├── LICENSE ├── README.md └── Scripts └── LINEPLOT.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Ruby Damodar Poddar 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ![Marine Corps Personal](https://see.fontimg.com/api/rf5/ax9Mo/YmJhZjU1MDUzZmY0NDI1MGJjMDE2OGMzMTlhMWRlNmIub3Rm/UGxvdFBybzogTWFzdGVyaW5nIE1hdHBsb3RsaWIg/marine-corps-personal-used.png?r=fs&h=26&w=1250&fg=579292&bg=FFFFFF&tb=1&s=21) 4 | 5 | # 🎨 **PlotPro: Mastering Matplotlib** 📊 6 | 7 | Welcome to **PlotPro**, your ultimate guide to mastering **Matplotlib** — a deeply comprehensive repository for creating stunning data visualizations in Python! 🚀 8 | 9 | Here’s a complete syllabus for diving deep into Matplotlib, covering everything from the basics to the most advanced features. 💡 10 | --- 11 | 12 | ### 1. **Introduction to Matplotlib** 🖥️ 13 | - Overview of Matplotlib 🌟 14 | - Installing Matplotlib ⚙️ 15 | - Understanding the anatomy of a Matplotlib figure (Figure, Axes, Axis) 🖼️ 16 | 17 | ### 2. **Basic Plotting** 📈 18 | - Creating simple line plots ✏️ 19 | - Adding titles, labels, and legends 🏷️ 20 | - Customizing lines (color, line style, markers) 🎨 21 | - Plotting multiple lines on the same plot 🔀 22 | - Plotting data from Pandas 🐼 23 | 24 | ### 3. **Figures, Axes, and Subplots** 🔢 25 | - Creating multiple figures and axes 🎛️ 26 | - Understanding figure size and resolution (DPI) 📐 27 | - Arranging multiple plots using `subplot()`, `subplots()`, and `GridSpec` 🗃️ 28 | - Adding insets and broken axes 🧩 29 | 30 | ### 4. **Plot Styles** 🖌️ 31 | - Using Matplotlib stylesheets (`plt.style.use`) 🖼️ 32 | - Creating custom styles 🎨 33 | - Themes: Seaborn styles integration with Matplotlib 🌊 34 | - Plot aesthetics and layout customization ✨ 35 | 36 | ### 5. **Working with Colors** 🌈 37 | - Basic color controls 🎛️ 38 | - Colormaps (Sequential, Diverging, and Qualitative colormaps) 📊 39 | - Colorbars for colormap-based plots 🖍️ 40 | - Using hex codes, RGB values, and named colors 🌐 41 | - Creating custom colormaps 🧑‍🎨 42 | 43 | ### 6. **Markers, Text, and Annotations** 📝 44 | - Customizing markers (types, size, and color) 🎯 45 | - Adding annotations to plots 🗨️ 46 | - Text positioning and customization (font styles, sizes, rotations) 🖋️ 47 | - Mathematical expressions using LaTeX in plots 🔢 48 | 49 | ### 7. **Legends and Layouts** 🗺️ 50 | - Customizing legends (location, number of columns, font, border) 🏷️ 51 | - Using `bbox_to_anchor` for precise legend placement 🧭 52 | - Fine-tuning layout with `tight_layout()`, `constrained_layout()` 🧩 53 | - Grids and Axes alignment for a professional look 📏 54 | 55 | ### 8. **Plot Types** 📊 56 | - **Line plots:** Customizations and advanced styling 📈 57 | - **Bar charts:** Vertical, horizontal, stacked bars, and bar customization 📊 58 | - **Histograms:** Binning, cumulative, density, and overlaid histograms 📉 59 | - **Scatter plots:** Adding color maps, bubble plots, transparency 🎯 60 | - **Pie charts:** Exploding, shadow effects, and adding percentages 🥧 61 | - **Box plots:** Customizing whiskers, notches, and comparing distributions 📦 62 | - **Violin plots:** Understanding KDE and density distribution 🎻 63 | - **Area plots:** Stacked area and filled plots 📐 64 | - **Error bars:** Adding error bars to any type of plot ⚠️ 65 | - **3D plots:** Line, scatter, surface, and wireframe plotting with `mpl_toolkits.mplot3d` 🏞️ 66 | - **Heatmaps:** Using `imshow()`, `pcolormesh()`, and annotating heatmaps 🌡️ 67 | 68 | ### 9. **Handling Dates and Times** ⏳ 69 | - Plotting time series data ⏱️ 70 | - Formatting date axes using `matplotlib.dates` 📅 71 | - Handling timezones and daylight saving time 🌐 72 | - Rolling averages, moving window plots, and seasonal decomposition 📆 73 | 74 | ### 10. **Advanced Plotting Techniques** 🎛️ 75 | - Customizing tick labels (rotation, formatting, hiding) 🔧 76 | - Customizing axes: log scale, symlog, dual axes (`twinx()`, `twiny()`) 📐 77 | - Plotting with polar coordinates 