"Bar plot of df.iloc[2] (using 'kind' argument)\", \n", " edgecolor=\"white\",\n", " alpha=0.9,\n", " fontsize=8);\n", "plt.title(\"Bar plot of df.iloc[2]\", fontsize=10);" ] }, { "cell_type": "markdown", "id": "7a856f51", "metadata": {}, "source": [ "Another example of using the `.plot(kind=)` syntax to generate plots. In this case, the time series dataset you generate earlier." ] }, { "cell_type": "code", "execution_count": 43, "id": "96ced8fe", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:45.048663Z", "iopub.status.busy": "2026-08-31T06:00:45.048610Z", "iopub.status.idle": "2026-08-31T06:00:45.084666Z", "shell.execute_reply": "2026-08-31T06:00:45.084470Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2023-01-01 0.496714\n", "2023-01-02 0.358450\n", "2023-01-03 1.006138\n", "Freq: D, dtype: float64\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(ts.head(3))\n", "ts.plot(kind=\"hist\", bins=30, figsize=(4, 3), ### plot a histogram of the time series, 30 intervals\n", " title=\"Histogram of ts\",\n", " edgecolor=\"white\",\n", " alpha=0.9)" ] }, { "cell_type": "markdown", "id": "ad668407", "metadata": {}, "source": [ "The same plot can be produced using the **`plot.hist()`** method. " ] }, { "cell_type": "code", "execution_count": 16, "id": "d18642e5", "metadata": { "execution": { "iopub.execute_input": "2026-08-31T06:00:45.085694Z", "iopub.status.busy": "2026-08-31T06:00:45.085642Z", "iopub.status.idle": "2026-08-31T06:00:45.119607Z", "shell.execute_reply": "2026-08-31T06:00:45.119419Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ts.plot.hist(bins=30, figsize=(4, 3),\n", " title=\"Histogram of ts (using method)\",\n", " edgecolor=\"white\",\n", " alpha=0.9)" ] }, { "cell_type": "markdown", "id": "32dfdfb3", "metadata": {}, "source": [ "For example, for histograms:\n", " \n", " 1. `df.plot.hist()`: Uses Pandas’s unified `.plot()` interface (which wraps Matplotlib) to draw a histogram.\n", " 2. `df.plot(kind='hist')`: The same as df.plot.hist(); a generic form of the same call.\n", " 3. Pandas has two top-level plotting methods: `df.hist()` (a quick wrapper around Matplotlib’s `plt.hist()` for all numeric columns) and `df.boxplot()`.\n", "\n", "\n", "\n", "| # | Method | Applies to | Subplots | Overlays Multiple Columns | Based On | Typical Use |\n", "|:-:|:---------------------- |:------------------- |:--------------------- |:------------------------- |:------------------------ |:------------------------------------- |\n", "| 1 | `df.hist()` | DataFrame only | Yes (grid of plots) | No | `plt.hist()` | Quick overview of all numeric columns |\n", "| 2 | `df.plot.hist()` | DataFrame or Series | Single plot | Yes | Pandas `.plot()` wrapper | Custom, combined histograms |\n", "\n" ] }, { "cell_type": "markdown", "id": "4cd22161", "metadata": {}, "source": [ "## Custom parameters\n", "\n", " Most plotting calls accept `Matplotlib` keyword arguments and return a Matplotlib Axes object for further customization. Often used parameters from pandas API reference ([pandas.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html)) include:\n", " 1. `data`: Series or DataFrame. The object for which the method is called.\n", " 2. `x`: label or position, default None. Only used if data is a DataFrame.\n", " 3. `y`: label, position or list of label, positions, default None. Allows plotting of one column versus another. Only used if data is a DataFrame.\n", " 4. `kind`: str. The kind of plot to produce: line (default), bar, barh, hist, box, kde/density, area, pie, scatter (DataFrame only), hexbin (DataFrame only).