diff --git a/Basic_Pandas.ipynb b/Basic_Pandas.ipynb new file mode 100644 index 0000000..62d69bb --- /dev/null +++ b/Basic_Pandas.ipynb @@ -0,0 +1,1230 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Basic Pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/amartyabhattacharya/Assignment-3/blob/amartyabhattacharya/Basic_Pandas.ipynb)" + ] + }, + { + "metadata": { + "id": "cGbE814_Xaf9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas\n", + "\n", + "Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.\n", + "\n", + "\n", + "## Import pandas and numpy" + ] + }, + { + "metadata": { + "id": "irlVYeeAXPDL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BI2J-zdMbGwE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### This is your playground feel free to explore other functions on pandas\n", + "\n", + "#### Create Series from numpy array, list and dict\n", + "\n", + "Don't know what a series is?\n", + "\n", + "[Series Doc](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.html)" + ] + }, + { + "metadata": { + "id": "GeEct691YGE3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145 + }, + "outputId": "cb8e8c8c-fae9-4efc-c2ee-cce1eb6f9b6b" + }, + "cell_type": "code", + "source": [ + "a_ascii = ord('A')\n", + "z_ascii = ord('Z')\n", + "alphabets = [chr(i) for i in range(a_ascii, z_ascii+1)]\n", + "\n", + "print(alphabets)\n", + "\n", + "numbers = np.arange(26)\n", + "\n", + "print(numbers)\n", + "\n", + "print(type(alphabets), type(numbers))\n", + "\n", + "alpha_numbers = dict(zip(alphabets, numbers))\n", + "\n", + "print(alpha_numbers)\n", + "\n", + "print(type(alpha_numbers))" + ], + "execution_count": 2, + "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": 503 + }, + "outputId": "13406ca6-f607-4f09-c8cd-172cf87d1570" + }, + "cell_type": "code", + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)" + ], + "execution_count": 3, + "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": 503 + }, + "outputId": "f61dab6c-0985-4161-8540-5f68586c87f4" + }, + "cell_type": "code", + "source": [ + "series2 = pd.Series(numbers)\n", + "print(series2)" + ], + "execution_count": 4, + "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": 503 + }, + "outputId": "46797c43-8d22-4512-efe1-9eb3c552c94c" + }, + "cell_type": "code", + "source": [ + "series3 = pd.Series(alpha_numbers)\n", + "print(series3)" + ], + "execution_count": 5, + "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": 125 + }, + "outputId": "390a899b-a90c-4f74-92b2-542303208119" + }, + "cell_type": "code", + "source": [ + "#replace head() with head(n) where n can be any number between [0-25] and observe the output in deach case \n", + "series3.head()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "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": "6363e260-15e3-49b7-8dc7-557241d6c70b" + }, + "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": 7, + "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": 8 + } + ] + }, + { + "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": 330 + }, + "outputId": "0480a6c2-890d-4b47-c9a5-84134329497f" + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n", + "pos = [0, 4, 8, 14, 20]\n", + "\n", + "# vowels = ser.take(pos)\n", + "vowels = ser[pos]\n", + "\n", + "print(vowels)\n", + "print(type(vowels))\n", + "\n", + "df = pd.DataFrame(vowels)#, columns=['vowels'])\n", + "\n", + "df.columns = ['vowels']\n", + "\n", + "#df.index = [0, 1, 2, 3, 4]\n", + "\n", + "df" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "0 a\n", + "4 e\n", + "8 i\n", + "14 o\n", + "20 u\n", + "dtype: object\n", + "\n" + ], + "name": "stdout" + }, + { + "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": 12 + } + ] + }, + { + "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": 53 + }, + "outputId": "de47f8cf-0c54-4631-e09a-b878c1b7c8f7" + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(['we', 