🧭 78 | - Complex layouts with `subplot2grid`, `gridspec_kw` 🗃️ 79 | - Sharing axes between subplots and linked zoom 🔍 80 | 81 | ### 11. **Interactive and Animated Plots** 🎥 82 | - Making interactive plots with `mpl_connect` and `mplcursors` 💻 83 | - Basic animations using `FuncAnimation` 🌀 84 | - Controlling animation frames, intervals, and updating plots 🎬 85 | - Saving animations as GIFs or video files 🎞️ 86 | - Creating interactive widgets with Jupyter notebooks (using `ipympl` or `ipywidgets`) 🧩 87 | 88 | ### 12. **3D Plotting** 🌍 89 | - Setting up 3D axes (`mpl_toolkits.mplot3d`) 🛠️ 90 | - 3D line plots, scatter plots, and surface plots 🗺️ 91 | - Controlling 3D views (elevation, azimuth, zoom) 🎛️ 92 | - 3D wireframes and contour plots 🌄 93 | - Projections and interactive rotations 🔄 94 | 95 | ### 13. **Visualizing Statistical Data** 📊 96 | - Error bars, confidence intervals ⚠️ 97 | - Kernel Density Estimation (KDE) plots 🎻 98 | - Plotting regression lines 📈 99 | - Visualizing distributions (using histogram, boxplot, violin plot, etc.) 🧮 100 | - Creating pair plots for multivariate data 🔗 101 | 102 | ### 14. **Customizing and Exporting Plots** 💾 103 | - Exporting plots to different formats (PNG, PDF, SVG, etc.) 🖼️ 104 | - Controlling resolution and DPI of exports 🖨️ 105 | - Customizing figure background, axis spines, and grids 🎛️ 106 | - Saving and loading plot styles 🧩 107 | 108 | ### 15. **Embedding Matplotlib in Applications** 🛠️ 109 | - Integrating Matplotlib with GUI frameworks (Tkinter, PyQt, wxPython) 🖥️ 110 | - Embedding Matplotlib in web applications using Flask/Django 🌍 111 | - Using `mpld3` for web-ready interactive plots 🌐 112 | 113 | ### 16. **Performance Optimization** ⚡ 114 | - Reducing memory footprint for large datasets 🧠 115 | - Optimizing plot rendering time ⏱️ 116 | - Fast updating for real-time data visualization 🏃‍♂️ 117 | - Working with large datasets using downsampling techniques 📉 118 | 119 | ### 17. **Exploring External Libraries Built on Matplotlib** 🛠️ 120 | - **Seaborn:** Advanced statistical visualizations, color palettes 🌊 121 | - **Plotly:** Interactive plots with Matplotlib styling 📊 122 | - **Cartopy:** Creating geospatial maps with Matplotlib 🌍 123 | - **Basemap:** Plotting geographical data 🗺️ 124 | 125 | ### 18. **Advanced Topics** 🔬 126 | - Writing custom plot functions 🧑‍💻 127 | - Working with complex data (e.g., multi-dimensional datasets) 🧠 128 | - Advanced control of figure aesthetics using low-level Matplotlib API 🎨 129 | - Designing publication-quality plots for research papers 📄 130 | 131 | 132 | 133 | Creating the **bestest README** for your project focusing on **Matplotlib plot types** involves explaining each plot type deeply with engaging descriptions, usage, customization, and examples. Here's a detailed, well-structured README with an **Indian-style touch**, ensuring it's thorough, appealing, and covers everything: 134 | 135 | --- 136 | 137 | 138 | ## **Table of Contents** 📚 139 | 140 | 1. [Introduction](#introduction) 141 | 2. [Installation](#installation) 142 | 3. [Line Plot](#1-line-plot-📈) 143 | 4. [Bar Chart](#2-bar-chart-📊) 144 | 5. [Histogram](#3-histogram-📊) 145 | 6. [Scatter Plot](#4-scatter-plot-⚪) 146 | 7. [Pie Chart](#5-pie-chart-🍰) 147 | 8. [Box Plot](#6-box-plot-📦) 148 | 9. [Violin Plot](#7-violin-plot-🎻) 149 | 10. [Area Plot](#8-area-plot-🗻) 150 | 11. [3D Plot](#9-3d-plot-🌍) 151 | 12. [Heatmap](#10-heatmap-🔥) 152 | 13. [Error Bars](#11-error-bars-🚦) 153 | 14. [Conclusion](#conclusion) 154 | 155 | --- 156 | 157 | ## **Introduction** 🌟 158 | 159 | In the world of data, visualizing your findings is just as important as analyzing them. Matplotlib, one of Python's most powerful plotting libraries, enables you to create stunning, informative, and customizable visualizations. 160 | 161 | This guide will walk you through **11 essential types of plots**, with practical examples, use cases, and customization tips, ensuring you can turn raw data into beautiful stories. 