From 2d88e14baa8f3020ce26d9b4c437f2a06f34a78a Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:43:07 +0530 Subject: [PATCH 1/8] Created using Colaboratory --- Basic_Pandas.ipynb | 1026 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1026 insertions(+) create mode 100644 Basic_Pandas.ipynb diff --git a/Basic_Pandas.ipynb b/Basic_Pandas.ipynb new file mode 100644 index 0000000..28fbc0a --- /dev/null +++ b/Basic_Pandas.ipynb @@ -0,0 +1,1026 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Basic Pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "f5238ca4-7558-49bf-b095-cac689c2b77c" + }, + "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": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25]\n", + " \n", + "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19, 'U': 20, 'V': 21, 'W': 22, 'X': 23, 'Y': 24, 'Z': 25}\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "6ouDfjWab_Mc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "3b6db407-a594-40f1-a9cd-df4f4577e00f" + }, + "cell_type": "code", + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 A\n", + "1 B\n", + "2 C\n", + "3 D\n", + "4 E\n", + "5 F\n", + "6 G\n", + "7 H\n", + "8 I\n", + "9 J\n", + "10 K\n", + "11 L\n", + "12 M\n", + "13 N\n", + "14 O\n", + "15 P\n", + "16 Q\n", + "17 R\n", + "18 S\n", + "19 T\n", + "20 U\n", + "21 V\n", + "22 W\n", + "23 X\n", + "24 Y\n", + "25 Z\n", + "dtype: object\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "At7nY7vVcBZ3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "e7e5899f-fb37-4ebf-c81f-900e907e0a3d" + }, + "cell_type": "code", + "source": [ + "series2 = pd.Series(numbers)\n", + "print(series2)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n", + "10 10\n", + "11 11\n", + "12 12\n", + "13 13\n", + "14 14\n", + "15 15\n", + "16 16\n", + "17 17\n", + "18 18\n", + "19 19\n", + "20 20\n", + "21 21\n", + "22 22\n", + "23 23\n", + "24 24\n", + "25 25\n", + "dtype: int64\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "J5z-2CWAdH6N", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 476 + }, + "outputId": "a7debffb-e831-4291-e3b5-62113c463301" + }, + "cell_type": "code", + "source": [ + "series3 = pd.Series(alpha_numbers)\n", + "print(series3)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "F 5\n", + "G 6\n", + "H 7\n", + "I 8\n", + "J 9\n", + "K 10\n", + "L 11\n", + "M 12\n", + "N 13\n", + "O 14\n", + "P 15\n", + "Q 16\n", + "R 17\n", + "S 18\n", + "T 19\n", + "U 20\n", + "V 21\n", + "W 22\n", + "X 23\n", + "Y 24\n", + "Z 25\n", + "dtype: int64\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "fYzblGGudKjO", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 119 + }, + "outputId": "6ae55281-4ec5-4bc4-bd65-90369c27ac1d" + }, + "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()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "OwsJIf5feTtg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create DataFrame from lists\n", + "\n", + "[DataFrame Doc](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)" + ] + }, + { + "metadata": { + "id": "73UTZ07EdWki", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "33434e79-ae08-494a-e6a6-dc3e6cc4244c" + }, + "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": 8, + "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": 8 + } + ] + }, + { + "metadata": { + "id": "uaK_1EO9etGS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "outputId": "54d800e0-240a-41fa-86ab-cd3187ebcc06" + }, + "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": 9, + "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": 9 + } + ] + }, + { + "metadata": { + "id": "ZYonoaW8gEAJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Extract Items from a series" + ] + }, + { + "metadata": { + "id": "tc1-KX_Bfe7U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "2133e72c-2844-4939-be3f-c5fa4bac0644" + }, + "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": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " vowels\n", + "0 a\n", + "4 e\n", + "8 i\n", + "14 o\n", + "20 u" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "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": "7b7269c1-f08d-47fb-8b11-c26af622b5d0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "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": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['We', 'Are', 'Learning', 'Pandas']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "qn47ee-MkZN8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Reindexing" + ] + }, + { + "metadata": { + "id": "h5R0JL2NjuFS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "188b653a-fdd8-4f5d-8c8b-fc2c33f149f9" + }, + "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": 12, + "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": 13 + } + ] + } + ] +} \ No newline at end of file From 19388c6e5be2d8ea6718c0c178ce5ffa7717c701 Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:47:08 +0530 Subject: [PATCH 2/8] Created using Colaboratory --- Get_to_know_your_Data.ipynb | 2354 +++++++++++++++++++++++++++++++++++ 1 file changed, 2354 insertions(+) create mode 100644 Get_to_know_your_Data.ipynb diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb new file mode 100644 index 0000000..ba0e9aa --- /dev/null +++ b/Get_to_know_your_Data.ipynb @@ -0,0 +1,2354 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "J82LU53m_OU0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] + }, + { + "metadata": { + "id": "ZyO1UXL8mtSj", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yXTzTowtnwGI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "metadata": { + "id": "H1Bjlb5wm9f-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "metadata": { + "id": "HY2Ps7xMn4ao", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "c1b40e2b-a748-488d-a0c3-b4b88f801fc4" + }, + "cell_type": "code", + "source": [ + "iris_df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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14.93.01.40.2setosa