'are', 'learning', 'pandas'])\n", + "\n", + "tit = ser.map(lambda x : x.title())\n", + "\n", + "titles = [i.title() for i in ser]\n", + "\n", + "print(titles)\n", + "print(list(tit))" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['We', 'Are', 'Learning', 'Pandas']\n", + "['We', 'Are', 'Learning', 'Pandas']\n" + ], + "name": "stdout" + } + ] + }, + { + "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": "c2dcd9a4-ccd1-4a2e-aa28-05b9d4eec876" + }, + "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": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " lower values upper values\n", + "2 a A\n", + "5 b B\n", + "4 c C\n", + "3 d D\n", + "1 e E" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 22 + } + ] + }, + { + "metadata": { + "id": "ycK74ws1utXg", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Exercise.ipynb b/Exercise.ipynb new file mode 100644 index 0000000..00b6ad2 --- /dev/null +++ b/Exercise.ipynb @@ -0,0 +1,3616 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Exercise.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/amartyabhattacharya/Assignment-3/blob/amartyabhattacharya/Exercise.ipynb)" + ] + }, + { + "metadata": { + "id": "2LTtpUJEibjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "metadata": { + "id": "c3_UBbMRhiKx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "tp-cTCyWi8mR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n", + "\n", + "This is a wine dataset\n", + "\n" + ] + }, + { + "metadata": { + "id": "DMojQY3thrRi", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "7d025873-785f-4bc3-c4fd-a8c8639208de" + }, + "cell_type": "code", + "source": [ + "wine_df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\")\n", + "wine_df.shape" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(177, 14)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 2 + } + ] + }, + { + "metadata": { + "id": "BF9MMjoZjSlg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### print first five rows" + ] + }, + { + "metadata": { + "id": "1vSMQdnHjYNU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "8c831227-2607-4295-9202-20abd6352447" + }, + "cell_type": "code", + "source": [ + "wine_df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 1 14.23 1.71 2.43 15.6 127 2.8 3.06 .28 2.29 5.64 1.04 3.92 \\\n", + "0 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", + "1 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", + "2 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n", + "4 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05 2.85 \n", + "\n", + " 1065 \n", + "0 1050 \n", + "1 1185 \n", + "2 1480 \n", + "3 735 \n", + "4 1450 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "Tet6P2DvjY3T", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy\n", + "\n", + "[Hint](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)" + ] + }, + { + "metadata": { + "id": "CMj3qSdJjx0u", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 + }, + "outputId": "4076641f-3f71-4270-a82a-5ea3e0446354" + }, + "cell_type": "code", + "source": [ + "wine_df_copy = wine_df.copy()\n", + "# wine_df_copy.iloc[::2]\n", + "# wine_df_copy.head()\n", + "wine_df_copy = wine_df_copy.drop(range(1, wine_df_copy.shape[0], 2))\n", + "wine_df_copy" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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"#### Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) Alcohol \n", + "2) Malic acid \n", + "3) Ash \n", + "4) Alcalinity of ash \n", + "5) Magnesium \n", + "6) Total phenols \n", + "7) Flavanoids \n", + "8) Nonflavanoid phenols \n", + "9) Proanthocyanins \n", + "10)Color intensity \n", + "11)Hue \n", + "12)OD280/OD315 of diluted wines \n", + "13)Proline " + ] + }, + { + "metadata": { + "id": "my8HB4V4j779", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 + }, + "outputId": "8ca76d52-1676-49c1-8e85-a4e3022ede4f" + }, + "cell_type": "code", + "source": [ + "columns = ['Category', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',\n", + " 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']\n", + "wine_df.columns = columns\n", + "wine_df.head()" + ], + 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" + ], + "text/plain": [ + " Category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 13.20 1.78 2.14 11.2 100 \n", + "1 1 13.16 2.36 2.67 18.6 101 \n", + "2 1 14.37 1.95 2.50 16.8 113 \n", + "3 1 13.24 2.59 2.87 21.0 118 \n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.65 2.76 0.26 1.28 \n", + "1 2.80 3.24 0.30 2.81 \n", + "2 3.85 3.49 0.24 2.18 \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 4.38 1.05 3.40 1050 \n", + "1 5.68 1.03 3.17 1185 \n", + "2 7.80 0.86 3.45 1480 \n", + "3 4.32 1.04 2.93 735 \n", + "4 6.75 1.05 2.85 1450 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "Zqi7hwWpkNbH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Set the values of the first 3 rows from alcohol as NaN\n", + "\n", + "Hint- Use iloc to select 3 rows of wine_df" + ] + }, + { + "metadata": { + "id": "buyT4vX4kPMl", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 + }, + "outputId": "3e5391e2-b3f8-4885-9b98-a2014e965c1a" + }, + "cell_type": "code", + "source": [ + "alc_index = columns.index('Alcohol')\n", + "wine_df.iloc[0:3:1, alc_index] = None\n", + "wine_df.head()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.78 2.14 11.2 100 \n", + "1 1 NaN 2.36 2.67 18.6 101 \n", + "2 1 NaN 1.95 2.50 16.8 113 \n", + "3 1 13.24 2.59 2.87 21.0 118 \n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.65 2.76 0.26 1.28 \n", + "1 2.80 3.24 0.30 2.81 \n", + "2 3.85 3.49 0.24 2.18 \n", + "3 2.80 2.69 0.39 1.82 \n", + "4 3.27 3.39 0.34 1.97 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 4.38 1.05 3.40 1050 \n", + "1 5.68 1.03 3.17 1185 \n", + "2 7.80 0.86 3.45 1480 \n", + "3 4.32 1.04 2.93 735 \n", + "4 6.75 1.05 2.85 1450 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 6 + } + ] + }, + { + "metadata": { + "id": "RQMNI2UHkP3o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "metadata": { + "id": "xunmCjaEmDwZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "6b9cabfa-c02a-41f8-c627-8a2527960ce1" + }, + "cell_type": "code", + "source": [ + "import random as rnd\n", + "random = [rnd.randrange(10) for _ in range(10)]\n", + "random" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[7, 7, 6, 0, 6, 3, 5, 1, 2, 7]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "hELUakyXmFSu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "metadata": { + "id": "zMgaNnNHmP01", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 413 + }, + "outputId": "7205f80b-6b27-4920-9f38-47b31d19384e" + }, + "cell_type": "code", + "source": [ + "wine_df.iloc[random, alc_index] = None\n", + "wine_df.head(10)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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CategoryAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.782.1411.21002.652.760.261.284.381.053.401050
11NaN2.362.6718.61012.803.240.302.815.681.033.171185
21NaN1.952.5016.81133.853.490.242.187.800.863.451480
31NaN2.592.8721.01182.802.690.391.824.321.042.93735
4114.201.762.4515.21123.273.390.341.976.751.052.851450
51NaN1.872.4514.6962.502.520.301.985.251.023.581290
61NaN2.152.6117.61212.602.510.311.255.051.063.581295
71NaN1.642.1714.0972.802.980.291.985.201.082.851045
8113.861.352.2716.0982.983.150.221.857.221.013.551045
9114.102.162.3018.01052.953.320.222.385.751.253.171510
