![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_sfWZU_eST2O"
+ },
+ "source": [
+ "**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n",
+ "\n",
+ "# Exercises\n",
+ "\n",
+ "Follow the instructions to recreate the plots using this data:\n",
+ "\n",
+ "## Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "l0eZ10dLST2X"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "x = np.arange(0,100)\n",
+ "y = x*2\n",
+ "z = x**2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "veY4-lsGST2c"
+ },
+ "source": [
+ "**Import matplotlib.pyplot as plt**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "tAOmEcNKST2e"
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5j_39gsVST2f"
+ },
+ "source": [
+ "## Exercise 1\n",
+ "\n",
+ "**Follow along with these steps**\n",
+ "* Create a figure object called fig using plt.figure()\n",
+ "* Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax.\n",
+ "* Plot (x,y) on that axes and set the labels and titles to match the plot below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "03Ts4r5SST2g",
+ "outputId": "45b0291a-4b7a-4f4c-c1ee-9998e43f6a98",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 582
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HfJB9JnS9xlK"
+ },
+ "source": [
+ "# NumPy Exercises\n",
+ "\n",
+ "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "u9POEVg29xlM"
+ },
+ "source": [
+ "#### Import NumPy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yyS-PuO_9xlM"
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ehlpKUPc9xlM"
+ },
+ "source": [
+ "#### Create an array of 10 zeros"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aEQkK-Dw9xlN"
+ },
+ "outputs": [],
+ "source": [
+ "arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6QHp05Ei9xlN"
+ },
+ "source": [
+ "#### Create an array of 10 ones"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true,
+ "id": "ebe9xrC29xlN"
+ },
+ "outputs": [],
+ "source": [
+ "arr2=np.array([1,1,1,1,1,1,1,1,1,1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ziS-l3xp9xlO"
+ },
+ "source": [
+ "#### Create an array of 10 fives"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "fFC7bqLM9xlO",
+ "outputId": "119c96dc-0306-4ccf-fe06-9ffa456a0436"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr3=np.array([5,5,5,5,5,5,5,5,5,5])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kBugMXlC9xlO"
+ },
+ "source": [
+ "#### Create an array of the integers from 10 to 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JsO6qS9R9xlO"
+ },
+ "outputs": [],
+ "source": [
+ "arr4=np.arange(10,51)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dMw4V2L79xlO"
+ },
+ "source": [
+ "#### Create an array of all the even integers from 10 to 50"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4lK-5SQV9xlO",
+ "outputId": "2e053b90-8713-42fa-9739-ee16b9ccd102"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
+ " 44, 46, 48, 50])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr6=np.arange(10,51,2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "g__F0rB39xlP"
+ },
+ "source": [
+ "#### Create a 3x3 matrix with values ranging from 0 to 8"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MmVXCn0K9xlP"
+ },
+ "outputs": [],
+ "source": [
+ "arr7=np.array([[0,1,2],[3,4,5],[6,7,8]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4YfT0aLo9xlP"
+ },
+ "source": [
+ "#### Create a 3x3 identity matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UHa42UpQ9xlP",
+ "outputId": "ac24486e-3249-41f3-e6ca-49b88c6a2125"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1., 0., 0.],\n",
+ " [0., 1., 0.],\n",
+ " [0., 0., 1.]])"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr8=np.eye(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bfwDbjhI9xlP"
+ },
+ "source": [
+ "#### Use NumPy to generate a random number between 0 and 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Z0OroZxW9xlP"
+ },
+ "outputs": [],
+ "source": [
+ "arr9=np.random.rand(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rLW0Jjzp9xlP"
+ },
+ "source": [
