diff --git a/002_Python_Matplotlib_Exercise_1.ipynb b/002_Python_Matplotlib_Exercise_1.ipynb index 702b96f..def5beb 100644 --- a/002_Python_Matplotlib_Exercise_1.ipynb +++ b/002_Python_Matplotlib_Exercise_1.ipynb @@ -30,8 +30,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:49:39.007971Z", - "start_time": "2021-06-15T05:49:37.834152Z" + "end_time": "2021-07-04T12:44:01.238492Z", + "start_time": "2021-07-04T12:44:00.049041Z" } }, "outputs": [], @@ -53,8 +53,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:45.108793Z", - "start_time": "2021-06-15T05:30:43.223070Z" + "end_time": "2021-07-04T12:30:48.486380Z", + "start_time": "2021-07-04T12:30:46.481499Z" } }, "outputs": [], @@ -112,7 +112,7 @@ "source": [ "The line plot graph should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -128,8 +128,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:45.671294Z", - "start_time": "2021-06-15T05:30:45.115633Z" + "end_time": "2021-07-04T12:30:49.327203Z", + "start_time": "2021-07-04T12:30:48.497612Z" } }, "outputs": [], @@ -154,7 +154,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The line bar chart should look like this:\n", "
\n", "\n", "
" @@ -165,7 +165,11 @@ "metadata": {}, "source": [ "# Real World Examples\n", - "Download data from my Github (**[gas_prices.csv](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/gas_prices.csv)** & **[fifa_data.csv](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/fifa_data.csv)**)" + "\n", + "Download datasets from my Github: \n", + "1. **[gas_prices.csv](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/gas_prices.csv)** \n", + "2. **[fifa_data.csv](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/fifa_data.csv)**\n", + "3. **[iris_data.csv](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/iris_data.csv)**" ] }, { @@ -180,8 +184,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:46.855851Z", - "start_time": "2021-06-15T05:30:45.682033Z" + "end_time": "2021-07-04T12:30:50.709523Z", + "start_time": "2021-07-04T12:30:49.336967Z" } }, "outputs": [], @@ -219,9 +223,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The line graph should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -237,8 +241,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:49:48.334080Z", - "start_time": "2021-06-15T05:49:47.671029Z" + "end_time": "2021-07-04T12:44:11.002158Z", + "start_time": "2021-07-04T12:44:10.222864Z" } }, "outputs": [ @@ -474,8 +478,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:49.232301Z", - "start_time": "2021-06-15T05:30:48.253795Z" + "end_time": "2021-07-04T12:30:53.151909Z", + "start_time": "2021-07-04T12:30:51.834036Z" } }, "outputs": [], @@ -501,9 +505,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The histogram should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -514,13 +518,20 @@ "### Pie Chart" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pie Chart #1" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:49.539913Z", - "start_time": "2021-06-15T05:30:49.240114Z" + "end_time": "2021-07-04T12:30:53.536672Z", + "start_time": "2021-07-04T12:30:53.161675Z" } }, "outputs": [], @@ -544,9 +555,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The piechart should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -554,7 +565,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Pie Chart #2" + "#### Pie Chart #2" ] }, { @@ -562,8 +573,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:50.148308Z", - "start_time": "2021-06-15T05:30:49.565307Z" + "end_time": "2021-07-04T12:30:54.220755Z", + "start_time": "2021-07-04T12:30:53.542534Z" } }, "outputs": [], @@ -594,9 +605,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The piechart should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, @@ -604,7 +615,116 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Box and Whiskers Chart" + "#### Pie Chart #3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:44:38.377634Z", + "start_time": "2021-07-04T12:44:38.353221Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IdSepalLengthCmSepalWidthCmPetalLengthCmPetalWidthCmSpecies
015.13.51.40.2Iris-setosa
124.93.01.40.2Iris-setosa
234.73.21.30.2Iris-setosa
344.63.11.50.2Iris-setosa
455.03.61.40.2Iris-setosa
\n", + "