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" + ], + "text/plain": [ + " Category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.78 2.14 11.2 100 \n", + "1 1 NaN 2.36 2.67 18.6 101 \n", + "2 1 NaN 1.95 2.50 16.8 113 \n", + "3 1 NaN 2.59 2.87 21.0 118 \n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "5 1 NaN 1.87 2.45 14.6 96 \n", + "6 1 NaN 2.15 2.61 17.6 121 \n", + "7 1 NaN 1.64 2.17 14.0 97 \n", + "8 1 13.86 1.35 2.27 16.0 98 \n", + "9 1 14.10 2.16 2.30 18.0 105 \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", + "5 2.50 2.52 0.30 1.98 \n", + "6 2.60 2.51 0.31 1.25 \n", + "7 2.80 2.98 0.29 1.98 \n", + "8 2.98 3.15 0.22 1.85 \n", + "9 2.95 3.32 0.22 2.38 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 4.38 1.05 3.40 1050 \n", + "1 5.68 1.03 3.17 1185 \n", + "2 7.80 0.86 3.45 1480 \n", + "3 4.32 1.04 2.93 735 \n", + "4 6.75 1.05 2.85 1450 \n", + "5 5.25 1.02 3.58 1290 \n", + "6 5.05 1.06 3.58 1295 \n", + "7 5.20 1.08 2.85 1045 \n", + "8 7.22 1.01 3.55 1045 \n", + "9 5.75 1.25 3.17 1510 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "PHyK_vRsmRwV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "metadata": { + "id": "EnOYhmEqmfKp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "b443827e-1f82-45ef-cb4c-589f9750ca9e" + }, + "cell_type": "code", + "source": [ + "status = pd.isnull(wine_df['Alcohol'])\n", + "alc_nan_enum = list(filter(lambda x: x[1], enumerate(list(status))))\n", + "# print(alc_nan_enum)\n", + "alc_nan = [iter[0] for iter in alc_nan_enum]\n", + "# print(alc_nan)\n", + "print(len(alc_nan))" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "7\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "As7IC6Ktms8-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2023 + }, + "outputId": "db28ae9a-77d9-4d15-d24b-409a48358a01" + }, + "cell_type": "code", + "source": [ + "wine_df.drop(alc_nan, axis = 0, inplace = True)\n", + "wine_df" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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CategoryAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
4114.201.762.4515.21123.273.390.341.976.7500001.052.851450
8113.861.352.2716.0982.983.150.221.857.2200001.013.551045
9114.102.162.3018.01052.953.320.222.385.7500001.253.171510
10114.121.482.3216.8952.202.430.261.575.0000001.172.821280
11113.751.732.4116.0892.602.760.291.815.6000001.152.901320
12114.751.732.3911.4913.103.690.432.815.4000001.252.731150
13114.381.872.3812.01023.303.640.292.967.5000001.203.001547
14113.631.812.7017.21122.852.910.301.467.3000001.282.881310
15114.301.922.7220.01202.803.140.331.976.2000001.072.651280
16113.831.572.6220.01152.953.400.401.726.6000001.132.571130
17114.191.592.4816.51083.303.930.321.868.7000001.232.821680
18113.643.102.5615.21162.703.030.171.665.1000000.963.36845
19114.061.632.2816.01263.003.170.242.105.6500001.093.71780
20112.933.802.6518.61022.412.410.251.984.5000001.033.52770
21113.711.862.3616.61012.612.880.271.693.8000001.114.001035
22112.851.602.5217.8952.482.370.261.463.9300001.093.631015
23113.501.812.6120.0962.532.610.281.663.5200001.123.82845
24113.052.053.2225.01242.632.680.471.923.5800001.133.20830
25113.391.772.6216.1932.852.940.341.454.8000000.923.221195
26113.301.722.1417.0942.402.190.271.353.9500001.022.771285
27113.871.902.8019.41072.952.970.371.764.5000001.253.40915
28114.021.682.2116.0962.652.330.261.984.7000001.043.591035
29113.731.502.7022.51013.003.250.292.385.7000001.192.711285
30113.581.662.3619.11062.863.190.221.956.9000001.092.881515
31113.681.832.3617.21042.422.690.421.973.8400001.232.87990
32113.761.532.7019.51322.952.740.501.355.4000001.253.001235
33113.511.802.6519.01102.352.530.291.544.2000001.102.871095
34113.481.812.4120.51002.702.980.261.865.1000001.043.47920
35113.281.642.8415.51102.602.680.341.364.6000001.092.78880
36113.051.652.5518.0982.452.430.291.444.2500001.122.511105
.............................................
147313.323.242.3821.5921.930.760.451.258.4200000.551.62650
148313.083.902.3621.51131.411.390.341.149.4000000.571.33550
149313.503.122.6224.01231.401.570.221.258.6000000.591.30500
150312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
151313.111.902.7525.51162.201.280.261.567.1000000.611.33425
