diff --git a/ArnabG99.ipynb b/ArnabG99.ipynb
index 9e2543a..51e5dfe 100644
--- a/ArnabG99.ipynb
+++ b/ArnabG99.ipynb
@@ -1,32 +1,42 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "ArnabG99.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "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": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dQmdW6EN-S2E",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Basic_Pandas.ipynb b/Basic_Pandas.ipynb
new file mode 100644
index 0000000..83112c6
--- /dev/null
+++ b/Basic_Pandas.ipynb
@@ -0,0 +1,1047 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Basic Pandas.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "[View in Colaboratory](hhttps://colab.research.google.com/github/ArnabG99/Assignment-3/blob/ArnabG99/ArnabG99.ipynb)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cGbE814_Xaf9",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Pandas\n",
+ "\n",
+ "Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.\n",
+ "\n",
+ "\n",
+ "## Import pandas and numpy"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "irlVYeeAXPDL",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "BI2J-zdMbGwE",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### This is your playground feel free to explore other functions on pandas\n",
+ "\n",
+ "#### Create Series from numpy array, list and dict\n",
+ "\n",
+ "Don't know what a series is?\n",
+ "\n",
+ "[Series Doc](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.html)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "GeEct691YGE3",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 143
+ },
+ "outputId": "370a6f61-ad3a-4b1c-ab1f-327c4a768cce"
+ },
+ "cell_type": "code",
+ "source": [
+ "a_ascii = ord('A')\n",
+ "z_ascii = ord('Z')\n",
+ "alphabets = [chr(i) for i in range(a_ascii, z_ascii+1)]\n",
+ "\n",
+ "print(alphabets)\n",
+ "\n",
+ "numbers = np.arange(26)\n",
+ "\n",
+ "print(numbers)\n",
+ "\n",
+ "print(type(alphabets), type(numbers))\n",
+ "\n",
+ "alpha_numbers = dict(zip(alphabets, numbers))\n",
+ "\n",
+ "print(alpha_numbers)\n",
+ "\n",
+ "print(type(alpha_numbers))"
+ ],
+ "execution_count": 34,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n",
+ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+ " 24 25]\n",
+ " \n",
+ "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19, 'U': 20, 'V': 21, 'W': 22, 'X': 23, 'Y': 24, 'Z': 25}\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6ouDfjWab_Mc",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 496
+ },
+ "outputId": "3245028d-c7b3-4e79-95fe-dd1aab01d3b7"
+ },
+ "cell_type": "code",
+ "source": [
+ "series1 = pd.Series(alphabets)\n",
+ "print(series1)"
+ ],
+ "execution_count": 33,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0 A\n",
+ "1 B\n",
+ "2 C\n",
+ "3 D\n",
+ "4 E\n",
+ "5 F\n",
+ "6 G\n",
+ "7 H\n",
+ "8 I\n",
+ "9 J\n",
+ "10 K\n",
+ "11 L\n",
+ "12 M\n",
+ "13 N\n",
+ "14 O\n",
+ "15 P\n",
+ "16 Q\n",
+ "17 R\n",
+ "18 S\n",
+ "19 T\n",
+ "20 U\n",
+ "21 V\n",
+ "22 W\n",
+ "23 X\n",
+ "24 Y\n",
+ "25 Z\n",
+ "dtype: object\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "At7nY7vVcBZ3",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 496
+ },
+ "outputId": "764cc23b-a746-41a2-cf0c-5385fc3ce2c0"
+ },
+ "cell_type": "code",
+ "source": [
+ "series2 = pd.Series(numbers)\n",
+ "print(series2)"
+ ],
+ "execution_count": 32,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0 0\n",
+ "1 1\n",
+ "2 2\n",
+ "3 3\n",
+ "4 4\n",
+ "5 5\n",
+ "6 6\n",
+ "7 7\n",
+ "8 8\n",
+ "9 9\n",
+ "10 10\n",
+ "11 11\n",
+ "12 12\n",
+ "13 13\n",
+ "14 14\n",
+ "15 15\n",
+ "16 16\n",
+ "17 17\n",
+ "18 18\n",
+ "19 19\n",
+ "20 20\n",
+ "21 21\n",
+ "22 22\n",
+ "23 23\n",
+ "24 24\n",
+ "25 25\n",
+ "dtype: int64\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "J5z-2CWAdH6N",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 496
+ },
+ "outputId": "a666fc70-f97c-4ac6-b983-a94618e77e88"
+ },
+ "cell_type": "code",
+ "source": [
+ "series3 = pd.Series(alpha_numbers)\n",
+ "print(series3)"
+ ],
+ "execution_count": 31,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "A 0\n",
+ "B 1\n",
+ "C 2\n",
+ "D 3\n",
+ "E 4\n",
+ "F 5\n",
+ "G 6\n",
+ "H 7\n",
+ "I 8\n",
+ "J 9\n",
+ "K 10\n",
+ "L 11\n",
+ "M 12\n",
+ "N 13\n",
+ "O 14\n",
+ "P 15\n",
+ "Q 16\n",
+ "R 17\n",
+ "S 18\n",
+ "T 19\n",
+ "U 20\n",
+ "V 21\n",
+ "W 22\n",
+ "X 23\n",
+ "Y 24\n",
+ "Z 25\n",
+ "dtype: int64\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "fYzblGGudKjO",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "outputId": "828b59a1-9652-4788-af97-aef6f5e51b0b"
+ },
+ "cell_type": "code",
+ "source": [
+ "#replace head() with head(n) where n can be any number between [0-25] and observe the output in deach case \n",
+ "series3.head(13)"
+ ],
+ "execution_count": 27,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "A 0\n",
+ "B 1\n",
+ "C 2\n",
+ "D 3\n",
+ "E 4\n",
+ "F 5\n",
+ "G 6\n",
+ "H 7\n",
+ "I 8\n",
+ "J 9\n",
+ "K 10\n",
+ "L 11\n",
+ "M 12\n",
+ "dtype: int64"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 27
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "OwsJIf5feTtg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Create DataFrame from lists\n",
+ "\n",
+ "[DataFrame Doc](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "73UTZ07EdWki",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 827
+ },
+ "outputId": "0019af9c-1671-4572-97ed-965199b6892b"
+ },
+ "cell_type": "code",
+ "source": [
+ "data = {'alphabets': alphabets, 'values': numbers}\n",
+ "\n",
+ "df = pd.DataFrame(data)\n",
+ "\n",
+ "#Lets Change the column `values` to `alpha_numbers`\n",
+ "\n",
+ "df.columns = ['alphabets', 'alpha_numbers']\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": 30,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
+ " | \n",
+ " alphabets | \n",
+ " alpha_numbers | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " A | \n",
+ " 0 | \n",
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+ " 9 | \n",
+ "
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+ " \n",
+ " | 11 | \n",
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+ " \n",
+ " | 12 | \n",
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+ " 12 | \n",
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+ " | 13 | \n",
+ " N | \n",
+ " 13 | \n",
+ "
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+ " | 14 | \n",
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+ " 14 | \n",
+ "
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+ " \n",
+ " | 15 | \n",
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+ " 15 | \n",
+ "
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+ " \n",
+ " | 16 | \n",
+ " Q | \n",
+ " 16 | \n",
+ "
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+ " \n",
+ " | 17 | \n",
+ " R | \n",
+ " 17 | \n",
+ "
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+ " \n",
+ " | 18 | \n",
+ " S | \n",
+ " 18 | \n",
+ "
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+ " \n",
+ " | 19 | \n",
+ " T | \n",
+ " 19 | \n",
+ "
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+ " \n",
+ " | 20 | \n",
+ " U | \n",
+ " 20 | \n",
+ "
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+ " \n",
+ " | 21 | \n",
+ " V | \n",
+ " 21 | \n",
+ "
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+ " \n",
+ " | 22 | \n",
+ " W | \n",
+ " 22 | \n",
+ "
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+ " \n",
+ " | 23 | \n",
+ " X | \n",
+ " 23 | \n",
+ "
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+ " \n",
+ " | 24 | \n",
+ " Y | \n",
+ " 24 | \n",
+ "
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+ " \n",
+ " | 25 | \n",
