From ff7b7f363cd418e2b9fe983ffdaf714261cfbe64 Mon Sep 17 00:00:00 2001 From: captain-pool Date: Wed, 26 Sep 2018 06:57:33 +0530 Subject: [PATCH 1/4] Code Committed for SoumanPaul --- SoumanPaul.ipynb | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 SoumanPaul.ipynb diff --git a/SoumanPaul.ipynb b/SoumanPaul.ipynb new file mode 100644 index 0000000..9e2543a --- /dev/null +++ b/SoumanPaul.ipynb @@ -0,0 +1,32 @@ +{ + "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 1a158af79b279f087c0fb71115ca9d147860d96e Mon Sep 17 00:00:00 2001 From: Soumanpaul Date: Mon, 8 Oct 2018 23:07:41 +0530 Subject: [PATCH 2/4] Completed NumpyExercises --- Basic_Pandas-checkpoint.ipynb | 1044 +++++++++++ Exercise-checkpoint.ipynb | 2277 +++++++++++++++++++++++ Get_to_know_your_Data-checkpoint.ipynb | 2354 ++++++++++++++++++++++++ 3 files changed, 5675 insertions(+) create mode 100644 Basic_Pandas-checkpoint.ipynb create mode 100644 Exercise-checkpoint.ipynb create mode 100644 Get_to_know_your_Data-checkpoint.ipynb diff --git a/Basic_Pandas-checkpoint.ipynb b/Basic_Pandas-checkpoint.ipynb new file mode 100644 index 0000000..a9be676 --- /dev/null +++ b/Basic_Pandas-checkpoint.ipynb @@ -0,0 +1,1044 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Basic Pandas.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "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": "49a1f081-7c31-4abc-c8e1-e3c87f53afaa" + }, + "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))\n" + ], + "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": "26cb349e-34ff-46aa-daf7-9102bbb66ce8" + }, + "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": "ddd95b3b-56ca-4a6a-a3cb-ad46a61b956f" + }, + "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": "96fbd118-de14-44ea-bcdb-705c13e0b51f" + }, + "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": 374 + }, + "outputId": "721edece-a3ac-4a7e-be36-e1e40328d2c8" + }, + "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(20)" + ], + "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", + "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", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 855 + }, + "outputId": "ae8e8bd7-11d2-4fb6-dd26-f9dfdbbcf1a9" + }, + "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": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alphabets values\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": 11 + } + ] + }, + { + "metadata": { + "id": "uaK_1EO9etGS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "outputId": "8a7ed095-a63d-4b1d-e719-3bf1a4a0437c" + }, + "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": 12, + "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 23 \\\n", + "alphabets A B C D E F G H I J ... Q R S T U V W X \n", + "values 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 23 \n", + "\n", + " 24 25 \n", + "alphabets Y Z \n", + "values 24 25 \n", + "\n", + "[2 rows x 26 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "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": "4699fc83-bcd0-4b4a-ad74-dc362a1cbe4a" + }, + "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", + "df\n", + "\n", + "\n" + ], + "execution_count": 20, + "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": 20 + } + ] + }, + { + "metadata": { + "id": "cmDxwtDNjWpO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Change the first character of each word to upper case in each word of ser" + ] + }, + { + "metadata": { + "id": "5KagP9PpgV2F", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "1eb68607-3bd9-4e3c-b006-b5fdd037a7e4" + }, + "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\n" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['We', 'Are', 'Learning', 'Pandas']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 28 + } + ] + }, + { + "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": "cb61a5fe-6fb0-44bd-b73d-b1372d1d4bdc" + }, + "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": 29, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "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" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 29 + } + ] + }, + { + "metadata": { + "id": "G_Frvc3mk93k", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "ad2c0077-bf21-484e-ee9d-716dfd598113" + }, + "cell_type": "code", + "source": [ + "new_index = [2, 5, 4, 3, 1]\n", + "\n", + "df1.reindex(index = new_index)" + ], + "execution_count": 30, + "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": 30 + } + ] + }, + { + "metadata": { + "id": "adCU_4N1ca5r", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Exercise-checkpoint.ipynb b/Exercise-checkpoint.ipynb new file mode 100644 index 0000000..18ae2fd --- /dev/null +++ b/Exercise-checkpoint.ipynb @@ -0,0 +1,2277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2LTtpUJEibjg" + }, + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c3_UBbMRhiKx" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tp-cTCyWi8mR" + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DMojQY3thrRi" + }, + "outputs": [], + "source": [ + "wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BF9MMjoZjSlg" + }, + "source": [ + "#### print first five rows" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "colab_type": "code", + "id": "1vSMQdnHjYNU", + "outputId": "d5521c9a-0974-41fe-b0a6-12c26fc828f3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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[i for i in range(1,len(wine_df_copy.count(axis = 1))) if i%2!=0]\n", + "wine_df_copy.drop(odd_rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o6Cs6T1Rjz71" + }, + "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 " + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "my8HB4V4j779" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
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" + ], + "text/plain": [ + " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium Total phenols \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN \n", + "3 1 13.24 2.59 2.87 21 118 2.8 \n", + "4 1 14.2 1.76 2.45 15.2 112 3.27 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 2.69 0.39 1.82 4.32 1.04 \n", + "4 3.39 0.34 1.97 6.75 1.05 \n", + "\n", + " OD280/OD315 OF diluted wines Proline \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 2.93 735 \n", + "4 2.85 1450 " + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.iloc[:3]='NaN'\n", + "wine_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RQMNI2UHkP3o" + }, + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xunmCjaEmDwZ" + }, + "outputs": [], + "source": [ + "from random import *\n", + "random=[]\n", + "for i in range(10):\n", + " random.append(randrange(0,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hELUakyXmFSu" + }, + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zMgaNnNHmP01" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3113.242.592.87211182.82.690.391.824.321.042.93735
4114.21.762.4515.21123.273.390.341.976.751.052.851450
5114.391.872.4514.6962.52.520.31.985.251.023.581290
61NaN2.152.6117.61212.62.510.311.255.051.063.581295
71NaN1.642.1714972.82.980.291.985.21.082.851045
81NaN1.352.2716982.983.150.221.857.221.013.551045
9114.12.162.3181052.953.320.222.385.751.253.171510
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" + ], + "text/plain": [ + " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium Total phenols \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN \n", + "3 1 13.24 2.59 2.87 21 118 2.8 \n", + "4 1 14.2 1.76 2.45 15.2 112 3.27 \n", + "5 1 14.39 1.87 2.45 14.6 96 2.5 \n", + "6 1 NaN 2.15 2.61 17.6 121 2.6 \n", + "7 1 NaN 1.64 2.17 14 97 2.8 \n", + "8 1 NaN 1.35 2.27 16 98 2.98 \n", + "9 1 14.1 2.16 2.3 18 105 2.95 \n", + "\n", + " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", + "0 NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN \n", + "3 2.69 0.39 1.82 4.32 1.04 \n", + "4 3.39 0.34 1.97 6.75 1.05 \n", + "5 2.52 0.3 1.98 5.25 1.02 \n", + "6 2.51 0.31 1.25 5.05 1.06 \n", + "7 2.98 0.29 1.98 5.2 1.08 \n", + "8 3.15 0.22 1.85 7.22 1.01 \n", + "9 3.32 0.22 2.38 5.75 1.25 \n", + "\n", + " OD280/OD315 OF diluted wines Proline \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \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 " + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.loc[random,'Alcohol']='NaN'\n", + "wine_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PHyK_vRsmRwV" + }, + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EnOYhmEqmfKp" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Count 0\n", + "Alcohol 0\n", + "MAlic acid 0\n", + "Ash 0\n", + "Alcalinity of ash 0\n", + "Magnesium 0\n", + "Total phenols 0\n", + "Flavanoids 0\n", + "Nonflavanoid phenols 0\n", + "Proanthocyanins 0\n", + "Color intensity 0\n", + "Hue 0\n", + "OD280/OD315 OF diluted wines 0\n", + "Proline 0\n", + "dtype: int64" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-Fd4WBklmf1_" + }, + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "As7IC6Ktms8-" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DlpG8drhmz7W" + }, + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mD40T0Cnm5SA" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Exercise.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Get_to_know_your_Data-checkpoint.ipynb b/Get_to_know_your_Data-checkpoint.ipynb new file mode 100644 index 0000000..dbf59d4 --- /dev/null +++ b/Get_to_know_your_Data-checkpoint.ipynb @@ -0,0 +1,2354 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "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')" + ], + "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": "7d342633-1b72-4558-fcec-76ebe66d7430" + }, + "cell_type": "code", + "source": [ + "iris_df.head()" + ], + "execution_count": 4, + "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