162 | 163 | --- 164 | 165 | ## **Installation** ⚙️ 166 | 167 | Before we dive into plotting, let’s make sure you have Matplotlib installed. 168 | 169 | ```bash 170 | pip install matplotlib 171 | ``` 172 | 173 | To make your plots even more interesting, you may also install **Seaborn** for enhanced aesthetics: 174 | 175 | ```bash 176 | pip install seaborn 177 | ``` 178 | 179 | --- 180 | 181 | ## **1. Line Plot** 📈 182 | 183 | A **Line Plot** is the simplest and most fundamental plot type. It’s like the **backbone** of data visualization—simple yet powerful. It shows the relationship between two continuous variables. 184 | 185 | ### **When to use it**: 186 | 187 | - To visualize trends or changes over time (e.g., temperature variations across days or stock market fluctuations). 188 | 189 | ### **Example**: 190 | 191 | ```python 192 | import matplotlib.pyplot as plt 193 | 194 | x = [1, 2, 3, 4, 5] 195 | y = [10, 20, 25, 30, 40] 196 | 197 | plt.plot(x, y, color='blue', linestyle='--', marker='o') 198 | plt.title('Simple Line Plot') 199 | plt.xlabel('X-axis') 200 | plt.ylabel('Y-axis') 201 | plt.show() 202 | ``` 203 | 204 | ### **Customization**: 205 | 206 | - **Color**: You can change the color to make it more appealing. 207 | - **Line Style**: Solid, dashed, dotted, or any other fancy line you prefer. 208 | - **Markers**: Add markers to highlight individual data points. 209 | 210 | --- 211 | 212 | ## **2. Bar Chart** 📊 213 | 214 | A **Bar Chart** is great for comparing categories. It's like a **full thali**, giving you a wholesome view of categorical data. The length of each bar represents the magnitude of the data point. 215 | 216 | ### **When to use it**: 217 | 218 | - Comparing sales of different products, student marks across subjects, or population across regions. 219 | 220 | ### **Example**: 221 | 222 | ```python 223 | categories = ['A', 'B', 'C', 'D'] 224 | values = [10, 25, 17, 35] 225 | 226 | plt.bar(categories, values, color='orange') 227 | plt.title('Bar Chart Example') 228 | plt.show() 229 | ``` 230 | 231 | ### **Customization**: 232 | 233 | - Use **stacked** or **grouped bars** for comparing multiple categories. 234 | - **Horizontal bars** for a fresh, innovative look! 235 | 236 | --- 237 | 238 | ## **3. Histogram** 📊 239 | 240 | A **Histogram** is like a **filter coffee**—it reveals the underlying distribution of data. It groups continuous data into bins and shows how frequently each bin occurs. 241 | 242 | ### **When to use it**: 243 | 244 | - To explore data distribution, like age groups in a population or frequency of scores in an exam. 245 | 246 | ### **Example**: 247 | 248 | ```python 249 | import numpy as np 250 | 251 | data = np.random.randn(1000) 252 | plt.hist(data, bins=30, color='green', edgecolor='black') 253 | plt.title('Histogram Example') 254 | plt.show() 255 | ``` 256 | 257 | ### **Customization**: 258 | 259 | - **Bins**: Increase or decrease the number of bins for more or less granularity. 260 | - Display a **cumulative** histogram to track totals. 261 | 262 | --- 263 | 264 | ## **4. Scatter Plot** ⚪ 265 | 266 | A **Scatter Plot** helps to visualize relationships between two variables by plotting points. It’s like arranging a **rangoli**—every point adds value to the overall picture. 267 | 268 | ### **When to use it**: 269 | 270 | - Visualize correlations, like price vs. demand or age vs. height. 271 | 272 | ### **Example**: 273 | 274 | ```python 275 | plt.scatter(x, y, color='purple', s=100) 276 | plt.title('Scatter Plot Example') 277 | plt.xlabel('X-axis') 278 | plt.ylabel('Y-axis') 279 | plt.show() 280 | ``` 281 | 282 | ### **Customization**: 283 | 284 | - Vary the **size** and **color** of points to represent another dimension, like population size. 285 | 286 | --- 287 | 288 | ## **5. Pie Chart** 🍰 289 | 290 | A **Pie Chart** breaks data into proportions, just like slicing a **gulab jamun**. Each slice of the pie represents a percentage of the whole. 