+ " Z | \n",
+ " 25 | \n",
+ "
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+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " alphabets alpha_numbers\n",
+ "0 A 0\n",
+ "1 B 1\n",
+ "2 C 2\n",
+ "3 D 3\n",
+ "4 E 4\n",
+ "5 F 5\n",
+ "6 G 6\n",
+ "7 H 7\n",
+ "8 I 8\n",
+ "9 J 9\n",
+ "10 K 10\n",
+ "11 L 11\n",
+ "12 M 12\n",
+ "13 N 13\n",
+ "14 O 14\n",
+ "15 P 15\n",
+ "16 Q 16\n",
+ "17 R 17\n",
+ "18 S 18\n",
+ "19 T 19\n",
+ "20 U 20\n",
+ "21 V 21\n",
+ "22 W 22\n",
+ "23 X 23\n",
+ "24 Y 24\n",
+ "25 Z 25"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 30
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "uaK_1EO9etGS",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 136
+ },
+ "outputId": "d6a38677-52c8-4838-d59a-edc16f02c28b"
+ },
+ "cell_type": "code",
+ "source": [
+ "# transpose\n",
+ "\n",
+ "df.T\n",
+ "\n",
+ "# there are many more operations which we can perform look at the documentation with the subsequent exercises we will learn more"
+ ],
+ "execution_count": 29,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 6 | \n",
+ " 7 | \n",
+ " 8 | \n",
+ " 9 | \n",
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+ " 16 | \n",
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+ " 18 | \n",
+ " 19 | \n",
+ " 20 | \n",
+ " 21 | \n",
+ " 22 | \n",
+ " 23 | \n",
+ " 24 | \n",
+ " 25 | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | alphabets | \n",
+ " A | \n",
+ " B | \n",
+ " C | \n",
+ " D | \n",
+ " E | \n",
+ " F | \n",
+ " G | \n",
+ " H | \n",
+ " I | \n",
+ " J | \n",
+ " ... | \n",
+ " Q | \n",
+ " R | \n",
+ " S | \n",
+ " T | \n",
+ " U | \n",
+ " V | \n",
+ " W | \n",
+ " X | \n",
+ " Y | \n",
+ " Z | \n",
+ "
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+ " \n",
+ " | alpha_numbers | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 6 | \n",
+ " 7 | \n",
+ " 8 | \n",
+ " 9 | \n",
+ " ... | \n",
+ " 16 | \n",
+ " 17 | \n",
+ " 18 | \n",
+ " 19 | \n",
+ " 20 | \n",
+ " 21 | \n",
+ " 22 | \n",
+ " 23 | \n",
+ " 24 | \n",
+ " 25 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
2 rows × 26 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \\\n",
+ "alphabets A B C D E F G H I J ... Q R S T U V W \n",
+ "alpha_numbers 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \n",
+ "\n",
+ " 23 24 25 \n",
+ "alphabets X Y Z \n",
+ "alpha_numbers 23 24 25 \n",
+ "\n",
+ "[2 rows x 26 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 29
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ZYonoaW8gEAJ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Extract Items from a series"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "tc1-KX_Bfe7U",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 304
+ },
+ "outputId": "e96360a7-13bf-4f7d-c513-c161383b1eee"
+ },
+ "cell_type": "code",
+ "source": [
+ "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n",
+ "pos = [0, 4, 8, 14, 20]\n",
+ "\n",
+ "vowels = ser.take(pos)\n",
+ "\n",
+ "df = pd.DataFrame(vowels, columns= ['vowels'])#, columns=['vowels'])\n",
+ "\n",
+ "df.columns = ['vowels']\n",
+ "print(df)\n",
+ "\n",
+ "df.index = [0, 1, 2, 3, 4]\n",
+ "\n",
+ "df"
+ ],
+ "execution_count": 43,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " vowels\n",
+ "0 a\n",
+ "4 e\n",
+ "8 i\n",
+ "14 o\n",
+ "20 u\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " vowels | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " a | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " e | \n",
+ "
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+ " \n",
+ " | 2 | \n",
+ " i | \n",
+ "
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+ " \n",
+ " | 3 | \n",
+ " o | \n",
+ "
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+ " \n",
+ " | 4 | \n",
+ " u | \n",
+ "
\n",
+ " \n",
+ "
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+ "
"
+ ],
+ "text/plain": [
+ " vowels\n",
+ "0 a\n",
+ "1 e\n",
+ "2 i\n",
+ "3 o\n",
+ "4 u"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 43
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cmDxwtDNjWpO",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Change the first character of each word to upper case in each word of ser"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5KagP9PpgV2F",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "9627d7f3-685e-4d52-871c-429907b377a7"
+ },
+ "cell_type": "code",
+ "source": [
+ "ser = pd.Series(['we', 'are', 'learning', 'pandas'])\n",
+ "\n",
+ "ser.map(lambda x : x.title())\n",
+ "\n",
+ "titles = [i.title() for i in ser]\n",
+ "\n",
+ "titles"
+ ],
+ "execution_count": 44,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "['We', 'Are', 'Learning', 'Pandas']"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 44
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "qn47ee-MkZN8",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Reindexing"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "h5R0JL2NjuFS",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 197
+ },
+ "outputId": "e34a155f-8068-49aa-f329-3bb003c741a1"
+ },
+ "cell_type": "code",
+ "source": [
+ "my_index = [1, 2, 3, 4, 5]\n",
+ "\n",
+ "df1 = pd.DataFrame({'upper values': ['A', 'B', 'C', 'D', 'E'],\n",
+ " 'lower values': ['a', 'b', 'c', 'd', 'e']},\n",
+ " index = my_index)\n",
+ "\n",
+ "df1"
+ ],
+ "execution_count": 45,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
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+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 45
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+ ]
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+ {
+ "metadata": {
+ "id": "G_Frvc3mk93k",
+ "colab_type": "code",
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+ "base_uri": "https://localhost:8080/",
+ "height": 197
+ },
+ "outputId": "ca5a9322-5ac9-4b64-f234-3caa4103b2d7"
+ },
+ "cell_type": "code",
+ "source": [
+ "new_index = [2, 5, 4, 3, 1]\n",
+ "\n",
+ "df1.reindex(index = new_index)"
+ ],
+ "execution_count": 46,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
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+}
diff --git a/Exercise.ipynb b/Exercise.ipynb
new file mode 100644
index 0000000..2f887b0
--- /dev/null
+++ b/Exercise.ipynb
@@ -0,0 +1,3792 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Exercise.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "[View in Colaboratory](https://colab.research.google.com/github/ArnabG99/Assignment-3/blob/ArnabG99/Exercise.ipynb)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "2LTtpUJEibjg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Pandas Exercise :\n",
+ "\n",
+ "\n",
+ "#### import necessary modules"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "c3_UBbMRhiKx",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "tp-cTCyWi8mR",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n",
+ "\n",
+ "This is a wine dataset\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "DMojQY3thrRi",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1906
+ },
+ "outputId": "59abbbb2-9f4b-4970-b2d6-d3e01afd2dd3"
+ },
+ "cell_type": "code",
+ "source": [
+ "wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\")\n",
+ "wine_df\n"
+ ],
+ "execution_count": 116,
+ "outputs": [
+ {
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
+ "
177 rows × 14 columns
\n",
+ "
"
+ ],
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+ "26 2.77 1285 \n",
+ "27 3.40 915 \n",
+ "28 3.59 1035 \n",
+ "29 2.71 1285 \n",
+ ".. ... ... \n",
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+ "173 1.56 750 \n",
+ "174 1.56 835 \n",
+ "175 1.62 840 \n",
+ "176 1.60 560 \n",
+ "\n",
+ "[177 rows x 14 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 116