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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": 4 + } + ] + }, + { + "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": 34 + }, + "outputId": "2fe6e676-1bec-453f-8e2b-3bc21ac0a999" + }, + "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" + ], + "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": "798d5ef2-0f98-4972-c98c-c896f879af64" + }, + "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": "21786651-605d-4636-d253-5694edb6f969" + }, + "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": "11e956f5-d253-4025-9c3a-1e88f54528b2" + }, + "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", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 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": "5f8ae149-0829-45fa-a537-ed81be0bd743" + }, + "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", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 31.0 1.5 0.2 setosa\n", + "115 6.4 32.0 5.3 2.3 virginica\n", + "113 5.7 25.0 5.0 2.0 virginica\n", + "43 5.0 35.0 1.6 0.6 setosa\n", + "126 6.2 28.0 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 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": "ee9c12fa-f9da-468a-dead-9d3ced177e8b" + }, + "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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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
1366.33.45.62.4virginica
165.43.91.30.4setosa
244.83.41.90.2setosa
395.13.41.50.2setosa
275.23.51.50.2setosa
856.03.44.51.6versicolor
1097.23.66.12.5virginica
55.43.91.70.4setosa
195.13.81.50.3setosa
105.43.71.50.2setosa
45.03.61.40.2setosa
1317.93.86.42.0virginica
155.74.41.50.4setosa
365.53.51.30.2setosa
205.43.41.70.2setosa
145.84.01.20.2setosa
445.13.81.90.4setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
285.23.41.40.2setosa
114.83.41.60.2setosa
465.13.81.60.2setosa
315.43.41.50.4setosa
64.63.41.40.3setosa
325.24.11.50.1setosa
1486.23.45.42.3virginica
265.03.41.60.4setosa
05.13.51.40.2setosa
485.33.71.50.2setosa
405.03.51.30.3setosa
335.54.21.40.2setosa
175.13.51.40.3setosa
1177.73.86.72.2virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "32 5.2 4.1 1.5 0.1 setosa\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "33 5.5 4.2 1.4 0.2 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "117 7.7 3.8 6.7 2.2 virginica" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "gH3DnhCq2Cbl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor" + ] + }, + { + "metadata": { + "id": "4U7ksr_R2H7M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "outputId": "a245bbce-09c2-41e0-fb41-6138e8f8eddc" + }, + "cell_type": "code", + "source": [ + "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + ], + "execution_count": 11, + "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": 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": "c38831d3-1fd3-4728-bfbf-c9355e4a96a5" + }, + "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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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
626.02.24.01.0versicolor
686.22.24.51.5versicolor
1196.02.25.01.5virginica
876.32.34.41.3versicolor
535.52.34.01.3versicolor
935.02.33.31.0versicolor
414.52.31.30.3setosa
574.92.43.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
726.32.54.91.5versicolor
1086.72.55.81.8virginica
895.52.54.01.3versicolor
985.12.53.01.1versicolor
1064.92.54.51.7virginica
695.62.53.91.1versicolor
1466.32.55.01.9virginica
1135.72.55.02.0virginica
795.72.63.51.0versicolor
1187.72.66.92.3virginica
925.82.64.01.2versicolor
905.52.64.41.2versicolor
1346.12.65.61.4virginica
675.82.74.11.0versicolor
595.22.73.91.4versicolor
945.62.74.21.3versicolor
836.02.75.11.6versicolor
1236.32.74.91.8virginica
1015.82.75.11.9virginica
..................
1486.23.45.42.3virginica
1366.33.45.62.4virginica
856.03.44.51.6versicolor
265.03.41.60.4setosa
244.83.41.90.2setosa
205.43.41.70.2setosa
365.53.51.30.2setosa
175.13.51.40.3setosa
405.03.51.30.3setosa
435.03.51.60.6setosa
05.13.51.40.2setosa
275.23.51.50.2setosa
224.63.61.00.2setosa
45.03.61.40.2setosa
1097.23.66.12.5virginica
215.13.71.50.4setosa
485.33.71.50.2setosa
105.43.71.50.2setosa
445.13.81.90.4setosa
185.73.81.70.3setosa
465.13.81.60.2setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
1177.73.86.72.2virginica
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
\n", + "

150 rows × 5 columns

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34.63.11.50.2setosa
435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
424.43.21.30.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "42 4.4 3.2 1.3 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "bf3850ab-a0eb-45fa-9816-fdd4c97517d2" + }, + "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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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "90 5.5 2.6 4.4 1.2 versicolor\n", + "93 5.0 2.3 3.3 1.0 versicolor\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "62 6.0 2.2 4.0 1.0 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": "58e878f8-2aed-49b2-9529-545308332808" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": "e1111c09-8877-43f8-ac45-f92b53899a5c" + }, + "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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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pl4RPzBfl5mI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From cc99112dc4c9b6153c9143e841d370a74a517825 Mon Sep 17 00:00:00 2001 From: Soumanpaul Date: Wed, 10 Oct 2018 21:51:16 +0530 Subject: [PATCH 3/4] Completed Assignment 3 --- .../Exercise-checkpoint-checkpoint.ipynb | 3544 +++++++++++++++++ Exercise-checkpoint.ipynb | 1531 ++++++- 2 files changed, 4943 insertions(+), 132 deletions(-) create mode 100644 .ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb diff --git a/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb b/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb new file mode 100644 index 0000000..e7d30e9 --- /dev/null +++ b/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb @@ -0,0 +1,3544 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "2LTtpUJEibjg" + }, + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "c3_UBbMRhiKx" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tp-cTCyWi8mR" + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DMojQY3thrRi" + }, + "outputs": [], + "source": [ + "wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BF9MMjoZjSlg" + }, + "source": [ + "#### print first five rows" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "colab_type": "code", + "id": "1vSMQdnHjYNU", + "outputId": "d5521c9a-0974-41fe-b0a6-12c26fc828f3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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[i for i in range(1,len(wine_df_copy.count(axis = 1))) if i%2!=0]\n", + "wine_df_copy.drop(odd_rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "o6Cs6T1Rjz71" + }, + "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 " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "my8HB4V4j779" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
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" + ], + "text/plain": [ + " Count 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 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.columns = ['Count','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()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Zqi7hwWpkNbH" + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "buyT4vX4kPMl" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count 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 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.iloc[:3]=np.nan\n", + "wine_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RQMNI2UHkP3o" + }, + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xunmCjaEmDwZ" + }, + "outputs": [], + "source": [ + "from random import *\n", + "random=[]\n", + "for i in range(10):\n", + " random.append(randrange(0,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hELUakyXmFSu" + }, + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zMgaNnNHmP01" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
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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
81.013.861.352.2716.098.02.983.150.221.857.221.013.551045.0
91.0NaN2.162.3018.0105.02.953.320.222.385.751.253.171510.0
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" + ], + "text/plain": [ + " Count 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", + "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 13.86 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 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.loc[random,'Alcohol']=np.nan\n", + "wine_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PHyK_vRsmRwV" + }, + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EnOYhmEqmfKp" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "46" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.isnull()\n", + "wine_df.isnull().sum().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-Fd4WBklmf1_" + }, + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "As7IC6Ktms8-" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
31.013.242.592.8721.0118.02.802.690.391.824.3200001.042.93735.0
41.014.201.762.4515.2112.03.273.390.341.976.7500001.052.851450.0
81.013.861.352.2716.098.02.983.150.221.857.2200001.013.551045.0
101.014.121.482.3216.895.02.202.430.261.575.0000001.172.821280.0
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
.............................................