24.73.21.30.2setosa
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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": 3 + } + ] + }, + { + "metadata": { + "id": "ZQXekIodqOZu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Find number of rows and columns\n" + ] + }, + { + "metadata": { + "id": "6Y-A-lbFqR82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "764285d6-9303-4fdf-e3d9-6bb96d4b0b8a" + }, + "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": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "150\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4ckCiGPhrC_t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Print all columns" + ] + }, + { + "metadata": { + "id": "S6jgMyRDrF2a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "1134eda4-9a4d-46b3-f0da-c20ffe82a7d9" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "kVav5-ACtIqS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Check Index\n" + ] + }, + { + "metadata": { + "id": "iu3I9zIGtLDX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "fa4774d9-dc67-4771-8a1d-7c0223a942c8" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "psCc7PborOCQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Right now the iris_data set has all the species grouped together let's shuffle it" + ] + }, + { + "metadata": { + "id": "Bxc8i6avrZPw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "2a2faa4a-c854-45b4-db7b-1a675fe31519" + }, + "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": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "130 7.4 2.8 6.1 1.9 virginica\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "j32h8022sRT8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### We can also apply an operation on whole column of iris_df" + ] + }, + { + "metadata": { + "id": "seYXHXsYsYJI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "outputId": "f5bb81e2-46b7-4850-ae80-ca05912232ab" + }, + "cell_type": "code", + "source": [ + "#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": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "130 7.4 2.8 6.1 1.9 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "21 5.1 37.0 1.5 0.4 setosa\n", + "8 4.4 29.0 1.4 0.2 setosa\n", + "31 5.4 34.0 1.5 0.4 setosa\n", + "35 5.0 32.0 1.2 0.2 setosa\n", + "130 7.4 28.0 6.1 1.9 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "130 7.4 2.8 6.1 1.9 virginica\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "R-Ca-LBLzjiF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Show all the rows where sepal_width > 3.3" + ] + }, + { + "metadata": { + "id": "WJ7W-F-d0AoZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1165 + }, + "outputId": "408f5d34-8ac8-41cf-c142-a84fe5e2242d" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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1177.73.86.72.2virginica
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64.63.41.40.3setosa
325.24.11.50.1setosa
395.13.41.50.2setosa
105.43.71.50.2setosa
1317.93.86.42.0virginica
1366.33.45.62.4virginica
485.33.71.50.2setosa
856.03.44.51.6versicolor
465.13.81.60.2setosa
1097.23.66.12.5virginica
405.03.51.30.3setosa
365.53.51.30.2setosa
224.63.61.00.2setosa
55.43.91.70.4setosa
285.23.41.40.2setosa
155.74.41.50.4setosa
1486.23.45.42.3virginica
45.03.61.40.2setosa
185.73.81.70.3setosa
05.13.51.40.2setosa
145.84.01.20.2setosa
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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": 11 + } + ] + }, + { + "metadata": { + "id": "1lmnB3ot2u7I", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Sorting a column by value" + ] + }, + { + "metadata": { + "id": "K7KIj6fv2zWP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 + }, + "outputId": "6e227ef9-1d6c-4e67-b5d3-663a2dea4318" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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605.02.03.51.0versicolor
1196.02.25.01.5virginica
626.02.24.01.0versicolor
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535.52.34.01.3versicolor
876.32.34.41.3versicolor
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414.52.31.30.3setosa
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805.52.43.81.1versicolor
574.92.43.31.0versicolor
695.62.53.91.1versicolor
895.52.54.01.3versicolor
985.12.53.01.1versicolor
726.32.54.91.5versicolor
1466.32.55.01.9virginica
1135.72.55.02.0virginica
1064.92.54.51.7virginica
1086.72.55.81.8virginica
1346.12.65.61.4virginica
925.82.64.01.2versicolor
1187.72.66.92.3virginica
795.72.63.51.0versicolor
905.52.64.41.2versicolor
1116.42.75.31.9virginica
1015.82.75.11.9virginica
1425.82.75.11.9virginica
825.82.73.91.2versicolor
1236.32.74.91.8virginica
595.22.73.91.4versicolor
..................