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "BF9MMjoZjSlg",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### print first five rows"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "1vSMQdnHjYNU",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 197
+ },
+ "outputId": "78bb77f6-02ed-4cec-a277-43cb27ac0bc9"
+ },
+ "cell_type": "code",
+ "source": [
+ "wine_df.head(5)"
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+ "execution_count": 118,
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+ {
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+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 118
+ }
+ ]
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+ {
+ "metadata": {
+ "id": "Tet6P2DvjY3T",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy\n",
+ "\n",
+ "[Hint](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "CMj3qSdJjx0u",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1906
+ },
+ "outputId": "665b54d3-004e-4872-e052-034147629db6"
+ },
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "wine_df_copy = wine_df.copy()\n",
+ "wine_df_copy.drop(index=np.arange(1, wine_df_copy.shape[0], 2), inplace = True)\n",
+ "wine_df_copy\n",
+ "\n",
+ "\n"
+ ],
+ "execution_count": 119,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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\n",
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89 rows × 14 columns
\n",
+ "
"
+ ],
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+ "170 3 12.77 2.39 2.28 19.5 86 1.39 0.51 0.48 0.64 9.899999 0.57 \n",
+ "172 3 13.71 5.65 2.45 20.5 95 1.68 0.61 0.52 1.06 7.700000 0.64 \n",
+ "174 3 13.27 4.28 2.26 20.0 120 1.59 0.69 0.43 1.35 10.200000 0.59 \n",
+ "176 3 14.13 4.10 2.74 24.5 96 2.05 0.76 0.56 1.35 9.200000 0.61 \n",
+ "\n",
+ " 3.92 1065 \n",
+ "0 3.40 1050 \n",
+ "2 3.45 1480 \n",
+ "4 2.85 1450 \n",
+ "6 3.58 1295 \n",
+ "8 3.55 1045 \n",
+ "10 2.82 1280 \n",
+ "12 2.73 1150 \n",
+ "14 2.88 1310 \n",
+ "16 2.57 1130 \n",
+ "18 3.36 845 \n",
+ "20 3.52 770 \n",
+ "22 3.63 1015 \n",
+ "24 3.20 830 \n",
+ "26 2.77 1285 \n",
+ "28 3.59 1035 \n",
+ "30 2.88 1515 \n",
+ "32 3.00 1235 \n",
+ "34 3.47 920 \n",
+ "36 2.51 1105 \n",
+ "38 3.53 760 \n",
+ "40 3.00 1035 \n",
+ "42 3.00 680 \n",
+ "44 3.33 1080 \n",
+ "46 3.33 985 \n",
+ "48 3.10 1260 \n",
+ "50 3.37 1265 \n",
+ "52 2.93 1375 \n",
+ "54 3.03 1120 \n",
+ "56 2.84 1270 \n",
+ "58 1.82 520 \n",
+ ".. ... ... \n",
+ "118 3.05 564 \n",
+ "120 3.69 465 \n",
+ "122 3.10 380 \n",
+ "124 3.28 378 \n",
+ "126 2.44 466 \n",
+ "128 2.57 580 \n",
+ "130 1.42 530 \n",
+ "132 1.29 600 \n",
+ "134 1.58 695 \n",
+ "136 1.69 515 \n",
+ "138 2.15 590 \n",
+ "140 2.47 780 \n",
+ "142 2.05 550 \n",
+ "144 1.68 830 \n",
+ "146 1.86 625 \n",
+ "148 1.33 550 \n",
+ "150 1.47 480 \n",
+ "152 1.51 675 \n",
+ "154 1.48 725 \n",
+ "156 1.73 880 \n",
+ "158 1.78 620 \n",
+ "160 1.82 680 \n",
+ "162 1.75 675 \n",
+ "164 1.75 520 \n",
+ "166 1.75 685 \n",
+ "168 1.92 630 \n",
+ "170 1.63 470 \n",
+ "172 1.74 740 \n",
+ "174 1.56 835 \n",
+ "176 1.60 560 \n",
+ "\n",
+ "[89 rows x 14 columns]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 119
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "o6Cs6T1Rjz71",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Assign the columns as below:\n",
+ "\n",
+ "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n",
+ "1) Alcohol \n",
+ "2) Malic acid \n",
+ "3) Ash \n",
+ "4) Alcalinity of ash \n",
+ "5) Magnesium \n",
+ "6) Total phenols \n",
+ "7) Flavanoids \n",
+ "8) Nonflavanoid phenols \n",
+ "9) Proanthocyanins \n",
+ "10)Color intensity \n",
+ "11)Hue \n",
+ "12)OD280/OD315 of diluted wines \n",
+ "13)Proline "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "my8HB4V4j779",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ },
+ "outputId": "5312cbf0-f396-4fa4-cba7-019d3c0b5691"
+ },
+ "cell_type": "code",
+ "source": [
+ "wine_df.columns = ['category', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', ' Flavanoids', 'Total phenols', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']\n",
+ "wine_df.head()"
+ ],
+ "execution_count": 120,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " category | \n",
+ " Alcohol | \n",
+ " Malic acid | \n",
+ " Ash | \n",
+ " Alcalinity of ash | \n",
+ " Magnesium | \n",
+ " Flavanoids | \n",
+ " Total phenols | \n",
+ " Nonflavanoid phenols | \n",
+ " Proanthocyanins | \n",
+ " Color intensity | \n",
+ " Hue | \n",
+ " OD280/OD315 of diluted wines | \n",
+ " Proline | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 1 | \n",
+ " 13.20 | \n",
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+ " 11.2 | \n",
+ " 100 | \n",
+ " 2.65 | \n",
+ " 2.76 | \n",
+ " 0.26 | \n",
+ " 1.28 | \n",
+ " 4.38 | \n",
+ " 1.05 | \n",
+ " 3.40 | \n",
+ " 1050 | \n",
+ "
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+ " \n",
+ " | 1 | \n",
+ " 1 | \n",
+ " 13.16 | \n",
+ " 2.36 | \n",
+ " 2.67 | \n",
+ " 18.6 | \n",
+ " 101 | \n",
+ " 2.80 | \n",
+ " 3.24 | \n",
+ " 0.30 | \n",
+ " 2.81 | \n",
+ " 5.68 | \n",
+ " 1.03 | \n",
+ " 3.17 | \n",
+ " 1185 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 1 | \n",
+ " 14.37 | \n",
+ " 1.95 | \n",
+ " 2.50 | \n",
+ " 16.8 | \n",
+ " 113 | \n",
+ " 3.85 | \n",
+ " 3.49 | \n",
+ " 0.24 | \n",
+ " 2.18 | \n",
+ " 7.80 | \n",
+ " 0.86 | \n",
+ " 3.45 | \n",
+ " 1480 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 1 | \n",
+ " 13.24 | \n",
+ " 2.59 | \n",
+ " 2.87 | \n",
+ " 21.0 | \n",
+ " 118 | \n",
+ " 2.80 | \n",
+ " 2.69 | \n",
+ " 0.39 | \n",
+ " 1.82 | \n",
+ " 4.32 | \n",
+ " 1.04 | \n",
+ " 2.93 | \n",
+ " 735 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1 | \n",
+ " 14.20 | \n",
+ " 1.76 | \n",
+ " 2.45 | \n",
+ " 15.2 | \n",
+ " 112 | \n",
+ " 3.27 | \n",
+ " 3.39 | \n",
+ " 0.34 | \n",
+ " 1.97 | \n",
+ " 6.75 | \n",
+ " 1.05 | \n",
+ " 2.85 | \n",
+ " 1450 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n",
+ "0 1 13.20 1.78 2.14 11.2 100 \n",
+ "1 1 13.16 2.36 2.67 18.6 101 \n",
+ "2 1 14.37 1.95 2.50 16.8 113 \n",
+ "3 1 13.24 2.59 2.87 21.0 118 \n",
+ "4 1 14.20 1.76 2.45 15.2 112 \n",
+ "\n",
+ " Flavanoids Total phenols Nonflavanoid phenols Proanthocyanins \\\n",
+ "0 2.65 2.76 0.26 1.28 \n",
+ "1 2.80 3.24 0.30 2.81 \n",
+ "2 3.85 3.49 0.24 2.18 \n",
+ "3 2.80 2.69 0.39 1.82 \n",
+ "4 3.27 3.39 0.34 1.97 \n",
+ "\n",
+ " Color intensity Hue OD280/OD315 of diluted wines Proline \n",
+ "0 4.38 1.05 3.40 1050 \n",
+ "1 5.68 1.03 3.17 1185 \n",
+ "2 7.80 0.86 3.45 1480 \n",
+ "3 4.32 1.04 2.93 735 \n",
+ "4 6.75 1.05 2.85 1450 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 120
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Zqi7hwWpkNbH",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Set the values of the first 3 rows from alcohol as NaN\n",
+ "\n",
+ "Hint- Use iloc to select 3 rows of wine_df"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "buyT4vX4kPMl",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ },
+ "outputId": "28815039-f3ca-4068-c95f-9fe61758a12a"
+ },
+ "cell_type": "code",
+ "source": [
+ "wine_df.iloc[0:3, wine_df.columns.get_loc('Alcohol')] = np.nan\n",
+ "wine_df.head()"
+ ],
+ "execution_count": 121,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " category | \n",
+ " Alcohol | \n",
+ " Malic acid | \n",
+ " Ash | \n",
+ " Alcalinity of ash | \n",
+ " Magnesium | \n",
+ " Flavanoids | \n",
+ " Total phenols | \n",
+ " Nonflavanoid phenols | \n",
+ " Proanthocyanins | \n",
+ " Color intensity | \n",
+ " Hue | \n",
+ " OD280/OD315 of diluted wines | \n",
+ " Proline | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 1 | \n",
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+ " 100 | \n",