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
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170 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\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", + "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", + "10 1.0 14.12 1.48 2.32 16.8 95.0 \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", + ".. ... ... ... ... ... ... \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", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "8 2.98 3.15 0.22 1.85 \n", + "10 2.20 2.43 0.26 1.57 \n", + "11 2.60 2.76 0.29 1.81 \n", + "12 3.10 3.69 0.43 2.81 \n", + "13 3.30 3.64 0.29 2.96 \n", + "14 2.85 2.91 0.30 1.46 \n", + "15 2.80 3.14 0.33 1.97 \n", + "16 2.95 3.40 0.40 1.72 \n", + "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", + ".. ... ... ... ... \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", + "3 4.320000 1.04 2.93 735.0 \n", + "4 6.750000 1.05 2.85 1450.0 \n", + "8 7.220000 1.01 3.55 1045.0 \n", + "10 5.000000 1.17 2.82 1280.0 \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", + ".. ... ... ... ... \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", + "[170 rows x 14 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.dropna()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DlpG8drhmz7W" + }, + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "mD40T0Cnm5SA" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Exercise.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Exercise-checkpoint.ipynb b/Exercise-checkpoint.ipynb index 18ae2fd..e7d30e9 100644 --- a/Exercise-checkpoint.ipynb +++ b/Exercise-checkpoint.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -219,7 +219,7 @@ "4 1450 " ] }, - "execution_count": 170, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1463,7 +1463,7 @@ "[89 rows x 14 columns]" ] }, - "execution_count": 171, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1502,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 172, + "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", @@ -1659,7 +1659,7 @@ "4 6.75 1.05 2.85 1450 " ] }, - "execution_count": 172, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 20, "metadata": { "colab": {}, "colab_type": "code", @@ -1781,29 +1781,29 @@ " \n", " \n", " 3\n", - " 1\n", + " 1.0\n", " 13.24\n", " 2.59\n", " 2.87\n", - " 21\n", - " 118\n", - " 2.8\n", + " 21.0\n", + " 118.0\n", + " 2.80\n", " 2.69\n", " 0.39\n", " 1.82\n", " 4.32\n", " 1.04\n", " 2.93\n", - " 735\n", + " 735.0\n", " \n", " \n", " 4\n", - " 1\n", - " 14.2\n", + " 1.0\n", + " 14.20\n", " 1.76\n", " 2.45\n", " 15.2\n", - " 112\n", + " 112.0\n", " 3.27\n", " 3.39\n", " 0.34\n", @@ -1811,42 +1811,42 @@ " 6.75\n", " 1.05\n", " 2.85\n", - " 1450\n", + " 1450.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium Total phenols \\\n", - "0 NaN NaN NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN NaN NaN \n", - "3 1 13.24 2.59 2.87 21 118 2.8 \n", - "4 1 14.2 1.76 2.45 15.2 112 3.27 \n", + " Count 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", - " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 2.69 0.39 1.82 4.32 1.04 \n", - "4 3.39 0.34 1.97 6.75 1.05 \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", - " OD280/OD315 OF diluted wines Proline \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 2.93 735 \n", - "4 2.85 1450 " + " 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 " ] }, - "execution_count": 173, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wine_df.iloc[:3]='NaN'\n", + "wine_df.iloc[:3]=np.nan\n", "wine_df.head()" ] }, @@ -1862,7 +1862,7 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 21, "metadata": { "colab": {}, "colab_type": "code", @@ -1888,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 22, "metadata": { "colab": {}, "colab_type": "code", @@ -1986,29 +1986,29 @@ " \n", " \n", " 3\n", - " 1\n", + " 1.0\n", " 13.24\n", " 2.59\n", " 2.87\n", - " 21\n", - " 118\n", - " 2.8\n", + " 21.0\n", + " 118.0\n", + " 2.80\n", " 2.69\n", " 0.39\n", " 1.82\n", " 4.32\n", " 1.04\n", " 2.93\n", - " 735\n", + " 735.0\n", " \n", " \n", " 4\n", - " 1\n", - " 14.2\n", + " 1.0\n", + " 14.20\n", " 1.76\n", " 2.45\n", " 15.2\n", - " 112\n", + " 112.0\n", " 3.27\n", " 3.39\n", " 0.34\n", @@ -2016,67 +2016,67 @@ " 6.75\n", " 1.05\n", " 2.85\n", - " 1450\n", + " 1450.0\n", " \n", " \n", " 5\n", - " 1\n", - " 14.39\n", + " 1.0\n", + " NaN\n", " 1.87\n", " 2.45\n", " 14.6\n", - " 96\n", - " 2.5\n", + " 96.0\n", + " 2.50\n", " 2.52\n", - " 0.3\n", + " 0.30\n", " 1.98\n", " 5.25\n", " 1.02\n", " 3.58\n", - " 1290\n", + " 1290.0\n", " \n", " \n", " 6\n", - " 1\n", + " 1.0\n", " NaN\n", " 2.15\n", " 2.61\n", " 17.6\n", - " 121\n", - " 2.6\n", + " 121.0\n", + " 2.60\n", " 2.51\n", " 0.31\n", " 1.25\n", " 5.05\n", " 1.06\n", " 3.58\n", - " 1295\n", + " 1295.0\n", " \n", " \n", " 7\n", - " 1\n", + " 1.0\n", " NaN\n", " 1.64\n", " 2.17\n", - " 14\n", - " 97\n", - " 2.8\n", + " 14.0\n", + " 97.0\n", + " 2.80\n", " 2.98\n", " 0.29\n", " 1.98\n", - " 5.2\n", + " 5.20\n", " 1.08\n", " 2.85\n", - " 1045\n", + " 1045.0\n", " \n", " \n", " 8\n", - " 1\n", - " NaN\n", + " 1.0\n", + " 13.86\n", " 1.35\n", " 2.27\n", - " 16\n", - " 98\n", + " 16.0\n", + " 98.0\n", " 2.98\n", " 3.15\n", " 0.22\n", @@ -2084,16 +2084,16 @@ " 7.22\n", " 1.01\n", " 3.55\n", - " 1045\n", + " 1045.0\n", " \n", " \n", " 9\n", - " 1\n", - " 14.1\n", + " 1.0\n", + " NaN\n", " 2.16\n", - " 2.3\n", - " 18\n", - " 105\n", + " 2.30\n", + " 18.0\n", + " 105.0\n", " 2.95\n", " 3.32\n", " 0.22\n", @@ -2101,57 +2101,57 @@ " 5.75\n", " 1.25\n", " 3.17\n", - " 1510\n", + " 1510.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium Total phenols \\\n", - "0 NaN NaN NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN NaN NaN \n", - "3 1 13.24 2.59 2.87 21 118 2.8 \n", - "4 1 14.2 1.76 2.45 15.2 112 3.27 \n", - "5 1 14.39 1.87 2.45 14.6 96 2.5 \n", - "6 1 NaN 2.15 2.61 17.6 121 2.6 \n", - "7 1 NaN 1.64 2.17 14 97 2.8 \n", - "8 1 NaN 1.35 2.27 16 98 2.98 \n", - "9 1 14.1 2.16 2.3 18 105 2.95 \n", + " Count 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", + "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 13.86 1.35 2.27 16.0 98.0 \n", + "9 1.0 NaN 2.16 2.30 18.0 105.0 \n", "\n", - " Flavanoids Nonflavanoid phenols Proanthocyanins Color intensity Hue \\\n", - "0 NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN \n", - "3 2.69 0.39 1.82 4.32 1.04 \n", - "4 3.39 0.34 1.97 6.75 1.05 \n", - "5 2.52 0.3 1.98 5.25 1.02 \n", - "6 2.51 0.31 1.25 5.05 1.06 \n", - "7 2.98 0.29 1.98 5.2 1.08 \n", - "8 3.15 0.22 1.85 7.22 1.01 \n", - "9 3.32 0.22 2.38 5.75 1.25 \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", - " OD280/OD315 OF diluted wines Proline \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \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 " + " 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 " ] }, - "execution_count": 175, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wine_df.loc[random,'Alcohol']='NaN'\n", + "wine_df.loc[random,'Alcohol']=np.nan\n", "wine_df.head(10)" ] }, @@ -2169,7 +2169,7 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 25, "metadata": { "colab": {}, "colab_type": "code", @@ -2179,30 +2179,17 @@ { "data": { "text/plain": [ - "Count 0\n", - "Alcohol 0\n", - "MAlic acid 0\n", - "Ash 0\n", - "Alcalinity of ash 0\n", - "Magnesium 0\n", - "Total phenols 0\n", - "Flavanoids 0\n", - "Nonflavanoid phenols 0\n", - "Proanthocyanins 0\n", - "Color intensity 0\n", - "Hue 0\n", - "OD280/OD315 OF diluted wines 0\n", - "Proline 0\n", - "dtype: int64" + "46" ] }, - "execution_count": 182, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wine_df.isnull().sum()" + "wine_df.isnull()\n", + "wine_df.isnull().sum().sum()" ] }, { @@ 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CountAlcoholMAlic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 OF diluted winesProline
31.013.242.592.8721.0118.02.802.690.391.824.3200001.042.93735.0
41.014.201.762.4515.2112.03.273.390.341.976.7500001.052.851450.0
81.013.861.352.2716.098.02.983.150.221.857.2200001.013.551045.0
101.014.121.482.3216.895.02.202.430.261.575.0000001.172.821280.0
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
.............................................