\n", " 5. `ax`\n", " 6. `figsize` \n", " 7. `use_index`\n", " 8. `title`\n", " 9. ticks (xticks and yticks): sequence. Values to use for ticks. \n", " 10. lim (`xlim` and `ylim`): 2-tuple/list. Set the limits of the axes. \n", " 11. label (`xlabel` and `ylabel`: Name to use for the label on axis (default to index name).\n", " 12. `coloarmap`: str or matplotlib colormap object\n", " 13. `stacked`: bool, default False in line and bar plots, and True in area plot. If True, create stacked plot.\n", " 14. **kwargs ([matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html)): \n", " 1. `lw` \n", " 2. `alpha` \n", " \n", "\n", "3. **style sheets**: Style the plots to look globally with `plt.style.use(...)`.\n", " \n", "4. **color & size**: \n", " - For scatter plots you can color by a column (`c`) or \n", " - size by an array (`s=df['col']*scale`) and \n", " - use `cmap` for colormaps.\n", "\n", "5. **fine-tuning**: For large or dense data, consider hexbin or KDE plots instead of scatter.\n", "\n", "6. **Plot Types**\n", "The `pandas.DataFrame.plot` API lists 11 plots to be used with the `kind` parameter: The kind of plot to produce: `line`(default), `bar`, `barh` (horizontal bar plot), `hist`, `box`, `kde` (`density`), `area`, `pie`, `scatter`, `hexbin`. These plots can be summarized as:\n", "\n", "\n", "| # | Method | Shorthand | Description | Best For |\n", "|---|--------|-----------|-------------|----------|\n", "| 1 | `.plot(kind='line')` | `.plot.line()` | Line plot | Time series, continuous trends |\n", "| 2 | `.plot(kind='bar')` | `.plot.bar()` | Vertical bar chart | Comparing categories side by side |\n", "| 3 | `.plot(kind='barh')` | `.plot.barh()` | Horizontal bar chart | Long category names, rankings |\n", "| 4 | `.plot(kind='hist')` | `.plot.hist()` | Histogram | Distribution of a single variable |\n", "| 5 | `.plot(kind='box')` | `.plot.box()` | Box plot | Spotting outliers, comparing spread |\n", "| 6 | `.plot(kind='kde')` | `.plot.kde()` | KDE curve | Smooth distribution, comparing shapes |\n", "| 7 | `.plot(kind='density')` | `.plot.density()` | Alias for kde | Same as KDE |\n", "| 8 | `.plot(kind='area')` | `.plot.area()` | Area plot | Cumulative totals, part-to-whole over time |\n", "| 9 | `.plot(kind='pie')` | `.plot.pie()` | Pie chart | Proportions of a whole (few categories) |\n", "| 10 | `.plot(kind='scatter', x='col1', y='col2')` | `.plot.scatter(x='col1', y='col2')` | Scatter plot | Correlation between two numeric variables |\n", "| 11 | `.plot(kind='hexbin', x='col1', y='col2')` | `.plot.hexbin(x='col1', y='col2')` | Hexbin plot | Large datasets, 2D density visualization |\n", "\n", "**Notes:**\n", "- `kde` and `density` are aliases — same plot, counted once for unique types\n", "- `scatter` and `hexbin` **require** `x` and `y` arguments\n", "- `pie` requires a single column (`y=` or per-column)\n", "- `scatter` and `hexbin` are **DataFrame only**\n", "\n", "\n", "\n", "Note: Jupyter Notebooks enable inline plotting (no pop-up) with `%matplotlib inline`, which is an IPython magic command that tells Jupyter Notebook to render Matplotlib plots directly inside the notebook output cell instead of in a separate window. You don’t need %matplotlib inline in Jupyter environment with IPython >= 7.\n", "\n", "Later, when you learn Matplotlib, you will see why these methods of plotting are a lot easier to use. Pandas visualization balances ease of use with control over the figure. A lot of the plot calls also accept additional arguments of their parent matplotlib.plt call. \n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.13.13)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.13" } }, "nbformat": 4, "nbformat_minor": 5 }