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+ " 2.76 | \n",
+ " 0.26 | \n",
+ " 1.28 | \n",
+ " 4.38 | \n",
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+ " 3.40 | \n",
+ " 1050 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " 2.36 | \n",
+ " 2.67 | \n",
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+ " 101 | \n",
+ " 2.80 | \n",
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+ " 5.68 | \n",
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+ " 1185 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 1 | \n",
+ " NaN | \n",
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+ " 113 | \n",
+ " 3.85 | \n",
+ " 3.49 | \n",
+ " 0.24 | \n",
+ " 2.18 | \n",
+ " 7.80 | \n",
+ " 0.86 | \n",
+ " 3.45 | \n",
+ " 1480 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 1 | \n",
+ " 13.24 | \n",
+ " 2.59 | \n",
+ " 2.87 | \n",
+ " 21.0 | \n",
+ " 118 | \n",
+ " 2.80 | \n",
+ " 2.69 | \n",
+ " 0.39 | \n",
+ " 1.82 | \n",
+ " 4.32 | \n",
+ " 1.04 | \n",
+ " 2.93 | \n",
+ " 735 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1 | \n",
+ " 14.20 | \n",
+ " 1.76 | \n",
+ " 2.45 | \n",
+ " 15.2 | \n",
+ " 112 | \n",
+ " 3.27 | \n",
+ " 3.39 | \n",
+ " 0.34 | \n",
+ " 1.97 | \n",
+ " 6.75 | \n",
+ " 1.05 | \n",
+ " 2.85 | \n",
+ " 1450 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n",
+ "0 1 NaN 1.78 2.14 11.2 100 \n",
+ "1 1 NaN 2.36 2.67 18.6 101 \n",
+ "2 1 NaN 1.95 2.50 16.8 113 \n",
+ "3 1 13.24 2.59 2.87 21.0 118 \n",
+ "4 1 14.20 1.76 2.45 15.2 112 \n",
+ "\n",
+ " Flavanoids Total phenols Nonflavanoid phenols Proanthocyanins \\\n",
+ "0 2.65 2.76 0.26 1.28 \n",
+ "1 2.80 3.24 0.30 2.81 \n",
+ "2 3.85 3.49 0.24 2.18 \n",
+ "3 2.80 2.69 0.39 1.82 \n",
+ "4 3.27 3.39 0.34 1.97 \n",
+ "\n",
+ " Color intensity Hue OD280/OD315 of diluted wines Proline \n",
+ "0 4.38 1.05 3.40 1050 \n",
+ "1 5.68 1.03 3.17 1185 \n",
+ "2 7.80 0.86 3.45 1480 \n",
+ "3 4.32 1.04 2.93 735 \n",
+ "4 6.75 1.05 2.85 1450 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 121
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "RQMNI2UHkP3o",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "xunmCjaEmDwZ",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "ae6865f1-e069-4149-db26-5713e2869c1f"
+ },
+ "cell_type": "code",
+ "source": [
+ "random = np.random.randint(10, size= 10)\n",
+ "random.sort()\n",
+ "print(random)"
+ ],
+ "execution_count": 122,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[0 0 0 1 4 4 5 8 9 9]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "hELUakyXmFSu",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "zMgaNnNHmP01",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "outputId": "639a4d74-d9a0-4733-f046-96a360def085"
+ },
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "wine_df.iloc[random, wine_df.columns.get_loc('Alcohol')] = np.nan\n",
+ "wine_df.head(10)"
+ ],
+ "execution_count": 123,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " category | \n",
+ " Alcohol | \n",
+ " Malic acid | \n",
+ " Ash | \n",
+ " Alcalinity of ash | \n",
+ " Magnesium | \n",
+ " Flavanoids | \n",
+ " Total phenols | \n",
+ " Nonflavanoid phenols | \n",
+ " Proanthocyanins | \n",
+ " Color intensity | \n",
+ " Hue | \n",
+ " OD280/OD315 of diluted wines | \n",
+ " Proline | \n",
+ "
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+ " \n",
+ " \n",
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+ " 1 | \n",
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+ " 3.85 | \n",
+ " 3.49 | \n",
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+ " 0.86 | \n",
+ " 3.45 | \n",
+ " 1480 | \n",
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+ " \n",
+ " | 3 | \n",
+ " 1 | \n",
+ " 13.24 | \n",
+ " 2.59 | \n",
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+ " 118 | \n",
+ " 2.80 | \n",
+ " 2.69 | \n",
+ " 0.39 | \n",
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+ " 2.93 | \n",
+ " 735 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " 1.76 | \n",
+ " 2.45 | \n",
+ " 15.2 | \n",
+ " 112 | \n",
+ " 3.27 | \n",
+ " 3.39 | \n",
+ " 0.34 | \n",
+ " 1.97 | \n",
+ " 6.75 | \n",
+ " 1.05 | \n",
+ " 2.85 | \n",
+ " 1450 | \n",
+ "
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+ " \n",
+ " | 5 | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " 1.87 | \n",
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+ " 2.50 | \n",
+ " 2.52 | \n",
+ " 0.30 | \n",
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+ " 1.02 | \n",
+ " 3.58 | \n",
+ " 1290 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 1 | \n",
+ " 14.06 | \n",
+ " 2.15 | \n",
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+ " 121 | \n",
+ " 2.60 | \n",
+ " 2.51 | \n",
+ " 0.31 | \n",
+ " 1.25 | \n",
+ " 5.05 | \n",
+ " 1.06 | \n",
+ " 3.58 | \n",
+ " 1295 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 1 | \n",
+ " 14.83 | \n",
+ " 1.64 | \n",
+ " 2.17 | \n",
+ " 14.0 | \n",
+ " 97 | \n",
+ " 2.80 | \n",
+ " 2.98 | \n",
+ " 0.29 | \n",
+ " 1.98 | \n",
+ " 5.20 | \n",
+ " 1.08 | \n",
+ " 2.85 | \n",
+ " 1045 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " 1.35 | \n",
+ " 2.27 | \n",
+ " 16.0 | \n",
+ " 98 | \n",
+ " 2.98 | \n",
+ " 3.15 | \n",
+ " 0.22 | \n",
+ " 1.85 | \n",
+ " 7.22 | \n",
+ " 1.01 | \n",
+ " 3.55 | \n",
+ " 1045 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 1 | \n",
+ " NaN | \n",
+ " 2.16 | \n",
+ " 2.30 | \n",
+ " 18.0 | \n",
+ " 105 | \n",
+ " 2.95 | \n",
+ " 3.32 | \n",
+ " 0.22 | \n",
+ " 2.38 | \n",
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+ " 1510 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n",
+ "0 1 NaN 1.78 2.14 11.2 100 \n",
+ "1 1 NaN 2.36 2.67 18.6 101 \n",
+ "2 1 NaN 1.95 2.50 16.8 113 \n",
+ "3 1 13.24 2.59 2.87 21.0 118 \n",
+ "4 1 NaN 1.76 2.45 15.2 112 \n",
+ "5 1 NaN 1.87 2.45 14.6 96 \n",
+ "6 1 14.06 2.15 2.61 17.6 121 \n",
+ "7 1 14.83 1.64 2.17 14.0 97 \n",
+ "8 1 NaN 1.35 2.27 16.0 98 \n",
+ "9 1 NaN 2.16 2.30 18.0 105 \n",
+ "\n",
+ " Flavanoids Total phenols Nonflavanoid phenols Proanthocyanins \\\n",
+ "0 2.65 2.76 0.26 1.28 \n",
+ "1 2.80 3.24 0.30 2.81 \n",
+ "2 3.85 3.49 0.24 2.18 \n",
+ "3 2.80 2.69 0.39 1.82 \n",
+ "4 3.27 3.39 0.34 1.97 \n",
+ "5 2.50 2.52 0.30 1.98 \n",
+ "6 2.60 2.51 0.31 1.25 \n",
+ "7 2.80 2.98 0.29 1.98 \n",
+ "8 2.98 3.15 0.22 1.85 \n",
+ "9 2.95 3.32 0.22 2.38 \n",
+ "\n",
+ " Color intensity Hue OD280/OD315 of diluted wines Proline \n",
+ "0 4.38 1.05 3.40 1050 \n",
+ "1 5.68 1.03 3.17 1185 \n",
+ "2 7.80 0.86 3.45 1480 \n",
+ "3 4.32 1.04 2.93 735 \n",
+ "4 6.75 1.05 2.85 1450 \n",
+ "5 5.25 1.02 3.58 1290 \n",
+ "6 5.05 1.06 3.58 1295 \n",
+ "7 5.20 1.08 2.85 1045 \n",
+ "8 7.22 1.01 3.55 1045 \n",
+ "9 5.75 1.25 3.17 1510 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 123
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "PHyK_vRsmRwV",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### How many missing values do we have? \n",
+ "\n",
+ "Hint: you can use isnull() and sum()"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "EnOYhmEqmfKp",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "904d00bf-9428-4da9-c272-ed98d8eb4a2c"
+ },
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "nan_index = wine_df[wine_df.isnull().any(axis = 1)].index\n",
+ "print(nan_index)\n",
+ "print('No of missing values: ' + str(nan_index.size))\n",
+ "\n",
+ "\n"
+ ],
+ "execution_count": 124,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Int64Index([0, 1, 2, 4, 5, 8, 9], dtype='int64')\n",
+ "No of missing values: 7\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-Fd4WBklmf1_",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Delete the rows that contain missing values "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "As7IC6Ktms8-",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "outputId": "f253338d-d9dc-4089-c5e4-32194d2450f7"