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
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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
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\n", + "

170 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\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", + "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", + "10 1.0 14.12 1.48 2.32 16.8 95.0 \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", + ".. ... ... ... ... ... ... \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", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "8 2.98 3.15 0.22 1.85 \n", + "10 2.20 2.43 0.26 1.57 \n", + "11 2.60 2.76 0.29 1.81 \n", + "12 3.10 3.69 0.43 2.81 \n", + "13 3.30 3.64 0.29 2.96 \n", + "14 2.85 2.91 0.30 1.46 \n", + "15 2.80 3.14 0.33 1.97 \n", + "16 2.95 3.40 0.40 1.72 \n", + "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", + ".. ... ... ... ... \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", + "3 4.320000 1.04 2.93 735.0 \n", + "4 6.750000 1.05 2.85 1450.0 \n", + "8 7.220000 1.01 3.55 1045.0 \n", + "10 5.000000 1.17 2.82 1280.0 \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", + ".. ... ... ... ... \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", + "[170 rows x 14 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_df.dropna()" + ] }, { "cell_type": "markdown", From 6eace9510181a82064d80f4425291063d8730957 Mon Sep 17 00:00:00 2001 From: Soumanpaul Date: Wed, 10 Oct 2018 22:26:24 +0530 Subject: [PATCH 4/4] Completed Assignment 3 --- .../Exercise-checkpoint-checkpoint.ipynb | 173 +- ...know_your_Data-checkpoint-checkpoint.ipynb | 2354 +++++++++ Exercise-checkpoint.ipynb | 173 +- Get_to_know_your_Data-checkpoint.ipynb | 4597 +++++++++-------- 4 files changed, 4886 insertions(+), 2411 deletions(-) create mode 100644 .ipynb_checkpoints/Get_to_know_your_Data-checkpoint-checkpoint.ipynb diff --git a/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb b/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb index e7d30e9..02b14e5 100644 --- a/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb +++ b/.ipynb_checkpoints/Exercise-checkpoint-checkpoint.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -219,7 +219,7 @@ "4 1450 " ] }, - "execution_count": 17, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1463,7 +1463,7 @@ "[89 rows x 14 columns]" ] }, - "execution_count": 18, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1502,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", @@ -1659,7 +1659,7 @@ "4 6.75 1.05 2.85 1450 " ] }, - "execution_count": 19, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", @@ -1840,7 +1840,7 @@ "4 6.75 1.05 2.85 1450.0 " ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1862,7 +1862,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", @@ -1888,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", @@ -1987,7 +1987,7 @@ " \n", " 3\n", " 1.0\n", - " 13.24\n", + " NaN\n", " 2.59\n", " 2.87\n", " 21.0\n", @@ -2004,7 +2004,7 @@ " \n", " 4\n", " 1.0\n", - " 14.20\n", + " NaN\n", " 1.76\n", " 2.45\n", " 15.2\n", @@ -2038,7 +2038,7 @@ " \n", " 6\n", " 1.0\n", - " NaN\n", + " 14.06\n", " 2.15\n", " 2.61\n", " 17.6\n", @@ -2055,7 +2055,7 @@ " \n", " 7\n", " 1.0\n", - " NaN\n", + " 14.83\n", " 1.64\n", " 2.17\n", " 14.0\n", @@ -2112,11 +2112,11 @@ "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", + "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", + "6 1.0 14.06 2.15 2.61 17.6 121.0 \n", + "7 1.0 14.83 1.64 2.17 14.0 97.0 \n", "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", "9 1.0 NaN 2.16 2.30 18.0 105.0 \n", "\n", @@ -2145,7 +2145,7 @@ "9 5.75 1.25 3.17 1510.0 " ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2169,7 +2169,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", @@ -2182,7 +2182,7 @@ "46" ] }, - "execution_count": 25, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2204,7 +2204,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", @@ -2250,38 +2250,38 @@ " \n", " \n", " \n", - " 3\n", + " 6\n", " 1.0\n", - " 13.24\n", - " 2.59\n", - " 2.87\n", - " 21.0\n", - " 118.0\n", - " 2.80\n", - " 2.69\n", - " 0.39\n", - " 1.82\n", - " 4.320000\n", - " 1.04\n", - " 2.93\n", - " 735.0\n", + " 14.06\n", + " 2.15\n", + " 2.61\n", + " 17.6\n", + " 121.0\n", + " 2.60\n", + " 2.51\n", + " 0.31\n", + " 1.25\n", + " 5.050000\n", + " 1.06\n", + " 3.58\n", + " 1295.0\n", " \n", " \n", - " 4\n", + " 7\n", " 1.0\n", - " 14.20\n", - " 1.76\n", - " 2.45\n", - " 15.2\n", - " 112.0\n", - " 3.27\n", - " 3.39\n", - " 0.34\n", - " 1.97\n", - " 6.750000\n", - " 1.05\n", + " 14.83\n", + " 1.64\n", + " 2.17\n", + " 14.0\n", + " 97.0\n", + " 2.80\n", + " 2.98\n", + " 0.29\n", + " 1.98\n", + " 5.200000\n", + " 1.08\n", " 2.85\n", - " 1450.0\n", + " 1045.0\n", " \n", " \n", " 8\n", @@ -3293,8 +3293,8 @@ ], "text/plain": [ " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\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", + "6 1.0 14.06 2.15 2.61 17.6 121.0 \n", + "7 1.0 14.83 1.64 2.17 14.0 97.0 \n", "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", "10 1.0 14.12 1.48 2.32 16.8 95.0 \n", "11 1.0 13.75 1.73 2.41 16.0 89.0 \n", @@ -3356,8 +3356,8 @@ "176 3.0 14.13 4.10 2.74 24.5 96.0 \n", "\n", " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", - "3 2.80 2.69 0.39 1.82 \n", - "4 3.27 3.39 0.34 1.97 \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", "10 2.20 2.43 0.26 1.57 \n", "11 2.60 2.76 0.29 1.81 \n", @@ -3419,8 +3419,8 @@ "176 2.05 0.76 0.56 1.35 \n", "\n", " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", - "3 4.320000 1.04 2.93 735.0 \n", - "4 6.750000 1.05 2.85 1450.0 \n", + "6 5.050000 1.06 3.58 1295.0 \n", + "7 5.200000 1.08 2.85 1045.0 \n", "8 7.220000 1.01 3.55 1045.0 \n", "10 5.000000 1.17 2.82 1280.0 \n", "11 5.600000 1.15 2.90 1320.0 \n", @@ -3484,7 +3484,7 @@ "[170 rows x 14 columns]" ] }, - "execution_count": 34, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3505,12 +3505,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": {}, "colab_type": "code", "id": "mD40T0Cnm5SA" }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"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')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + 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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": 4 + } + ] + }, + { + "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": 34 + }, + "outputId": "2fe6e676-1bec-453f-8e2b-3bc21ac0a999" + }, + "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" + ], + "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": "798d5ef2-0f98-4972-c98c-c896f879af64" + }, + "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": "21786651-605d-4636-d253-5694edb6f969" + }, + "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": "11e956f5-d253-4025-9c3a-1e88f54528b2" + }, + "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", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 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": "5f8ae149-0829-45fa-a537-ed81be0bd743" + }, + "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", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 31.0 1.5 0.2 setosa\n", + "115 6.4 32.0 5.3 2.3 virginica\n", + "113 5.7 25.0 5.0 2.0 virginica\n", + "43 5.0 35.0 1.6 0.6 setosa\n", + "126 6.2 28.0 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 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": "ee9c12fa-f9da-468a-dead-9d3ced177e8b" + }, + "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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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
1366.33.45.62.4virginica
165.43.91.30.4setosa
244.83.41.90.2setosa
395.13.41.50.2setosa
275.23.51.50.2setosa
856.03.44.51.6versicolor
1097.23.66.12.5virginica
55.43.91.70.4setosa
195.13.81.50.3setosa
105.43.71.50.2setosa
45.03.61.40.2setosa
1317.93.86.42.0virginica
155.74.41.50.4setosa
365.53.51.30.2setosa
205.43.41.70.2setosa
145.84.01.20.2setosa
445.13.81.90.4setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
285.23.41.40.2setosa
114.83.41.60.2setosa
465.13.81.60.2setosa
315.43.41.50.4setosa
64.63.41.40.3setosa
325.24.11.50.1setosa
1486.23.45.42.3virginica
265.03.41.60.4setosa
05.13.51.40.2setosa
485.33.71.50.2setosa
405.03.51.30.3setosa
335.54.21.40.2setosa
175.13.51.40.3setosa
1177.73.86.72.2virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "32 5.2 4.1 1.5 0.1 setosa\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "33 5.5 4.2 1.4 0.2 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "117 7.7 3.8 6.7 2.2 virginica" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "gH3DnhCq2Cbl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor" + ] + }, + { + "metadata": { + "id": "4U7ksr_R2H7M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "outputId": "a245bbce-09c2-41e0-fb41-6138e8f8eddc" + }, + "cell_type": "code", + "source": [ + "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + ], + "execution_count": 11, + "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": 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": "c38831d3-1fd3-4728-bfbf-c9355e4a96a5" + }, + "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
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1086.72.55.81.8virginica
895.52.54.01.3versicolor
985.12.53.01.1versicolor
1064.92.54.51.7virginica
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1187.72.66.92.3virginica
925.82.64.01.2versicolor
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1346.12.65.61.4virginica
675.82.74.11.0versicolor
595.22.73.91.4versicolor
945.62.74.21.3versicolor
836.02.75.11.6versicolor
1236.32.74.91.8virginica
1015.82.75.11.9virginica
..................