291 | 292 | ### **When to use it**: 293 | 294 | - When you want to visualize proportions or parts of a whole, like market shares or survey results. 295 | 296 | ### **Example**: 297 | 298 | ```python 299 | sizes = [30, 20, 25, 25] 300 | labels = ['Product A', 'Product B', 'Product C', 'Product D'] 301 | 302 | plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140) 303 | plt.title('Pie Chart Example') 304 | plt.show() 305 | ``` 306 | 307 | ### **Customization**: 308 | 309 | - **Explode** a slice to emphasize it! 310 | - Add shadows for a more 3D look. 311 | 312 | --- 313 | 314 | ## **6. Box Plot** 📦 315 | 316 | A **Box Plot** shows data distribution using quartiles, helping you find outliers. Think of it like opening a **treasure chest**—you see the full spread of the data in one glance. 317 | 318 | ### **When to use it**: 319 | 320 | - Comparing data distributions, especially for groups. 321 | 322 | ### **Example**: 323 | 324 | ```python 325 | data = [np.random.randn(100) for _ in range(4)] 326 | plt.boxplot(data, notch=True) 327 | plt.title('Box Plot Example') 328 | plt.show() 329 | ``` 330 | 331 | ### **Customization**: 332 | 333 | - Add **notches** for more detail about the median. 334 | - Change colors and positions for multiple box plots. 335 | 336 | --- 337 | 338 | ## **7. Violin Plot** 🎻 339 | 340 | A **Violin Plot** combines a box plot and a density plot, giving you insights into the distribution of data. It’s like adding a **spicy twist** to your box plot. 341 | 342 | ### **When to use it**: 343 | 344 | - To visualize distributions in more detail, especially when comparing multiple categories. 345 | 346 | ### **Example**: 347 | 348 | ```python 349 | import seaborn as sns 350 | 351 | sns.violinplot(x="category", y="value", data=data) 352 | plt.title('Violin Plot Example') 353 | plt.show() 354 | ``` 355 | 356 | --- 357 | 358 | ## **8. Area Plot** 🗻 359 | 360 | An **Area Plot** is like layering a **delicious lasagna**—it shows how different parts contribute to a whole over time. Similar to a line plot, but with filled areas. 361 | 362 | ### **When to use it**: 363 | 364 | - Best for visualizing stacked data over time, like revenue from multiple sources. 365 | 366 | ### **Example**: 367 | 368 | ```python 369 | plt.fill_between(x, y1, y2, color='skyblue', alpha=0.5) 370 | plt.title('Area Plot Example') 371 | plt.show() 372 | ``` 373 | 374 | --- 375 | 376 | ## **9. 3D Plot** 🌍 377 | 378 | A **3D Plot** adds depth to your data, letting you visualize multiple dimensions. It’s like adding a third layer to your **favorite biryani**. 379 | 380 | ### **When to use it**: 381 | 382 | - Ideal for visualizing geographical data or any data requiring three dimensions. 383 | 384 | ### **Example**: 385 | 386 | ```python 387 | from mpl_toolkits.mplot3d import Axes3D 388 | 389 | ax = plt.axes(projection='3d') 390 | ax.plot3D(x, y, z, 'gray') 391 | plt.title('3D Plot Example') 392 | plt.show() 393 | ``` 394 | 395 | --- 396 | 397 | ## **10. Heatmap** 🔥 398 | 399 | A **Heatmap** uses color gradients to represent data values. It’s like spicing up your data visualization with **chutneys**—hot, spicy, and revealing. 400 | 401 | ### **When to use it**: 402 | 403 | - Ideal for visualizing correlations, frequencies, or any tabular data. 404 | 405 | ### **Example**: 406 | 407 | ```python 408 | sns.heatmap(data, annot=True, cmap="coolwarm") 409 | plt.title('Heatmap Example') 410 | plt.show() 411 | ``` 412 | 413 | --- 414 | 415 | ## **11. Error Bars** 🚦 416 | 417 | **Error Bars** are like adding caution to your plotting—letting you visualize the uncertainty or variability in your data. 418 | 419 | ### **When to use it**: 420 | 421 | - To show variability in data, especially in scientific experiments. 