" + ], + "text/plain": [ + " Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n", + "0 1 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 2 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 3 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5 5.0 3.6 1.4 0.2 Iris-setosa" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "data = pd.read_csv(\"iris_data.csv\")\n", + "data.head()" ] }, { @@ -612,8 +732,55 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-06-15T05:30:50.480340Z", - "start_time": "2021-06-15T05:30:50.156124Z" + "end_time": "2021-07-04T12:30:57.738331Z", + "start_time": "2021-07-04T12:30:54.329154Z" + } + }, + "outputs": [], + "source": [ + "SepalLength = data['SepalLengthCm'].value_counts()\n", + "\n", + "# Plot a pie chart\n", + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "SepalLength.plot(kind='pie', title='Sepal Length', figsize=(9,9))\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The piechart should look like this:\n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Box and Whiskers Chart\n", + "\n", + "A box and whisker plot(box plot) *displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum.*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Box plot #1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:30:58.134328Z", + "start_time": "2021-07-04T12:30:57.745170Z" } }, "outputs": [], @@ -647,9 +814,156 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The line plot graph should look like this:\n", + "The box plot should look like this:\n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Box plot #2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:44:46.874699Z", + "start_time": "2021-07-04T12:44:46.856146Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Name Salary Hours Grade\n", + "0 John 60000 41 50\n", + "1 Rad 64000 40 50\n", + "2 Var 60000 36 46\n", + "3 Mathew 289000 30 95\n", + "4 Alina 66000 35 50\n", + "5 Lee 50000 39 5\n", + "6 Rogers 60000 40 57\n" + ] + } + ], + "source": [ + "#cateating data\n", + "import pandas as pd\n", + "df = pd.DataFrame({'Name': ['John', 'Rad', 'Var', 'Mathew', 'Alina', 'Lee', 'Rogers'],\n", + " 'Salary':[60000,64000,60000,289000,66000,50000,60000],\n", + " 'Hours':[41,40,36,30,35,39,40],\n", + " 'Grade':[50,50,46,95,50,5,57]})\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:44:53.560730Z", + "start_time": "2021-07-04T12:44:53.537293Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25 35.5\n", + "0.50 39.0\n", + "0.75 40.0\n", + "Name: Hours, dtype: float64\n" + ] + } + ], + "source": [ + "# Quartiles of Hours\n", + "print(df['Hours'].quantile([0.25, 0.5, 0.75]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:30:58.755910Z", + "start_time": "2021-07-04T12:30:58.341361Z" + } + }, + "outputs": [], + "source": [ + "# Plot a box-whisker chart\n", + "import matplotlib.pyplot as plt\n", + "df['Hours'].plot(kind='box', title='Weekly Hours Distribution', figsize=(10,8))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The box plot should look like this:\n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:44:57.581721Z", + "start_time": "2021-07-04T12:44:57.561215Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.25 60000.0\n", + "0.50 60000.0\n", + "0.75 65000.0\n", + "Name: Salary, dtype: float64\n" + ] + } + ], + "source": [ + "# Quartiles of Salary\n", + "print(df['Salary'].quantile([0.25, 0.5, 0.75]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2021-07-04T12:30:59.252980Z", + "start_time": "2021-07-04T12:30:58.796927Z" + } + }, + "outputs": [], + "source": [ + "# Plot a box-whisker chart\n", + "df['Salary'].plot(kind='box', title='Salary Distribution', figsize=(10,8))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The box plot should look like this:\n", "
\n", - "\n", + "\n", "
" ] }, diff --git a/LICENSE b/LICENSE index 1642570..257d417 100644 --- a/LICENSE +++ b/LICENSE @@ -1,49 +1,21 @@ -The instructions and text in this tutorial (the "software") are licensed -under the zlib License. - - (C) 2019-2021 milaan9 - - This software is provided 'as-is', without any express or implied - warranty. In no event will the authors be held liable for any damages - arising from the use of this software. - - Permission is granted to anyone to use this software for any purpose, - including commercial applications, and to alter it and redistribute it - freely, subject to the following restrictions: - - 1. The origin of this software must not be misrepresented; you must not - claim that you wrote the original