1486.23.45.42.3virginica
1366.33.45.62.4virginica
856.03.44.51.6versicolor
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45.03.61.40.2setosa
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105.43.71.50.2setosa
445.13.81.90.4setosa
185.73.81.70.3setosa
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195.13.81.50.3setosa
1317.93.86.42.0virginica
1177.73.86.72.2virginica
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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34.63.11.50.2setosa
435.03.51.60.6setosa
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424.43.21.30.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "42 4.4 3.2 1.3 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "bf3850ab-a0eb-45fa-9816-fdd4c97517d2" + }, + "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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905.52.64.41.2versicolor
935.02.33.31.0versicolor
507.03.24.71.4versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "90 5.5 2.6 4.4 1.2 versicolor\n", + "93 5.0 2.3 3.3 1.0 versicolor\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "62 6.0 2.2 4.0 1.0 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": "58e878f8-2aed-49b2-9529-545308332808" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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min4.300002.3000001.0000000.10000
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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": "e1111c09-8877-43f8-ac45-f92b53899a5c" + }, + "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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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "pl4RPzBfl5mI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Exercise-checkpoint.ipynb b/Exercise-checkpoint.ipynb index e7d30e9..02b14e5 100644 --- a/Exercise-checkpoint.ipynb +++ b/Exercise-checkpoint.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -219,7 +219,7 @@ "4 1450 " ] }, - "execution_count": 17, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1463,7 +1463,7 @@ "[89 rows x 14 columns]" ] }, - "execution_count": 18, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1502,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", @@ -1659,7 +1659,7 @@ "4 6.75 1.05 2.85 1450 " ] }, - "execution_count": 19, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1683,7 +1683,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", @@ -1840,7 +1840,7 @@ "4 6.75 1.05 2.85 1450.0 " ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1862,7 +1862,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", @@ -1888,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", @@ -1987,7 +1987,7 @@ " \n", " 3\n", " 1.0\n", - " 13.24\n", + " NaN\n", " 2.59\n", " 2.87\n", " 21.0\n", @@ -2004,7 +2004,7 @@ " \n", " 4\n", " 1.0\n", - " 14.20\n", + " NaN\n", " 1.76\n", " 2.45\n", " 15.2\n", @@ -2038,7 +2038,7 @@ " \n", " 6\n", " 1.0\n", - " NaN\n", + " 14.06\n", " 2.15\n", " 2.61\n", " 17.6\n", @@ -2055,7 +2055,7 @@ " \n", " 7\n", " 1.0\n", - " NaN\n", + " 14.83\n", " 1.64\n", " 2.17\n", " 14.0\n", @@ -2112,11 +2112,11 @@ "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", + "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", + "6 1.0 14.06 2.15 2.61 17.6 121.0 \n", + "7 1.0 14.83 1.64 2.17 14.0 97.0 \n", "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", "9 1.0 NaN 2.16 2.30 18.0 105.0 \n", "\n", @@ -2145,7 +2145,7 @@ "9 5.75 1.25 3.17 1510.0 " ] }, - "execution_count": 22, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2169,7 +2169,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", @@ -2182,7 +2182,7 @@ "46" ] }, - "execution_count": 25, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2204,7 +2204,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", @@ -2250,38 +2250,38 @@ " \n", " \n", " \n", - " 3\n", + " 6\n", " 1.0\n", - " 13.24\n", - " 2.59\n", - " 2.87\n", - " 21.0\n", - " 118.0\n", - " 2.80\n", - " 2.69\n", - " 0.39\n", - " 1.82\n", - " 4.320000\n", - " 1.04\n", - " 2.93\n", - " 735.0\n", + " 14.06\n", + " 2.15\n", + " 2.61\n", + " 17.6\n", + " 121.0\n", + " 2.60\n", + " 2.51\n", + " 0.31\n", + " 1.25\n", + " 5.050000\n", + " 1.06\n", + " 3.58\n", + " 1295.0\n", " \n", " \n", - " 4\n", + " 7\n", " 1.0\n", - " 14.20\n", - " 1.76\n", - " 2.45\n", - " 15.2\n", - " 112.0\n", - " 3.27\n", - " 3.39\n", - " 0.34\n", - " 1.97\n", - " 6.750000\n", - " 1.05\n", + " 14.83\n", + " 1.64\n", + " 2.17\n", + " 14.0\n", + " 97.0\n", + " 2.80\n", + " 2.98\n", + " 0.29\n", + " 1.98\n", + " 5.200000\n", + " 1.08\n", " 2.85\n", - " 1450.0\n", + " 1045.0\n", " \n", " \n", " 8\n", @@ -3293,8 +3293,8 @@ ], "text/plain": [ " Count Alcohol MAlic acid Ash Alcalinity of ash Magnesium \\\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", + "6 1.0 14.06 2.15 2.61 17.6 121.0 \n", + "7 1.0 14.83 1.64 2.17 14.0 97.0 \n", "8 1.0 13.86 1.35 2.27 16.0 98.0 \n", "10 1.0 14.12 1.48 2.32 16.8 95.0 \n", "11 1.0 13.75 1.73 2.41 16.0 89.0 \n", @@ -3356,8 +3356,8 @@ "176 3.0 14.13 4.10 2.74 24.5 96.0 \n", "\n", " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", - "3 2.80 2.69 0.39 1.82 \n", - "4 3.27 3.39 0.34 1.97 \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", "10 2.20 2.43 0.26 1.57 \n", "11 2.60 2.76 0.29 1.81 \n", @@ -3419,8 +3419,8 @@ "176 2.05 0.76 0.56 1.35 \n", "\n", " Color intensity Hue OD280/OD315 OF diluted wines Proline \n", - "3 4.320000 1.04 2.93 735.0 \n", - "4 6.750000 1.05 2.85 1450.0 \n", + "6 5.050000 1.06 3.58 1295.0 \n", + "7 5.200000 1.08 2.85 1045.0 \n", "8 7.220000 1.01 3.55 1045.0 \n", "10 5.000000 1.17 2.82 1280.0 \n", "11 5.600000 1.15 2.90 1320.0 \n", @@ -3484,7 +3484,7 @@ "[170 rows x 14 columns]" ] }, - "execution_count": 34, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3505,12 +3505,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": {}, "colab_type": "code", "id": "mD40T0Cnm5SA" }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ]], dtype=object)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "wine_df.hist(bins=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [] } diff --git a/Get_to_know_your_Data-checkpoint.ipynb b/Get_to_know_your_Data-checkpoint.ipynb index dbf59d4..d6ff895 100644 --- a/Get_to_know_your_Data-checkpoint.ipynb +++ b/Get_to_know_your_Data-checkpoint.ipynb @@ -1,2354 +1,2365 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Get to know your Data.ipynb", - "version": "0.3.2", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "J82LU53m_OU0" + }, + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] }, - "cells": [ - { - "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')" - ], - "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": "7d342633-1b72-4558-fcec-76ebe66d7430" - }, - "cell_type": "code", - "source": [ - "iris_df.head()" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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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": 4 - } - ] - }, - { - "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": 34 - }, - "outputId": "2fe6e676-1bec-453f-8e2b-3bc21ac0a999" - }, - "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" - ], - "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": "798d5ef2-0f98-4972-c98c-c896f879af64" - }, - "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": "21786651-605d-4636-d253-5694edb6f969" - }, - "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" - ] + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ZyO1UXL8mtSj" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "yXTzTowtnwGI" + }, + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "H1Bjlb5wm9f-" + }, + "outputs": [], + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KE-k7b_Mn5iN" + }, + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 }, + "colab_type": "code", + "id": "HY2Ps7xMn4ao", + "outputId": "7d342633-1b72-4558-fcec-76ebe66d7430" + }, + "outputs": [ { - "metadata": { - "id": "Bxc8i6avrZPw", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 221 - }, - "outputId": "11e956f5-d253-4025-9c3a-1e88f54528b2" - }, - "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())" + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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" ], - "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", - "3 4.6 3.1 1.5 0.2 setosa\n", - "115 6.4 3.2 5.3 2.3 virginica\n", - "113 5.7 2.5 5.0 2.0 virginica\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "126 6.2 2.8 4.8 1.8 virginica\n" - ], - "name": "stdout" - } + "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" ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ZQXekIodqOZu" + }, + "source": [ + "#### Find number of rows and columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "colab_type": "code", + "id": "6Y-A-lbFqR82", + "outputId": "2fe6e676-1bec-453f-8e2b-3bc21ac0a999" + }, + "outputs": [ { - "metadata": { - "id": "j32h8022sRT8", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### We can also apply an operation on whole column of