+ },
+ "cell_type": "code",
+ "source": [
+ "wine_df.drop(index = nan_index, inplace = True)\n",
+ "wine_df.head(10)\n"
+ ],
+ "execution_count": 125,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " category | \n",
+ " Alcohol | \n",
+ " Malic acid | \n",
+ " Ash | \n",
+ " Alcalinity of ash | \n",
+ " Magnesium | \n",
+ " Flavanoids | \n",
+ " Total phenols | \n",
+ " Nonflavanoid phenols | \n",
+ " Proanthocyanins | \n",
+ " Color intensity | \n",
+ " Hue | \n",
+ " OD280/OD315 of diluted wines | \n",
+ " Proline | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 3 | \n",
+ " 1 | \n",
+ " 13.24 | \n",
+ " 2.59 | \n",
+ " 2.87 | \n",
+ " 21.0 | \n",
+ " 118 | \n",
+ " 2.80 | \n",
+ " 2.69 | \n",
+ " 0.39 | \n",
+ " 1.82 | \n",
+ " 4.32 | \n",
+ " 1.04 | \n",
+ " 2.93 | \n",
+ " 735 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 1 | \n",
+ " 14.06 | \n",
+ " 2.15 | \n",
+ " 2.61 | \n",
+ " 17.6 | \n",
+ " 121 | \n",
+ " 2.60 | \n",
+ " 2.51 | \n",
+ " 0.31 | \n",
+ " 1.25 | \n",
+ " 5.05 | \n",
+ " 1.06 | \n",
+ " 3.58 | \n",
+ " 1295 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 1 | \n",
+ " 14.83 | \n",
+ " 1.64 | \n",
+ " 2.17 | \n",
+ " 14.0 | \n",
+ " 97 | \n",
+ " 2.80 | \n",
+ " 2.98 | \n",
+ " 0.29 | \n",
+ " 1.98 | \n",
+ " 5.20 | \n",
+ " 1.08 | \n",
+ " 2.85 | \n",
+ " 1045 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 1 | \n",
+ " 14.12 | \n",
+ " 1.48 | \n",
+ " 2.32 | \n",
+ " 16.8 | \n",
+ " 95 | \n",
+ " 2.20 | \n",
+ " 2.43 | \n",
+ " 0.26 | \n",
+ " 1.57 | \n",
+ " 5.00 | \n",
+ " 1.17 | \n",
+ " 2.82 | \n",
+ " 1280 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 1 | \n",
+ " 13.75 | \n",
+ " 1.73 | \n",
+ " 2.41 | \n",
+ " 16.0 | \n",
+ " 89 | \n",
+ " 2.60 | \n",
+ " 2.76 | \n",
+ " 0.29 | \n",
+ " 1.81 | \n",
+ " 5.60 | \n",
+ " 1.15 | \n",
+ " 2.90 | \n",
+ " 1320 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 1 | \n",
+ " 14.75 | \n",
+ " 1.73 | \n",
+ " 2.39 | \n",
+ " 11.4 | \n",
+ " 91 | \n",
+ " 3.10 | \n",
+ " 3.69 | \n",
+ " 0.43 | \n",
+ " 2.81 | \n",
+ " 5.40 | \n",
+ " 1.25 | \n",
+ " 2.73 | \n",
+ " 1150 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 1 | \n",
+ " 14.38 | \n",
+ " 1.87 | \n",
+ " 2.38 | \n",
+ " 12.0 | \n",
+ " 102 | \n",
+ " 3.30 | \n",
+ " 3.64 | \n",
+ " 0.29 | \n",
+ " 2.96 | \n",
+ " 7.50 | \n",
+ " 1.20 | \n",
+ " 3.00 | \n",
+ " 1547 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 1 | \n",
+ " 13.63 | \n",
+ " 1.81 | \n",
+ " 2.70 | \n",
+ " 17.2 | \n",
+ " 112 | \n",
+ " 2.85 | \n",
+ " 2.91 | \n",
+ " 0.30 | \n",
+ " 1.46 | \n",
+ " 7.30 | \n",
+ " 1.28 | \n",
+ " 2.88 | \n",
+ " 1310 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 1 | \n",
+ " 14.30 | \n",
+ " 1.92 | \n",
+ " 2.72 | \n",
+ " 20.0 | \n",
+ " 120 | \n",
+ " 2.80 | \n",
+ " 3.14 | \n",
+ " 0.33 | \n",
+ " 1.97 | \n",
+ " 6.20 | \n",
+ " 1.07 | \n",
+ " 2.65 | \n",
+ " 1280 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 1 | \n",
+ " 13.83 | \n",
+ " 1.57 | \n",
+ " 2.62 | \n",
+ " 20.0 | \n",
+ " 115 | \n",
+ " 2.95 | \n",
+ " 3.40 | \n",
+ " 0.40 | \n",
+ " 1.72 | \n",
+ " 6.60 | \n",
+ " 1.13 | \n",
+ " 2.57 | \n",
+ " 1130 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n",
+ "3 1 13.24 2.59 2.87 21.0 118 \n",
+ "6 1 14.06 2.15 2.61 17.6 121 \n",
+ "7 1 14.83 1.64 2.17 14.0 97 \n",
+ "10 1 14.12 1.48 2.32 16.8 95 \n",
+ "11 1 13.75 1.73 2.41 16.0 89 \n",
+ "12 1 14.75 1.73 2.39 11.4 91 \n",
+ "13 1 14.38 1.87 2.38 12.0 102 \n",
+ "14 1 13.63 1.81 2.70 17.2 112 \n",
+ "15 1 14.30 1.92 2.72 20.0 120 \n",
+ "16 1 13.83 1.57 2.62 20.0 115 \n",
+ "\n",
+ " Flavanoids Total phenols Nonflavanoid phenols Proanthocyanins \\\n",
+ "3 2.80 2.69 0.39 1.82 \n",
+ "6 2.60 2.51 0.31 1.25 \n",
+ "7 2.80 2.98 0.29 1.98 \n",
+ "10 2.20 2.43 0.26 1.57 \n",
+ "11 2.60 2.76 0.29 1.81 \n",
+ "12 3.10 3.69 0.43 2.81 \n",
+ "13 3.30 3.64 0.29 2.96 \n",
+ "14 2.85 2.91 0.30 1.46 \n",
+ "15 2.80 3.14 0.33 1.97 \n",
+ "16 2.95 3.40 0.40 1.72 \n",
+ "\n",
+ " Color intensity Hue OD280/OD315 of diluted wines Proline \n",
+ "3 4.32 1.04 2.93 735 \n",
+ "6 5.05 1.06 3.58 1295 \n",
+ "7 5.20 1.08 2.85 1045 \n",
+ "10 5.00 1.17 2.82 1280 \n",
+ "11 5.60 1.15 2.90 1320 \n",
+ "12 5.40 1.25 2.73 1150 \n",
+ "13 7.50 1.20 3.00 1547 \n",
+ "14 7.30 1.28 2.88 1310 \n",
+ "15 6.20 1.07 2.65 1280 \n",
+ "16 6.60 1.13 2.57 1130 "
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 125
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "DlpG8drhmz7W",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "### BONUS: Play with the data set below"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "mD40T0Cnm5SA",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ ""
+ ],
+ "execution_count": 0,
+ "outputs": []
+ }
+ ]
+}
diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb
new file mode 100644
index 0000000..af47857
--- /dev/null
+++ b/Get_to_know_your_Data.ipynb
@@ -0,0 +1,1943 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "Get to know your Data.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "
"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "J82LU53m_OU0",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Get to know your Data\n",
+ "\n",
+ "\n",
+ "#### Import necessary modules\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ZyO1UXL8mtSj",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "yXTzTowtnwGI",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Loading CSV Data to a DataFrame"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "H1Bjlb5wm9f-",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "KE-k7b_Mn5iN",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### See the top 10 rows\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "HY2Ps7xMn4ao",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359
+ },
+ "outputId": "829f589e-39b1-435d-a288-2ddb7df85856"
+ },
+ "cell_type": "code",
+ "source": [
+ "iris_df.head(10)"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 5.4 | \n",
+ " 3.9 | \n",
+ " 1.7 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 4.6 | \n",
+ " 3.4 | \n",
+ " 1.4 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 5.0 | \n",
+ " 3.4 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 4.4 | \n",
+ " 2.9 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 4.9 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.1 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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\n",
+ "5 5.4 3.9 1.7 0.4 setosa\n",
+ "6 4.6 3.4 1.4 0.3 setosa\n",
+ "7 5.0 3.4 1.5 0.2 setosa\n",
+ "8 4.4 2.9 1.4 0.2 setosa\n",
+ "9 4.9 3.1 1.5 0.1 setosa"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "ZQXekIodqOZu",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Find number of rows and columns\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6Y-A-lbFqR82",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "af0b4150-fed4-46e1-fc96-5dd4d06f7799"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(iris_df.shape)\n",
+ "\n",
+ "#first is row and second is column\n",
+ "#select row by simple indexing\n",
+ "\n",
+ "print(iris_df.shape[0])\n",
+ "print(iris_df.shape[1])"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "(150, 5)\n",
+ "150\n",
+ "5\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4ckCiGPhrC_t",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Print all columns"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "S6jgMyRDrF2a",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "7576bcf3-8f63-4cbb-d1ea-a0d36092cafc"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(iris_df.columns)"