+ "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "szluy14n9xlP",
+ "outputId": "176f3078-97b7-4a7d-e885-5a1224b13fec"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.11617859, -0.13631661, 0.7150532 , -0.27734985, 0.06538679,\n",
+ " -1.16994467, -1.45669789, 1.56838663, 1.13525628, -0.69958045,\n",
+ " 0.06961791, -0.07441215, 0.99718638, 0.28416225, -0.14591454,\n",
+ " 0.02658355, -2.05644532, 0.66783981, 2.57474944, 1.3365862 ,\n",
+ " -0.35696454, -1.57424745, 0.76840978, -0.79557035, 0.91504545])"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr10=np.random.rand(25)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_GhI8LYn9xlP"
+ },
+ "source": [
+ "#### Create the following matrix:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wS1ZBddV9xlP",
+ "outputId": "92588415-77b9-49c7-cfdd-9b67602a3386"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
+ " [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
+ " [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
+ " [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
+ " [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
+ " [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
+ " [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
+ " [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
+ " [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
+ " [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr11=np.arange(0.01,1.01,0.01)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3OqA-QtL9xlQ"
+ },
+ "source": [
+ "#### Create an array of 20 linearly spaced points between 0 and 1:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "FNXTugQ29xlQ",
+ "outputId": "674dbbff-9a87-4463-933f-07b2bbd15ce2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
+ " 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
+ " 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
+ " 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "elx0EaxE9xlQ"
+ },
+ "source": [
+ "## Numpy Indexing and Selection\n",
+ "\n",
+ "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2Tbm9kVf9xlQ"
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ft8P8e249xlQ",
+ "outputId": "1112818b-f5ce-4e4a-e25d-b0ef19de4054",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 1, 2, 3, 4, 5],\n",
+ " [ 6, 7, 8, 9, 10],\n",
+ " [11, 12, 13, 14, 15],\n",
+ " [16, 17, 18, 19, 20],\n",
+ " [21, 22, 23, 24, 25]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ],
+ "source": [
+ "arr11=np.arange(1,26).reshape(5,5)\n",
+ "arr11[0:, 0:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WrcFkpGL9xlQ",
+ "outputId": "9c1836e0-2c62-4367-dd37-95b7fc758318",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[12, 13, 14, 15],\n",
+ " [17, 18, 19, 20],\n",
+ " [22, 23, 24, 25]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 12
+ }
+ ],
+ "source": [
+ "arr11 = np.arange(1,26).reshape(5,5)\n",
+ "arr11[2:, 1:]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rnUSntEa9xlQ",
+ "outputId": "8d609eca-1571-45eb-8e6c-b4da64181169",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "20"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 15
+ }
+ ],
+ "source": [
+ "20"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3DMBC_wp9xlQ",
+ "outputId": "a0208df9-d118-435c-ea9d-01249ebbeb99",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 2],\n",
+ " [ 7],\n",
+ " [12],\n",
+ " [17]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 17
+ }
+ ],
+ "source": [
+ "arr11=np.arange(1,26).reshape(5,5)\n",
+ "arr11[:4, 1:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "pGjD4HUK9xlQ",
+ "outputId": "202cdaec-43e4-4c6c-f68f-4a71ed1cd448",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[21, 22, 23, 24, 25]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 18
+ }
+ ],
+ "source": [
+ "arr11=np.arange(1,26).reshape(5,5)\n",
+ "arr11[4:, 0:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dqsdpxUo9xlR",
+ "outputId": "e0346ff5-2b64-4b8b-b60a-1f27a3bacfc3",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[16, 17, 18, 19, 20],\n",