152313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
153312.581.292.1020.01031.480.580.531.407.6000000.581.55640
154313.175.192.3222.0931.740.630.611.557.9000000.601.48725
155313.844.122.3819.5891.800.830.481.569.0100000.571.64480
156312.453.032.6427.0971.900.580.631.147.5000000.671.73880
157314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
158313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
159312.363.832.3821.0882.300.920.501.047.6500000.561.58520
160313.693.262.5420.01071.830.560.500.805.8800000.961.82680
161312.853.272.5822.01061.650.600.600.965.5800000.872.11570
162312.963.452.3518.51061.390.700.400.945.2800000.681.75675
163313.782.762.3022.0901.350.680.411.039.5800000.701.68615
164313.734.362.2622.5881.280.470.521.156.6200000.781.75520
165313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
166312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
167313.582.582.6924.51051.550.840.391.548.6600000.741.80750
168313.404.602.8625.01121.980.960.271.118.5000000.671.92630
169312.203.032.3219.0961.250.490.400.735.5000000.661.83510
170312.772.392.2819.5861.390.510.480.649.8999990.571.63470
171314.162.512.4820.0911.680.700.441.249.7000000.621.71660
172313.715.652.4520.5951.680.610.521.067.7000000.641.74740
173313.403.912.4823.01021.800.750.431.417.3000000.701.56750
174313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
175313.172.592.3720.01201.650.680.531.469.3000000.601.62840
176314.134.102.7424.5962.050.760.561.359.2000000.611.60560
\n", + "

170 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Category Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "4 1 14.20 1.76 2.45 15.2 112 \n", + "8 1 13.86 1.35 2.27 16.0 98 \n", + "9 1 14.10 2.16 2.30 18.0 105 \n", + "10 1 14.12 1.48 2.32 16.8 95 \n", + "11 1 13.75 1.73 2.41 16.0 89 \n", + "12 1 14.75 1.73 2.39 11.4 91 \n", + "13 1 14.38 1.87 2.38 12.0 102 \n", + "14 1 13.63 1.81 2.70 17.2 112 \n", + "15 1 14.30 1.92 2.72 20.0 120 \n", + "16 1 13.83 1.57 2.62 20.0 115 \n", + "17 1 14.19 1.59 2.48 16.5 108 \n", + "18 1 13.64 3.10 2.56 15.2 116 \n", + "19 1 14.06 1.63 2.28 16.0 126 \n", + "20 1 12.93 3.80 2.65 18.6 102 \n", + "21 1 13.71 1.86 2.36 16.6 101 \n", + "22 1 12.85 1.60 2.52 17.8 95 \n", + "23 1 13.50 1.81 2.61 20.0 96 \n", + "24 1 13.05 2.05 3.22 25.0 124 \n", + "25 1 13.39 1.77 2.62 16.1 93 \n", + "26 1 13.30 1.72 2.14 17.0 94 \n", + "27 1 13.87 1.90 2.80 19.4 107 \n", + "28 1 14.02 1.68 2.21 16.0 96 \n", + "29 1 13.73 1.50 2.70 22.5 101 \n", + "30 1 13.58 1.66 2.36 19.1 106 \n", + "31 1 13.68 1.83 2.36 17.2 104 \n", + "32 1 13.76 1.53 2.70 19.5 132 \n", + "33 1 13.51 1.80 2.65 19.0 110 \n", + "34 1 13.48 1.81 2.41 20.5 100 \n", + "35 1 13.28 1.64 2.84 15.5 110 \n", + "36 1 13.05 1.65 2.55 18.0 98 \n", + ".. ... ... ... ... ... ... \n", + "147 3 13.32 3.24 2.38 21.5 92 \n", + "148 3 13.08 3.90 2.36 21.5 113 \n", + "149 3 13.50 3.12 2.62 24.0 123 \n", + "150 3 12.79 2.67 2.48 22.0 112 \n", + "151 3 13.11 1.90 2.75 25.5 116 \n", + "152 3 13.23 3.30 2.28 18.5 98 \n", + "153 3 12.58 1.29 2.10 20.0 103 \n", + "154 3 13.17 5.19 2.32 22.0 93 \n", + "155 3 13.84 4.12 2.38 19.5 89 \n", + "156 3 12.45 3.03 2.64 27.0 97 \n", + "157 3 14.34 1.68 2.70 25.0 98 \n", + "158 3 13.48 1.67 2.64 22.5 89 \n", + "159 3 12.36 3.83 2.38 21.0 88 \n", + "160 3 13.69 3.26 2.54 20.0 107 \n", + "161 3 12.85 3.27 2.58 22.0 106 \n", + "162 3 12.96 3.45 2.35 18.5 106 \n", + "163 3 13.78 2.76 2.30 22.0 90 \n", + "164 3 13.73 4.36 2.26 22.5 88 \n", + "165 3 13.45 3.70 2.60 23.0 111 \n", + "166 3 12.82 3.37 2.30 19.5 88 \n", + "167 3 13.58 2.58 2.69 24.5 105 \n", + "168 3 13.40 4.60 2.86 25.0 112 \n", + "169 3 12.20 3.03 2.32 19.0 96 \n", + "170 3 12.77 2.39 2.28 19.5 86 \n", + "171 3 14.16 2.51 2.48 20.0 91 \n", + "172 3 13.71 5.65 2.45 20.5 95 \n", + "173 3 13.40 3.91 2.48 23.0 102 \n", + "174 3 13.27 4.28 2.26 20.0 120 \n", + "175 3 13.17 2.59 