+ ],
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
+ " 'species'],\n",
+ " dtype='object')\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "kVav5-ACtIqS",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Check Index\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "iu3I9zIGtLDX",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "cc6d8cbf-8938-4710-8eb5-256a678bab3a"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(iris_df.index)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "RangeIndex(start=0, stop=150, step=1)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "psCc7PborOCQ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Right now the iris_data set has all the species grouped together let's shuffle it"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Bxc8i6avrZPw",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 221
+ },
+ "outputId": "271b01bc-d21f-4e4d-e91c-2e964124c42a"
+ },
+ "cell_type": "code",
+ "source": [
+ "#generate a random permutaion on index\n",
+ "\n",
+ "print(iris_df.head())\n",
+ "\n",
+ "new_index = np.random.permutation(iris_df.index)\n",
+ "iris_df = iris_df.reindex(index = new_index)\n",
+ "\n",
+ "print(iris_df.head())"
+ ],
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " 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\n",
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "84 5.4 3.0 4.5 1.5 versicolor\n",
+ "126 6.2 2.8 4.8 1.8 virginica\n",
+ "55 5.7 2.8 4.5 1.3 versicolor\n",
+ "12 4.8 3.0 1.4 0.1 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "j32h8022sRT8",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### We can also apply an operation on whole column of iris_df"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "seYXHXsYsYJI",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 323
+ },
+ "outputId": "cd43cfe6-8753-4434-b155-c2f2c33437d9"
+ },
+ "cell_type": "code",
+ "source": [
+ "#original\n",
+ "\n",
+ "\n",
+ "reset_index = np.sort(iris_df.index)\n",
+ "iris_df = iris_df.reindex(index = reset_index)\n",
+ "print(iris_df.head())\n",
+ "\n",
+ "\n",
+ "iris_df['sepal_width'] *= 10\n",
+ "\n",
+ "#changed\n",
+ "\n",
+ "print(iris_df.head())\n",
+ "\n",
+ "#lets undo the operation\n",
+ "\n",
+ "iris_df['sepal_width'] /= 10\n",
+ "\n",
+ "print(iris_df.head())"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " 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\n",
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "0 5.1 35.0 1.4 0.2 setosa\n",
+ "1 4.9 30.0 1.4 0.2 setosa\n",
+ "2 4.7 32.0 1.3 0.2 setosa\n",
+ "3 4.6 31.0 1.5 0.2 setosa\n",
+ "4 5.0 36.0 1.4 0.2 setosa\n",
+ " 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\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "R-Ca-LBLzjiF",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Show all the rows where sepal_width > 3.3"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "WJ7W-F-d0AoZ",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1165
+ },
+ "outputId": "dbf8ae62-6a0d-498f-8716-88d089927845"
+ },
+ "cell_type": "code",
+ "source": [
+ "iris_df[iris_df['sepal_width']>3.3]"
+ ],
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 5.4 | \n",
+ " 3.9 | \n",
+ " 1.7 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 4.6 | \n",
+ " 3.4 | \n",
+ " 1.4 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 5.0 | \n",
+ " 3.4 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 5.4 | \n",
+ " 3.7 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 4.8 | \n",
+ " 3.4 | \n",
+ " 1.6 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 5.8 | \n",
+ " 4.0 | \n",
+ " 1.2 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 5.7 | \n",
+ " 4.4 | \n",
+ " 1.5 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 5.4 | \n",
+ " 3.9 | \n",
+ " 1.3 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " 5.7 | \n",
+ " 3.8 | \n",
+ " 1.7 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " 5.1 | \n",
+ " 3.8 | \n",
+ " 1.5 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " 5.4 | \n",
+ " 3.4 | \n",
+ " 1.7 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 21 | \n",
+ " 5.1 | \n",
+ " 3.7 | \n",
+ " 1.5 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 22 | \n",
+ " 4.6 | \n",
+ " 3.6 | \n",
+ " 1.0 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 24 | \n",
+ " 4.8 | \n",
+ " 3.4 | \n",
+ " 1.9 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 26 | \n",
+ " 5.0 | \n",
+ " 3.4 | \n",
+ " 1.6 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 27 | \n",
+ " 5.2 | \n",
+ " 3.5 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 28 | \n",
+ " 5.2 | \n",
+ " 3.4 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 31 | \n",
+ " 5.4 | \n",
+ " 3.4 | \n",
+ " 1.5 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 32 | \n",
+ " 5.2 | \n",
+ " 4.1 | \n",
+ " 1.5 | \n",
+ " 0.1 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 33 | \n",
+ " 5.5 | \n",
+ " 4.2 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 36 | \n",
+ " 5.5 | \n",
+ " 3.5 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 39 | \n",
+ " 5.1 | \n",
+ " 3.4 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 40 | \n",
+ " 5.0 | \n",
+ " 3.5 | \n",
+ " 1.3 | \n",
+ " 0.3 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 43 | \n",
+ " 5.0 | \n",
+ " 3.5 | \n",
+ " 1.6 | \n",
+ " 0.6 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 44 | \n",
+ " 5.1 | \n",
+ " 3.8 | \n",
+ " 1.9 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 46 | \n",
+ " 5.1 | \n",
+ " 3.8 | \n",
+ " 1.6 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 48 | \n",
+ " 5.3 | \n",
+ " 3.7 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 85 | \n",
+ " 6.0 | \n",
+ " 3.4 | \n",
+ " 4.5 | \n",
+ " 1.6 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ " | 109 | \n",
+ " 7.2 | \n",
+ " 3.6 | \n",
+ " 6.1 | \n",
+ " 2.5 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 117 | \n",
+ " 7.7 | \n",
+ " 3.8 | \n",
+ " 6.7 | \n",
+ " 2.2 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 131 | \n",
+ " 7.9 | \n",
+ " 3.8 | \n",
+ " 6.4 | \n",
+ " 2.0 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 136 | \n",
+ " 6.3 | \n",
+ " 3.4 | \n",
+ " 5.6 | \n",
+ " 2.4 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 148 | \n",
+ " 6.2 | \n",
+ " 3.4 | \n",
+ " 5.4 | \n",
+ " 2.3 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa\n",
+ "5 5.4 3.9 1.7 0.4 setosa\n",
+ "6 4.6 3.4 1.4 0.3 setosa\n",
+ "7 5.0 3.4 1.5 0.2 setosa\n",
+ "10 5.4 3.7 1.5 0.2 setosa\n",
+ "11 4.8 3.4 1.6 0.2 setosa\n",
+ "14 5.8 4.0 1.2 0.2 setosa\n",
+ "15 5.7 4.4 1.5 0.4 setosa\n",
+ "16 5.4 3.9 1.3 0.4 setosa\n",
+ "17 5.1 3.5 1.4 0.3 setosa\n",
+ "18 5.7 3.8 1.7 0.3 setosa\n",
+ "19 5.1 3.8 1.5 0.3 setosa\n",
+ "20 5.4 3.4 1.7 0.2 setosa\n",
+ "21 5.1 3.7 1.5 0.4 setosa\n",
+ "22 4.6 3.6 1.0 0.2 setosa\n",
+ "24 4.8 3.4 1.9 0.2 setosa\n",
+ "26 5.0 3.4 1.6 0.4 setosa\n",
+ "27 5.2 3.5 1.5 0.2 setosa\n",
+ "28 5.2 3.4 1.4 0.2 setosa\n",
+ "31 5.4 3.4 1.5 0.4 setosa\n",
+ "32 5.2 4.1 1.5 0.1 setosa\n",
+ "33 5.5 4.2 1.4 0.2 setosa\n",
+ "36 5.5 3.5 1.3 0.2 setosa\n",
+ "39 5.1 3.4 1.5 0.2 setosa\n",
+ "40 5.0 3.5 1.3 0.3 setosa\n",
+ "43 5.0 3.5 1.6 0.6 setosa\n",
+ "44 5.1 3.8 1.9 0.4 setosa\n",
+ "46 5.1 3.8 1.6 0.2 setosa\n",
+ "48 5.3 3.7 1.5 0.2 setosa\n",
+ "85 6.0 3.4 4.5 1.6 versicolor\n",
+ "109 7.2 3.6 6.1 2.5 virginica\n",
+ "117 7.7 3.8 6.7 2.2 virginica\n",
+ "131 7.9 3.8 6.4 2.0 virginica\n",
+ "136 6.3 3.4 5.6 2.4 virginica\n",
+ "148 6.2 3.4 5.4 2.3 virginica"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "gH3DnhCq2Cbl",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4U7ksr_R2H7M",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 80