64.63.41.40.3setosa
265.03.41.60.4setosa
205.43.41.70.2setosa
856.03.44.51.6versicolor
285.23.41.40.2setosa
1486.23.45.42.3virginica
435.03.51.60.6setosa
275.23.51.50.2setosa
405.03.51.30.3setosa
365.53.51.30.2setosa
05.13.51.40.2setosa
175.13.51.40.3setosa
45.03.61.40.2setosa
224.63.61.00.2setosa
1097.23.66.12.5virginica
215.13.71.50.4setosa
485.33.71.50.2setosa
105.43.71.50.2setosa
1177.73.86.72.2virginica
465.13.81.60.2setosa
185.73.81.70.3setosa
445.13.81.90.4setosa
1317.93.86.42.0virginica
195.13.81.50.3setosa
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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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
215.13.71.50.4setosa
84.42.91.40.2setosa
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355.03.21.20.2setosa
195.13.81.50.3setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "8 4.4 2.9 1.4 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "a47dd0a5-33ce-4643-9095-5f350e257f88" + }, + "cell_type": "code", + "source": [ + "# do the same for other 2 species \n", + "versicolor = iris_df[iris_df['species'] == species[1]]\n", + "\n", + "versicolor.head()" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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705.93.24.81.8versicolor
526.93.14.91.5versicolor
656.73.14.41.4versicolor
935.02.33.31.0versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "70 5.9 3.2 4.8 1.8 versicolor\n", + "52 6.9 3.1 4.9 1.5 versicolor\n", + "65 6.7 3.1 4.4 1.4 versicolor\n", + "93 5.0 2.3 3.3 1.0 versicolor\n", + "91 6.1 3.0 4.6 1.4 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "-y1wDc8SpdQs", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Describe each created species to see the difference\n", + "\n" + ] + }, + { + "metadata": { + "id": "eHrn3ZVRpOk5", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "52f1600a-6aa9-44a1-b8a1-9e2fda384c20" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count50.0000050.00000050.00000050.00000
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "count 50.000000 50.000000 50.000000 50.000000\n", + "mean 5.936000 2.770000 4.260000 1.326000\n", + "std 0.516171 0.313798 0.469911 0.197753\n", + "min 4.900000 2.000000 3.000000 1.000000\n", + "25% 5.600000 2.525000 4.000000 1.200000\n", + "50% 5.900000 2.800000 4.350000 1.300000\n", + "75% 6.300000 3.000000 4.600000 1.500000\n", + "max 7.000000 3.400000 5.100000 1.800000" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "metadata": { + "id": "Vdu0ulZWtr09", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Let's plot and see the difference" + ] + }, + { + "metadata": { + "id": "PEVMzRvpttmD", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### import matplotlib.pyplot " + ] + }, + { + "metadata": { + "id": "rqDXuuAtt7C3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 + }, + "outputId": "1922eb5f-f268-4dec-c37d-e9d4a6972d70" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "#hist creates a histogram there are many more plots(see the documentation) you can play with it.\n", + "\n", + "plt.hist(setosa['sepal_length'])\n", + "plt.hist(versicolor['sepal_length'])\n", + "plt.hist(virginica['sepal_length'])" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 4., 1., 6., 10., 5., 8., 5., 3., 5., 3.]),\n", + " array([4.9 , 5.11, 5.32, 5.53, 5.74, 5.95, 6.16, 6.37, 6.58, 6.79, 7. ]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 20 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 7e8bd27fee7a8b1c8dd2cebb5ae9f534faea8bfa Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:48:05 +0530 Subject: [PATCH 3/8] Delete ardev472.ipynb --- ardev472.ipynb | 32 -------------------------------- 1 file changed, 32 deletions(-) delete mode 100644 ardev472.ipynb diff --git a/ardev472.ipynb b/ardev472.ipynb deleted file mode 100644 index 9e2543a..0000000 --- a/ardev472.ipynb +++ /dev/null @@ -1,32 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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 -} From cecc76aa011f135bb73bc46d9b83ae0e0b1a42b0 Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:54:51 +0530 Subject: [PATCH 4/8] Create ardev472.ipynb --- ardev472.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 ardev472.ipynb diff --git a/ardev472.ipynb b/ardev472.ipynb new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/ardev472.ipynb @@ -0,0 +1 @@ + From 972326622c65ac3361a2327b8213fb3b0a9ad8ac Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:55:47 +0530 Subject: [PATCH 5/8] Delete ardev472.ipynb --- ardev472.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 100644 ardev472.ipynb diff --git a/ardev472.ipynb b/ardev472.ipynb deleted file mode 100644 index 8b13789..0000000 --- a/ardev472.ipynb +++ /dev/null @@ -1 +0,0 @@ - From 4eeac863d204209561709fe7025ad84ef79059be Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Wed, 13 Feb 2019 22:56:41 +0530 Subject: [PATCH 6/8] Created using Colaboratory --- ardev472.ipynb | 41 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 ardev472.ipynb diff --git a/ardev472.ipynb b/ardev472.ipynb new file mode 100644 index 0000000..7652cd1 --- /dev/null +++ b/ardev472.ipynb @@ -0,0 +1,41 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Untitled0.