iris_df" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "(150, 5)\n" + ] + } + ], + "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])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4ckCiGPhrC_t" + }, + "source": [ + "#### Print all columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 }, + "colab_type": "code", + "id": "S6jgMyRDrF2a", + "outputId": "798d5ef2-0f98-4972-c98c-c896f879af64" + }, + "outputs": [ { - "metadata": { - "id": "seYXHXsYsYJI", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 323 - }, - "outputId": "5f8ae149-0829-45fa-a537-ed81be0bd743" - }, - "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", - "3 4.6 3.1 1.5 0.2 setosa\n", - "115 6.4 3.2 5.3 2.3 virginica\n", - "113 5.7 2.5 5.0 2.0 virginica\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "126 6.2 2.8 4.8 1.8 virginica\n", - " sepal_length sepal_width petal_length petal_width species\n", - "3 4.6 31.0 1.5 0.2 setosa\n", - "115 6.4 32.0 5.3 2.3 virginica\n", - "113 5.7 25.0 5.0 2.0 virginica\n", - "43 5.0 35.0 1.6 0.6 setosa\n", - "126 6.2 28.0 4.8 1.8 virginica\n", - " sepal_length sepal_width petal_length petal_width species\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "115 6.4 3.2 5.3 2.3 virginica\n", - "113 5.7 2.5 5.0 2.0 virginica\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "126 6.2 2.8 4.8 1.8 virginica\n" - ], - "name": "stdout" - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(iris_df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kVav5-ACtIqS" + }, + "source": [ + "#### Check Index\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "colab_type": "code", + "id": "iu3I9zIGtLDX", + "outputId": "21786651-605d-4636-d253-5694edb6f969" + }, + "outputs": [ { - "metadata": { - "id": "R-Ca-LBLzjiF", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Show all the rows where sepal_width > 3.3" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ] + } + ], + "source": [ + "print(iris_df.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "psCc7PborOCQ" + }, + "source": [ + "#### Right now the iris_data set has all the species grouped together let's shuffle it" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 }, + "colab_type": "code", + "id": "Bxc8i6avrZPw", + "outputId": "11e956f5-d253-4025-9c3a-1e88f54528b2" + }, + "outputs": [ { - "metadata": { - "id": "WJ7W-F-d0AoZ", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1165 - }, - "outputId": "ee9c12fa-f9da-468a-dead-9d3ced177e8b" - }, - "cell_type": "code", - "source": [ - "iris_df[iris_df['sepal_width']>3.3]" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - 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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
1366.33.45.62.4virginica
165.43.91.30.4setosa
244.83.41.90.2setosa
395.13.41.50.2setosa
275.23.51.50.2setosa
856.03.44.51.6versicolor
1097.23.66.12.5virginica
55.43.91.70.4setosa
195.13.81.50.3setosa
105.43.71.50.2setosa
45.03.61.40.2setosa
1317.93.86.42.0virginica
155.74.41.50.4setosa
365.53.51.30.2setosa
205.43.41.70.2setosa
145.84.01.20.2setosa
445.13.81.90.4setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
285.23.41.40.2setosa
114.83.41.60.2setosa
465.13.81.60.2setosa
315.43.41.50.4setosa
64.63.41.40.3setosa
325.24.11.50.1setosa
1486.23.45.42.3virginica
265.03.41.60.4setosa
05.13.51.40.2setosa
485.33.71.50.2setosa
405.03.51.30.3setosa
335.54.21.40.2setosa
175.13.51.40.3setosa
1177.73.86.72.2virginica
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "7 5.0 3.4 1.5 0.2 setosa\n", - "18 5.7 3.8 1.7 0.3 setosa\n", - "136 6.3 3.4 5.6 2.4 virginica\n", - "16 5.4 3.9 1.3 0.4 setosa\n", - "24 4.8 3.4 1.9 0.2 setosa\n", - "39 5.1 3.4 1.5 0.2 setosa\n", - "27 5.2 3.5 1.5 0.2 setosa\n", - "85 6.0 3.4 4.5 1.6 versicolor\n", - "109 7.2 3.6 6.1 2.5 virginica\n", - "5 5.4 3.9 1.7 0.4 setosa\n", - "19 5.1 3.8 1.5 0.3 setosa\n", - "10 5.4 3.7 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa\n", - "131 7.9 3.8 6.4 2.0 virginica\n", - "15 5.7 4.4 1.5 0.4 setosa\n", - "36 5.5 3.5 1.3 0.2 setosa\n", - "20 5.4 3.4 1.7 0.2 setosa\n", - "14 5.8 4.0 1.2 0.2 setosa\n", - "44 5.1 3.8 1.9 0.4 setosa\n", - "21 5.1 3.7 1.5 0.4 setosa\n", - "22 4.6 3.6 1.0 0.2 setosa\n", - "28 5.2 3.4 1.4 0.2 setosa\n", - "11 4.8 3.4 1.6 0.2 setosa\n", - "46 5.1 3.8 1.6 0.2 setosa\n", - "31 5.4 3.4 1.5 0.4 setosa\n", - "6 4.6 3.4 1.4 0.3 setosa\n", - "32 5.2 4.1 1.5 0.1 setosa\n", - "148 6.2 3.4 5.4 2.3 virginica\n", - "26 5.0 3.4 1.6 0.4 setosa\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "48 5.3 3.7 1.5 0.2 setosa\n", - "40 5.0 3.5 1.3 0.3 setosa\n", - "33 5.5 4.2 1.4 0.2 setosa\n", - "17 5.1 3.5 1.4 0.3 setosa\n", - "117 7.7 3.8 6.7 2.2 virginica" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 10 - } - ] + "name": "stdout", + "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", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 virginica\n" + ] + } + ], + "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())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "j32h8022sRT8" + }, + "source": [ + "#### We can also apply an operation on whole column of iris_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 }, + "colab_type": "code", + "id": "seYXHXsYsYJI", + "outputId": "5f8ae149-0829-45fa-a537-ed81be0bd743" + }, + "outputs": [ { - "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" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 31.0 1.5 0.2 setosa\n", + "115 6.4 32.0 5.3 2.3 virginica\n", + "113 5.7 25.0 5.0 2.0 virginica\n", + "43 5.0 35.0 1.6 0.6 setosa\n", + "126 6.2 28.0 4.8 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "126 6.2 2.8 4.8 1.8 virginica\n" + ] + } + ], + "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())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "R-Ca-LBLzjiF" + }, + "source": [ + "#### Show all the rows where sepal_width > 3.3" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1165 }, + "colab_type": "code", + "id": "WJ7W-F-d0AoZ", + "outputId": "ee9c12fa-f9da-468a-dead-9d3ced177e8b" + }, + "outputs": [ { - "metadata": { - "id": "4U7ksr_R2H7M", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 80 - }, - "outputId": "a245bbce-09c2-41e0-fb41-6138e8f8eddc" - }, - "cell_type": "code", - "source": [ - "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + "data": { + "text/html": [ + "
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435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
1366.33.45.62.4virginica
165.43.91.30.4setosa
244.83.41.90.2setosa
395.13.41.50.2setosa
275.23.51.50.2setosa
856.03.44.51.6versicolor
1097.23.66.12.5virginica
55.43.91.70.4setosa
195.13.81.50.3setosa
105.43.71.50.2setosa
45.03.61.40.2setosa
1317.93.86.42.0virginica
155.74.41.50.4setosa
365.53.51.30.2setosa
205.43.41.70.2setosa
145.84.01.20.2setosa
445.13.81.90.4setosa
215.13.71.50.4setosa
224.63.61.00.2setosa
285.23.41.40.2setosa
114.83.41.60.2setosa
465.13.81.60.2setosa
315.43.41.50.4setosa
64.63.41.40.3setosa
325.24.11.50.1setosa
1486.23.45.42.3virginica
265.03.41.60.4setosa
05.13.51.40.2setosa
485.33.71.50.2setosa
405.03.51.30.3setosa
335.54.21.40.2setosa
175.13.51.40.3setosa
1177.73.86.72.2virginica
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" ], - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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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" + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "32 5.2 4.1 1.5 0.1 setosa\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "33 5.5 4.2 1.4 0.2 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "117 7.7 3.8 6.7 2.2 virginica" ] + }, + "execution_count": 10, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "gH3DnhCq2Cbl" + }, + "source": [ + "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 }, + "colab_type": "code", + "id": "4U7ksr_R2H7M", + "outputId": "a245bbce-09c2-41e0-fb41-6138e8f8eddc" + }, + "outputs": [ { - "metadata": { - "id": "K7KIj6fv2zWP", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1969 - }, - "outputId": "c38831d3-1fd3-4728-bfbf-c9355e4a96a5" - }, - "cell_type": "code", - "source": [ - "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", - "#pass ascending = False for descending order" + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
856.03.44.51.6versicolor
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
626.02.24.01.0versicolor
686.22.24.51.5versicolor
1196.02.25.01.5virginica
876.32.34.41.3versicolor
535.52.34.01.3versicolor
935.02.33.31.0versicolor
414.52.31.30.3setosa
574.92.43.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
726.32.54.91.5versicolor
1086.72.55.81.8virginica
895.52.54.01.3versicolor
985.12.53.01.1versicolor
1064.92.54.51.7virginica
695.62.53.91.1versicolor
1466.32.55.01.9virginica
1135.72.55.02.0virginica
795.72.63.51.0versicolor
1187.72.66.92.3virginica
925.82.64.01.2versicolor
905.52.64.41.2versicolor
1346.12.65.61.4virginica
675.82.74.11.0versicolor
595.22.73.91.4versicolor
945.62.74.21.3versicolor
836.02.75.11.6versicolor
1236.32.74.91.8virginica
1015.82.75.11.9virginica
..................