422 | 423 | ### **Example**: 424 | 425 | ```python 426 | plt.errorbar(x, y, yerr=0.2, fmt='o', ecolor=' 427 | 428 | black', capsize=5) 429 | plt.title('Error Bars Example') 430 | plt.show() 431 | ``` 432 | 433 | --- 434 | 435 | ## **Conclusion** 🎉 436 | 437 | With this guide, you’re now equipped with a **comprehensive understanding** of the core plot types in Matplotlib. Whether you're working on simple trend analysis, complex statistical visualizations, or just exploring data, these plots will help you convey your insights clearly and effectively. 438 | 439 | -------------------------------------------------------------------------------- /Scripts/LINEPLOT.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Understanding the Basics of Matplotlib" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "### What is Matplotlib?\n", 15 | "\n", 16 | "Matplotlib is a very powerful library for creating visualizations in Python. Imagine it like this—whenever we want to create graphs or plots for any data, Matplotlib is like our 'graph paper and pen.' It helps us visualize data in the form of graphs, charts, etc. For now, we will focus on line plots." 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "### Importing Matplotlib\n", 24 | "\n", 25 | "Before we do anything, we must import Matplotlib. This is like saying, \"Matplotlib, I need your help, please come!\" So we use the following line:" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 1, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "import matplotlib.pyplot as plt" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "- `import`: This is how we bring the library into our code.\n", 42 | "\n", 43 | "- `matplotlib.pyplot`: This is the specific part of `Matplotlib` that deals with plotting. We are only importing the `pyplot` module, which is where all the plotting functions live.\n", 44 | "as plt: We are giving `matplotlib.pyplot` a nickname (plt). This saves us from writing `matplotlib.pyplot` again and again. Instead, we just use plt." 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": {}, 50 | "source": [ 51 | "### Introduction to Matplotlib Line Plots\n", 52 | "\n", 53 | "🌼 Welcome to our exciting journey into the world of data visualization with Matplotlib! Today, we will dive into the colorful realm of line plots—a fundamental yet powerful tool for representing data. Whether you’re a student, a data enthusiast, or someone simply curious about visualizing trends, you’re in the right place!" 54 | ] 55 | }, 56 | { 57 | "cell_type": "markdown", 58 | "metadata": {}, 59 | "source": [ 60 | "### Why Line Plots?\n", 61 | "Before we jump into coding, let's take a moment to appreciate why line plots are so important. Think of a line plot as a bridge connecting data points. Just as a bridge helps you cross from one side to the other, a line plot helps you visualize relationships and trends over time or across categories. It’s particularly useful for:\n", 62 | "\n", 63 | "- Displaying trends: You can easily see how data changes over time.\n", 64 | "- Comparing data sets: With multiple lines, you can compare different categories or groups side by side.\n", 65 | "- Identifying patterns: Line plots help in spotting trends, peaks, and valleys in data." 66 | ] 67 | }, 68 | { 69 | "cell_type": "markdown", 70 | "metadata": {}, 71 | "source": [ 72 | "### Learning Approach\n", 73 | "We will start from the absolute basics—don’t worry if you’ve never coded before! I will guide you through every step, explaining even the tiniest details. We will progress gradually, from creating our very first line plot to exploring advanced techniques that will make your visualizations shine!" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "metadata": {}, 79 | "source": [ 80 | "### The Plan\n", 81 | "1. Setting Up Your Environment: We’ll ensure you have everything ready to start coding.\n", 82 | "2. Creating Your First Line Plot: We’ll write our first few lines of code and see a simple line plot come to life!\n", 83 | "3. Customizing Line Plots: Learn how to change colors, styles, and add markers to make your plot unique.