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "9J6v6bw65Pdb", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 9f2d2866e2a85ff362ad1c672bce970bb3ec8ec5 Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Thu, 14 Feb 2019 00:27:17 +0530 Subject: [PATCH 7/8] Created using Colaboratory --- Exercise.ipynb | 309 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 309 insertions(+) create mode 100644 Exercise.ipynb diff --git a/Exercise.ipynb b/Exercise.ipynb new file mode 100644 index 0000000..9f08baf --- /dev/null +++ b/Exercise.ipynb @@ -0,0 +1,309 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Exercise.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "2LTtpUJEibjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "metadata": { + "id": "c3_UBbMRhiKx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "tp-cTCyWi8mR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n", + "\n", + "This is a wine dataset\n", + "\n" + ] + }, + { + "metadata": { + "id": "DMojQY3thrRi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BF9MMjoZjSlg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### print first five rows" + ] + }, + { + "metadata": { + "id": "1vSMQdnHjYNU", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Tet6P2DvjY3T", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy\n", + "\n", + "[Hint](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)" + ] + }, + { + "metadata": { + "id": "CMj3qSdJjx0u", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "o6Cs6T1Rjz71", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) Alcohol \n", + "2) Malic acid \n", + "3) Ash \n", + "4) Alcalinity of ash \n", + "5) Magnesium \n", + "6) Total phenols \n", + "7) Flavanoids \n", + "8) Nonflavanoid phenols \n", + "9) Proanthocyanins \n", + "10)Color intensity \n", + "11)Hue \n", + "12)OD280/OD315 of diluted wines \n", + "13)Proline " + ] + }, + { + "metadata": { + "id": "my8HB4V4j779", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Zqi7hwWpkNbH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Set the values of the first 3 rows from alcohol as NaN\n", + "\n", + "Hint- Use iloc to select 3 rows of wine_df" + ] + }, + { + "metadata": { + "id": "buyT4vX4kPMl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RQMNI2UHkP3o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "metadata": { + "id": "xunmCjaEmDwZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hELUakyXmFSu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "metadata": { + "id": "zMgaNnNHmP01", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PHyK_vRsmRwV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "metadata": { + "id": "EnOYhmEqmfKp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "As7IC6Ktms8-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DlpG8drhmz7W", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "metadata": { + "id": "mD40T0Cnm5SA", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From ba073f1b22047ca22b9ab9ea25e099fe69f16950 Mon Sep 17 00:00:00 2001 From: Amit Rai <42401957+ardev472@users.noreply.github.com> Date: Thu, 14 Feb 2019 00:48:15 +0530 Subject: [PATCH 8/8] Created using Colaboratory --- Exercise.ipynb | 3398 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 3361 insertions(+), 37 deletions(-) diff --git a/Exercise.ipynb b/Exercise.ipynb index 9f08baf..0d33f24 100644 --- a/Exercise.ipynb +++ b/Exercise.ipynb @@ -72,7 +72,7 @@ }, "cell_type": "code", "source": [ - "" + "wine_df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data')" ], "execution_count": 0, "outputs": [] @@ -91,14 +91,168 @@ "metadata": { "id": "1vSMQdnHjYNU", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "9b08cf51-99fc-4e12-abc7-45503b273261" }, "cell_type": "code", "source": [ - "" + "wine_df.head()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + 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"colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "4fe88d4c-d692-435c-ecf1-963f7d271fa2" }, "cell_type": "code", "source": [ - "" + "wine_df.columns = ['Column 1','Alcohol','MAlic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids','Nonflavanoid phenols', 'Proanthocyanins','Color intensity','Hue', 'OD280/OD315 OF diluted wines', 'Proline']\n", + "wine_df.head()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Column 1AlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
0113.201.782.1411.21002.652.760.261.284.381.053.401050
1113.162.362.6718.61012.803.240.302.815.681.033.171185
2114.371.952.5016.81133.853.490.242.187.800.863.451480
3113.242.592.8721.01182.802.690.391.824.321.042.93735
4114.201.762.4515.21123.273.390.341.976.751.052.851450
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" + ], + "text/plain": [ + " Column 1 Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 13.20 1.78 2.14 11.2 100 \n", + "1 1 13.16 2.36 2.67 18.6 101 \n", + "2 1 14.37 1.95 2.50 16.8 113 \n", + "3 1 13.24 2.59 2.87 21.0 118 \n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.65 2.76 0.26 1.28 \n", + "1 2.80 3.24 0.30 2.81 \n", + "2 3.85 3.49 0.24 2.18 \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "\n", + " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", + "0 4.38 1.05 3.40 1050 \n", + "1 5.68 1.03 3.17 1185 \n", + "2 7.80 0.86 3.45 1480 \n", + "3 4.32 1.04 2.93 735 \n", + "4 6.75 1.05 2.85 1450 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] }, { "metadata": { @@ -179,14 +1717,176 @@ "metadata": { "id": "buyT4vX4kPMl", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "e150178e-4937-48dc-9ea7-18c578212897" }, "cell_type": "code", "source": [ - "" + "wine_df.iloc[:3]=np.nan\n", + "wine_df.head()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Column 1AlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31.013.242.592.8721.0118.02.802.690.391.824.321.042.93735.0