+ " [21, 22, 23, 24, 25]])"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ],
+ "source": [
+ "arr11=np.arange(1,26).reshape(5,5)\n",
+ "arr11[3:, 0:]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true,
+ "id": "6vtHuVJU9xlf"
+ },
+ "source": [
+ "# Great Job!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.9.12"
+ },
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment 1/Assignment1_Rayeshwar/Rayeshwar_Assignment1_Pandas.ipynb b/Assignment 1/Assignment1_Rayeshwar/Rayeshwar_Assignment1_Pandas.ipynb
new file mode 100644
index 00000000..b1f6ed9f
--- /dev/null
+++ b/Assignment 1/Assignment1_Rayeshwar/Rayeshwar_Assignment1_Pandas.ipynb
@@ -0,0 +1,2860 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## General Instructions for all Assignments"
+ ],
+ "metadata": {
+ "id": "AWDeauE3Bdzs"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "1. Fork the repository and make a copy of this notebook and rename it as your name_Assignment1_Pandas\n",
+ "2. Generate a pull request. The pull request message should also be yourname_Assignment1"
+ ],
+ "metadata": {
+ "id": "Xaf9uMvXBJEC"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "1. This code notebook is composed of cells. Each cell is either text or Python code.\n",
+ "2. To run a cell of code, click the \"play button\" icon to the left of the cell or click on the cell and press \"Shift+Enter\" on your keyboard. This will execute the Python code contained in the cell. Executing a cell that defines a variable is important before executing or authoring a cell that depends on that previously created variable assignment.\n"
+ ],
+ "metadata": {
+ "id": "4_6uxHVGB_XT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [],
+ "metadata": {
+ "id": "b68xNjkKCBIF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BiD4gl6_cAYs"
+ },
+ "outputs": [],
+ "source": [
+ "#import pandas library\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JUtm9KricAYu"
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv('/content/drive/MyDrive/DATASETS/Iris (1).csv') #enter path for the Iris csv in the brackets\n",
+ "#reads the csv file and stores it in a data frame(define in the pandas library)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qzUnSkfRcAYu"
+ },
+ "outputs": [],
+ "source": [
+ "dictionary = {'col':[4,2,3], 'col2':[4,7,6], 'col3':[7,10,9]}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "vKjWRiCtwXq1",
+ "outputId": "f6c0aea0-e7ab-449e-e230-8cf995d9debd"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ACswu4BXcAYu"
+ },
+ "outputs": [],
+ "source": [
+ "dictionarydf = pd.DataFrame.from_dict(dictionary)\n",
+ "#it creats a new datafram which stores the dictionary made above"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.head()\n",
+ "#shows top 5 rows (row indexing starts from 0)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "MvYNz8E2dp3K",
+ "outputId": "a1550e32-f99c-49b5-a3db-f1a30f915e43"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "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"
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| 4 | \n", + "5 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
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| 6 | \n", + "7 | \n", + "4.6 | \n", + "3.4 | \n", + "1.4 | \n", + "0.3 | \n", + "Iris-setosa | \n", + "
| 7 | \n", + "8 | \n", + "5.0 | \n", + "3.4 | \n", + "1.5 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
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| 9 | \n", + "10 | \n", + "4.9 | \n", + "3.1 | \n", + "1.5 | \n", + "0.1 | \n", + "Iris-setosa | \n", + "
| \n", + " | Id | \n", + "SepalLengthCm | \n", + "SepalWidthCm | \n", + "PetalLengthCm | \n", + "PetalWidthCm | \n", + "Species | \n", + "
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| 136 | \n", + "137 | \n", + "6.3 | \n", + "3.4 | \n", + "5.6 | \n", + "2.4 | \n", + "Iris-virginica | \n", + "
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| 139 | \n", + "140 | \n", + "6.9 | \n", + "3.1 | \n", + "5.4 | \n", + "2.1 | \n", + "Iris-virginica | \n", + "