1486.23.45.42.3virginica
1366.33.45.62.4virginica
856.03.44.51.6versicolor
265.03.41.60.4setosa
244.83.41.90.2setosa
205.43.41.70.2setosa
365.53.51.30.2setosa
175.13.51.40.3setosa
405.03.51.30.3setosa
435.03.51.60.6setosa
05.13.51.40.2setosa
275.23.51.50.2setosa
224.63.61.00.2setosa
45.03.61.40.2setosa
1097.23.66.12.5virginica
215.13.71.50.4setosa
485.33.71.50.2setosa
105.43.71.50.2setosa
445.13.81.90.4setosa
185.73.81.70.3setosa
465.13.81.60.2setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
1177.73.86.72.2virginica
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", - "62 6.0 2.2 4.0 1.0 versicolor\n", - "68 6.2 2.2 4.5 1.5 versicolor\n", - "119 6.0 2.2 5.0 1.5 virginica\n", - "87 6.3 2.3 4.4 1.3 versicolor\n", - "53 5.5 2.3 4.0 1.3 versicolor\n", - "93 5.0 2.3 3.3 1.0 versicolor\n", - "41 4.5 2.3 1.3 0.3 setosa\n", - "57 4.9 2.4 3.3 1.0 versicolor\n", - "81 5.5 2.4 3.7 1.0 versicolor\n", - "80 5.5 2.4 3.8 1.1 versicolor\n", - "72 6.3 2.5 4.9 1.5 versicolor\n", - "108 6.7 2.5 5.8 1.8 virginica\n", - "89 5.5 2.5 4.0 1.3 versicolor\n", - "98 5.1 2.5 3.0 1.1 versicolor\n", - "106 4.9 2.5 4.5 1.7 virginica\n", - "69 5.6 2.5 3.9 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", - "79 5.7 2.6 3.5 1.0 versicolor\n", - "118 7.7 2.6 6.9 2.3 virginica\n", - "92 5.8 2.6 4.0 1.2 versicolor\n", - "90 5.5 2.6 4.4 1.2 versicolor\n", - "134 6.1 2.6 5.6 1.4 virginica\n", - "67 5.8 2.7 4.1 1.0 versicolor\n", - "59 5.2 2.7 3.9 1.4 versicolor\n", - "94 5.6 2.7 4.2 1.3 versicolor\n", - "83 6.0 2.7 5.1 1.6 versicolor\n", - "123 6.3 2.7 4.9 1.8 virginica\n", - "101 5.8 2.7 5.1 1.9 virginica\n", - ".. ... ... ... ... ...\n", - "148 6.2 3.4 5.4 2.3 virginica\n", - "136 6.3 3.4 5.6 2.4 virginica\n", - "85 6.0 3.4 4.5 1.6 versicolor\n", - "26 5.0 3.4 1.6 0.4 setosa\n", - "24 4.8 3.4 1.9 0.2 setosa\n", - "20 5.4 3.4 1.7 0.2 setosa\n", - "36 5.5 3.5 1.3 0.2 setosa\n", - "17 5.1 3.5 1.4 0.3 setosa\n", - "40 5.0 3.5 1.3 0.3 setosa\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "27 5.2 3.5 1.5 0.2 setosa\n", - "22 4.6 3.6 1.0 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa\n", - "109 7.2 3.6 6.1 2.5 virginica\n", - "21 5.1 3.7 1.5 0.4 setosa\n", - "48 5.3 3.7 1.5 0.2 setosa\n", - "10 5.4 3.7 1.5 0.2 setosa\n", - "44 5.1 3.8 1.9 0.4 setosa\n", - "18 5.7 3.8 1.7 0.3 setosa\n", - "46 5.1 3.8 1.6 0.2 setosa\n", - "19 5.1 3.8 1.5 0.3 setosa\n", - "131 7.9 3.8 6.4 2.0 virginica\n", - "117 7.7 3.8 6.7 2.2 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": 12 - } - ] - }, - { - "metadata": { - "id": "9jg_Z4YCoMSV", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### List all the unique species" + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "85 6.0 3.4 4.5 1.6 versicolor" ] + }, + "execution_count": 11, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1lmnB3ot2u7I" + }, + "source": [ + "#### Sorting a column by value" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 }, + "colab_type": "code", + "id": "K7KIj6fv2zWP", + "outputId": "c38831d3-1fd3-4728-bfbf-c9355e4a96a5" + }, + "outputs": [ { - "metadata": { - "id": "M6EN78ufoJY7", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "4ae0c425-f973-486f-817f-63cbe6897bdf" - }, - "cell_type": "code", - "source": [ - "species = iris_df['species'].unique()\n", - "\n", - "print(species)" + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
605.02.03.51.0versicolor
626.02.24.01.0versicolor
686.22.24.51.5versicolor
1196.02.25.01.5virginica
876.32.34.41.3versicolor
535.52.34.01.3versicolor
935.02.33.31.0versicolor
414.52.31.30.3setosa
574.92.43.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
726.32.54.91.5versicolor
1086.72.55.81.8virginica
895.52.54.01.3versicolor
985.12.53.01.1versicolor
1064.92.54.51.7virginica
695.62.53.91.1versicolor
1466.32.55.01.9virginica
1135.72.55.02.0virginica
795.72.63.51.0versicolor
1187.72.66.92.3virginica
925.82.64.01.2versicolor
905.52.64.41.2versicolor
1346.12.65.61.4virginica
675.82.74.11.0versicolor
595.22.73.91.4versicolor
945.62.74.21.3versicolor
836.02.75.11.6versicolor
1236.32.74.91.8virginica
1015.82.75.11.9virginica
..................