software. If you use this software - in a product, an acknowledgment in the product documentation would be - appreciated but is not required. - 2. Altered source versions must be plainly marked as such, and must not be - misrepresented as being the original software. - 3. This notice may not be removed or altered from any source distribution. - - - -The code examples (the "software") are unlicensed: - - This is free and unencumbered software released into the public domain. - - Anyone is free to copy, modify, publish, use, compile, sell, or - distribute this software, either in source code form or as a compiled - binary, for any purpose, commercial or non-commercial, and by any - means. - - In jurisdictions that recognize copyright laws, the author or authors - of this software dedicate any and all copyright interest in the - software to the public domain. We make this dedication for the benefit - of the public at large and to the detriment of our heirs and - successors. We intend this dedication to be an overt act of - relinquishment in perpetuity of all present and future rights to this - software under copyright law. - - THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, - EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF - MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. - IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR - OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, - ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR - OTHER DEALINGS IN THE SOFTWARE. - - For more information, please refer to +MIT License + +Copyright (c) 2021 milaan9 + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Matplotlib Cheat Sheet Plotting in Python.pdf b/Matplotlib Cheat Sheet Plotting in Python.pdf new file mode 100644 index 0000000..79283cf Binary files /dev/null and b/Matplotlib Cheat Sheet Plotting in Python.pdf differ diff --git a/README.md b/README.md index bbd3b5a..0074d26 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,25 @@ +

+Last Commit + + + +Stars Badge +Forks Badge +Size +Pull Requests Badge +Issues Badge +Language +MIT License +

+ + + +

+binder +colab +

+ # 11_Python_Matplotlib_Module ## Introduction ๐Ÿ‘‹ @@ -11,57 +33,64 @@ Matploitlib is a Python Library used for plotting, this python library provides **Is Matplotlib Included in Python?** Matplotlib is not a part of the Standard Libraries which is installed by default when Python, there are several toolkits which are available that extend python matplotlib functionality. Some of them are separate downloads, others can be shipped with the matplotlib source code but have external dependencies. +--- ## Table of contents ๐Ÿ“‹ +| **No.** | **Name** | +| ------- | -------- | +| 01 | **[Python_Matplotlib_pyplot](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/001_Python_Matplotlib_pyplot.ipynb)** | +| 02 | **[Python_Matplotlib_Exercise_1](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/002_Python_Matplotlib_Exercise_1.ipynb)** | +| 03 | **[Python_Matplotlib_Exercise_2](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/003_Python_Matplotlib_Exercise_2.ipynb)** | +| | **[gas_prices](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/gas_prices.csv)** | +| | **[fifa_data](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/fifa_data.csv)** | +| | **[company_sales_data](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/company_sales_data.csv)** | +| | **[iris_data](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/iris_data.csv)** | +| 04 | **[Matplotlib Cheat Sheet Plotting in Python.pdf](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/Matplotlib%20Cheat%20Sheet%20Plotting%20in%20Python.pdf)** | -[001_Python_Matplotlib_pyplot](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/001_Python_Matplotlib_pyplot.ipynb) - - -[002_Python_Matplotlib_Exercise_1](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/002_Python_Matplotlib_Exercise_1.ipynb) - - -[003_Python_Matplotlib_Exercise_2](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/003_Python_Matplotlib_Exercise_2.ipynb) +These are online **read-only** versions. However you can **`Run โ–ถ`** all the codes **online** by clicking here โžž binder -[gas_prices](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/gas_prices.csv) +--- +## Install Matplotlib Module: -[fifa_data](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/fifa_data.csv) +Open your [![Anaconda](https://img.shields.io/badge/Anaconda-342B029.svg?&style=flate&logo=anaconda&logoColor=white)](https://www.anaconda.com/products/individual) Prompt propmt and type and run the following command (individually): + - pip install matplotlib + -[company_sales_data](https://github.com/milaan9/11_Python_Matplotlib_Module/blob/main/company_sales_data.csv) - - -These are online read-only versions. +Once Installed now we can import it inside our python code. +--- ## Frequently asked questions โ” ### How can I thank you for writing and sharing this tutorial? ๐ŸŒท -You can โญ star this tutorial. Starring is free for you, but it tells me and other people that it was helpful and you like this tutorial. +You can Star Badge and Fork Badge Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial. -Go [here](https://github.com/milaan9/11_Python_Matplotlib_Module) if you aren't here already and click the "โญ Star" button in the top right corner. You will be asked to create a GitHub account if you don't already have one. +Go [**`here`**](https://github.com/milaan9/11_Python_Matplotlib_Module) if you aren't here already and click โžž **`โœฐ Star`** and **`โต– Fork`** button in the top right corner. You'll be asked to create a GitHub account if you don't already have one. -### How can I read this tutorial without an Internet connection? ๐Ÿค” +--- -1. Go [here](https://github.com/milaan9/11_Python_Matplotlib_Module) if you aren't here already. - -2. Click the big green "Clone or download" button in the top right of the page, then click "[Download ZIP](https://github.com/milaan9/11_Python_Matplotlib_Module/archive/refs/heads/main.zip)". +### How can I read this tutorial without an Internet connection? GIF + +1. Go [**`here`**](https://github.com/milaan9/11_Python_Matplotlib_Module) and click the big green โžž **`Code`** button in the top right of the page, then click โžž [**`Download ZIP`**](https://github.com/milaan9/11_Python_Matplotlib_Module/archive/refs/heads/main.zip). ![Download ZIP](img/dnld_rep.png) -3. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run. +2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run. -4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them +3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them - `Cell > All Output > Clear` + **`Kernel > Restart & Clear Output`** This will clear all the outputs and now you can understand each statement and learn interactively. If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again. +--- ## Authors โœ๏ธ @@ -73,6 +102,7 @@ If you have trouble with this tutorial please tell me about it by [Create an iss If you like this tutorial, please [give it a โญ star](https://github.com/milaan9/11_Python_Matplotlib_Module). +--- ## Licence ๐Ÿ“œ diff --git a/img/ex1_10.png b/img/ex1_10.png new file mode 100644 index 0000000..4dde116 Binary files /dev/null and b/img/ex1_10.png differ diff --git a/img/ex1_7.png b/img/ex1_7.png index 5528517..4a93f8a 100644 Binary files a/img/ex1_7.png and b/img/ex1_7.png differ diff --git a/img/ex1_8.png b/img/ex1_8.png new file mode 100644 index 0000000..5528517 Binary files /dev/null and b/img/ex1_8.png differ diff --git a/img/ex1_9.png b/img/ex1_9.png new file mode 100644 index 0000000..df84d63 Binary files /dev/null and b/img/ex1_9.png differ diff --git a/iris_data.csv b/iris_data.csv new file mode 100644 index 0000000..1bf42f2 --- /dev/null +++ b/iris_data.csv @@ -0,0 +1,151 @@ +Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species +1,5.1,3.5,1.4,0.2,Iris-setosa +2,4.9,3.0,1.4,0.2,Iris-setosa +3,4.7,3.2,1.3,0.2,Iris-setosa +4,4.6,3.1,1.5,0.2,Iris-setosa +5,5.0,3.6,1.4,0.2,Iris-setosa +6,5.4,3.9,1.7,0.4,Iris-setosa +7,4.6,3.4,1.4,0.3,Iris-setosa +8,5.0,3.4,1.5,0.2,Iris-setosa +9,4.4,2.9,1.4,0.2,Iris-setosa +10,4.9,3.1,1.5,0.1,Iris-setosa +11,5.4,3.7,1.5,0.2,Iris-setosa +12,4.8,3.4,1.6,0.2,Iris-setosa +13,4.8,3.0,1.4,0.1,Iris-setosa +14,4.3,3.0,1.1,0.1,Iris-setosa +15,5.8,4.0,1.2,0.2,Iris-setosa +16,5.7,4.4,1.5,0.4,Iris-setosa +17,5.4,3.9,1.3,0.4,Iris-setosa +18,5.1,3.5,1.4,0.3,Iris-setosa +19,5.7,3.8,1.7,0.3,Iris-setosa +20,5.1,3.8,1.5,0.3,Iris-setosa +21,5.4,3.4,1.7,0.2,Iris-setosa +22,5.1,3.7,1.5,0.4,Iris-setosa +23,4.6,3.6,1.0,0.2,Iris-setosa +24,5.1,3.3,1.7,0.5,Iris-setosa +25,4.8,3.4,1.9,0.2,Iris-setosa 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