2.37 20.0 120 \n", + "176 3 14.13 4.10 2.74 24.5 96 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "4 3.27 3.39 0.34 1.97 \n", + "8 2.98 3.15 0.22 1.85 \n", + "9 2.95 3.32 0.22 2.38 \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", + "4 6.750000 1.05 2.85 1450 \n", + "8 7.220000 1.01 3.55 1045 \n", + "9 5.750000 1.25 3.17 1510 \n", + "10 5.000000 1.17 2.82 1280 \n", + "11 5.600000 1.15 2.90 1320 \n", + "12 5.400000 1.25 2.73 1150 \n", + "13 7.500000 1.20 3.00 1547 \n", + "14 7.300000 1.28 2.88 1310 \n", + "15 6.200000 1.07 2.65 1280 \n", + "16 6.600000 1.13 2.57 1130 \n", + "17 8.700000 1.23 2.82 1680 \n", + "18 5.100000 0.96 3.36 845 \n", + "19 5.650000 1.09 3.71 780 \n", + "20 4.500000 1.03 3.52 770 \n", + "21 3.800000 1.11 4.00 1035 \n", + "22 3.930000 1.09 3.63 1015 \n", + "23 3.520000 1.12 3.82 845 \n", + "24 3.580000 1.13 3.20 830 \n", + "25 4.800000 0.92 3.22 1195 \n", + "26 3.950000 1.02 2.77 1285 \n", + "27 4.500000 1.25 3.40 915 \n", + "28 4.700000 1.04 3.59 1035 \n", + "29 5.700000 1.19 2.71 1285 \n", + "30 6.900000 1.09 2.88 1515 \n", + "31 3.840000 1.23 2.87 990 \n", + "32 5.400000 1.25 3.00 1235 \n", + "33 4.200000 1.10 2.87 1095 \n", + "34 5.100000 1.04 3.47 920 \n", + "35 4.600000 1.09 2.78 880 \n", + "36 4.250000 1.12 2.51 1105 \n", + ".. ... ... ... ... \n", + "147 8.420000 0.55 1.62 650 \n", + "148 9.400000 0.57 1.33 550 \n", + "149 8.600000 0.59 1.30 500 \n", + "150 10.800000 0.48 1.47 480 \n", + "151 7.100000 0.61 1.33 425 \n", + "152 10.520000 0.56 1.51 675 \n", + "153 7.600000 0.58 1.55 640 \n", + "154 7.900000 0.60 1.48 725 \n", + "155 9.010000 0.57 1.64 480 \n", + "156 7.500000 0.67 1.73 880 \n", + "157 13.000000 0.57 1.96 660 \n", + "158 11.750000 0.57 1.78 620 \n", + "159 7.650000 0.56 1.58 520 \n", + "160 5.880000 0.96 1.82 680 \n", + "161 5.580000 0.87 2.11 570 \n", + "162 5.280000 0.68 1.75 675 \n", + "163 9.580000 0.70 1.68 615 \n", + "164 6.620000 0.78 1.75 520 \n", + "165 10.680000 0.85 1.56 695 \n", + "166 10.260000 0.72 1.75 685 \n", + "167 8.660000 0.74 1.80 750 \n", + "168 8.500000 0.67 1.92 630 \n", + "169 5.500000 0.66 1.83 510 \n", + "170 9.899999 0.57 1.63 470 \n", + "171 9.700000 0.62 1.71 660 \n", + "172 7.700000 0.64 1.74 740 \n", + "173 7.300000 0.70 1.56 750 \n", + "174 10.200000 0.59 1.56 835 \n", + "175 9.300000 0.60 1.62 840 \n", + "176 9.200000 0.61 1.60 560 \n", + "\n", + "[170 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "DlpG8drhmz7W", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "metadata": { + "id": "mD40T0Cnm5SA", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb new file mode 100644 index 0000000..dcd3e8e --- /dev/null +++ b/Get_to_know_your_Data.ipynb @@ -0,0 +1,2417 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "[View in Colaboratory](https://colab.research.google.com/github/amartyabhattacharya/Assignment-3/blob/amartyabhattacharya/Get_to_know_your_Data.ipynb)" + ] + }, + { + "metadata": { + "id": "J82LU53m_OU0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] + }, + { + "metadata": { + "id": "ZyO1UXL8mtSj", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yXTzTowtnwGI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "metadata": { + "id": "H1Bjlb5wm9f-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "metadata": { + "id": "HY2Ps7xMn4ao", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "311fde04-ab5c-4d91-b56b-c91432985003" + }, + "cell_type": "code", + "source": [ + "iris_df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
05.13.51.40.2setosa