41.014.201.762.4515.2112.03.273.390.341.976.751.052.851450.0
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" + ], + "text/plain": [ + " Column 1 Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN \n", + "3 1.0 13.24 2.59 2.87 21.0 118.0 \n", + "4 1.0 14.20 1.76 2.45 15.2 112.0 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "\n", + " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 4.32 1.04 2.93 735.0 \n", + "4 6.75 1.05 2.85 1450.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] }, { "metadata": { @@ -202,14 +1902,28 @@ "metadata": { "id": "xunmCjaEmDwZ", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "0ad33615-ece8-4b2c-d6ef-1c98634a59d9" }, "cell_type": "code", "source": [ - "" + "import random\n", + "random =random.sample(range(1,11),10)\n", + "print(random)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[8, 2, 9, 5, 10, 1, 7, 4, 6, 3]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -225,14 +1939,276 @@ "metadata": { "id": "zMgaNnNHmP01", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + }, + "outputId": "f470c62d-d209-4b07-f05f-549e762bd2a6" }, "cell_type": "code", "source": [ - "" + "wine_df.loc[random,'Alcohol']=np.nan\n", + "wine_df.head(10)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Column 1AlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31.0NaN2.592.8721.0118.02.802.690.391.824.321.042.93735.0
41.0NaN1.762.4515.2112.03.273.390.341.976.751.052.851450.0
51.0NaN1.872.4514.696.02.502.520.301.985.251.023.581290.0
61.0NaN2.152.6117.6121.02.602.510.311.255.051.063.581295.0
71.0NaN1.642.1714.097.02.802.980.291.985.201.082.851045.0
81.0NaN1.352.2716.098.02.983.150.221.857.221.013.551045.0
91.0NaN2.162.3018.0105.02.953.320.222.385.751.253.171510.0
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" + ], + "text/plain": [ + " Column 1 Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\n", + "0 NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN \n", + "3 1.0 NaN 2.59 2.87 21.0 118.0 \n", + "4 1.0 NaN 1.76 2.45 15.2 112.0 \n", + "5 1.0 NaN 1.87 2.45 14.6 96.0 \n", + "6 1.0 NaN 2.15 2.61 17.6 121.0 \n", + "7 1.0 NaN 1.64 2.17 14.0 97.0 \n", + "8 1.0 NaN 1.35 2.27 16.0 98.0 \n", + "9 1.0 NaN 2.16 2.30 18.0 105.0 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "5 2.50 2.52 0.30 1.98 \n", + "6 2.60 2.51 0.31 1.25 \n", + "7 2.80 2.98 0.29 1.98 \n", + "8 2.98 3.15 0.22 1.85 \n", + "9 2.95 3.32 0.22 2.38 \n", + "\n", + " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 4.32 1.04 2.93 735.0 \n", + "4 6.75 1.05 2.85 1450.0 \n", + "5 5.25 1.02 3.58 1290.0 \n", + "6 5.05 1.06 3.58 1295.0 \n", + "7 5.20 1.08 2.85 1045.0 \n", + "8 7.22 1.01 3.55 1045.0 \n", + "9 5.75 1.25 3.17 1510.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] }, { "metadata": { @@ -250,14 +2226,32 @@ "metadata": { "id": "EnOYhmEqmfKp", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "688a7fb3-ebdd-4a1d-88fe-264be9a08bbc" }, "cell_type": "code", "source": [ - "" + "wine_df.isnull()\n", + "wine_df.isnull().sum().sum()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "50" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] }, { "metadata": { @@ -273,14 +2267,1298 @@ "metadata": { "id": "As7IC6Ktms8-", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1986 + }, + "outputId": "486f5b08-fbbb-40f0-c3ce-dcb3b33356f0" }, "cell_type": "code", "source": [ - "" + "wine_df.dropna()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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Column 1AlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
111.013.751.732.4116.089.02.602.760.291.815.6000001.152.901320.0
121.014.751.732.3911.491.03.103.690.432.815.4000001.252.731150.0
131.014.381.872.3812.0102.03.303.640.292.967.5000001.203.001547.0
141.013.631.812.7017.2112.02.852.910.301.467.3000001.282.881310.0
151.014.301.922.7220.0120.02.803.140.331.976.2000001.072.651280.0
161.013.831.572.6220.0115.02.953.400.401.726.6000001.132.571130.0
171.014.191.592.4816.5108.03.303.930.321.868.7000001.232.821680.0
181.013.643.102.5615.2116.02.703.030.171.665.1000000.963.36845.0
191.014.061.632.2816.0126.03.003.170.242.105.6500001.093.71780.0
201.012.933.802.6518.6102.02.412.410.251.984.5000001.033.52770.0
211.013.711.862.3616.6101.02.612.880.271.693.8000001.114.001035.0
221.012.851.602.5217.895.02.482.370.261.463.9300001.093.631015.0
231.013.501.812.6120.096.02.532.610.281.663.5200001.123.82845.0
241.013.052.053.2225.0124.02.632.680.471.923.5800001.133.20830.0
251.013.391.772.6216.193.02.852.940.341.454.8000000.923.221195.0
261.013.301.722.1417.094.02.402.190.271.353.9500001.022.771285.0
271.013.871.902.8019.4107.02.952.970.371.764.5000001.253.40915.0
281.014.021.682.2116.096.02.652.330.261.984.7000001.043.591035.0
291.013.731.502.7022.5101.03.003.250.292.385.7000001.192.711285.0
301.013.581.662.3619.1106.02.863.190.221.956.9000001.092.881515.0
311.013.681.832.3617.2104.02.422.690.421.973.8400001.232.87990.0
321.013.761.532.7019.5132.02.952.740.501.355.4000001.253.001235.0
331.013.511.802.6519.0110.02.352.530.291.544.2000001.102.871095.0
341.013.481.812.4120.5100.02.702.980.261.865.1000001.043.47920.0
351.013.281.642.8415.5110.02.602.680.341.364.6000001.092.78880.0
361.013.051.652.5518.098.02.452.430.291.444.2500001.122.511105.0
371.013.071.502.1015.598.02.402.640.281.373.7000001.182.691020.0
381.014.223.992.5113.2128.03.003.040.202.085.1000000.893.53760.0
391.013.561.712.3116.2117.03.153.290.342.346.1300000.953.38795.0
401.013.413.842.1218.890.02.452.680.271.484.2800000.913.001035.0
.............................................