\n", 84 | "4. Adding Titles and Labels: A great plot is not just pretty; it tells a story! We will add meaningful titles and labels.\n", 85 | "5. Multiple Lines: Discover how to compare multiple datasets in a single plot.\n", 86 | "6. Advanced Customizations: We’ll explore techniques like adding grids, annotations, and legends.\n", 87 | "7. Interactivity: If time permits, we might delve into creating interactive plots." 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "### Getting Started\n", 95 | "So, let’s roll up our sleeves and jump into this exciting adventure! First, make sure you have Python and Matplotlib installed. If you haven’t installed Matplotlib yet, don’t worry! Just run the following command in your terminal or command prompt:" 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "`pip install matplotlib`" 103 | ] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "metadata": {}, 108 | "source": [ 109 | "### Are you all set? Let’s begin our journey into the world of line plots! 🌟" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": {}, 115 | "source": [ 116 | "### Creating Your First Line Plot\n", 117 | "Now, let’s create our very first line plot. Open your Python environment (like Jupyter Notebook or any IDE you prefer), and follow along with the code below:" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 1, 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "data": { 127 | "image/png": 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", 128 | "text/plain": [ 129 | "
" 130 | ] 131 | }, 132 | "metadata": {}, 133 | "output_type": "display_data" 134 | } 135 | ], 136 | "source": [ 137 | "import matplotlib.pyplot as plt\n", 138 | "x = [1, 2, 3, 4, 5]\n", 139 | "y = [2, 3, 5, 7, 11]\n", 140 | "\n", 141 | "# Creating the line plot\n", 142 | "plt.plot(x, y)\n", 143 | "plt.show()" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "- **Importing Matplotlib**: We start by importing the `pyplot` module from Matplotlib, which provides a simple interface for creating plots.\n", 151 | "- **Sample Data**: Here, we define two lists: `x` and `y`. These will be our data points for the plot.\n", 152 | "- **Creating the Plot**: The `plt.plot(x, y)` function creates the line plot using our data.\n", 153 | "- **Displaying the Plot**: Finally, `plt.show()` displays the plot.\n", 154 | "\n", 155 | "### Next ....\n", 156 | "\n", 157 | "Run the above code, and you should see your first line plot! Take a moment to appreciate your achievement. **Isn’t it amazing how a few lines of code can visualize data?** \n", 158 | "\n", 159 | "Now, let’s move on to customizing our plot to make it even more informative and visually appealing. Are you ready? Let's go! 🌈" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | " Now that we have created our first line plot, let’s customize it to make it more informative and visually appealing. This time, we will explore how to add titles, labels, and customize the appearance of our plot.\n", 167 | "\n", 168 | "### Customizing Your Line Plot\n", 169 | "\n", 170 | "We will build upon our previous example and add the following enhancements:\n", 171 | "\n", 172 | "1. **Title**: Every great plot needs a title that summarizes what it represents.\n", 173 | "2. **Axis Labels**: We will label the x-axis and y-axis to provide context.\n", 174 | "3. **Line Style and Color**: We can change the style and color of the line to make it stand out." 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": 2, 180 | "metadata": {}, 181 | "outputs": [ 182 | { 183 | "data": { 184 | "image/png": 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", 185 | "text/plain": [ 186 | "