| 140 | \n", + "141 | \n", + "6.7 | \n", + "3.1 | \n", + "5.6 | \n", + "2.4 | \n", + "Iris-virginica | \n", + "
| 141 | \n", + "142 | \n", + "6.9 | \n", + "3.1 | \n", + "5.1 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 142 | \n", + "143 | \n", + "5.8 | \n", + "2.7 | \n", + "5.1 | \n", + "1.9 | \n", + "Iris-virginica | \n", + "
| 143 | \n", + "144 | \n", + "6.8 | \n", + "3.2 | \n", + "5.9 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 144 | \n", + "145 | \n", + "6.7 | \n", + "3.3 | \n", + "5.7 | \n", + "2.5 | \n", + "Iris-virginica | \n", + "
| 145 | \n", + "146 | \n", + "6.7 | \n", + "3.0 | \n", + "5.2 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 146 | \n", + "147 | \n", + "6.3 | \n", + "2.5 | \n", + "5.0 | \n", + "1.9 | \n", + "Iris-virginica | \n", + "
| 147 | \n", + "148 | \n", + "6.5 | \n", + "3.0 | \n", + "5.2 | \n", + "2.0 | \n", + "Iris-virginica | \n", + "
| 148 | \n", + "149 | \n", + "6.2 | \n", + "3.4 | \n", + "5.4 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 149 | \n", + "150 | \n", + "5.9 | \n", + "3.0 | \n", + "5.1 | \n", + "1.8 | \n", + "Iris-virginica | \n", + "
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| \n", + " | Id | \n", + "SepalLengthCm | \n", + "SepalWidthCm | \n", + "PetalLengthCm | \n", + "PetalWidthCm | \n", + "Species | \n", + "
|---|---|---|---|---|---|---|
| 22 | \n", + "23 | \n", + "4.6 | \n", + "3.6 | \n", + "1.0 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 23 | \n", + "24 | \n", + "5.1 | \n", + "3.3 | \n", + "1.7 | \n", + "0.5 | \n", + "Iris-setosa | \n", + "
| 24 | \n", + "25 | \n", + "4.8 | \n", + "3.4 | \n", + "1.9 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 25 | \n", + "26 | \n", + "5.0 | \n", + "3.0 | \n", + "1.6 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 26 | \n", + "27 | \n", + "5.0 | \n", + "3.4 | \n", + "1.6 | \n", + "0.4 | \n", + "Iris-setosa | \n", + "
| 27 | \n", + "28 | \n", + "5.2 | \n", + "3.5 | \n", + "1.5 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 28 | \n", + "29 | \n", + "5.2 | \n", + "3.4 | \n", + "1.4 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 29 | \n", + "30 | \n", + "4.7 | \n", + "3.2 | \n", + "1.6 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 30 | \n", + "31 | \n", + "4.8 | \n", + "3.1 | \n", + "1.6 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 31 | \n", + "32 | \n", + "5.4 | \n", + "3.4 | \n", + "1.5 | \n", + "0.4 | \n", + "Iris-setosa | \n", + "
| 32 | \n", + "33 | \n", + "5.2 | \n", + "4.1 | \n", + "1.5 | \n", + "0.1 | \n", + "Iris-setosa | \n", + "
| \n", + " | Id | \n", + "SepalLengthCm | \n", + "SepalWidthCm | \n", + "PetalLengthCm | \n", + "PetalWidthCm | \n", + "Species | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "1 | \n", + "5.1 | \n", + "3.5 | \n", + "1.4 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 1 | \n", + "2 | \n", + "4.9 | \n", + "3.0 | \n", + "1.4 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 2 | \n", + "3 | \n", + "4.7 | \n", + "3.2 | \n", + "1.3 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 3 | \n", + "4 | \n", + "4.6 | \n", + "3.1 | \n", + "1.5 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| 4 | \n", + "5 | \n", + "5.0 | \n", + "3.6 | \n", + "1.4 | \n", + "0.2 | \n", + "Iris-setosa | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 145 | \n", + "146 | \n", + "6.7 | \n", + "3.0 | \n", + "5.2 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 146 | \n", + "147 | \n", + "6.3 | \n", + "2.5 | \n", + "5.0 | \n", + "1.9 | \n", + "Iris-virginica | \n", + "
| 147 | \n", + "148 | \n", + "6.5 | \n", + "3.0 | \n", + "5.2 | \n", + "2.0 | \n", + "Iris-virginica | \n", + "
| 148 | \n", + "149 | \n", + "6.2 | \n", + "3.4 | \n", + "5.4 | \n", + "2.3 | \n", + "Iris-virginica | \n", + "
| 149 | \n", + "150 | \n", + "5.9 | \n", + "3.0 | \n", + "5.1 | \n", + "1.8 | \n", + "Iris-virginica | \n", + "
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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Pg-TwyBV-QHh",
+ "outputId": "6949ef2c-5fcb-4bc1-a8e5-1b7417c9f87e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: yfinance in /usr/local/lib/python3.12/dist-packages (0.2.66)\n",