+ },
+ "outputId": "a448ee3a-15d6-42ef-e2db-416aee992b8a"
+ },
+ "cell_type": "code",
+ "source": [
+ "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] "
+ ],
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 85 | \n",
+ " 6.0 | \n",
+ " 3.4 | \n",
+ " 4.5 | \n",
+ " 1.6 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "85 6.0 3.4 4.5 1.6 versicolor"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "1lmnB3ot2u7I",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Sorting a column by value"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "K7KIj6fv2zWP",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 2193
+ },
+ "outputId": "50829d4f-3591-468f-c515-9507c398a9fe"
+ },
+ "cell_type": "code",
+ "source": [
+ "print(iris_df.sort_values(by='sepal_width'))#, ascending = False)\n",
+ "#pass ascending = False for descending order\n",
+ "print(iris_df.sort_values(by='sepal_width', ascending = False))"
+ ],
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "60 5.0 2.0 3.5 1.0 versicolor\n",
+ "62 6.0 2.2 4.0 1.0 versicolor\n",
+ "119 6.0 2.2 5.0 1.5 virginica\n",
+ "68 6.2 2.2 4.5 1.5 versicolor\n",
+ "41 4.5 2.3 1.3 0.3 setosa\n",
+ "53 5.5 2.3 4.0 1.3 versicolor\n",
+ "93 5.0 2.3 3.3 1.0 versicolor\n",
+ "87 6.3 2.3 4.4 1.3 versicolor\n",
+ "81 5.5 2.4 3.7 1.0 versicolor\n",
+ "80 5.5 2.4 3.8 1.1 versicolor\n",
+ "57 4.9 2.4 3.3 1.0 versicolor\n",
+ "72 6.3 2.5 4.9 1.5 versicolor\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "98 5.1 2.5 3.0 1.1 versicolor\n",
+ "113 5.7 2.5 5.0 2.0 virginica\n",
+ "108 6.7 2.5 5.8 1.8 virginica\n",
+ "69 5.6 2.5 3.9 1.1 versicolor\n",
+ "89 5.5 2.5 4.0 1.3 versicolor\n",
+ "106 4.9 2.5 4.5 1.7 virginica\n",
+ "92 5.8 2.6 4.0 1.2 versicolor\n",
+ "79 5.7 2.6 3.5 1.0 versicolor\n",
+ "90 5.5 2.6 4.4 1.2 versicolor\n",
+ "118 7.7 2.6 6.9 2.3 virginica\n",
+ "134 6.1 2.6 5.6 1.4 virginica\n",
+ "101 5.8 2.7 5.1 1.9 virginica\n",
+ "94 5.6 2.7 4.2 1.3 versicolor\n",
+ "59 5.2 2.7 3.9 1.4 versicolor\n",
+ "111 6.4 2.7 5.3 1.9 virginica\n",
+ "82 5.8 2.7 3.9 1.2 versicolor\n",
+ "67 5.8 2.7 4.1 1.0 versicolor\n",
+ ".. ... ... ... ... ...\n",
+ "85 6.0 3.4 4.5 1.6 versicolor\n",
+ "39 5.1 3.4 1.5 0.2 setosa\n",
+ "31 5.4 3.4 1.5 0.4 setosa\n",
+ "20 5.4 3.4 1.7 0.2 setosa\n",
+ "148 6.2 3.4 5.4 2.3 virginica\n",
+ "26 5.0 3.4 1.6 0.4 setosa\n",
+ "43 5.0 3.5 1.6 0.6 setosa\n",
+ "40 5.0 3.5 1.3 0.3 setosa\n",
+ "36 5.5 3.5 1.3 0.2 setosa\n",
+ "27 5.2 3.5 1.5 0.2 setosa\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "17 5.1 3.5 1.4 0.3 setosa\n",
+ "22 4.6 3.6 1.0 0.2 setosa\n",
+ "109 7.2 3.6 6.1 2.5 virginica\n",
+ "4 5.0 3.6 1.4 0.2 setosa\n",
+ "10 5.4 3.7 1.5 0.2 setosa\n",
+ "48 5.3 3.7 1.5 0.2 setosa\n",
+ "21 5.1 3.7 1.5 0.4 setosa\n",
+ "131 7.9 3.8 6.4 2.0 virginica\n",
+ "117 7.7 3.8 6.7 2.2 virginica\n",
+ "46 5.1 3.8 1.6 0.2 setosa\n",
+ "44 5.1 3.8 1.9 0.4 setosa\n",
+ "18 5.7 3.8 1.7 0.3 setosa\n",
+ "19 5.1 3.8 1.5 0.3 setosa\n",
+ "5 5.4 3.9 1.7 0.4 setosa\n",
+ "16 5.4 3.9 1.3 0.4 setosa\n",
+ "14 5.8 4.0 1.2 0.2 setosa\n",
+ "32 5.2 4.1 1.5 0.1 setosa\n",
+ "33 5.5 4.2 1.4 0.2 setosa\n",
+ "15 5.7 4.4 1.5 0.4 setosa\n",
+ "\n",
+ "[150 rows x 5 columns]\n",
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "15 5.7 4.4 1.5 0.4 setosa\n",
+ "33 5.5 4.2 1.4 0.2 setosa\n",
+ "32 5.2 4.1 1.5 0.1 setosa\n",
+ "14 5.8 4.0 1.2 0.2 setosa\n",
+ "16 5.4 3.9 1.3 0.4 setosa\n",
+ "5 5.4 3.9 1.7 0.4 setosa\n",
+ "19 5.1 3.8 1.5 0.3 setosa\n",
+ "44 5.1 3.8 1.9 0.4 setosa\n",
+ "46 5.1 3.8 1.6 0.2 setosa\n",
+ "131 7.9 3.8 6.4 2.0 virginica\n",
+ "117 7.7 3.8 6.7 2.2 virginica\n",
+ "18 5.7 3.8 1.7 0.3 setosa\n",
+ "48 5.3 3.7 1.5 0.2 setosa\n",
+ "10 5.4 3.7 1.5 0.2 setosa\n",
+ "21 5.1 3.7 1.5 0.4 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa\n",
+ "22 4.6 3.6 1.0 0.2 setosa\n",
+ "109 7.2 3.6 6.1 2.5 virginica\n",
+ "36 5.5 3.5 1.3 0.2 setosa\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "17 5.1 3.5 1.4 0.3 setosa\n",
+ "40 5.0 3.5 1.3 0.3 setosa\n",
+ "43 5.0 3.5 1.6 0.6 setosa\n",
+ "27 5.2 3.5 1.5 0.2 setosa\n",
+ "39 5.1 3.4 1.5 0.2 setosa\n",
+ "28 5.2 3.4 1.4 0.2 setosa\n",
+ "136 6.3 3.4 5.6 2.4 virginica\n",
+ "31 5.4 3.4 1.5 0.4 setosa\n",
+ "85 6.0 3.4 4.5 1.6 versicolor\n",
+ "7 5.0 3.4 1.5 0.2 setosa\n",
+ ".. ... ... ... ... ...\n",
+ "83 6.0 2.7 5.1 1.6 versicolor\n",
+ "67 5.8 2.7 4.1 1.0 versicolor\n",
+ "101 5.8 2.7 5.1 1.9 virginica\n",
+ "82 5.8 2.7 3.9 1.2 versicolor\n",
+ "111 6.4 2.7 5.3 1.9 virginica\n",
+ "94 5.6 2.7 4.2 1.3 versicolor\n",
+ "79 5.7 2.6 3.5 1.0 versicolor\n",
+ "90 5.5 2.6 4.4 1.2 versicolor\n",
+ "92 5.8 2.6 4.0 1.2 versicolor\n",
+ "118 7.7 2.6 6.9 2.3 virginica\n",
+ "134 6.1 2.6 5.6 1.4 virginica\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "89 5.5 2.5 4.0 1.3 versicolor\n",
+ "98 5.1 2.5 3.0 1.1 versicolor\n",
+ "106 4.9 2.5 4.5 1.7 virginica\n",
+ "108 6.7 2.5 5.8 1.8 virginica\n",
+ "72 6.3 2.5 4.9 1.5 versicolor\n",
+ "69 5.6 2.5 3.9 1.1 versicolor\n",
+ "113 5.7 2.5 5.0 2.0 virginica\n",
+ "57 4.9 2.4 3.3 1.0 versicolor\n",
+ "80 5.5 2.4 3.8 1.1 versicolor\n",
+ "81 5.5 2.4 3.7 1.0 versicolor\n",
+ "93 5.0 2.3 3.3 1.0 versicolor\n",
+ "53 5.5 2.3 4.0 1.3 versicolor\n",
+ "41 4.5 2.3 1.3 0.3 setosa\n",
+ "87 6.3 2.3 4.4 1.3 versicolor\n",
+ "62 6.0 2.2 4.0 1.0 versicolor\n",
+ "68 6.2 2.2 4.5 1.5 versicolor\n",
+ "119 6.0 2.2 5.0 1.5 virginica\n",
+ "60 5.0 2.0 3.5 1.0 versicolor\n",
+ "\n",
+ "[150 rows x 5 columns]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "9jg_Z4YCoMSV",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### List all the unique species"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "M6EN78ufoJY7",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "dd37c611-5ef4-4495-ae00-e2f584c0de2d"
+ },
+ "cell_type": "code",
+ "source": [
+ "species = iris_df['species'].unique()\n",
+ "\n",
+ "print(species)"
+ ],
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "['setosa' 'versicolor' 'virginica']\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "wG1i5nxBodmB",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Selecting a particular species using boolean mask (learnt in previous exercise)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "gZvpbKBwoVUe",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "1b9c963c-6f09-4ff9-9e60-50139a1aa564"
+ },
+ "cell_type": "code",
+ "source": [
+ "setosa = iris_df[iris_df['species'] == species[0]]\n",
+ "\n",
+ "setosa.head()"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "7tumfZ3DotPG",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "1e4cbc14-51ef-4ea1-c2d6-d70e7c0aec77"
+ },
+ "cell_type": "code",
+ "source": [
+ "# do the same for other 2 species \n",
+ "versicolor = iris_df[iris_df['species'] == species[1]]\n",
+ "\n",
+ "versicolor.head()"
+ ],
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 50 | \n",
+ " 7.0 | \n",
+ " 3.2 | \n",
+ " 4.7 | \n",
+ " 1.4 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ " | 51 | \n",
+ " 6.4 | \n",
+ " 3.2 | \n",
+ " 4.5 | \n",
+ " 1.5 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ " | 52 | \n",
+ " 6.9 | \n",
+ " 3.1 | \n",
+ " 4.9 | \n",
+ " 1.5 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ " | 53 | \n",
+ " 5.5 | \n",
+ " 2.3 | \n",
+ " 4.0 | \n",
+ " 1.3 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ " | 54 | \n",
+ " 6.5 | \n",
+ " 2.8 | \n",
+ " 4.6 | \n",
+ " 1.5 | \n",
+ " versicolor | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "50 7.0 3.2 4.7 1.4 versicolor\n",
+ "51 6.4 3.2 4.5 1.5 versicolor\n",
+ "52 6.9 3.1 4.9 1.5 versicolor\n",
+ "53 5.5 2.3 4.0 1.3 versicolor\n",