1473.013.323.242.3821.592.01.930.760.451.258.4200000.551.62650.0
1483.013.083.902.3621.5113.01.411.390.341.149.4000000.571.33550.0
1493.013.503.122.6224.0123.01.401.570.221.258.6000000.591.30500.0
1503.012.792.672.4822.0112.01.481.360.241.2610.8000000.481.47480.0
1513.013.111.902.7525.5116.02.201.280.261.567.1000000.611.33425.0
1523.013.233.302.2818.598.01.800.830.611.8710.5200000.561.51675.0
1533.012.581.292.1020.0103.01.480.580.531.407.6000000.581.55640.0
1543.013.175.192.3222.093.01.740.630.611.557.9000000.601.48725.0
1553.013.844.122.3819.589.01.800.830.481.569.0100000.571.64480.0
1563.012.453.032.6427.097.01.900.580.631.147.5000000.671.73880.0
1573.014.341.682.7025.098.02.801.310.532.7013.0000000.571.96660.0
1583.013.481.672.6422.589.02.601.100.522.2911.7500000.571.78620.0
1593.012.363.832.3821.088.02.300.920.501.047.6500000.561.58520.0
1603.013.693.262.5420.0107.01.830.560.500.805.8800000.961.82680.0
1613.012.853.272.5822.0106.01.650.600.600.965.5800000.872.11570.0
1623.012.963.452.3518.5106.01.390.700.400.945.2800000.681.75675.0
1633.013.782.762.3022.090.01.350.680.411.039.5800000.701.68615.0
1643.013.734.362.2622.588.01.280.470.521.156.6200000.781.75520.0
1653.013.453.702.6023.0111.01.700.920.431.4610.6800000.851.56695.0
1663.012.823.372.3019.588.01.480.660.400.9710.2600000.721.75685.0
1673.013.582.582.6924.5105.01.550.840.391.548.6600000.741.80750.0
1683.013.404.602.8625.0112.01.980.960.271.118.5000000.671.92630.0
1693.012.203.032.3219.096.01.250.490.400.735.5000000.661.83510.0
1703.012.772.392.2819.586.01.390.510.480.649.8999990.571.63470.0
1713.014.162.512.4820.091.01.680.700.441.249.7000000.621.71660.0
1723.013.715.652.4520.595.01.680.610.521.067.7000000.641.74740.0
1733.013.403.912.4823.0102.01.800.750.431.417.3000000.701.56750.0
1743.013.274.282.2620.0120.01.590.690.431.3510.2000000.591.56835.0
1753.013.172.592.3720.0120.01.650.680.531.469.3000000.601.62840.0
1763.014.134.102.7424.596.02.050.760.561.359.2000000.611.60560.0
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166 rows × 14 columns

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" + ], + "text/plain": [ + " Column 1 Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\n", + "11 1.0 13.75 1.73 2.41 16.0 89.0 \n", + "12 1.0 14.75 1.73 2.39 11.4 91.0 \n", + "13 1.0 14.38 1.87 2.38 12.0 102.0 \n", + "14 1.0 13.63 1.81 2.70 17.2 112.0 \n", + "15 1.0 14.30 1.92 2.72 20.0 120.0 \n", + "16 1.0 13.83 1.57 2.62 20.0 115.0 \n", + "17 1.0 14.19 1.59 2.48 16.5 108.0 \n", + "18 1.0 13.64 3.10 2.56 15.2 116.0 \n", + "19 1.0 14.06 1.63 2.28 16.0 126.0 \n", + "20 1.0 12.93 3.80 2.65 18.6 102.0 \n", + "21 1.0 13.71 1.86 2.36 16.6 101.0 \n", + "22 1.0 12.85 1.60 2.52 17.8 95.0 \n", + "23 1.0 13.50 1.81 2.61 20.0 96.0 \n", + "24 1.0 13.05 2.05 3.22 25.0 124.0 \n", + "25 1.0 13.39 1.77 2.62 16.1 93.0 \n", + "26 1.0 13.30 1.72 2.14 17.0 94.0 \n", + "27 1.0 13.87 1.90 2.80 19.4 107.0 \n", + "28 1.0 14.02 1.68 2.21 16.0 96.0 \n", + "29 1.0 13.73 1.50 2.70 22.5 101.0 \n", + "30 1.0 13.58 1.66 2.36 19.1 106.0 \n", + "31 1.0 13.68 1.83 2.36 17.2 104.0 \n", + "32 1.0 13.76 1.53 2.70 19.5 132.0 \n", + "33 1.0 13.51 1.80 2.65 19.0 110.0 \n", + "34 1.0 13.48 1.81 2.41 20.5 100.0 \n", + "35 1.0 13.28 1.64 2.84 15.5 110.0 \n", + "36 1.0 13.05 1.65 2.55 18.0 98.0 \n", + "37 