" 187 | ] 188 | }, 189 | "metadata": {}, 190 | "output_type": "display_data" 191 | } 192 | ], 193 | "source": [ 194 | "import matplotlib.pyplot as plt\n", 195 | "\n", 196 | "# Sample data\n", 197 | "x = [1, 2, 3, 4, 5]\n", 198 | "y = [2, 3, 5, 7, 11]\n", 199 | "\n", 200 | "# Creating the line plot with customizations\n", 201 | "plt.plot(x, y, color='blue', linestyle='--', marker='o', markersize=8, label='Prime Numbers')\n", 202 | "\n", 203 | "# Adding title and labels\n", 204 | "plt.title('Line Plot of Prime Numbers')\n", 205 | "plt.xlabel('Index')\n", 206 | "plt.ylabel('Prime Value')\n", 207 | "\n", 208 | "# Adding a legend\n", 209 | "plt.legend()\n", 210 | "\n", 211 | "# Display the plot\n", 212 | "plt.show()" 213 | ] 214 | }, 215 | { 216 | "cell_type": "markdown", 217 | "metadata": {}, 218 | "source": [ 219 | " Let's break down the process of adding the title, axis labels, and other customizations step by step. This way, you can understand each component clearly." 220 | ] 221 | }, 222 | { 223 | "cell_type": "markdown", 224 | "metadata": {}, 225 | "source": [ 226 | "> 1. Creating the Basic Line Plot\n", 227 | "First, we’ll create the basic line plot without any customizations. Here’s the starting point:" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 3, 233 | "metadata": {}, 234 | "outputs": [ 235 | { 236 | "data": { 237 | "image/png": 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", 238 | "text/plain": [ 239 | "
" 240 | ] 241 | }, 242 | "metadata": {}, 243 | "output_type": "display_data" 244 | } 245 | ], 246 | "source": [ 247 | "import matplotlib.pyplot as plt\n", 248 | "\n", 249 | "# Sample data\n", 250 | "x = [1, 2, 3, 4, 5]\n", 251 | "y = [2, 3, 5, 7, 11]\n", 252 | "\n", 253 | "# Creating the basic line plot\n", 254 | "plt.plot(x, y)\n", 255 | "\n", 256 | "# Display the plot\n", 257 | "plt.show()" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": {}, 263 | "source": [ 264 | "> 2: Adding a Title\n", 265 | "Now, let’s add a title to our plot to give it context. A title tells viewers what the plot is about." 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 4, 271 | "metadata": {}, 272 | "outputs": [ 273 | { 274 | "data": { 275 | "image/png": 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", 276 | "text/plain": [ 277 | "
" 278 | ] 279 | }, 280 | "metadata": {}, 281 | "output_type": "display_data" 282 | } 283 | ], 284 | "source": [ 285 | "import matplotlib.pyplot as plt\n", 286 | "\n", 287 | "# Sample data\n", 288 | "x = [1, 2, 3, 4, 5]\n", 289 | "y = [2, 3, 5, 7, 11]\n", 290 | "\n", 291 | "# Creating the line plot\n", 292 | "plt.plot(x, y)\n", 293 | "\n", 294 | "# Adding a title\n", 295 | "plt.title('Line Plot of Prime Numbers')\n", 296 | "\n", 297 | "# Display the plot\n", 298 | "plt.show()" 299 | ] 300 | }, 301 | { 302 | "cell_type": "markdown", 303 | "metadata": {}, 304 | "source": [ 305 | "> Title: `plt.title('Line Plot of Prime Numbers')`\n", 306 | "\n", 307 | " adds a title to the plot. You can change the text to reflect what your data represents." 308 | ] 309 | }, 310 | { 311 | "cell_type": "markdown", 312 | "metadata": {}, 313 | "source": [ 314 | "> Adding Axis Labels\n", 315 | "Next, we’ll label the x-axis and y-axis. This helps viewers understand what each axis represents." 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": 5, 321 | "metadata": {}, 322 | "outputs": [ 323 | { 324 | "data": { 325 | "image/png": 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", 326 | "text/plain": [ 327 | "
" 328 | ] 329 | }, 330 | "metadata": {}, 331 | "output_type": "display_data" 332 | } 333 | ], 334 | "source": [ 335 | "import matplotlib.pyplot as plt\n", 336 | "\n", 337 | "# Sample data\n", 338 | "x = [1, 2, 3, 4, 5]\n", 339 | "y = [2, 3, 5, 7, 11]\n", 340 | "\n", 341 | "# Creating the line plot\n", 342 | "plt.plot(x, y)\n", 343 | "\n", 344 | "# Adding a title\n", 345 | "plt.title('Line Plot of Prime Numbers')\n", 346 | "\n", 347 | "# Adding x-axis label\n", 348 | "plt.xlabel('Index')\n", 349 | "\n", 350 | "# Adding y-axis label\n", 351 | "plt.ylabel('Prime Value')\n", 352 | "\n", 353 | "# Display the plot\n", 354 | "plt.show()" 355 | ] 356 | }, 357 | { 358 | "cell_type": "markdown", 359 | "metadata": {}, 360 | "source": [ 361 | "- X-axis Label: `plt.xlabel('Index')` labels the x-axis as \"Index.\"\n", 362 | "- Y-axis Label: `plt.ylabel('Prime Value')` labels the y-axis as \"Prime Value.\"" 363 | ] 364 | }, 365 | { 366 | "cell_type": "markdown", 367 | "metadata": {}, 368 | "source": [ 369 | "#### Customizing the Line Style and Color\n", 370 | "Now, let’s customize the appearance of our line to make it more visually appealing." 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": 6, 376 | "metadata": {}, 377 | "outputs": [ 378 | { 379 | "data": { 380 | "image/png": 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", 381 | "text/plain": [ 382 | "
