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    "# Visualization\n"
   ]
  },
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    "<h2>Overview</h2>\n",
    "\n",
    "This chapter introduces visualization as a management decision tool. You will learn how to choose chart types, create quick plots with pandas, compare common chart families, and polish figures so patterns are easier to read.\n",
    "\n",
    "<h2>Chapter Video</h2>\n",
    "\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube-nocookie.com/embed/hEWY6kkBdpo\" title=\"Charts Are Like Pasta - Data Visualization Part 1: Crash Course Statistics #5\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n",
    "\n",
    "<div class=\"video-credit\">\n",
    "<p><strong>Video credit:</strong> CrashCourse. (2018, February 21). <em>Charts Are Like Pasta - Data Visualization Part 1: Crash Course Statistics #5</em>. YouTube. <a href=\"https://youtube.com/watch?v=hEWY6kkBdpo\">Watch on YouTube</a>.</p>\n",
    "<p class=\"video-rights\">This embedded video is provided for educational reference only. All rights are retained by the respective copyright holder.</p>\n",
    "</div>\n"
   ]
  },
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    "<h2>Learning Objectives</h2>\n",
    "\n",
    "By the end of this chapter, you will be able to:\n",
    "\n",
    "1. Choose chart types that match categorical, quantitative, relationship, and time-based questions.\n",
    "2. Create quick exploratory plots from pandas Series and DataFrame objects.\n",
    "3. Compare line, bar, histogram, box, and scatter plots for common management analyses.\n",
    "4. Adjust labels, scales, legends, and styling so a chart supports a clear interpretation.\n",
    "5. Explain when pandas, Matplotlib, and Seaborn are useful in a visualization workflow.\n"
   ]
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    "<h2>Chapter Flow</h2>\n",
    "\n",
    "```{div} chapter-flow\n",
    "{doc}`5.1 Pandas Plotting Basics <0501-pandas-vis>`\n",
    "\n",
    "{doc}`5.2 Pandas Chart Types <0502-pandas-chart-types>`\n",
    "\n",
    "{doc}`5.3 Pandas Plot Styling <0503-pandas-plot-styling>`\n",
    "```\n"
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    "<h2>Glossary</h2>\n",
    "\n",
    "| # | Term | Explanation |\n",
    "|---:|---|---|\n",
    "| 1 | Data visualization | Using charts and graphs to reveal patterns in data. |\n",
    "| 2 | Audience | The people who need to understand or act on a chart. |\n",
    "| 3 | Chart type | A visual form such as a bar chart, line chart, or scatter plot. |\n",
    "| 4 | Categorical variable | A variable whose values are labels or groups. |\n",
    "| 5 | Quantitative variable | A variable measured with numeric values. |\n",
    "| 6 | Frequency | The count of observations in a category or interval. |\n",
    "| 7 | Relative frequency | A frequency expressed as a proportion or percentage. |\n",
    "| 8 | Bar chart | A chart that compares values across categories. |\n",
    "| 9 | Line chart | A chart that shows change across an ordered sequence. |\n",
    "| 10 | Scatter plot | A chart that displays paired numeric values as points. |\n",
    "| 11 | Histogram | A chart that shows the distribution of one numeric variable. |\n",
    "| 12 | Box plot | A compact summary of median, quartiles, and possible outliers. |\n",
    "| 13 | Axis | A reference line that provides the scale for values. |\n",
    "| 14 | Legend | A guide that maps colors or symbols to groups. |\n",
    "| 15 | Label | Text that names axes, categories, or marks. |\n",
    "| 16 | Scale | The mapping between data values and visual positions. |\n",
    "| 17 | Outlier | An observation that sits far from the main pattern. |\n",
    "| 18 | Pandas plotting | The DataFrame and Series plotting interface built on Matplotlib. |\n",
    "| 19 | Matplotlib | A flexible Python library for building and customizing plots. |\n",
    "| 20 | Seaborn | A statistical visualization library built on Matplotlib. |\n",
    "| 21 | Encoding | The use of position, color, size, or shape to represent data. |\n"
   ]
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   "source": [
    "<h2><a href=\"overview.html\" target=\"_blank\" rel=\"noopener noreferrer\" style=\"color: var(--pst-color-link, #176de8);\">Chapter Overview Slides</a></h2>\n"
   ]
  }
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