1.0 13.07 1.50 2.10 15.5 98.0 \n", + "38 1.0 14.22 3.99 2.51 13.2 128.0 \n", + "39 1.0 13.56 1.71 2.31 16.2 117.0 \n", + "40 1.0 13.41 3.84 2.12 18.8 90.0 \n", + ".. ... ... ... ... ... ... \n", + "147 3.0 13.32 3.24 2.38 21.5 92.0 \n", + "148 3.0 13.08 3.90 2.36 21.5 113.0 \n", + "149 3.0 13.50 3.12 2.62 24.0 123.0 \n", + "150 3.0 12.79 2.67 2.48 22.0 112.0 \n", + "151 3.0 13.11 1.90 2.75 25.5 116.0 \n", + "152 3.0 13.23 3.30 2.28 18.5 98.0 \n", + "153 3.0 12.58 1.29 2.10 20.0 103.0 \n", + "154 3.0 13.17 5.19 2.32 22.0 93.0 \n", + "155 3.0 13.84 4.12 2.38 19.5 89.0 \n", + "156 3.0 12.45 3.03 2.64 27.0 97.0 \n", + "157 3.0 14.34 1.68 2.70 25.0 98.0 \n", + "158 3.0 13.48 1.67 2.64 22.5 89.0 \n", + "159 3.0 12.36 3.83 2.38 21.0 88.0 \n", + "160 3.0 13.69 3.26 2.54 20.0 107.0 \n", + "161 3.0 12.85 3.27 2.58 22.0 106.0 \n", + "162 3.0 12.96 3.45 2.35 18.5 106.0 \n", + "163 3.0 13.78 2.76 2.30 22.0 90.0 \n", + "164 3.0 13.73 4.36 2.26 22.5 88.0 \n", + "165 3.0 13.45 3.70 2.60 23.0 111.0 \n", + "166 3.0 12.82 3.37 2.30 19.5 88.0 \n", + "167 3.0 13.58 2.58 2.69 24.5 105.0 \n", + "168 3.0 13.40 4.60 2.86 25.0 112.0 \n", + "169 3.0 12.20 3.03 2.32 19.0 96.0 \n", + "170 3.0 12.77 2.39 2.28 19.5 86.0 \n", + "171 3.0 14.16 2.51 2.48 20.0 91.0 \n", + "172 3.0 13.71 5.65 2.45 20.5 95.0 \n", + "173 3.0 13.40 3.91 2.48 23.0 102.0 \n", + "174 3.0 13.27 4.28 2.26 20.0 120.0 \n", + "175 3.0 13.17 2.59 2.37 20.0 120.0 \n", + "176 3.0 14.13 4.10 2.74 24.5 96.0 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "11 2.60 2.76 0.29 1.81 \n", + "12 3.10 3.69 0.43 2.81 \n", + "13 3.30 3.64 0.29 2.96 \n", + "14 2.85 2.91 0.30 1.46 \n", + "15 2.80 3.14 0.33 1.97 \n", + "16 2.95 3.40 0.40 1.72 \n", + "17 3.30 3.93 0.32 1.86 \n", + "18 2.70 3.03 0.17 1.66 \n", + "19 3.00 3.17 0.24 2.10 \n", + "20 2.41 2.41 0.25 1.98 \n", + "21 2.61 2.88 0.27 1.69 \n", + "22 2.48 2.37 0.26 1.46 \n", + "23 2.53 2.61 0.28 1.66 \n", + "24 2.63 2.68 0.47 1.92 \n", + "25 2.85 2.94 0.34 1.45 \n", + "26 2.40 2.19 0.27 1.35 \n", + "27 2.95 2.97 0.37 1.76 \n", + "28 2.65 2.33 0.26 1.98 \n", + "29 3.00 3.25 0.29 2.38 \n", + "30 2.86 3.19 0.22 1.95 \n", + "31 2.42 2.69 0.42 1.97 \n", + "32 2.95 2.74 0.50 1.35 \n", + "33 2.35 2.53 0.29 1.54 \n", + "34 2.70 2.98 0.26 1.86 \n", + "35 2.60 2.68 0.34 1.36 \n", + "36 2.45 2.43 0.29 1.44 \n", + "37 2.40 2.64 0.28 1.37 \n", + "38 3.00 3.04 0.20 2.08 \n", + "39 3.15 3.29 0.34 2.34 \n", + "40 2.45 2.68 0.27 1.48 \n", + ".. ... ... ... ... \n", + "147 1.93 0.76 0.45 1.25 \n", + "148 1.41 1.39 0.34 1.14 \n", + "149 1.40 1.57 0.22 1.25 \n", + "150 1.48 1.36 0.24 1.26 \n", + "151 2.20 1.28 0.26 1.56 \n", + "152 1.80 0.83 0.61 1.87 \n", + "153 1.48 0.58 0.53 1.40 \n", + "154 1.74 0.63 0.61 1.55 \n", + "155 1.80 0.83 0.48 1.56 \n", + "156 1.90 0.58 0.63 1.14 \n", + "157 2.80 1.31 0.53 2.70 \n", + "158 2.60 1.10 0.52 2.29 \n", + "159 2.30 0.92 0.50 1.04 \n", + "160 1.83 0.56 0.50 0.80 \n", + "161 1.65 0.60 0.60 0.96 \n", + "162 1.39 0.70 0.40 0.94 \n", + "163 1.35 0.68 0.41 1.03 \n", + "164 1.28 0.47 0.52 1.15 \n", + "165 1.70 0.92 0.43 1.46 \n", + "166 1.48 0.66 0.40 0.97 \n", + "167 1.55 0.84 0.39 1.54 \n", + "168 1.98 0.96 0.27 1.11 \n", + "169 1.25 0.49 0.40 0.73 \n", + "170 1.39 0.51 0.48 0.64 \n", + "171 1.68 0.70 0.44 1.24 \n", + "172 