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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": 3 + } + ] + }, + { + "metadata": { + "id": "ZQXekIodqOZu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Find number of rows and columns\n" + ] + }, + { + "metadata": { + "id": "6Y-A-lbFqR82", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "5560ae57-7c4b-4546-e09b-f52080d3a4b3" + }, + "cell_type": "code", + "source": [ + "print(iris_df.shape)\n", + "\n", + "#first is row and second is column\n", + "#select row by simple indexing\n", + "\n", + "#print(iris_df.shape[0])\n", + "#print(iris_df.shape[1])" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n" + ], + "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": 71 + }, + "outputId": "f91ee7ec-e80b-40dc-b993-b6191fc2ed56" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "kVav5-ACtIqS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Check Index\n" + ] + }, + { + "metadata": { + "id": "iu3I9zIGtLDX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "ce90018d-bad2-4dc7-b988-43cedc2f65f6" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "psCc7PborOCQ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Right now the iris_data set has all the species grouped together let's shuffle it" + ] + }, + { + "metadata": { + "id": "Bxc8i6avrZPw", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 233 + }, + "outputId": "2eb8f65e-56b2-479b-9851-072768eaee1d" + }, + "cell_type": "code", + "source": [ + "#generate a random permutaion on index\n", + "\n", + "print(iris_df.head())\n", + "\n", + "new_index = np.random.permutation(iris_df.index)\n", + "iris_df = iris_df.reindex(index = new_index)\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "102 7.1 3.0 5.9 2.1 virginica\n", + "66 5.6 3.0 4.5 1.5 versicolor\n", + "104 6.5 3.0 5.8 2.2 virginica\n", + "133 6.3 2.8 5.1 1.5 virginica\n", + "95 5.7 3.0 4.2 1.2 versicolor\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": 341 + }, + "outputId": "08e30d6c-f88a-4e40-dcfe-f076a249ef14" + }, + "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": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "102 7.1 3.0 5.9 2.1 virginica\n", + "66 5.6 3.0 4.5 1.5 versicolor\n", + "104 6.5 3.0 5.8 2.2 virginica\n", + "133 6.3 2.8 5.1 1.5 virginica\n", + "95 5.7 3.0 4.2 1.2 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "102 7.1 30.0 5.9 2.1 virginica\n", + "66 5.6 30.0 4.5 1.5 versicolor\n", + "104 6.5 30.0 5.8 2.2 virginica\n", + "133 6.3 28.0 5.1 1.5 virginica\n", + "95 5.7 30.0 4.2 1.2 versicolor\n", + " sepal_length sepal_width petal_length petal_width species\n", + "102 7.1 3.0 5.9 2.1 virginica\n", + "66 5.6 3.0 4.5 1.5 versicolor\n", + "104 6.5 3.0 5.8 2.2 virginica\n", + "133 6.3 2.8 5.1 1.5 virginica\n", + "95 5.7 3.0 4.2 1.2 versicolor\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "R-Ca-LBLzjiF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Show all the rows where sepal_width > 3.3" + ] + }, + { + "metadata": { + "id": "WJ7W-F-d0AoZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1165 + }, + "outputId": "d76703b4-442b-4fb1-de2c-8e12c5b99db4" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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405.03.51.30.3setosa
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05.13.51.40.2setosa
325.24.11.50.1setosa
185.73.81.70.3setosa
55.43.91.70.4setosa
1177.73.86.72.2virginica
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365.53.51.30.2setosa
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75.03.41.50.2setosa
1486.23.45.42.3virginica
485.33.71.50.2setosa
224.63.61.00.2setosa
145.84.01.20.2setosa
244.83.41.90.2setosa
395.13.41.50.2setosa
1366.33.45.62.4virginica
1317.93.86.42.0virginica
105.43.71.50.2setosa
335.54.21.40.2setosa
275.23.51.50.2setosa
435.03.51.60.6setosa
155.74.41.50.4setosa
445.13.81.90.4setosa
175.13.51.40.3setosa
285.23.41.40.2setosa
195.13.81.50.3setosa
856.03.44.51.6versicolor
215.13.71.50.4setosa