+ "54 6.5 2.8 4.6 1.5 versicolor"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "cUYm5UqVpDPy",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "78cac6d9-6b0c-4235-dd35-d3e1871edd81"
+ },
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "\n",
+ "virginica = iris_df[iris_df['species'] == species[2]]\n",
+ "\n",
+ "virginica.head() "
+ ],
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 100 | \n",
+ " 6.3 | \n",
+ " 3.3 | \n",
+ " 6.0 | \n",
+ " 2.5 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 101 | \n",
+ " 5.8 | \n",
+ " 2.7 | \n",
+ " 5.1 | \n",
+ " 1.9 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 102 | \n",
+ " 7.1 | \n",
+ " 3.0 | \n",
+ " 5.9 | \n",
+ " 2.1 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 103 | \n",
+ " 6.3 | \n",
+ " 2.9 | \n",
+ " 5.6 | \n",
+ " 1.8 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " | 104 | \n",
+ " 6.5 | \n",
+ " 3.0 | \n",
+ " 5.8 | \n",
+ " 2.2 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width species\n",
+ "100 6.3 3.3 6.0 2.5 virginica\n",
+ "101 5.8 2.7 5.1 1.9 virginica\n",
+ "102 7.1 3.0 5.9 2.1 virginica\n",
+ "103 6.3 2.9 5.6 1.8 virginica\n",
+ "104 6.5 3.0 5.8 2.2 virginica"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-y1wDc8SpdQs",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Describe each created species to see the difference\n",
+ "\n"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "eHrn3ZVRpOk5",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 297
+ },
+ "outputId": "ba30f410-e825-4750-c131-d6dd3ad4af28"
+ },
+ "cell_type": "code",
+ "source": [
+ "setosa.describe()"
+ ],
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 50.00000 | \n",
+ " 50.000000 | \n",
+ " 50.000000 | \n",
+ " 50.00000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 5.00600 | \n",
+ " 3.418000 | \n",
+ " 1.464000 | \n",
+ " 0.24400 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 0.35249 | \n",
+ " 0.381024 | \n",
+ " 0.173511 | \n",
+ " 0.10721 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 4.30000 | \n",
+ " 2.300000 | \n",
+ " 1.000000 | \n",
+ " 0.10000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 4.80000 | \n",
+ " 3.125000 | \n",
+ " 1.400000 | \n",
+ " 0.20000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 5.00000 | \n",
+ " 3.400000 | \n",
+ " 1.500000 | \n",
+ " 0.20000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 5.20000 | \n",
+ " 3.675000 | \n",
+ " 1.575000 | \n",
+ " 0.30000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 5.80000 | \n",
+ " 4.400000 | \n",
+ " 1.900000 | \n",
+ " 0.60000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width\n",
+ "count 50.00000 50.000000 50.000000 50.00000\n",
+ "mean 5.00600 3.418000 1.464000 0.24400\n",
+ "std 0.35249 0.381024 0.173511 0.10721\n",
+ "min 4.30000 2.300000 1.000000 0.10000\n",
+ "25% 4.80000 3.125000 1.400000 0.20000\n",
+ "50% 5.00000 3.400000 1.500000 0.20000\n",
+ "75% 5.20000 3.675000 1.575000 0.30000\n",
+ "max 5.80000 4.400000 1.900000 0.60000"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 17
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "GwJFT2GlpwUv",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 297
+ },
+ "outputId": "bc0b99ae-e91e-456f-d64d-c905b4a93501"
+ },
+ "cell_type": "code",
+ "source": [
+ "versicolor.describe()"
+ ],
+ "execution_count": 18,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 50.000000 | \n",
+ " 50.000000 | \n",
+ " 50.000000 | \n",
+ " 50.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 5.936000 | \n",
+ " 2.770000 | \n",
+ " 4.260000 | \n",
+ " 1.326000 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 0.516171 | \n",
+ " 0.313798 | \n",
+ " 0.469911 | \n",
+ " 0.197753 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 4.900000 | \n",
+ " 2.000000 | \n",
+ " 3.000000 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 5.600000 | \n",
+ " 2.525000 | \n",
+ " 4.000000 | \n",
+ " 1.200000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 5.900000 | \n",
+ " 2.800000 | \n",
+ " 4.350000 | \n",
+ " 1.300000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 6.300000 | \n",
+ " 3.000000 | \n",
+ " 4.600000 | \n",
+ " 1.500000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 7.000000 | \n",
+ " 3.400000 | \n",
+ " 5.100000 | \n",
+ " 1.800000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width\n",
+ "count 50.000000 50.000000 50.000000 50.000000\n",
+ "mean 5.936000 2.770000 4.260000 1.326000\n",
+ "std 0.516171 0.313798 0.469911 0.197753\n",
+ "min 4.900000 2.000000 3.000000 1.000000\n",
+ "25% 5.600000 2.525000 4.000000 1.200000\n",
+ "50% 5.900000 2.800000 4.350000 1.300000\n",
+ "75% 6.300000 3.000000 4.600000 1.500000\n",
+ "max 7.000000 3.400000 5.100000 1.800000"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Ad4qhSZLpztf",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 297
+ },
+ "outputId": "71653ad0-ee7b-4282-cbdb-8d399de969ce"
+ },
+ "cell_type": "code",
+ "source": [
+ "virginica.describe()"
+ ],
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal_length | \n",
+ " sepal_width | \n",
+ " petal_length | \n",
+ " petal_width | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 50.00000 | \n",
+ " 50.000000 | \n",
+ " 50.000000 | \n",
+ " 50.00000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 6.58800 | \n",
+ " 2.974000 | \n",
+ " 5.552000 | \n",
+ " 2.02600 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 0.63588 | \n",
+ " 0.322497 | \n",
+ " 0.551895 | \n",
+ " 0.27465 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 4.90000 | \n",
+ " 2.200000 | \n",
+ " 4.500000 | \n",
+ " 1.40000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 6.22500 | \n",
+ " 2.800000 | \n",
+ " 5.100000 | \n",
+ " 1.80000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 6.50000 | \n",
+ " 3.000000 | \n",
+ " 5.550000 | \n",
+ " 2.00000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 6.90000 | \n",
+ " 3.175000 | \n",
+ " 5.875000 | \n",
+ " 2.30000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 7.90000 | \n",
+ " 3.800000 | \n",
+ " 6.900000 | \n",
+ " 2.50000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sepal_length sepal_width petal_length petal_width\n",
+ "count 50.00000 50.000000 50.000000 50.00000\n",
+ "mean 6.58800 2.974000 5.552000 2.02600\n",
+ "std 0.63588 0.322497 0.551895 0.27465\n",
+ "min 4.90000 2.200000 4.500000 1.40000\n",
+ "25% 6.22500 2.800000 5.100000 1.80000\n",
+ "50% 6.50000 3.000000 5.550000 2.00000\n",
+ "75% 6.90000 3.175000 5.875000 2.30000\n",
+ "max 7.90000 3.800000 6.900000 2.50000"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 19
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Vdu0ulZWtr09",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "#### Let's plot and see the difference"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "PEVMzRvpttmD",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "##### import matplotlib.pyplot "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "rqDXuuAtt7C3",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 398
+ },
+ "outputId": "07e60731-bd1d-420d-f35e-aaaae5f2ba9a"
+ },
+ "cell_type": "code",
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "#hist creates a histogram there are many more plots(see the documentation) you can play with it.\n",
+ "\n",
+ "plt.hist(setosa['sepal_length'])\n",
+ "plt.hist(versicolor['sepal_length'])\n",
+ "plt.hist(virginica['sepal_length'])"
+ ],
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([ 1., 0., 5., 5., 8., 9., 10., 5., 1., 6.]),\n",
+ " array([4.9, 5.2, 5.5, 5.8, 6.1, 6.4, 6.7, 7. , 7.3, 7.6, 7.9]),\n",
+ " )"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 20
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file