1.68 0.61 0.52 1.06 \n", + "173 1.80 0.75 0.43 1.41 \n", + "174 1.59 0.69 0.43 1.35 \n", + "175 1.65 0.68 0.53 1.46 \n", + "176 2.05 0.76 0.56 1.35 \n", + "\n", + " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", + "11 5.600000 1.15 2.90 1320.0 \n", + "12 5.400000 1.25 2.73 1150.0 \n", + "13 7.500000 1.20 3.00 1547.0 \n", + "14 7.300000 1.28 2.88 1310.0 \n", + "15 6.200000 1.07 2.65 1280.0 \n", + "16 6.600000 1.13 2.57 1130.0 \n", + "17 8.700000 1.23 2.82 1680.0 \n", + "18 5.100000 0.96 3.36 845.0 \n", + "19 5.650000 1.09 3.71 780.0 \n", + "20 4.500000 1.03 3.52 770.0 \n", + "21 3.800000 1.11 4.00 1035.0 \n", + "22 3.930000 1.09 3.63 1015.0 \n", + "23 3.520000 1.12 3.82 845.0 \n", + "24 3.580000 1.13 3.20 830.0 \n", + "25 4.800000 0.92 3.22 1195.0 \n", + "26 3.950000 1.02 2.77 1285.0 \n", + "27 4.500000 1.25 3.40 915.0 \n", + "28 4.700000 1.04 3.59 1035.0 \n", + "29 5.700000 1.19 2.71 1285.0 \n", + "30 6.900000 1.09 2.88 1515.0 \n", + "31 3.840000 1.23 2.87 990.0 \n", + "32 5.400000 1.25 3.00 1235.0 \n", + "33 4.200000 1.10 2.87 1095.0 \n", + "34 5.100000 1.04 3.47 920.0 \n", + "35 4.600000 1.09 2.78 880.0 \n", + "36 4.250000 1.12 2.51 1105.0 \n", + "37 3.700000 1.18 2.69 1020.0 \n", + "38 5.100000 0.89 3.53 760.0 \n", + "39 6.130000 0.95 3.38 795.0 \n", + "40 4.280000 0.91 3.00 1035.0 \n", + ".. ... ... ... ... \n", + "147 8.420000 0.55 1.62 650.0 \n", + "148 9.400000 0.57 1.33 550.0 \n", + "149 8.600000 0.59 1.30 500.0 \n", + "150 10.800000 0.48 1.47 480.0 \n", + "151 7.100000 0.61 1.33 425.0 \n", + "152 10.520000 0.56 1.51 675.0 \n", + "153 7.600000 0.58 1.55 640.0 \n", + "154 7.900000 0.60 1.48 725.0 \n", + "155 9.010000 0.57 1.64 480.0 \n", + "156 7.500000 0.67 1.73 880.0 \n", + "157 13.000000 0.57 1.96 660.0 \n", + "158 11.750000 0.57 1.78 620.0 \n", + "159 7.650000 0.56 1.58 520.0 \n", + "160 5.880000 0.96 1.82 680.0 \n", + "161 5.580000 0.87 2.11 570.0 \n", + "162 5.280000 0.68 1.75 675.0 \n", + "163 9.580000 0.70 1.68 615.0 \n", + "164 6.620000 0.78 1.75 520.0 \n", + "165 10.680000 0.85 1.56 695.0 \n", + "166 10.260000 0.72 1.75 685.0 \n", + "167 8.660000 0.74 1.80 750.0 \n", + "168 8.500000 0.67 1.92 630.0 \n", + "169 5.500000 0.66 1.83 510.0 \n", + "170 9.899999 0.57 1.63 470.0 \n", + "171 9.700000 0.62 1.71 660.0 \n", + "172 7.700000 0.64 1.74 740.0 \n", + "173 7.300000 0.70 1.56 750.0 \n", + "174 10.200000 0.59 1.56 835.0 \n", + "175 9.300000 0.60 1.62 840.0 \n", + "176 9.200000 0.61 1.60 560.0 \n", + "\n", + "[166 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] }, { "metadata": { @@ -296,14 +3574,60 @@ "metadata": { "id": "mD40T0Cnm5SA", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 651 + }, + "outputId": "e5597e71-c17e-44db-9ac4-9689207ed1ff" }, "cell_type": "code", "source": [ - "" + "import matplotlib.pyplot as plt\n", + "wine_df.hist()" ], - "execution_count": 0, - "outputs": [] + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ]],\n", + " dtype=object)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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cc1NRyrwkyxWMV7alrTxLIp68yrZEW8BqtSrvhUqJshJrWYnzWZW171nW4i2Nyto+LGvx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\n0NHRITz99NOCVqvtV1ltbW1CXFyckJycLGzZsqXfZRUUFAhLliwRBEE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