+ "Requirement already satisfied: plotly in /usr/local/lib/python3.12/dist-packages (5.24.1)\n",
+ "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.2.2)\n",
+ "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.0.2)\n",
+ "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.32.4)\n",
+ "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.0.12)\n",
+ "Requirement already satisfied: platformdirs>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.5.1)\n",
+ "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2025.2)\n",
+ "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.12/dist-packages (from yfinance) (2.4.7)\n",
+ "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.12/dist-packages (from yfinance) (3.18.3)\n",
+ "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.12/dist-packages (from yfinance) (4.13.5)\n",
+ "Requirement already satisfied: curl_cffi>=0.7 in /usr/local/lib/python3.12/dist-packages (from yfinance) (0.13.0)\n",
+ "Requirement already satisfied: protobuf>=3.19.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (5.29.5)\n",
+ "Requirement already satisfied: websockets>=13.0 in /usr/local/lib/python3.12/dist-packages (from yfinance) (15.0.1)\n",
+ "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.12/dist-packages (from plotly) (9.1.2)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from plotly) (25.0)\n",
+ "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.8)\n",
+ "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (4.15.0)\n",
+ "Requirement already satisfied: cffi>=1.12.0 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2.0.0)\n",
+ "Requirement already satisfied: certifi>=2024.2.2 in /usr/local/lib/python3.12/dist-packages (from curl_cffi>=0.7->yfinance) (2025.11.12)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.3.0->yfinance) (2025.2)\n",
+ "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.4.4)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (3.11)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.31->yfinance) (2.5.0)\n",
+ "Requirement already satisfied: pycparser in /usr/local/lib/python3.12/dist-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.23)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install yfinance plotly"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "import yfinance as yf\n",
+ "import pandas as pd\n",
+ "\n",
+ "stock = yf.Ticker(\"AAPL\")\n",
+ "\n",
+ "data = stock.history(period=\"6mo\")\n",
+ "\n",
+ "data = data.reset_index()\n",
+ "\n",
+ "data['Date'] = pd.to_datetime(data['Date']).dt.tz_localize(None)\n",
+ "\n",
+ "print(data.head())"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "r35REdYN-zx1",
+ "outputId": "15ed78c0-92ce-408d-9c98-365beb77ded0"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Date Open High Low Close Volume \\\n",
+ "0 2025-06-11 203.072450 204.070349 197.993148 198.362366 60989900 \n",
+ "1 2025-06-12 198.661736 199.260466 196.945348 198.781479 43904600 \n",
+ "2 2025-06-13 199.310370 199.949024 195.288838 196.037262 51447300 \n",
+ "3 2025-06-16 196.885486 198.272565 196.147035 198.003128 43020700 \n",
+ "4 2025-06-17 196.785678 197.973180 194.799869 195.228958 38856200 \n",
+ "\n",
+ " Dividends Stock Splits \n",
+ "0 0.0 0.0 \n",
+ "1 0.0 0.0 \n",
+ "2 0.0 0.0 \n",
+ "3 0.0 0.0 \n",
+ "4 0.0 0.0 \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "fig = go.Figure(data=[go.Candlestick(\n",
+ " x=data['Date'],\n",
+ " open=data['Open'],\n",
+ " high=data['High'],\n",
+ " low=data['Low'],\n",
+ " close=data['Close']\n",
+ ")])\n",
+ "\n",
+ "fig.update_layout(\n",
+ " title='Apple Inc(AAPL) Stock Price - Last 6 Months Data',\n",
+ " yaxis_title='Stock Price(US Dollar)',\n",
+ " xaxis_title='Date',\n",
+ " xaxis_rangeslider_visible=False\n",
+ ")\n",
+ "\n",
+ "fig.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 542
+ },
+ "id": "N6I0ETQr_BvV",
+ "outputId": "3fed2a78-c022-49e4-e73d-6e8fa91861fb"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ "