From 8e8c49649dc0cc9fc1f9488ce4b43db7869d31d8 Mon Sep 17 00:00:00 2001 From: shreyasi das <43550173+shreyasi17@users.noreply.github.com> Date: Wed, 2 Jan 2019 12:13:50 +0530 Subject: [PATCH 1/3] Basic Pandas --- shreyasi17.ipynb | 1073 ++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 1043 insertions(+), 30 deletions(-) diff --git a/shreyasi17.ipynb b/shreyasi17.ipynb index 9e2543a..6a95081 100644 --- a/shreyasi17.ipynb +++ b/shreyasi17.ipynb @@ -1,32 +1,1045 @@ { - "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": "shreyasi17.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": [ + "\"Open" + ] + }, + { + "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", + "outputId": "f42f4c26-28fe-4859-fdd2-5c0bfff23b28", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 147 + } + }, + "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": 4, + "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", + "outputId": "733e48b9-b2df-4975-9efc-01372c4ebbed", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + } + }, + "cell_type": "code", + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)" + ], + "execution_count": 5, + "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", + "outputId": "bef2690e-353e-4819-d794-4b5537206b22", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + } + }, + "cell_type": "code", + "source": [ + "series2 = pd.Series(numbers)\n", + "print(series2)" + ], + "execution_count": 6, + "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", + "outputId": "e7d917cb-018e-4662-cbc0-8a79833efc58", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 513 + } + }, + "cell_type": "code", + "source": [ + "series3 = pd.Series(alpha_numbers)\n", + "print(series3)" + ], + "execution_count": 7, + "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", + "outputId": "f6282396-07e6-4bfe-879f-8b3a6999dfdd", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + } + }, + "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(23)" + ], + "execution_count": 8, + "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", + "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", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "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", + "outputId": "0285d7f9-dfa8-49a2-a095-a22b4cb0de6d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 865 + } + }, + "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": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 9 + } + ] + }, + { + "metadata": { + "id": "uaK_1EO9etGS", + "colab_type": "code", + "outputId": "758805b4-2b4a-4f12-9c92-1246a57cac33", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141 + } + }, + "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": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "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": 10 + } + ] + }, + { + "metadata": { + "id": "ZYonoaW8gEAJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Extract Items from a series" + ] + }, + { + "metadata": { + "id": "tc1-KX_Bfe7U", + "colab_type": "code", + "outputId": "ebcf04cc-b546-4cfd-ad09-ae886d189d45", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "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'])\n", + "\n", + "df.columns = ['vowels']\n", + "\n", + "df.index = [0, 1, 2, 3, 4]\n", + "\n", + "df" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 11 + } + ] + }, + { + "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", + "outputId": "497037a2-7d45-489a-adb5-1c1e1290497a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "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": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['We', 'Are', 'Learning', 'Pandas']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "qn47ee-MkZN8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Reindexing" + ] + }, + { + "metadata": { + "id": "h5R0JL2NjuFS", + "colab_type": "code", + "outputId": "393e7fb4-c511-426d-f1e1-36ea4d4a626a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + } + }, + "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": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " lower values upper values\n", + "2 b B\n", + "5 e E\n", + "4 d D\n", + "3 c C\n", + "1 a A" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + } + ] +} \ No newline at end of file From 6f664216262d393a3fc048fe762b5489a977f7a0 Mon Sep 17 00:00:00 2001 From: shreyasi das <43550173+shreyasi17@users.noreply.github.com> Date: Wed, 2 Jan 2019 12:18:16 +0530 Subject: [PATCH 2/3] Get to know your Data --- shreyasi17.ipynb | 2430 +++++++++++++++++++++++++++++++++++----------- 1 file changed, 1870 insertions(+), 560 deletions(-) diff --git a/shreyasi17.ipynb b/shreyasi17.ipynb index 6a95081..9f7cd08 100644 --- a/shreyasi17.ipynb +++ b/shreyasi17.ipynb @@ -27,22 +27,20 @@ }, { "metadata": { - "id": "cGbE814_Xaf9", + "id": "J82LU53m_OU0", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "# Pandas\n", + "# Get to know your Data\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" + "#### Import necessary modules\n" ] }, { "metadata": { - "id": "irlVYeeAXPDL", + "id": "ZyO1UXL8mtSj", "colab_type": "code", "colab": {} }, @@ -56,61 +54,218 @@ }, { "metadata": { - "id": "BI2J-zdMbGwE", + "id": "yXTzTowtnwGI", "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)" + "#### 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": "GeEct691YGE3", + "id": "HY2Ps7xMn4ao", "colab_type": "code", - "outputId": "f42f4c26-28fe-4859-fdd2-5c0bfff23b28", + "outputId": "7cd4dc70-c448-44da-e8ba-2b43e0bf6736", "colab": { "base_uri": "https://localhost:8080/", - "height": 147 + "height": 206 } }, "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", + "iris_df.head()" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
14.93.01.40.2setosa
24.73.21.30.2setosa
34.63.11.50.2setosa
45.03.61.40.2setosa
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" + ], + "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": 42 + } + ] + }, + { + "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", + "outputId": "5086cdb6-0854-4f26-c3b1-c2a0e0e48ef5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + } + }, + "cell_type": "code", + "source": [ + "print(iris_df.shape)\n", "\n", - "print(alpha_numbers)\n", + "#first is row and second is column\n", + "#select row by simple indexing\n", "\n", - "print(type(alpha_numbers))" + "print(iris_df.shape[0])\n", + "print(iris_df.shape[1])" + ], + "execution_count": 43, + "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", + "outputId": "febc8804-c116-4069-fd9b-5af5b1bc3c00", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + } + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" ], - "execution_count": 4, + "execution_count": 44, "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" + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" ], "name": "stdout" } @@ -118,51 +273,34 @@ }, { "metadata": { - "id": "6ouDfjWab_Mc", + "id": "kVav5-ACtIqS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Check Index\n" + ] + }, + { + "metadata": { + "id": "iu3I9zIGtLDX", "colab_type": "code", - "outputId": "733e48b9-b2df-4975-9efc-01372c4ebbed", + "outputId": "7ee018bf-a911-4de2-8c46-da228dfb42cb", "colab": { "base_uri": "https://localhost:8080/", - "height": 513 + "height": 35 } }, "cell_type": "code", "source": [ - "series1 = pd.Series(alphabets)\n", - "print(series1)" + "print(iris_df.index)" ], - "execution_count": 5, + "execution_count": 45, "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" + "RangeIndex(start=0, stop=150, step=1)\n" ], "name": "stdout" } @@ -170,51 +308,52 @@ }, { "metadata": { - "id": "At7nY7vVcBZ3", + "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", - "outputId": "bef2690e-353e-4819-d794-4b5537206b22", + "outputId": "8734c31f-6e5c-459c-80c4-8fb8a8832144", "colab": { "base_uri": "https://localhost:8080/", - "height": 513 + "height": 237 } }, "cell_type": "code", "source": [ - "series2 = pd.Series(numbers)\n", - "print(series2)" + "#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": 6, + "execution_count": 46, "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" + " 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", + "126 6.2 2.8 4.8 1.8 virginica\n", + "135 7.7 3.0 6.1 2.3 virginica\n", + "128 6.4 2.8 5.6 2.1 virginica\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "56 6.3 3.3 4.7 1.6 versicolor\n" ], "name": "stdout" } @@ -222,51 +361,65 @@ }, { "metadata": { - "id": "J5z-2CWAdH6N", + "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", - "outputId": "e7d917cb-018e-4662-cbc0-8a79833efc58", + "outputId": "18305166-b616-4a75-b0ca-a52d1b6a286b", "colab": { "base_uri": "https://localhost:8080/", - "height": 513 + "height": 348 } }, "cell_type": "code", "source": [ - "series3 = pd.Series(alpha_numbers)\n", - "print(series3)" + "#original\n", + "\n", + "print(iris_df.head())\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": 7, + "execution_count": 47, "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" + " sepal_length sepal_width petal_length petal_width species\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + "135 7.7 3.0 6.1 2.3 virginica\n", + "128 6.4 2.8 5.6 2.1 virginica\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "56 6.3 3.3 4.7 1.6 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "126 6.2 28.0 4.8 1.8 virginica\n", + "135 7.7 30.0 6.1 2.3 virginica\n", + "128 6.4 28.0 5.6 2.1 virginica\n", + "60 5.0 20.0 3.5 1.0 versicolor\n", + "56 6.3 33.0 4.7 1.6 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + "135 7.7 3.0 6.1 2.3 virginica\n", + "128 6.4 2.8 5.6 2.1 virginica\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "56 6.3 3.3 4.7 1.6 versicolor\n" ], "name": "stdout" } @@ -274,93 +427,1167 @@ }, { "metadata": { - "id": "fYzblGGudKjO", + "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", - "outputId": "f6282396-07e6-4bfe-879f-8b3a6999dfdd", + "outputId": "d9caac74-c750-4e50-80e0-7e5cd0bda7db", "colab": { "base_uri": "https://localhost:8080/", - "height": 458 + "height": 1179 } }, "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(23)" + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
405.03.51.30.3setosa
465.13.81.60.2setosa
244.83.41.90.2setosa
185.73.81.70.3setosa
1486.23.45.42.3virginica
205.43.41.70.2setosa
195.13.81.50.3setosa
165.43.91.30.4setosa
114.83.41.60.2setosa
64.63.41.40.3setosa
395.13.41.50.2setosa
1366.33.45.62.4virginica
335.54.21.40.2setosa
1177.73.86.72.2virginica
45.03.61.40.2setosa
856.03.44.51.6versicolor
75.03.41.50.2setosa
224.63.61.00.2setosa
485.33.71.50.2setosa
1097.23.66.12.5virginica
145.84.01.20.2setosa
445.13.81.90.4setosa
435.03.51.60.6setosa
175.13.51.40.3setosa
275.23.51.50.2setosa
55.43.91.70.4setosa
315.43.41.50.4setosa
325.24.11.50.1setosa
365.53.51.30.2setosa
1317.93.86.42.0virginica
05.13.51.40.2setosa
155.74.41.50.4setosa
285.23.41.40.2setosa
105.43.71.50.2setosa
265.03.41.60.4setosa
215.13.71.50.4setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "33 5.5 4.2 1.4 0.2 setosa\n", + "117 7.7 3.8 6.7 2.2 virginica\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "32 5.2 4.1 1.5 0.1 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "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", + "outputId": "d2b29aeb-6c2d-46a2-9e6e-da1eb111df7d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81 + } + }, + "cell_type": "code", + "source": [ + "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + ], + "execution_count": 49, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
856.03.44.51.6versicolor
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" + ], + "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": 49 + } + ] + }, + { + "metadata": { + "id": "1lmnB3ot2u7I", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Sorting a column by value" + ] + }, + { + "metadata": { + "id": "K7KIj6fv2zWP", + "colab_type": "code", + "outputId": "79c7241d-bafd-4130-a4b3-9129aca11ac8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1992 + } + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 50, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
686.22.24.51.5versicolor
626.02.24.01.0versicolor
1196.02.25.01.5virginica
535.52.34.01.3versicolor
414.52.31.30.3setosa
935.02.33.31.0versicolor
876.32.34.41.3versicolor
815.52.43.71.0versicolor
574.92.43.31.0versicolor
805.52.43.81.1versicolor
985.12.53.01.1versicolor
1466.32.55.01.9virginica
1135.72.55.02.0virginica
895.52.54.01.3versicolor
1086.72.55.81.8virginica
1064.92.54.51.7virginica
695.62.53.91.1versicolor
726.32.54.91.5versicolor
905.52.64.41.2versicolor
925.82.64.01.2versicolor
1187.72.66.92.3virginica
795.72.63.51.0versicolor
1346.12.65.61.4virginica
1015.82.75.11.9virginica
1236.32.74.91.8virginica
1425.82.75.11.9virginica
945.62.74.21.3versicolor
675.82.74.11.0versicolor
836.02.75.11.6versicolor
..................
285.23.41.40.2setosa
1366.33.45.62.4virginica
75.03.41.50.2setosa
856.03.44.51.6versicolor
315.43.41.50.4setosa
395.13.41.50.2setosa
365.53.51.30.2setosa
405.03.51.30.3setosa
435.03.51.60.6setosa
05.13.51.40.2setosa
175.13.51.40.3setosa
275.23.51.50.2setosa
1097.23.66.12.5virginica
45.03.61.40.2setosa
224.63.61.00.2setosa
105.43.71.50.2setosa
485.33.71.50.2setosa
215.13.71.50.4setosa
185.73.81.70.3setosa
195.13.81.50.3setosa
1177.73.86.72.2virginica
465.13.81.60.2setosa
445.13.81.90.4setosa
1317.93.86.42.0virginica
165.43.91.30.4setosa
55.43.91.70.4setosa
145.84.01.20.2setosa
325.24.11.50.1setosa
335.54.21.40.2setosa
155.74.41.50.4setosa
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150 rows × 5 columns

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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "68 6.2 2.2 4.5 1.5 versicolor\n", + "62 6.0 2.2 4.0 1.0 versicolor\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "53 5.5 2.3 4.0 1.3 versicolor\n", + "41 4.5 2.3 1.3 0.3 setosa\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", + "57 4.9 2.4 3.3 1.0 versicolor\n", + "80 5.5 2.4 3.8 1.1 versicolor\n", + "98 5.1 2.5 3.0 1.1 versicolor\n", + "146 6.3 2.5 5.0 1.9 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "108 6.7 2.5 5.8 1.8 virginica\n", + "106 4.9 2.5 4.5 1.7 virginica\n", + "69 5.6 2.5 3.9 1.1 versicolor\n", + "72 6.3 2.5 4.9 1.5 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", + "79 5.7 2.6 3.5 1.0 versicolor\n", + "134 6.1 2.6 5.6 1.4 virginica\n", + "101 5.8 2.7 5.1 1.9 virginica\n", + "123 6.3 2.7 4.9 1.8 virginica\n", + "142 5.8 2.7 5.1 1.9 virginica\n", + "94 5.6 2.7 4.2 1.3 versicolor\n", + "67 5.8 2.7 4.1 1.0 versicolor\n", + "83 6.0 2.7 5.1 1.6 versicolor\n", + ".. ... ... ... ... ...\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "36 5.5 3.5 1.3 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", + "0 5.1 3.5 1.4 0.2 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "27 5.2 3.5 1.5 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", + "22 4.6 3.6 1.0 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", + "18 5.7 3.8 1.7 0.3 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\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", + "131 7.9 3.8 6.4 2.0 virginica\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "5 5.4 3.9 1.7 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]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 50 + } + ] + }, + { + "metadata": { + "id": "9jg_Z4YCoMSV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### List all the unique species" + ] + }, + { + "metadata": { + "id": "M6EN78ufoJY7", + "colab_type": "code", + "outputId": "39ad4226-53df-42cd-8d9f-32ffe1e1cd41", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "cell_type": "code", + "source": [ + "species = iris_df['species'].unique()\n", + "\n", + "print(species)" ], - "execution_count": 8, + "execution_count": 51, "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", - "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", - "dtype: int64" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 8 + "output_type": "stream", + "text": [ + "['virginica' 'versicolor' 'setosa']\n" + ], + "name": "stdout" } ] }, { "metadata": { - "id": "OwsJIf5feTtg", + "id": "wG1i5nxBodmB", "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)" + "#### Selecting a particular species using boolean mask (learnt in previous exercise)" ] }, { "metadata": { - "id": "73UTZ07EdWki", + "id": "gZvpbKBwoVUe", "colab_type": "code", - "outputId": "0285d7f9-dfa8-49a2-a095-a22b4cb0de6d", + "outputId": "d56f2768-8b3b-4af1-ba70-6933f8e8dd78", "colab": { "base_uri": "https://localhost:8080/", - "height": 865 + "height": 206 } }, "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", + "setosa = iris_df[iris_df['species'] == species[0]]\n", "\n", - "df.columns = ['alphabets', 'alpha_numbers']\n", - "\n", - "df" + "setosa.head()" ], - "execution_count": 9, + "execution_count": 52, "outputs": [ { "output_type": "execute_result", @@ -384,201 +1611,92 @@ " \n", " \n", " \n", - " alphabets\n", - " alpha_numbers\n", + " sepal_length\n", + " sepal_width\n", + " petal_length\n", + " petal_width\n", + " species\n", " \n", " \n", " \n", " \n", - " 0\n", - " A\n", - " 0\n", - " \n", - " \n", - " 1\n", - " B\n", - " 1\n", - " \n", - " \n", - " 2\n", - " C\n", - " 2\n", - " \n", - " \n", - " 3\n", - " D\n", - " 3\n", - " \n", - " \n", - " 4\n", - " E\n", - " 4\n", - " \n", - " \n", - " 5\n", - " F\n", - " 5\n", - " \n", - " \n", - " 6\n", - " G\n", - " 6\n", - " \n", - " \n", - " 7\n", - " H\n", - " 7\n", - " \n", - " \n", - " 8\n", - " I\n", - " 8\n", - " \n", - " \n", - " 9\n", - " J\n", - " 9\n", - " \n", - " \n", - " 10\n", - " K\n", - " 10\n", - " \n", - " \n", - " 11\n", - " L\n", - " 11\n", - " \n", - " \n", - " 12\n", - " M\n", - " 12\n", - " \n", - " \n", - " 13\n", - " N\n", - " 13\n", - " \n", - " \n", - " 14\n", - " O\n", - " 14\n", - " \n", - " \n", - " 15\n", - " P\n", - " 15\n", - " \n", - " \n", - " 16\n", - " Q\n", - " 16\n", - " \n", - " \n", - " 17\n", - " R\n", - " 17\n", - " \n", - " \n", - " 18\n", - " S\n", - " 18\n", - " \n", - " \n", - " 19\n", - " T\n", - " 19\n", - " \n", - " \n", - " 20\n", - " U\n", - " 20\n", - " \n", - " \n", - " 21\n", - " V\n", - " 21\n", + " 126\n", + " 6.2\n", + " 2.8\n", + " 4.8\n", + " 1.8\n", + " virginica\n", " \n", " \n", - " 22\n", - " W\n", - " 22\n", + " 135\n", + " 7.7\n", + " 3.0\n", + " 6.1\n", + " 2.3\n", + " virginica\n", " \n", " \n", - " 23\n", - " X\n", - " 23\n", + " 128\n", + " 6.4\n", + " 2.8\n", + " 5.6\n", + " 2.1\n", + " virginica\n", " \n", " \n", - " 24\n", - " Y\n", - " 24\n", + " 108\n", + " 6.7\n", + " 2.5\n", + " 5.8\n", + " 1.8\n", + " virginica\n", " \n", " \n", - " 25\n", - " Z\n", - " 25\n", + " 102\n", + " 7.1\n", + " 3.0\n", + " 5.9\n", + " 2.1\n", + " virginica\n", " \n", " \n", "\n", "" ], "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" + " sepal_length sepal_width petal_length petal_width species\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + "135 7.7 3.0 6.1 2.3 virginica\n", + "128 6.4 2.8 5.6 2.1 virginica\n", + "108 6.7 2.5 5.8 1.8 virginica\n", + "102 7.1 3.0 5.9 2.1 virginica" ] }, "metadata": { "tags": [] }, - "execution_count": 9 + "execution_count": 52 } ] }, { "metadata": { - "id": "uaK_1EO9etGS", + "id": "7tumfZ3DotPG", "colab_type": "code", - "outputId": "758805b4-2b4a-4f12-9c92-1246a57cac33", + "outputId": "896719b6-2850-43e4-b316-f57dc9ebcc75", "colab": { "base_uri": "https://localhost:8080/", - "height": 141 + "height": 206 } }, "cell_type": "code", "source": [ - "# transpose\n", + "# do the same for other 2 species \n", + "versicolor = iris_df[iris_df['species'] == species[1]]\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" + "versicolor.head()" ], - "execution_count": 10, + "execution_count": 53, "outputs": [ { "output_type": "execute_result", @@ -602,117 +1720,79 @@ " \n", " \n", " \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", + " sepal_length\n", + " sepal_width\n", + " petal_length\n", + " petal_width\n", + " species\n", " \n", " \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", - " \n", - " \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", + " 60\n", + " 5.0\n", + " 2.0\n", + " 3.5\n", + " 1.0\n", + " versicolor\n", + " \n", + " \n", + " 56\n", + " 6.3\n", + " 3.3\n", + " 4.7\n", + " 1.6\n", + " versicolor\n", + " \n", + " \n", + " 95\n", + " 5.7\n", + " 3.0\n", + " 4.2\n", + " 1.2\n", + " versicolor\n", + " \n", + " \n", + " 76\n", + " 6.8\n", + " 2.8\n", + " 4.8\n", + " 1.4\n", + " versicolor\n", + " \n", + " \n", + " 78\n", + " 6.0\n", + " 2.9\n", + " 4.5\n", + " 1.5\n", + " versicolor\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]" + " sepal_length sepal_width petal_length petal_width species\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "56 6.3 3.3 4.7 1.6 versicolor\n", + "95 5.7 3.0 4.2 1.2 versicolor\n", + "76 6.8 2.8 4.8 1.4 versicolor\n", + "78 6.0 2.9 4.5 1.5 versicolor" ] }, "metadata": { "tags": [] }, - "execution_count": 10 + "execution_count": 53 } ] }, { "metadata": { - "id": "ZYonoaW8gEAJ", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Extract Items from a series" - ] - }, - { - "metadata": { - "id": "tc1-KX_Bfe7U", + "id": "cUYm5UqVpDPy", "colab_type": "code", - "outputId": "ebcf04cc-b546-4cfd-ad09-ae886d189d45", + "outputId": "09f9a382-f213-4982-c8c0-fac805f4f6f6", "colab": { "base_uri": "https://localhost:8080/", "height": 206 @@ -720,20 +1800,13 @@ }, "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'])\n", "\n", - "df.columns = ['vowels']\n", "\n", - "df.index = [0, 1, 2, 3, 4]\n", + "virginica = iris_df[iris_df['species'] == species[2]]\n", "\n", - "df" + "virginica.head()" ], - "execution_count": 11, + "execution_count": 54, "outputs": [ { "output_type": "execute_result", @@ -757,127 +1830,224 @@ " \n", " \n", " \n", - " vowels\n", + " sepal_length\n", + " sepal_width\n", + " petal_length\n", + " petal_width\n", + " species\n", " \n", " \n", " \n", " \n", - " 0\n", - " a\n", + " 40\n", + " 5.0\n", + " 3.5\n", + " 1.3\n", + " 0.3\n", + " setosa\n", " \n", " \n", - " 1\n", - " e\n", + " 46\n", + " 5.1\n", + " 3.8\n", + " 1.6\n", + " 0.2\n", + " setosa\n", " \n", " \n", - " 2\n", - " i\n", + " 38\n", + " 4.4\n", + " 3.0\n", + " 1.3\n", + " 0.2\n", + " setosa\n", " \n", " \n", - " 3\n", - " o\n", + " 24\n", + " 4.8\n", + " 3.4\n", + " 1.9\n", + " 0.2\n", + " setosa\n", " \n", " \n", - " 4\n", - " u\n", + " 18\n", + " 5.7\n", + " 3.8\n", + " 1.7\n", + " 0.3\n", + " setosa\n", " \n", " \n", "\n", "" ], "text/plain": [ - " vowels\n", - "0 a\n", - "1 e\n", - "2 i\n", - "3 o\n", - "4 u" + " sepal_length sepal_width petal_length petal_width species\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "38 4.4 3.0 1.3 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa" ] }, "metadata": { "tags": [] }, - "execution_count": 11 + "execution_count": 54 } ] }, { "metadata": { - "id": "cmDxwtDNjWpO", + "id": "-y1wDc8SpdQs", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### Change the first character of each word to upper case in each word of ser" + "#### Describe each created species to see the difference\n", + "\n" ] }, { "metadata": { - "id": "5KagP9PpgV2F", + "id": "eHrn3ZVRpOk5", "colab_type": "code", - "outputId": "497037a2-7d45-489a-adb5-1c1e1290497a", + "outputId": "fc5247a4-25cf-4cc3-c514-9651ad6c1f05", "colab": { "base_uri": "https://localhost:8080/", - "height": 35 + "height": 300 } }, "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" + "setosa.describe()" ], - "execution_count": 12, + "execution_count": 55, "outputs": [ { "output_type": "execute_result", "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "['We', 'Are', 'Learning', 'Pandas']" + " 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": 12 + "execution_count": 55 } ] }, { "metadata": { - "id": "qn47ee-MkZN8", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Reindexing" - ] - }, - { - "metadata": { - "id": "h5R0JL2NjuFS", + "id": "GwJFT2GlpwUv", "colab_type": "code", - "outputId": "393e7fb4-c511-426d-f1e1-36ea4d4a626a", + "outputId": "feb2e1b2-57ae-42a8-dbf0-82fa18197ad7", "colab": { "base_uri": "https://localhost:8080/", - "height": 206 + "height": 300 } }, "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" + "versicolor.describe()" ], - "execution_count": 13, + "execution_count": 56, "outputs": [ { "output_type": "execute_result", @@ -901,73 +2071,107 @@ " \n", " \n", " \n", - " lower values\n", - " upper values\n", + " sepal_length\n", + " sepal_width\n", + " petal_length\n", + " petal_width\n", " \n", " \n", " \n", " \n", - " 1\n", - " a\n", - " A\n", + " count\n", + " 50.000000\n", + " 50.000000\n", + " 50.000000\n", + " 50.000000\n", " \n", " \n", - " 2\n", - " b\n", - " B\n", + " mean\n", + " 5.936000\n", + " 2.770000\n", + " 4.260000\n", + " 1.326000\n", " \n", " \n", - " 3\n", - " c\n", - " C\n", + " std\n", + " 0.516171\n", + " 0.313798\n", + " 0.469911\n", + " 0.197753\n", " \n", " \n", - " 4\n", - " d\n", - " D\n", + " min\n", + " 4.900000\n", + " 2.000000\n", + " 3.000000\n", + " 1.000000\n", " \n", " \n", - " 5\n", - " e\n", - " E\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": [ - " lower values upper values\n", - "1 a A\n", - "2 b B\n", - "3 c C\n", - "4 d D\n", - "5 e E" + " 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": 13 + "execution_count": 56 } ] }, { "metadata": { - "id": "G_Frvc3mk93k", + "id": "Ad4qhSZLpztf", "colab_type": "code", - "outputId": "2c602f98-6619-4cc4-d26c-868ea981f890", + "outputId": "5a28a668-fe2e-4e09-d5b5-06a8defc6b41", "colab": { "base_uri": "https://localhost:8080/", - "height": 206 + "height": 300 } }, "cell_type": "code", "source": [ - "new_index = [2, 5, 4, 3, 1]\n", - "\n", - "df1.reindex(index = new_index)" + "virginica.describe()" ], - "execution_count": 14, + "execution_count": 57, "outputs": [ { "output_type": "execute_result", @@ -991,53 +2195,159 @@ " \n", " \n", " \n", - " lower values\n", - " upper values\n", + " sepal_length\n", + " sepal_width\n", + " petal_length\n", + " petal_width\n", " \n", " \n", " \n", " \n", - " 2\n", - " b\n", - " B\n", + " count\n", + " 50.00000\n", + " 50.000000\n", + " 50.000000\n", + " 50.00000\n", " \n", " \n", - " 5\n", - " e\n", - " E\n", + " mean\n", + " 5.00600\n", + " 3.418000\n", + " 1.464000\n", + " 0.24400\n", " \n", " \n", - " 4\n", - " d\n", - " D\n", + " std\n", + " 0.35249\n", + " 0.381024\n", + " 0.173511\n", + " 0.10721\n", " \n", " \n", - " 3\n", - " c\n", - " C\n", + " min\n", + " 4.30000\n", + " 2.300000\n", + " 1.000000\n", + " 0.10000\n", " \n", " \n", - " 1\n", - " a\n", - " A\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": [ - " lower values upper values\n", - "2 b B\n", - "5 e E\n", - "4 d D\n", - "3 c C\n", - "1 a A" + " 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": 57 + } + ] + }, + { + "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", + "outputId": "c3577877-0f14-47aa-dd6c-f9ad73a0603f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 402 + } + }, + "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": 58, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 4., 1., 6., 5., 12., 8., 4., 5., 2., 3.]),\n", + " array([4.3 , 4.45, 4.6 , 4.75, 4.9 , 5.05, 5.2 , 5.35, 5.5 , 5.65, 5.8 ]),\n", + " )" ] }, "metadata": { "tags": [] }, - "execution_count": 14 + "execution_count": 58 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } } ] } From a9eb52de5c3d8f3fc6f42718ca0ad3e1444aa43f Mon Sep 17 00:00:00 2001 From: shreyasi das <43550173+shreyasi17@users.noreply.github.com> Date: Wed, 2 Jan 2019 12:30:16 +0530 Subject: [PATCH 3/3] Exercise 3 --- shreyasi17.ipynb | 6847 ++++++++++++++++++++++++++++++++++------------ 1 file changed, 5077 insertions(+), 1770 deletions(-) diff --git a/shreyasi17.ipynb b/shreyasi17.ipynb index 9f7cd08..2e62311 100644 --- a/shreyasi17.ipynb +++ b/shreyasi17.ipynb @@ -27,69 +27,212 @@ }, { "metadata": { - "id": "J82LU53m_OU0", + "id": "2LTtpUJEibjg", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "# Get to know your Data\n", + "# Pandas Exercise :\n", "\n", "\n", - "#### Import necessary modules\n" + "#### import necessary modules" ] }, { "metadata": { - "id": "ZyO1UXL8mtSj", + "id": "c3_UBbMRhiKx", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ - "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "import pandas as pd" ], "execution_count": 0, "outputs": [] }, { "metadata": { - "id": "yXTzTowtnwGI", + "id": "tp-cTCyWi8mR", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### Loading CSV Data to a DataFrame" + "#### 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": "H1Bjlb5wm9f-", + "id": "DMojQY3thrRi", "colab_type": "code", - "colab": {} + "outputId": "c3c029f6-ce24-4252-f737-e3328dd7aca5", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2350 + } }, "cell_type": "code", "source": [ - "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + "wine_df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')\n", + "print(wine_df)\n", + "wine_df = pd.DataFrame(wine_df)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "text": [ + " 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 \\\n", + "0 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.380000 1.05 \n", + "1 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.680000 1.03 \n", + "2 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.800000 0.86 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.320000 1.04 \n", + "4 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.750000 1.05 \n", + "5 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 1.98 5.250000 1.02 \n", + "6 1 14.06 2.15 2.61 17.6 121 2.60 2.51 0.31 1.25 5.050000 1.06 \n", + "7 1 14.83 1.64 2.17 14.0 97 2.80 2.98 0.29 1.98 5.200000 1.08 \n", + "8 1 13.86 1.35 2.27 16.0 98 2.98 3.15 0.22 1.85 7.220000 1.01 \n", + "9 1 14.10 2.16 2.30 18.0 105 2.95 3.32 0.22 2.38 5.750000 1.25 \n", + "10 1 14.12 1.48 2.32 16.8 95 2.20 2.43 0.26 1.57 5.000000 1.17 \n", + "11 1 13.75 1.73 2.41 16.0 89 2.60 2.76 0.29 1.81 5.600000 1.15 \n", + "12 1 14.75 1.73 2.39 11.4 91 3.10 3.69 0.43 2.81 5.400000 1.25 \n", + "13 1 14.38 1.87 2.38 12.0 102 3.30 3.64 0.29 2.96 7.500000 1.20 \n", + "14 1 13.63 1.81 2.70 17.2 112 2.85 2.91 0.30 1.46 7.300000 1.28 \n", + "15 1 14.30 1.92 2.72 20.0 120 2.80 3.14 0.33 1.97 6.200000 1.07 \n", + "16 1 13.83 1.57 2.62 20.0 115 2.95 3.40 0.40 1.72 6.600000 1.13 \n", + "17 1 14.19 1.59 2.48 16.5 108 3.30 3.93 0.32 1.86 8.700000 1.23 \n", + "18 1 13.64 3.10 2.56 15.2 116 2.70 3.03 0.17 1.66 5.100000 0.96 \n", + "19 1 14.06 1.63 2.28 16.0 126 3.00 3.17 0.24 2.10 5.650000 1.09 \n", + "20 1 12.93 3.80 2.65 18.6 102 2.41 2.41 0.25 1.98 4.500000 1.03 \n", + "21 1 13.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.800000 1.11 \n", + "22 1 12.85 1.60 2.52 17.8 95 2.48 2.37 0.26 1.46 3.930000 1.09 \n", + "23 1 13.50 1.81 2.61 20.0 96 2.53 2.61 0.28 1.66 3.520000 1.12 \n", + "24 1 13.05 2.05 3.22 25.0 124 2.63 2.68 0.47 1.92 3.580000 1.13 \n", + "25 1 13.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.800000 0.92 \n", + "26 1 13.30 1.72 2.14 17.0 94 2.40 2.19 0.27 1.35 3.950000 1.02 \n", + "27 1 13.87 1.90 2.80 19.4 107 2.95 2.97 0.37 1.76 4.500000 1.25 \n", + "28 1 14.02 1.68 2.21 16.0 96 2.65 2.33 0.26 1.98 4.700000 1.04 \n", + "29 1 13.73 1.50 2.70 22.5 101 3.00 3.25 0.29 2.38 5.700000 1.19 \n", + ".. .. ... ... ... ... ... ... ... ... ... ... ... \n", + "147 3 13.32 3.24 2.38 21.5 92 1.93 0.76 0.45 1.25 8.420000 0.55 \n", + "148 3 13.08 3.90 2.36 21.5 113 1.41 1.39 0.34 1.14 9.400000 0.57 \n", + "149 3 13.50 3.12 2.62 24.0 123 1.40 1.57 0.22 1.25 8.600000 0.59 \n", + "150 3 12.79 2.67 2.48 22.0 112 1.48 1.36 0.24 1.26 10.800000 0.48 \n", + "151 3 13.11 1.90 2.75 25.5 116 2.20 1.28 0.26 1.56 7.100000 0.61 \n", + "152 3 13.23 3.30 2.28 18.5 98 1.80 0.83 0.61 1.87 10.520000 0.56 \n", + "153 3 12.58 1.29 2.10 20.0 103 1.48 0.58 0.53 1.40 7.600000 0.58 \n", + "154 3 13.17 5.19 2.32 22.0 93 1.74 0.63 0.61 1.55 7.900000 0.60 \n", + "155 3 13.84 4.12 2.38 19.5 89 1.80 0.83 0.48 1.56 9.010000 0.57 \n", + "156 3 12.45 3.03 2.64 27.0 97 1.90 0.58 0.63 1.14 7.500000 0.67 \n", + "157 3 14.34 1.68 2.70 25.0 98 2.80 1.31 0.53 2.70 13.000000 0.57 \n", + "158 3 13.48 1.67 2.64 22.5 89 2.60 1.10 0.52 2.29 11.750000 0.57 \n", + "159 3 12.36 3.83 2.38 21.0 88 2.30 0.92 0.50 1.04 7.650000 0.56 \n", + "160 3 13.69 3.26 2.54 20.0 107 1.83 0.56 0.50 0.80 5.880000 0.96 \n", + "161 3 12.85 3.27 2.58 22.0 106 1.65 0.60 0.60 0.96 5.580000 0.87 \n", + "162 3 12.96 3.45 2.35 18.5 106 1.39 0.70 0.40 0.94 5.280000 0.68 \n", + "163 3 13.78 2.76 2.30 22.0 90 1.35 0.68 0.41 1.03 9.580000 0.70 \n", + "164 3 13.73 4.36 2.26 22.5 88 1.28 0.47 0.52 1.15 6.620000 0.78 \n", + "165 3 13.45 3.70 2.60 23.0 111 1.70 0.92 0.43 1.46 10.680000 0.85 \n", + "166 3 12.82 3.37 2.30 19.5 88 1.48 0.66 0.40 0.97 10.260000 0.72 \n", + "167 3 13.58 2.58 2.69 24.5 105 1.55 0.84 0.39 1.54 8.660000 0.74 \n", + "168 3 13.40 4.60 2.86 25.0 112 1.98 0.96 0.27 1.11 8.500000 0.67 \n", + "169 3 12.20 3.03 2.32 19.0 96 1.25 0.49 0.40 0.73 5.500000 0.66 \n", + "170 3 12.77 2.39 2.28 19.5 86 1.39 0.51 0.48 0.64 9.899999 0.57 \n", + "171 3 14.16 2.51 2.48 20.0 91 1.68 0.70 0.44 1.24 9.700000 0.62 \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", + "173 3 13.40 3.91 2.48 23.0 102 1.80 0.75 0.43 1.41 7.300000 0.70 \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", + "175 3 13.17 2.59 2.37 20.0 120 1.65 0.68 0.53 1.46 9.300000 0.60 \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", + "1 3.17 1185 \n", + "2 3.45 1480 \n", + "3 2.93 735 \n", + "4 2.85 1450 \n", + "5 3.58 1290 \n", + "6 3.58 1295 \n", + "7 2.85 1045 \n", + "8 3.55 1045 \n", + "9 3.17 1510 \n", + "10 2.82 1280 \n", + "11 2.90 1320 \n", + "12 2.73 1150 \n", + "13 3.00 1547 \n", + "14 2.88 1310 \n", + "15 2.65 1280 \n", + "16 2.57 1130 \n", + "17 2.82 1680 \n", + "18 3.36 845 \n", + "19 3.71 780 \n", + "20 3.52 770 \n", + "21 4.00 1035 \n", + "22 3.63 1015 \n", + "23 3.82 845 \n", + "24 3.20 830 \n", + "25 3.22 1195 \n", + "26 2.77 1285 \n", + "27 3.40 915 \n", + "28 3.59 1035 \n", + "29 2.71 1285 \n", + ".. ... ... \n", + "147 1.62 650 \n", + "148 1.33 550 \n", + "149 1.30 500 \n", + "150 1.47 480 \n", + "151 1.33 425 \n", + "152 1.51 675 \n", + "153 1.55 640 \n", + "154 1.48 725 \n", + "155 1.64 480 \n", + "156 1.73 880 \n", + "157 1.96 660 \n", + "158 1.78 620 \n", + "159 1.58 520 \n", + "160 1.82 680 \n", + "161 2.11 570 \n", + "162 1.75 675 \n", + "163 1.68 615 \n", + "164 1.75 520 \n", + "165 1.56 695 \n", + "166 1.75 685 \n", + "167 1.80 750 \n", + "168 1.92 630 \n", + "169 1.83 510 \n", + "170 1.63 470 \n", + "171 1.71 660 \n", + "172 1.74 740 \n", + "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]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { - "id": "KE-k7b_Mn5iN", + "id": "BF9MMjoZjSlg", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### See the top 10 rows\n" + "#### print first five rows" ] }, { "metadata": { - "id": "HY2Ps7xMn4ao", + "id": "1vSMQdnHjYNU", "colab_type": "code", - "outputId": "7cd4dc70-c448-44da-e8ba-2b43e0bf6736", + "outputId": "fdd1cf57-e394-46b1-8420-677c7f919472", "colab": { "base_uri": "https://localhost:8080/", "height": 206 @@ -97,9 +240,9 @@ }, "cell_type": "code", "source": [ - "iris_df.head()" + "wine_df.head(5)" ], - "execution_count": 42, + "execution_count": 3, "outputs": [ { "output_type": "execute_result", @@ -123,333 +266,164 @@ " \n", " \n", " \n", - " sepal_length\n", - " sepal_width\n", - " petal_length\n", - " petal_width\n", - " species\n", + " 1\n", + " 14.23\n", + " 1.71\n", + " 2.43\n", + " 15.6\n", + " 127\n", + " 2.8\n", + " 3.06\n", + " .28\n", + " 2.29\n", + " 5.64\n", + " 1.04\n", + " 3.92\n", + " 1065\n", " \n", " \n", " \n", " \n", " 0\n", - " 5.1\n", - " 3.5\n", - " 1.4\n", - " 0.2\n", - " setosa\n", + " 1\n", + " 13.20\n", + " 1.78\n", + " 2.14\n", + " 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", " \n", " \n", " 1\n", - " 4.9\n", - " 3.0\n", - " 1.4\n", - " 0.2\n", - " setosa\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", - " 4.7\n", - " 3.2\n", - " 1.3\n", - " 0.2\n", - " setosa\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", - " 4.6\n", - " 3.1\n", - " 1.5\n", - " 0.2\n", - " setosa\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", - " 5.0\n", - " 3.6\n", - " 1.4\n", - " 0.2\n", - " setosa\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": [ - " 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" + " 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 3.92 \\\n", + "0 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", + "1 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", + "2 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n", + "4 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 \n", + "\n", + " 1065 \n", + "0 1050 \n", + "1 1185 \n", + "2 1480 \n", + "3 735 \n", + "4 1450 " ] }, "metadata": { "tags": [] }, - "execution_count": 42 - } - ] - }, - { - "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", - "outputId": "5086cdb6-0854-4f26-c3b1-c2a0e0e48ef5", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 72 - } - }, - "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": 43, - "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", - "outputId": "febc8804-c116-4069-fd9b-5af5b1bc3c00", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 72 - } - }, - "cell_type": "code", - "source": [ - "print(iris_df.columns)" - ], - "execution_count": 44, - "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", - "outputId": "7ee018bf-a911-4de2-8c46-da228dfb42cb", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "cell_type": "code", - "source": [ - "print(iris_df.index)" - ], - "execution_count": 45, - "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", - "outputId": "8734c31f-6e5c-459c-80c4-8fb8a8832144", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 237 - } - }, - "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": 46, - "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", - "126 6.2 2.8 4.8 1.8 virginica\n", - "135 7.7 3.0 6.1 2.3 virginica\n", - "128 6.4 2.8 5.6 2.1 virginica\n", - "60 5.0 2.0 3.5 1.0 versicolor\n", - "56 6.3 3.3 4.7 1.6 versicolor\n" - ], - "name": "stdout" + "execution_count": 3 } ] }, { "metadata": { - "id": "j32h8022sRT8", + "id": "Tet6P2DvjY3T", "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", - "outputId": "18305166-b616-4a75-b0ca-a52d1b6a286b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 348 - } - }, - "cell_type": "code", - "source": [ - "#original\n", + "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy\n", "\n", - "print(iris_df.head())\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": 47, - "outputs": [ - { - "output_type": "stream", - "text": [ - " sepal_length sepal_width petal_length petal_width species\n", - "126 6.2 2.8 4.8 1.8 virginica\n", - "135 7.7 3.0 6.1 2.3 virginica\n", - "128 6.4 2.8 5.6 2.1 virginica\n", - "60 5.0 2.0 3.5 1.0 versicolor\n", - "56 6.3 3.3 4.7 1.6 versicolor\n", - " sepal_length sepal_width petal_length petal_width species\n", - "126 6.2 28.0 4.8 1.8 virginica\n", - "135 7.7 30.0 6.1 2.3 virginica\n", - "128 6.4 28.0 5.6 2.1 virginica\n", - "60 5.0 20.0 3.5 1.0 versicolor\n", - "56 6.3 33.0 4.7 1.6 versicolor\n", - " sepal_length sepal_width petal_length petal_width species\n", - "126 6.2 2.8 4.8 1.8 virginica\n", - "135 7.7 3.0 6.1 2.3 virginica\n", - "128 6.4 2.8 5.6 2.1 virginica\n", - "60 5.0 2.0 3.5 1.0 versicolor\n", - "56 6.3 3.3 4.7 1.6 versicolor\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "R-Ca-LBLzjiF", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Show all the rows where sepal_width > 3.3" + "[Hint](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)" ] }, { "metadata": { - "id": "WJ7W-F-d0AoZ", + "id": "CMj3qSdJjx0u", "colab_type": "code", - "outputId": "d9caac74-c750-4e50-80e0-7e5cd0bda7db", + "outputId": "8dd723aa-75f0-4873-aa96-280f8de4b409", "colab": { "base_uri": "https://localhost:8080/", - "height": 1179 + "height": 1992 } }, "cell_type": "code", "source": [ - "iris_df[iris_df['sepal_width']>3.3]" + "wine_df_copy = wine_df\n", + "wine_df_copy\n", + "wine_df_copy.iloc[::2]" ], - "execution_count": 48, + "execution_count": 4, "outputs": [ { "output_type": "execute_result", @@ -473,460 +447,1246 @@ " \n", " \n", " \n", - " sepal_length\n", - " sepal_width\n", - " petal_length\n", - " petal_width\n", - " species\n", + " 1\n", + " 14.23\n", + " 1.71\n", + " 2.43\n", + " 15.6\n", + " 127\n", + " 2.8\n", + " 3.06\n", + " .28\n", + " 2.29\n", + " 5.64\n", + " 1.04\n", + " 3.92\n", + " 1065\n", " \n", " \n", " \n", " \n", - " 40\n", - " 5.0\n", - " 3.5\n", - " 1.3\n", - " 0.3\n", - " setosa\n", - " \n", - " \n", - " 46\n", - " 5.1\n", - " 3.8\n", - " 1.6\n", - " 0.2\n", - " setosa\n", + " 0\n", + " 1\n", + " 13.20\n", + " 1.78\n", + " 2.14\n", + " 11.2\n", + " 100\n", + " 2.65\n", + " 2.76\n", + " 0.26\n", + " 1.28\n", + " 4.380000\n", + " 1.05\n", + " 3.40\n", + " 1050\n", " \n", " \n", - " 24\n", - " 4.8\n", - " 3.4\n", - " 1.9\n", - " 0.2\n", - " setosa\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", + " 7.800000\n", + " 0.86\n", + " 3.45\n", + " 1480\n", " \n", " \n", - " 18\n", - " 5.7\n", - " 3.8\n", - " 1.7\n", - " 0.3\n", - " setosa\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", + " 6.750000\n", + " 1.05\n", + " 2.85\n", + " 1450\n", " \n", " \n", - " 148\n", - " 6.2\n", - " 3.4\n", - " 5.4\n", - " 2.3\n", - " virginica\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", + " 5.050000\n", + " 1.06\n", + " 3.58\n", + " 1295\n", + " \n", + " \n", + " 8\n", + " 1\n", + " 13.86\n", + " 1.35\n", + " 2.27\n", + " 16.0\n", + " 98\n", + " 2.98\n", + " 3.15\n", + " 0.22\n", + " 1.85\n", + " 7.220000\n", + " 1.01\n", + " 3.55\n", + " 1045\n", " \n", " \n", - " 20\n", - " 5.4\n", - " 3.4\n", - " 1.7\n", - " 0.2\n", - " setosa\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", + " 5.000000\n", + " 1.17\n", + " 2.82\n", + " 1280\n", + " \n", + " \n", + " 12\n", + " 1\n", + " 14.75\n", + 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+ " 13.76\n", + " 1.53\n", + " 2.70\n", + " 19.5\n", + " 132\n", + " 2.95\n", + " 2.74\n", + " 0.50\n", + " 1.35\n", + " 5.400000\n", + " 1.25\n", + " 3.00\n", + " 1235\n", + " \n", + " \n", + " 34\n", + " 1\n", + " 13.48\n", + " 1.81\n", + " 2.41\n", + " 20.5\n", + " 100\n", + " 2.70\n", + " 2.98\n", + " 0.26\n", + " 1.86\n", + " 5.100000\n", + " 1.04\n", + " 3.47\n", + " 920\n", " \n", " \n", - " 48\n", - " 5.3\n", - " 3.7\n", - " 1.5\n", - " 0.2\n", - " setosa\n", + " 36\n", + " 1\n", + " 13.05\n", + " 1.65\n", + " 2.55\n", + " 18.0\n", + " 98\n", + " 2.45\n", + " 2.43\n", + " 0.29\n", + " 1.44\n", + " 4.250000\n", + " 1.12\n", + " 2.51\n", + " 1105\n", " \n", " \n", - " 109\n", - " 7.2\n", - " 3.6\n", - " 6.1\n", - " 2.5\n", - " virginica\n", + " 38\n", + " 1\n", + " 14.22\n", + " 3.99\n", + " 2.51\n", + " 13.2\n", + " 128\n", + " 3.00\n", + " 3.04\n", + " 0.20\n", + " 2.08\n", + " 5.100000\n", + " 0.89\n", + " 3.53\n", + " 760\n", " \n", " \n", - " 14\n", - " 5.8\n", - " 4.0\n", - 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89 rows × 14 columns

\n", "" ], "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "40 5.0 3.5 1.3 0.3 setosa\n", - "46 5.1 3.8 1.6 0.2 setosa\n", - "24 4.8 3.4 1.9 0.2 setosa\n", - "18 5.7 3.8 1.7 0.3 setosa\n", - "148 6.2 3.4 5.4 2.3 virginica\n", - "20 5.4 3.4 1.7 0.2 setosa\n", - "19 5.1 3.8 1.5 0.3 setosa\n", - "16 5.4 3.9 1.3 0.4 setosa\n", - "11 4.8 3.4 1.6 0.2 setosa\n", - "6 4.6 3.4 1.4 0.3 setosa\n", - "39 5.1 3.4 1.5 0.2 setosa\n", - "136 6.3 3.4 5.6 2.4 virginica\n", - "33 5.5 4.2 1.4 0.2 setosa\n", - "117 7.7 3.8 6.7 2.2 virginica\n", - "4 5.0 3.6 1.4 0.2 setosa\n", - "85 6.0 3.4 4.5 1.6 versicolor\n", - "7 5.0 3.4 1.5 0.2 setosa\n", - "22 4.6 3.6 1.0 0.2 setosa\n", - "48 5.3 3.7 1.5 0.2 setosa\n", - "109 7.2 3.6 6.1 2.5 virginica\n", - "14 5.8 4.0 1.2 0.2 setosa\n", - "44 5.1 3.8 1.9 0.4 setosa\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "17 5.1 3.5 1.4 0.3 setosa\n", - "27 5.2 3.5 1.5 0.2 setosa\n", - "5 5.4 3.9 1.7 0.4 setosa\n", - "31 5.4 3.4 1.5 0.4 setosa\n", - "32 5.2 4.1 1.5 0.1 setosa\n", - "36 5.5 3.5 1.3 0.2 setosa\n", - "131 7.9 3.8 6.4 2.0 virginica\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "15 5.7 4.4 1.5 0.4 setosa\n", - "28 5.2 3.4 1.4 0.2 setosa\n", - "10 5.4 3.7 1.5 0.2 setosa\n", - "26 5.0 3.4 1.6 0.4 setosa\n", - "21 5.1 3.7 1.5 0.4 setosa" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 48 - } - ] - }, - { - "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", - "outputId": "d2b29aeb-6c2d-46a2-9e6e-da1eb111df7d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81 - } - }, - "cell_type": "code", - "source": [ - "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " - ], - "execution_count": 49, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
856.03.44.51.6versicolor
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "85 6.0 3.4 4.5 1.6 versicolor" + "[89 rows x 14 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 49 + "execution_count": 4 } ] }, { "metadata": { - "id": "1lmnB3ot2u7I", + "id": "o6Cs6T1Rjz71", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### Sorting a column by value" + "#### 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) alcalinity_of_ash \n", + "4) magnesium \n", + "5) flavanoids \n", + "6) proanthocyanins \n", + "7) hue " ] }, { "metadata": { - "id": "K7KIj6fv2zWP", + "id": "my8HB4V4j779", "colab_type": "code", - "outputId": "79c7241d-bafd-4130-a4b3-9129aca11ac8", + "outputId": "bcd8472c-3660-4d5a-898c-8610d87f3983", "colab": { "base_uri": "https://localhost:8080/", - "height": 1992 + "height": 2010 } }, "cell_type": "code", "source": [ - "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", - "#pass ascending = False for descending order" + "#wine_df.columns = ['alcohol','malic_acid','alcalinity_of_ash','magnesium','flavanoids','proanthocyanins','hue']\n", + "print(wine_df.iloc[0][1])\n", + "wine_df" ], - "execution_count": 50, + "execution_count": 5, "outputs": [ + { + "output_type": "stream", + "text": [ + "13.2\n" + ], + "name": "stdout" + }, { "output_type": "execute_result", "data": { @@ -949,253 +1709,532 @@ " \n", " \n", " \n", - " sepal_length\n", - " sepal_width\n", - " petal_length\n", - " petal_width\n", - " species\n", + " 1\n", + " 14.23\n", + " 1.71\n", + " 2.43\n", + " 15.6\n", + " 127\n", + " 2.8\n", + " 3.06\n", + " .28\n", + " 2.29\n", + " 5.64\n", + " 1.04\n", + " 3.92\n", + " 1065\n", " \n", " \n", " \n", " \n", - " 60\n", - " 5.0\n", - " 2.0\n", - " 3.5\n", - " 1.0\n", - " versicolor\n", - " \n", - " \n", - " 68\n", - " 6.2\n", - " 2.2\n", - " 4.5\n", - " 1.5\n", - " versicolor\n", - " \n", - " \n", - " 62\n", - " 6.0\n", - " 2.2\n", - " 4.0\n", - " 1.0\n", - " versicolor\n", - " \n", - " \n", - " 119\n", - " 6.0\n", - " 2.2\n", - " 5.0\n", - " 1.5\n", - " virginica\n", - " \n", - " \n", - " 53\n", - " 5.5\n", - " 2.3\n", - " 4.0\n", - " 1.3\n", - " versicolor\n", - " \n", - " \n", - " 41\n", - " 4.5\n", - " 2.3\n", - " 1.3\n", - " 0.3\n", - " setosa\n", - " \n", - " \n", - " 93\n", - " 5.0\n", - " 2.3\n", - " 3.3\n", - " 1.0\n", - " versicolor\n", - " \n", - " \n", - " 87\n", - " 6.3\n", - " 2.3\n", - " 4.4\n", - " 1.3\n", - " versicolor\n", + " 0\n", + " 1\n", + " 13.20\n", + " 1.78\n", + " 2.14\n", + " 11.2\n", + " 100\n", + " 2.65\n", + " 2.76\n", + " 0.26\n", + " 1.28\n", + " 4.380000\n", + " 1.05\n", + " 3.40\n", + " 1050\n", " \n", " \n", - " 81\n", - " 5.5\n", - " 2.4\n", - " 3.7\n", - " 1.0\n", - " versicolor\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", + " 5.680000\n", + " 1.03\n", + " 3.17\n", + " 1185\n", " \n", " \n", - " 57\n", - " 4.9\n", - " 2.4\n", - " 3.3\n", - " 1.0\n", - " versicolor\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", + " 7.800000\n", + " 0.86\n", + " 3.45\n", + " 1480\n", " \n", " \n", - " 80\n", - " 5.5\n", - " 2.4\n", - " 3.8\n", - " 1.1\n", - " versicolor\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", + " 4.320000\n", + " 1.04\n", + " 2.93\n", + " 735\n", " \n", " \n", - " 98\n", - " 5.1\n", - " 2.5\n", - " 3.0\n", - " 1.1\n", - " versicolor\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", + " 6.750000\n", + " 1.05\n", + " 2.85\n", + " 1450\n", " \n", " \n", - " 146\n", - " 6.3\n", - " 2.5\n", - " 5.0\n", - " 1.9\n", - " virginica\n", + " 5\n", + " 1\n", + " 14.39\n", + " 1.87\n", + " 2.45\n", + " 14.6\n", + " 96\n", + " 2.50\n", + " 2.52\n", + " 0.30\n", + " 1.98\n", + " 5.250000\n", + " 1.02\n", + " 3.58\n", + " 1290\n", " \n", " \n", - " 113\n", - " 5.7\n", - " 2.5\n", - " 5.0\n", - " 2.0\n", - " virginica\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", + " 5.050000\n", + " 1.06\n", + " 3.58\n", + " 1295\n", " \n", " \n", - " 89\n", - " 5.5\n", - " 2.5\n", - " 4.0\n", - " 1.3\n", - " versicolor\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", + " 5.200000\n", + " 1.08\n", + " 2.85\n", + " 1045\n", + " \n", + " \n", + " 8\n", + " 1\n", + " 13.86\n", + " 1.35\n", + " 2.27\n", + " 16.0\n", + " 98\n", + " 2.98\n", + " 3.15\n", + " 0.22\n", + " 1.85\n", + " 7.220000\n", + " 1.01\n", + " 3.55\n", + " 1045\n", + " \n", + " 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150 rows × 5 columns

\n", + "

177 rows × 14 columns

\n", "" ], "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "60 5.0 2.0 3.5 1.0 versicolor\n", - "68 6.2 2.2 4.5 1.5 versicolor\n", - "62 6.0 2.2 4.0 1.0 versicolor\n", - "119 6.0 2.2 5.0 1.5 virginica\n", - "53 5.5 2.3 4.0 1.3 versicolor\n", - "41 4.5 2.3 1.3 0.3 setosa\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", - "57 4.9 2.4 3.3 1.0 versicolor\n", - "80 5.5 2.4 3.8 1.1 versicolor\n", - "98 5.1 2.5 3.0 1.1 versicolor\n", - "146 6.3 2.5 5.0 1.9 virginica\n", - "113 5.7 2.5 5.0 2.0 virginica\n", - "89 5.5 2.5 4.0 1.3 versicolor\n", - "108 6.7 2.5 5.8 1.8 virginica\n", - "106 4.9 2.5 4.5 1.7 virginica\n", - "69 5.6 2.5 3.9 1.1 versicolor\n", - "72 6.3 2.5 4.9 1.5 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", - "79 5.7 2.6 3.5 1.0 versicolor\n", - "134 6.1 2.6 5.6 1.4 virginica\n", - "101 5.8 2.7 5.1 1.9 virginica\n", - "123 6.3 2.7 4.9 1.8 virginica\n", - "142 5.8 2.7 5.1 1.9 virginica\n", - "94 5.6 2.7 4.2 1.3 versicolor\n", - "67 5.8 2.7 4.1 1.0 versicolor\n", - "83 6.0 2.7 5.1 1.6 versicolor\n", - ".. ... ... ... ... ...\n", - "28 5.2 3.4 1.4 0.2 setosa\n", - "136 6.3 3.4 5.6 2.4 virginica\n", - "7 5.0 3.4 1.5 0.2 setosa\n", - "85 6.0 3.4 4.5 1.6 versicolor\n", - "31 5.4 3.4 1.5 0.4 setosa\n", - "39 5.1 3.4 1.5 0.2 setosa\n", - "36 5.5 3.5 1.3 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", - "0 5.1 3.5 1.4 0.2 setosa\n", - "17 5.1 3.5 1.4 0.3 setosa\n", - "27 5.2 3.5 1.5 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", - "22 4.6 3.6 1.0 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", - "18 5.7 3.8 1.7 0.3 setosa\n", - "19 5.1 3.8 1.5 0.3 setosa\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", - "131 7.9 3.8 6.4 2.0 virginica\n", - "16 5.4 3.9 1.3 0.4 setosa\n", - "5 5.4 3.9 1.7 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", + " 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 \\\n", + "0 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.380000 1.05 \n", + "1 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.680000 1.03 \n", + "2 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.800000 0.86 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.320000 1.04 \n", + "4 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.750000 1.05 \n", + "5 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 1.98 5.250000 1.02 \n", + "6 1 14.06 2.15 2.61 17.6 121 2.60 2.51 0.31 1.25 5.050000 1.06 \n", + "7 1 14.83 1.64 2.17 14.0 97 2.80 2.98 0.29 1.98 5.200000 1.08 \n", + "8 1 13.86 1.35 2.27 16.0 98 2.98 3.15 0.22 1.85 7.220000 1.01 \n", + "9 1 14.10 2.16 2.30 18.0 105 2.95 3.32 0.22 2.38 5.750000 1.25 \n", + "10 1 14.12 1.48 2.32 16.8 95 2.20 2.43 0.26 1.57 5.000000 1.17 \n", + "11 1 13.75 1.73 2.41 16.0 89 2.60 2.76 0.29 1.81 5.600000 1.15 \n", + "12 1 14.75 1.73 2.39 11.4 91 3.10 3.69 0.43 2.81 5.400000 1.25 \n", + "13 1 14.38 1.87 2.38 12.0 102 3.30 3.64 0.29 2.96 7.500000 1.20 \n", + "14 1 13.63 1.81 2.70 17.2 112 2.85 2.91 0.30 1.46 7.300000 1.28 \n", + "15 1 14.30 1.92 2.72 20.0 120 2.80 3.14 0.33 1.97 6.200000 1.07 \n", + "16 1 13.83 1.57 2.62 20.0 115 2.95 3.40 0.40 1.72 6.600000 1.13 \n", + "17 1 14.19 1.59 2.48 16.5 108 3.30 3.93 0.32 1.86 8.700000 1.23 \n", + "18 1 13.64 3.10 2.56 15.2 116 2.70 3.03 0.17 1.66 5.100000 0.96 \n", + "19 1 14.06 1.63 2.28 16.0 126 3.00 3.17 0.24 2.10 5.650000 1.09 \n", + "20 1 12.93 3.80 2.65 18.6 102 2.41 2.41 0.25 1.98 4.500000 1.03 \n", + "21 1 13.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.800000 1.11 \n", + "22 1 12.85 1.60 2.52 17.8 95 2.48 2.37 0.26 1.46 3.930000 1.09 \n", + "23 1 13.50 1.81 2.61 20.0 96 2.53 2.61 0.28 1.66 3.520000 1.12 \n", + "24 1 13.05 2.05 3.22 25.0 124 2.63 2.68 0.47 1.92 3.580000 1.13 \n", + "25 1 13.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.800000 0.92 \n", + "26 1 13.30 1.72 2.14 17.0 94 2.40 2.19 0.27 1.35 3.950000 1.02 \n", + "27 1 13.87 1.90 2.80 19.4 107 2.95 2.97 0.37 1.76 4.500000 1.25 \n", + "28 1 14.02 1.68 2.21 16.0 96 2.65 2.33 0.26 1.98 4.700000 1.04 \n", + "29 1 13.73 1.50 2.70 22.5 101 3.00 3.25 0.29 2.38 5.700000 1.19 \n", + ".. .. ... ... ... ... ... ... ... ... ... ... ... \n", + "147 3 13.32 3.24 2.38 21.5 92 1.93 0.76 0.45 1.25 8.420000 0.55 \n", + "148 3 13.08 3.90 2.36 21.5 113 1.41 1.39 0.34 1.14 9.400000 0.57 \n", + "149 3 13.50 3.12 2.62 24.0 123 1.40 1.57 0.22 1.25 8.600000 0.59 \n", + "150 3 12.79 2.67 2.48 22.0 112 1.48 1.36 0.24 1.26 10.800000 0.48 \n", + "151 3 13.11 1.90 2.75 25.5 116 2.20 1.28 0.26 1.56 7.100000 0.61 \n", + "152 3 13.23 3.30 2.28 18.5 98 1.80 0.83 0.61 1.87 10.520000 0.56 \n", + "153 3 12.58 1.29 2.10 20.0 103 1.48 0.58 0.53 1.40 7.600000 0.58 \n", + "154 3 13.17 5.19 2.32 22.0 93 1.74 0.63 0.61 1.55 7.900000 0.60 \n", + "155 3 13.84 4.12 2.38 19.5 89 1.80 0.83 0.48 1.56 9.010000 0.57 \n", + "156 3 12.45 3.03 2.64 27.0 97 1.90 0.58 0.63 1.14 7.500000 0.67 \n", + "157 3 14.34 1.68 2.70 25.0 98 2.80 1.31 0.53 2.70 13.000000 0.57 \n", + "158 3 13.48 1.67 2.64 22.5 89 2.60 1.10 0.52 2.29 11.750000 0.57 \n", + "159 3 12.36 3.83 2.38 21.0 88 2.30 0.92 0.50 1.04 7.650000 0.56 \n", + "160 3 13.69 3.26 2.54 20.0 107 1.83 0.56 0.50 0.80 5.880000 0.96 \n", + "161 3 12.85 3.27 2.58 22.0 106 1.65 0.60 0.60 0.96 5.580000 0.87 \n", + "162 3 12.96 3.45 2.35 18.5 106 1.39 0.70 0.40 0.94 5.280000 0.68 \n", + "163 3 13.78 2.76 2.30 22.0 90 1.35 0.68 0.41 1.03 9.580000 0.70 \n", + "164 3 13.73 4.36 2.26 22.5 88 1.28 0.47 0.52 1.15 6.620000 0.78 \n", + "165 3 13.45 3.70 2.60 23.0 111 1.70 0.92 0.43 1.46 10.680000 0.85 \n", + "166 3 12.82 3.37 2.30 19.5 88 1.48 0.66 0.40 0.97 10.260000 0.72 \n", + "167 3 13.58 2.58 2.69 24.5 105 1.55 0.84 0.39 1.54 8.660000 0.74 \n", + "168 3 13.40 4.60 2.86 25.0 112 1.98 0.96 0.27 1.11 8.500000 0.67 \n", + "169 3 12.20 3.03 2.32 19.0 96 1.25 0.49 0.40 0.73 5.500000 0.66 \n", + "170 3 12.77 2.39 2.28 19.5 86 1.39 0.51 0.48 0.64 9.899999 0.57 \n", + "171 3 14.16 2.51 2.48 20.0 91 1.68 0.70 0.44 1.24 9.700000 0.62 \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", + "173 3 13.40 3.91 2.48 23.0 102 1.80 0.75 0.43 1.41 7.300000 0.70 \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", + "175 3 13.17 2.59 2.37 20.0 120 1.65 0.68 0.53 1.46 9.300000 0.60 \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", + "1 3.17 1185 \n", + "2 3.45 1480 \n", + "3 2.93 735 \n", + "4 2.85 1450 \n", + "5 3.58 1290 \n", + "6 3.58 1295 \n", + "7 2.85 1045 \n", + "8 3.55 1045 \n", + "9 3.17 1510 \n", + "10 2.82 1280 \n", + "11 2.90 1320 \n", + "12 2.73 1150 \n", + "13 3.00 1547 \n", + "14 2.88 1310 \n", + "15 2.65 1280 \n", + "16 2.57 1130 \n", + "17 2.82 1680 \n", + "18 3.36 845 \n", + "19 3.71 780 \n", + "20 3.52 770 \n", + "21 4.00 1035 \n", + "22 3.63 1015 \n", + "23 3.82 845 \n", + "24 3.20 830 \n", + "25 3.22 1195 \n", + "26 2.77 1285 \n", + "27 3.40 915 \n", + "28 3.59 1035 \n", + "29 2.71 1285 \n", + ".. ... ... \n", + "147 1.62 650 \n", + "148 1.33 550 \n", + "149 1.30 500 \n", + "150 1.47 480 \n", + "151 1.33 425 \n", + "152 1.51 675 \n", + "153 1.55 640 \n", + "154 1.48 725 \n", + "155 1.64 480 \n", + "156 1.73 880 \n", + "157 1.96 660 \n", + "158 1.78 620 \n", + "159 1.58 520 \n", + "160 1.82 680 \n", + "161 2.11 570 \n", + "162 1.75 675 \n", + "163 1.68 615 \n", + "164 1.75 520 \n", + "165 1.56 695 \n", + "166 1.75 685 \n", + "167 1.80 750 \n", + "168 1.92 630 \n", + "169 1.83 510 \n", + "170 1.63 470 \n", + "171 1.71 660 \n", + "172 1.74 740 \n", + "173 1.56 750 \n", + "174 1.56 835 \n", + "175 1.62 840 \n", + "176 1.60 560 \n", "\n", - "[150 rows x 5 columns]" + "[177 rows x 14 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 50 + "execution_count": 5 } ] }, { "metadata": { - "id": "9jg_Z4YCoMSV", + "id": "Zqi7hwWpkNbH", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### List all the unique species" - ] - }, - { - "metadata": { - "id": "M6EN78ufoJY7", - "colab_type": "code", - "outputId": "39ad4226-53df-42cd-8d9f-32ffe1e1cd41", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - } - }, - "cell_type": "code", - "source": [ - "species = iris_df['species'].unique()\n", + "#### Set the values of the first 3 rows from alcohol as NaN\n", "\n", - "print(species)" - ], - "execution_count": 51, - "outputs": [ - { - "output_type": "stream", - "text": [ - "['virginica' 'versicolor' 'setosa']\n" - ], - "name": "stdout" - } - ] - }, - { - "metadata": { - "id": "wG1i5nxBodmB", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Selecting a particular species using boolean mask (learnt in previous exercise)" + "Hint- Use iloc to select 3 rows of wine_df" ] }, { "metadata": { - "id": "gZvpbKBwoVUe", + "id": "buyT4vX4kPMl", "colab_type": "code", - "outputId": "d56f2768-8b3b-4af1-ba70-6933f8e8dd78", + "outputId": "04c526c8-9bf9-44e9-ca1d-baaf406c1ab2", "colab": { "base_uri": "https://localhost:8080/", - "height": 206 + "height": 1992 } }, "cell_type": "code", "source": [ - "setosa = iris_df[iris_df['species'] == species[0]]\n", - "\n", - "setosa.head()" + "wine_df.iloc[0:3,0] = np.nan\n", + "wine_df" ], - "execution_count": 52, + "execution_count": 6, "outputs": [ { "output_type": "execute_result", @@ -1611,443 +2956,1489 @@ " \n", " \n", " \n", - " sepal_length\n", - " sepal_width\n", - " petal_length\n", - " petal_width\n", - " species\n", + " 1\n", + " 14.23\n", + " 1.71\n", + " 2.43\n", + " 15.6\n", + " 127\n", + " 2.8\n", + " 3.06\n", + " .28\n", + " 2.29\n", + " 5.64\n", + " 1.04\n", + " 3.92\n", + " 1065\n", " \n", " \n", " \n", " \n", - " 126\n", - " 6.2\n", - " 2.8\n", - " 4.8\n", - " 1.8\n", - " virginica\n", + " 0\n", + " NaN\n", + " 13.20\n", + " 1.78\n", + " 2.14\n", + " 11.2\n", + " 100\n", + " 2.65\n", + " 2.76\n", + " 0.26\n", + " 1.28\n", + " 4.380000\n", + " 1.05\n", + " 3.40\n", + " 1050\n", " \n", " \n", - " 135\n", - " 7.7\n", - " 3.0\n", - " 6.1\n", - " 2.3\n", - " virginica\n", + " 1\n", + " NaN\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", + " 5.680000\n", + " 1.03\n", + " 3.17\n", + " 1185\n", " \n", " \n", - " 128\n", - " 6.4\n", - " 2.8\n", - " 5.6\n", - " 2.1\n", - " virginica\n", + " 2\n", + " NaN\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", + " 7.800000\n", + " 0.86\n", + " 3.45\n", + " 1480\n", " \n", " \n", - " 108\n", - " 6.7\n", - " 2.5\n", - " 5.8\n", - " 1.8\n", - " virginica\n", + " 3\n", + " 1.0\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", + " 4.320000\n", + " 1.04\n", + " 2.93\n", + " 735\n", " \n", " \n", - " 102\n", - " 7.1\n", - " 3.0\n", - " 5.9\n", - " 2.1\n", - " virginica\n", + " 4\n", + " 1.0\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", + " 6.750000\n", + " 1.05\n", + " 2.85\n", + " 1450\n", " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "126 6.2 2.8 4.8 1.8 virginica\n", - "135 7.7 3.0 6.1 2.3 virginica\n", - "128 6.4 2.8 5.6 2.1 virginica\n", - "108 6.7 2.5 5.8 1.8 virginica\n", - "102 7.1 3.0 5.9 2.1 virginica" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 52 - } - ] - }, - { - "metadata": { - "id": "7tumfZ3DotPG", - "colab_type": "code", - "outputId": "896719b6-2850-43e4-b316-f57dc9ebcc75", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - } - }, - "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": 53, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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51.014.391.872.4514.6962.502.520.301.985.2500001.023.581290
605.02.03.561.0versicolor14.062.152.6117.61212.602.510.311.255.0500001.063.581295
566.33.34.71.6versicolor71.014.831.642.1714.0972.802.980.291.985.2000001.082.851045
81.013.861.352.2716.0982.983.150.221.857.2200001.013.551045
91.014.102.162.3018.01052.953.320.222.385.7500001.253.171510
955.73.04.21.2versicolor101.014.121.482.3216.8952.202.430.261.575.0000001.172.821280
111.013.751.732.4116.0892.602.760.291.815.6000001.152.901320
121.014.751.732.3911.4913.103.690.432.815.4000001.252.731150
131.014.381.872.3812.01023.303.640.292.967.5000001.203.001547
141.013.631.812.7017.21122.852.910.301.467.3000001.282.881310
151.014.301.922.7220.01202.803.140.331.976.2000001.072.651280
161.013.831.572.6220.01152.953.400.401.726.6000001.132.571130
171.014.191.592.4816.51083.303.930.321.868.7000001.232.821680
181.013.643.102.5615.21162.703.030.171.665.1000000.963.36845
191.014.061.632.2816.01263.003.170.242.105.6500001.093.71780
201.012.933.802.6518.61022.412.410.251.984.5000001.033.52770
211.013.711.862.3616.61012.612.880.271.693.8000001.114.001035
766.82.84.81.4versicolor221.012.851.602.5217.8952.482.370.261.463.9300001.093.631015
231.013.501.812.6120.0962.532.610.281.663.5200001.123.82845
786.02.94.51.5versicolor241.013.052.053.2225.01242.632.680.471.923.5800001.133.20830
251.013.391.772.6216.1932.852.940.341.454.8000000.923.221195
261.013.301.722.1417.0942.402.190.271.353.9500001.022.771285
271.013.871.902.8019.41072.952.970.371.764.5000001.253.40915
281.014.021.682.2116.0962.652.330.261.984.7000001.043.591035
291.013.731.502.7022.51013.003.250.292.385.7000001.192.711285
.............................................
1473.013.323.242.3821.5921.930.760.451.258.4200000.551.62650
1483.013.083.902.3621.51131.411.390.341.149.4000000.571.33550
1493.013.503.122.6224.01231.401.570.221.258.6000000.591.30500
1503.012.792.672.4822.01121.481.360.241.2610.8000000.481.47480
1513.013.111.902.7525.51162.201.280.261.567.1000000.611.33425
1523.013.233.302.2818.5981.800.830.611.8710.5200000.561.51675
1533.012.581.292.1020.01031.480.580.531.407.6000000.581.55640
1543.013.175.192.3222.0931.740.630.611.557.9000000.601.48725
1553.013.844.122.3819.5891.800.830.481.569.0100000.571.64480
1563.012.453.032.6427.0971.900.580.631.147.5000000.671.73880
1573.014.341.682.7025.0982.801.310.532.7013.0000000.571.96660
1583.013.481.672.6422.5892.601.100.522.2911.7500000.571.78620
1593.012.363.832.3821.0882.300.920.501.047.6500000.561.58520
1603.013.693.262.5420.01071.830.560.500.805.8800000.961.82680
1613.012.853.272.5822.01061.650.600.600.965.5800000.872.11570
1623.012.963.452.3518.51061.390.700.400.945.2800000.681.75675
1633.013.782.762.3022.0901.350.680.411.039.5800000.701.68615
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"172 0.64 1.74 740 \n", + "173 0.70 1.56 750 \n", + "174 0.59 1.56 835 \n", + "175 0.60 1.62 840 \n", + "176 0.61 1.60 560 \n", + "\n", + "[177 rows x 14 columns]" ] }, "metadata": { "tags": [] }, - "execution_count": 53 + "execution_count": 6 } ] }, { "metadata": { - "id": "cUYm5UqVpDPy", + "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", - "outputId": "09f9a382-f213-4982-c8c0-fac805f4f6f6", + "outputId": "22415fa2-1aed-4c4a-978b-8b0db8578b84", "colab": { "base_uri": "https://localhost:8080/", - "height": 206 + "height": 35 } }, "cell_type": "code", "source": [ + "arr = np.array([])\n", + "import random\n", + "for i in range(10):\n", + " a = random.randint(0,10)\n", + " arr = np.append(arr,a)\n", "\n", - "\n", - "virginica = iris_df[iris_df['species'] == species[2]]\n", - "\n", - "virginica.head()" + "print(arr)" ], - "execution_count": 54, + "execution_count": 7, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
405.03.51.30.3setosa
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "40 5.0 3.5 1.3 0.3 setosa\n", - "46 5.1 3.8 1.6 0.2 setosa\n", - "38 4.4 3.0 1.3 0.2 setosa\n", - "24 4.8 3.4 1.9 0.2 setosa\n", - "18 5.7 3.8 1.7 0.3 setosa" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 54 + "output_type": "stream", + "text": [ + "[ 0. 4. 1. 9. 8. 9. 9. 3. 5. 10.]\n" + ], + "name": "stdout" } ] }, { "metadata": { - "id": "-y1wDc8SpdQs", + "id": "hELUakyXmFSu", "colab_type": "text" }, "cell_type": "markdown", "source": [ - "#### Describe each created species to see the difference\n", - "\n" + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" ] }, { "metadata": { - "id": "eHrn3ZVRpOk5", + "id": "zMgaNnNHmP01", "colab_type": "code", - "outputId": "fc5247a4-25cf-4cc3-c514-9651ad6c1f05", + "outputId": "d123c184-59be-4a7f-ea9d-0f4067eacae8", "colab": { "base_uri": "https://localhost:8080/", - "height": 300 + "height": 2350 } }, "cell_type": "code", "source": [ - "setosa.describe()" + "for i in arr:\n", + " i = int(i)\n", + " wine_df.iat[i,0] = np.nan\n", + " \n", + "print(wine_df)" ], - "execution_count": 55, + "execution_count": 8, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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+ "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", + "outputId": "f61cfd32-0447-4c74-d062-f163ead5f626", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + } + }, + "cell_type": "code", + "source": [ + "wine_df.isnull().sum()" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { "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" + "1 9\n", + "14.23 0\n", + "1.71 0\n", + "2.43 0\n", + "15.6 0\n", + "127 0\n", + "2.8 0\n", + "3.06 0\n", + ".28 0\n", + "2.29 0\n", + "5.64 0\n", + "1.04 0\n", + "3.92 0\n", + "1065 0\n", + "dtype: int64" ] }, "metadata": { "tags": [] }, - "execution_count": 55 + "execution_count": 9 } ] }, { "metadata": { - "id": "GwJFT2GlpwUv", + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "As7IC6Ktms8-", "colab_type": "code", - "outputId": "feb2e1b2-57ae-42a8-dbf0-82fa18197ad7", + "outputId": "974d849a-cdd2-45c9-9e14-56a7bf30780b", "colab": { "base_uri": "https://localhost:8080/", - "height": 300 + "height": 1992 } }, "cell_type": "code", "source": [ - "versicolor.describe()" + "wine_df.dropna()" ], - "execution_count": 56, + "execution_count": 10, "outputs": [ { "output_type": "execute_result", @@ -2071,283 +4462,1199 @@ " \n", " \n", " \n", - " sepal_length\n", 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sepal_lengthsepal_widthpetal_lengthpetal_width
191.014.061.632.2816.01263.003.170.242.105.6500001.093.71780
count50.0000050.00000050.00000050.00000201.012.933.802.6518.61022.412.410.251.984.5000001.033.52770
mean5.006003.4180001.4640000.24400211.013.711.862.3616.61012.612.880.271.693.8000001.114.001035
std0.352490.3810240.1735110.10721221.012.851.602.5217.8952.482.370.261.463.9300001.093.631015
231.013.501.812.6120.0962.532.610.281.663.5200001.123.82845
min4.300002.3000001.0000000.10000241.013.052.053.2225.01242.632.680.471.923.5800001.133.20830
251.013.391.772.6216.1932.852.940.341.454.8000000.923.221195
25%4.800003.1250001.4000000.20000261.013.301.722.1417.0942.402.190.271.353.9500001.022.771285
50%5.000003.4000001.5000000.20000271.013.871.902.8019.41072.952.970.371.764.5000001.253.40915
75%5.200003.6750001.5750000.30000281.014.021.682.2116.0962.652.330.261.984.7000001.043.591035
291.013.731.502.7022.51013.003.250.292.385.7000001.192.711285
301.013.581.662.3619.11062.863.190.221.956.9000001.092.881515
max5.800004.4000001.9000000.60000311.013.681.832.3617.21042.422.690.421.973.8400001.232.87990
321.013.761.532.7019.51322.952.740.501.355.4000001.253.001235
331.013.511.802.6519.01102.352.530.291.544.2000001.102.871095
341.013.481.812.4120.51002.702.980.261.865.1000001.043.47920
351.013.281.642.8415.51102.602.680.341.364.6000001.092.78880
361.013.051.652.5518.0982.452.430.291.444.2500001.122.511105
371.013.071.502.1015.5982.402.640.281.373.7000001.182.691020
381.014.223.992.5113.21283.003.040.202.085.1000000.893.53760
.............................................
1473.013.323.242.3821.5921.930.760.451.258.4200000.551.62650
1483.013.083.902.3621.51131.411.390.341.149.4000000.571.33550
1493.013.503.122.6224.01231.401.570.221.258.6000000.591.30500
1503.012.792.672.4822.01121.481.360.241.2610.8000000.481.47480
1513.013.111.902.7525.51162.201.280.261.567.1000000.611.33425
1523.013.233.302.2818.5981.800.830.611.8710.5200000.561.51675
1533.012.581.292.1020.01031.480.580.531.407.6000000.581.55640
1543.013.175.192.3222.0931.740.630.611.557.9000000.601.48725
1553.013.844.122.3819.5891.800.830.481.569.0100000.571.64480
1563.012.453.032.6427.0971.900.580.631.147.5000000.671.73880
1573.014.341.682.7025.0982.801.310.532.7013.0000000.571.96660
1583.013.481.672.6422.5892.601.100.522.2911.7500000.571.78620
1593.012.363.832.3821.0882.300.920.501.047.6500000.561.58520
1603.013.693.262.5420.01071.830.560.500.805.8800000.961.82680
1613.012.853.272.5822.01061.650.600.600.965.5800000.872.11570
1623.012.963.452.3518.51061.390.700.400.945.2800000.681.75675
1633.013.782.762.3022.0901.350.680.411.039.5800000.701.68615
1643.013.734.362.2622.5881.280.470.521.156.6200000.781.75520
1653.013.453.702.6023.01111.700.920.431.4610.6800000.851.56695
1663.012.823.372.3019.5881.480.660.400.9710.2600000.721.75685
1673.013.582.582.6924.51051.550.840.391.548.6600000.741.80750
1683.013.404.602.8625.01121.980.960.271.118.5000000.671.92630
1693.012.203.032.3219.0961.250.490.400.735.5000000.661.83510
1703.012.772.392.2819.5861.390.510.480.649.8999990.571.63470
1713.014.162.512.4820.0911.680.700.441.249.7000000.621.71660
1723.013.715.652.4520.5951.680.610.521.067.7000000.641.74740
1733.013.403.912.4823.01021.800.750.431.417.3000000.701.56750
1743.013.274.282.2620.01201.590.690.431.3510.2000000.591.56835
1753.013.172.592.3720.01201.650.680.531.469.3000000.601.62840
1763.014.134.102.7424.5962.050.760.561.359.2000000.611.60560
\n", + "

168 rows × 14 columns

\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": 57 - } - ] - }, - { - "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", - "outputId": "c3577877-0f14-47aa-dd6c-f9ad73a0603f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 402 - } - }, - "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": 58, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(array([ 4., 1., 6., 5., 12., 8., 4., 5., 2., 3.]),\n", - " array([4.3 , 4.45, 4.6 , 4.75, 4.9 , 5.05, 5.2 , 5.35, 5.5 , 5.65, 5.8 ]),\n", - " )" + " 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 \\\n", + "6 1.0 14.06 2.15 2.61 17.6 121 2.60 2.51 0.31 1.25 5.050000 \n", + "7 1.0 14.83 1.64 2.17 14.0 97 2.80 2.98 0.29 1.98 5.200000 \n", + "11 1.0 13.75 1.73 2.41 16.0 89 2.60 2.76 0.29 1.81 5.600000 \n", + "12 1.0 14.75 1.73 2.39 11.4 91 3.10 3.69 0.43 2.81 5.400000 \n", + "13 1.0 14.38 1.87 2.38 12.0 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