" 383 | ] 384 | }, 385 | "metadata": {}, 386 | "output_type": "display_data" 387 | } 388 | ], 389 | "source": [ 390 | "import matplotlib.pyplot as plt\n", 391 | "\n", 392 | "# Sample data\n", 393 | "x = [1, 2, 3, 4, 5]\n", 394 | "y = [2, 3, 5, 7, 11]\n", 395 | "\n", 396 | "# Creating the line plot with customizations\n", 397 | "plt.plot(x, y, color='blue', linestyle='--', marker='o', markersize=8)\n", 398 | "\n", 399 | "# Adding a title\n", 400 | "plt.title('Line Plot of Prime Numbers')\n", 401 | "\n", 402 | "# Adding x-axis label\n", 403 | "plt.xlabel('Index')\n", 404 | "\n", 405 | "# Adding y-axis label\n", 406 | "plt.ylabel('Prime Value')\n", 407 | "\n", 408 | "# Display the plot\n", 409 | "plt.show()" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": {}, 415 | "source": [ 416 | "- Line Color: color='blue' sets the color of the line to blue.\n", 417 | "- Line Style: linestyle='--' changes the line style to dashed.\n", 418 | "- Markers:\n", 419 | "- marker='o' adds circle markers at each data point.\n", 420 | "- markersize=8 sets the size of the markers." 421 | ] 422 | }, 423 | { 424 | "cell_type": "markdown", 425 | "metadata": {}, 426 | "source": [ 427 | "### Adding a Legend\n", 428 | "Finally, we can add a legend to identify what the line represents, especially useful if we add multiple lines later." 429 | ] 430 | }, 431 | { 432 | "cell_type": "code", 433 | "execution_count": 7, 434 | "metadata": {}, 435 | "outputs": [ 436 | { 437 | "data": { 438 | "image/png": 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", 439 | "text/plain": [ 440 | "
" 441 | ] 442 | }, 443 | "metadata": {}, 444 | "output_type": "display_data" 445 | } 446 | ], 447 | "source": [ 448 | "import matplotlib.pyplot as plt\n", 449 | "\n", 450 | "# Sample data\n", 451 | "x = [1, 2, 3, 4, 5]\n", 452 | "y = [2, 3, 5, 7, 11]\n", 453 | "\n", 454 | "# Creating the line plot with customizations\n", 455 | "plt.plot(x, y, color='blue', linestyle='--', marker='o', markersize=8, label='Prime Numbers')\n", 456 | "\n", 457 | "# Adding a title\n", 458 | "plt.title('Line Plot of Prime Numbers')\n", 459 | "\n", 460 | "# Adding x-axis label\n", 461 | "plt.xlabel('Index')\n", 462 | "\n", 463 | "# Adding y-axis label\n", 464 | "plt.ylabel('Prime Value')\n", 465 | "\n", 466 | "# Adding a legend\n", 467 | "plt.legend()\n", 468 | "\n", 469 | "# Display the plot\n", 470 | "plt.show()" 471 | ] 472 | }, 473 | { 474 | "cell_type": "markdown", 475 | "metadata": {}, 476 | "source": [ 477 | "- Legend: label='Prime Numbers' adds a label to the line.\n", 478 | "- Displaying the Legend: plt.legend() displays the legend on the plot." 479 | ] 480 | }, 481 | { 482 | "cell_type": "code", 483 | "execution_count": null, 484 | "metadata": {}, 485 | "outputs": [], 486 | "source": [ 487 | "here i will go " 488 | ] 489 | } 490 | ], 491 | "metadata": { 492 | "kernelspec": { 493 | "display_name": "Python 3", 494 | "language": "python", 495 | "name": "python3" 496 | }, 497 | "language_info": { 498 | "codemirror_mode": { 499 | "name": "ipython", 500 | "version": 3 501 | }, 502 | "file_extension": ".py", 503 | "mimetype": "text/x-python", 504 | "name": "python", 505 | "nbconvert_exporter": "python", 506 | "pygments_lexer": "ipython3", 507 | "version": "3.12.5" 508 | } 509 | }, 510 | "nbformat": 4, 511 | "nbformat_minor": 2 512 | } 513 | --------------------------------------------------------------------------------