1486.23.45.42.3virginica
1366.33.45.62.4virginica
856.03.44.51.6versicolor
265.03.41.60.4setosa
244.83.41.90.2setosa
205.43.41.70.2setosa
365.53.51.30.2setosa
175.13.51.40.3setosa
405.03.51.30.3setosa
435.03.51.60.6setosa
05.13.51.40.2setosa
275.23.51.50.2setosa
224.63.61.00.2setosa
45.03.61.40.2setosa
1097.23.66.12.5virginica
215.13.71.50.4setosa
485.33.71.50.2setosa
105.43.71.50.2setosa
445.13.81.90.4setosa
185.73.81.70.3setosa
465.13.81.60.2setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
1177.73.86.72.2virginica
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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" ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "text": [ - "['setosa' 'virginica' 'versicolor']\n" - ], - "name": "stdout" - } + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "62 6.0 2.2 4.0 1.0 versicolor\n", + "68 6.2 2.2 4.5 1.5 versicolor\n", + "119 6.0 2.2 5.0 1.5 virginica\n", + "87 6.3 2.3 4.4 1.3 versicolor\n", + "53 5.5 2.3 4.0 1.3 versicolor\n", + "93 5.0 2.3 3.3 1.0 versicolor\n", + "41 4.5 2.3 1.3 0.3 setosa\n", + "57 4.9 2.4 3.3 1.0 versicolor\n", + "81 5.5 2.4 3.7 1.0 versicolor\n", + "80 5.5 2.4 3.8 1.1 versicolor\n", + "72 6.3 2.5 4.9 1.5 versicolor\n", + "108 6.7 2.5 5.8 1.8 virginica\n", + "89 5.5 2.5 4.0 1.3 versicolor\n", + "98 5.1 2.5 3.0 1.1 versicolor\n", + "106 4.9 2.5 4.5 1.7 virginica\n", + "69 5.6 2.5 3.9 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", + "79 5.7 2.6 3.5 1.0 versicolor\n", + "118 7.7 2.6 6.9 2.3 virginica\n", + "92 5.8 2.6 4.0 1.2 versicolor\n", + "90 5.5 2.6 4.4 1.2 versicolor\n", + "134 6.1 2.6 5.6 1.4 virginica\n", + "67 5.8 2.7 4.1 1.0 versicolor\n", + "59 5.2 2.7 3.9 1.4 versicolor\n", + "94 5.6 2.7 4.2 1.3 versicolor\n", + "83 6.0 2.7 5.1 1.6 versicolor\n", + "123 6.3 2.7 4.9 1.8 virginica\n", + "101 5.8 2.7 5.1 1.9 virginica\n", + ".. ... ... ... ... ...\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "117 7.7 3.8 6.7 2.2 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]" ] + }, + "execution_count": 12, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "9jg_Z4YCoMSV" + }, + "source": [ + "#### List all the unique species" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "colab_type": "code", + "id": "M6EN78ufoJY7", + "outputId": "4ae0c425-f973-486f-817f-63cbe6897bdf" + }, + "outputs": [ { - "metadata": { - "id": "wG1i5nxBodmB", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "#### Selecting a particular species using boolean mask (learnt in previous exercise)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "['setosa' 'virginica' 'versicolor']\n" + ] + } + ], + "source": [ + "species = iris_df['species'].unique()\n", + "\n", + "print(species)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wG1i5nxBodmB" + }, + "source": [ + "#### Selecting a particular species using boolean mask (learnt in previous exercise)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 }, + "colab_type": "code", + "id": "gZvpbKBwoVUe", + "outputId": "213a3376-8e60-4f9f-cf7e-937e98475206" + }, + "outputs": [ { - "metadata": { - "id": "gZvpbKBwoVUe", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - }, - "outputId": "213a3376-8e60-4f9f-cf7e-937e98475206" - }, - "cell_type": "code", - "source": [ - "setosa = iris_df[iris_df['species'] == species[0]]\n", - "\n", - "setosa.head()" + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
34.63.11.50.2setosa
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75.03.41.50.2setosa
185.73.81.70.3setosa
424.43.21.30.2setosa
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" ], - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
34.63.11.50.2setosa
435.03.51.60.6setosa
75.03.41.50.2setosa
185.73.81.70.3setosa
424.43.21.30.2setosa
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "43 5.0 3.5 1.6 0.6 setosa\n", - "7 5.0 3.4 1.5 0.2 setosa\n", - "18 5.7 3.8 1.7 0.3 setosa\n", - "42 4.4 3.2 1.3 0.2 setosa" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 14 - } + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "42 4.4 3.2 1.3 0.2 setosa" ] + }, + "execution_count": 14, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "setosa = iris_df[iris_df['species'] == species[0]]\n", + "\n", + "setosa.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 }, + "colab_type": "code", + "id": "7tumfZ3DotPG", + "outputId": "bf3850ab-a0eb-45fa-9816-fdd4c97517d2" + }, + "outputs": [ { - "metadata": { - "id": "7tumfZ3DotPG", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - }, - "outputId": "bf3850ab-a0eb-45fa-9816-fdd4c97517d2" - }, - "cell_type": "code", - "source": [ - "# do the same for other 2 species \n", - "versicolor = iris_df[iris_df['species'] == species[1]]\n", - "\n", - "versicolor.head()" + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
1156.43.25.32.3virginica
1135.72.55.02.0virginica
1266.22.84.81.8virginica
1227.72.86.72.0virginica
1166.53.05.51.8virginica
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" ], - "execution_count": 15, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
1156.43.25.32.3virginica
1135.72.55.02.0virginica
1266.22.84.81.8virginica
1227.72.86.72.0virginica
1166.53.05.51.8virginica
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "115 6.4 3.2 5.3 2.3 virginica\n", - "113 5.7 2.5 5.0 2.0 virginica\n", - "126 6.2 2.8 4.8 1.8 virginica\n", - "122 7.7 2.8 6.7 2.0 virginica\n", - "116 6.5 3.0 5.5 1.8 virginica" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 15 - } + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "115 6.4 3.2 5.3 2.3 virginica\n", + "113 5.7 2.5 5.0 2.0 virginica\n", + "126 6.2 2.8 4.8 1.8 virginica\n", + "122 7.7 2.8 6.7 2.0 virginica\n", + "116 6.5 3.0 5.5 1.8 virginica" ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# do the same for other 2 species \n", + "versicolor = iris_df[iris_df['species'] == species[1]]\n", + "\n", + "versicolor.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 }, + "colab_type": "code", + "id": "cUYm5UqVpDPy", + "outputId": "a3cc4a4c-b06e-43c1-ed6a-b1f8fcb14de6" + }, + "outputs": [ { - "metadata": { - "id": "cUYm5UqVpDPy", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 204 - }, - "outputId": "a3cc4a4c-b06e-43c1-ed6a-b1f8fcb14de6" - }, - "cell_type": "code", - "source": [ - "\n", - "\n", - "virginica = iris_df[iris_df['species'] == species[2]]\n", - "\n", - "virginica.head()" + "data": { + "text/html": [ + "
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507.03.24.71.4versicolor
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" ], - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "90 5.5 2.6 4.4 1.2 versicolor\n", - "93 5.0 2.3 3.3 1.0 versicolor\n", - "50 7.0 3.2 4.7 1.4 versicolor\n", - "60 5.0 2.0 3.5 1.0 versicolor\n", - "62 6.0 2.2 4.0 1.0 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" + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "90 5.5 2.6 4.4 1.2 versicolor\n", + "93 5.0 2.3 3.3 1.0 versicolor\n", + "50 7.0 3.2 4.7 1.4 versicolor\n", + "60 5.0 2.0 3.5 1.0 versicolor\n", + "62 6.0 2.2 4.0 1.0 versicolor" ] + }, + "execution_count": 16, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "virginica = iris_df[iris_df['species'] == species[2]]\n", + "\n", + "virginica.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-y1wDc8SpdQs" + }, + "source": [ + "#### Describe each created species to see the difference\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 }, + "colab_type": "code", + "id": "eHrn3ZVRpOk5", + "outputId": "58e878f8-2aed-49b2-9529-545308332808" + }, + "outputs": [ { - "metadata": { - "id": "eHrn3ZVRpOk5", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 297 - }, - "outputId": "58e878f8-2aed-49b2-9529-545308332808" - }, - "cell_type": "code", - "source": [ - "setosa.describe()" + "data": { + "text/html": [ + "
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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" ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "virginica.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Vdu0ulZWtr09" + }, + "source": [ + "#### Let's plot and see the difference" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PEVMzRvpttmD" + }, + "source": [ + "##### import matplotlib.pyplot " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 }, + "colab_type": "code", + "id": "rqDXuuAtt7C3", + "outputId": "e1111c09-8877-43f8-ac45-f92b53899a5c" + }, + "outputs": [ { - "metadata": { - "id": "PEVMzRvpttmD", - "colab_type": "text" - }, - "cell_type": "markdown", - "source": [ - "##### import matplotlib.pyplot " + "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", + " )" ] + }, + "execution_count": 20, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" }, { - "metadata": { - "id": "rqDXuuAtt7C3", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 398 - }, - "outputId": "e1111c09-8877-43f8-ac45-f92b53899a5c" - }, - "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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+ "text/plain": [ + "" ] - }, - { - "metadata": { - "id": "pl4RPzBfl5mI", - "colab_type": "code", - "colab": {} - }, - "cell_type": "code", - "source": [ - "" - ], - "execution_count": 0, - "outputs": [] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" } - ] -} \ No newline at end of file + ], + "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'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "pl4RPzBfl5mI" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "provenance": [], + "version": "0.3.2" + }, + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}