465.13.81.60.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "109 7.2 3.6 6.1 2.5 virginica\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa\n", + "40 5.0 3.5 1.3 0.3 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "32 5.2 4.1 1.5 0.1 setosa\n", + "18 5.7 3.8 1.7 0.3 setosa\n", + "5 5.4 3.9 1.7 0.4 setosa\n", + "117 7.7 3.8 6.7 2.2 virginica\n", + "26 5.0 3.4 1.6 0.4 setosa\n", + "6 4.6 3.4 1.4 0.3 setosa\n", + "36 5.5 3.5 1.3 0.2 setosa\n", + "20 5.4 3.4 1.7 0.2 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "7 5.0 3.4 1.5 0.2 setosa\n", + "148 6.2 3.4 5.4 2.3 virginica\n", + "48 5.3 3.7 1.5 0.2 setosa\n", + "22 4.6 3.6 1.0 0.2 setosa\n", + "14 5.8 4.0 1.2 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "39 5.1 3.4 1.5 0.2 setosa\n", + "136 6.3 3.4 5.6 2.4 virginica\n", + "131 7.9 3.8 6.4 2.0 virginica\n", + "10 5.4 3.7 1.5 0.2 setosa\n", + "33 5.5 4.2 1.4 0.2 setosa\n", + "27 5.2 3.5 1.5 0.2 setosa\n", + "43 5.0 3.5 1.6 0.6 setosa\n", + "15 5.7 4.4 1.5 0.4 setosa\n", + "44 5.1 3.8 1.9 0.4 setosa\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "28 5.2 3.4 1.4 0.2 setosa\n", + "19 5.1 3.8 1.5 0.3 setosa\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "gH3DnhCq2Cbl", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Club two filters together - Find all samples where sepal_width > 3.3 and species is versicolor" + ] + }, + { + "metadata": { + "id": "4U7ksr_R2H7M", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "outputId": "fc882f0b-2c15-4a8b-80fc-3110bae0525f" + }, + "cell_type": "code", + "source": [ + "iris_df[(iris_df['sepal_width']>3.3) & (iris_df['species'] == 'versicolor')] " + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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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": 10 + } + ] + }, + { + "metadata": { + "id": "1lmnB3ot2u7I", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Sorting a column by value" + ] + }, + { + "metadata": { + "id": "K7KIj6fv2zWP", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 + }, + "outputId": "e8c1676e-a5f7-4714-d598-c5e104cc0142" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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605.02.03.51.0versicolor
1196.02.25.01.5virginica
686.22.24.51.5versicolor
626.02.24.01.0versicolor
414.52.31.30.3setosa
935.02.33.31.0versicolor
535.52.34.01.3versicolor
876.32.34.41.3versicolor
805.52.43.81.1versicolor
574.92.43.31.0versicolor
815.52.43.71.0versicolor
1086.72.55.81.8virginica
1466.32.55.01.9virginica
695.62.53.91.1versicolor
1064.92.54.51.7virginica
985.12.53.01.1versicolor
895.52.54.01.3versicolor
726.32.54.91.5versicolor
1135.72.55.02.0virginica
905.52.64.41.2versicolor
1187.72.66.92.3virginica
795.72.63.51.0versicolor
1346.12.65.61.4virginica
925.82.64.01.2versicolor
836.02.75.11.6versicolor
595.22.73.91.4versicolor
1425.82.75.11.9virginica
675.82.74.11.0versicolor
945.62.74.21.3versicolor
1236.32.74.91.8virginica
..................
315.43.41.50.4setosa
205.43.41.70.2setosa
265.03.41.60.4setosa
244.83.41.90.2setosa
1366.33.45.62.4virginica
1486.23.45.42.3virginica
435.03.51.60.6setosa
365.53.51.30.2setosa
05.13.51.40.2setosa
175.13.51.40.3setosa
405.03.51.30.3setosa
275.23.51.50.2setosa
1097.23.66.12.5virginica
224.63.61.00.2setosa
45.03.61.40.2setosa
215.13.71.50.4setosa
105.43.71.50.2setosa
485.33.71.50.2setosa
195.13.81.50.3setosa
465.13.81.60.2setosa
185.73.81.70.3setosa
1317.93.86.42.0virginica
445.13.81.90.4setosa
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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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
94.93.11.50.1setosa
414.52.31.30.3setosa
165.43.91.30.4setosa
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315.43.41.50.4setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "9 4.9 3.1 1.5 0.1 setosa\n", + "41 4.5 2.3 1.3 0.3 setosa\n", + "16 5.4 3.9 1.3 0.4 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "31 5.4 3.4 1.5 0.4 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 31 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "5f354b12-0188-44f7-f809-d246971f12ce" + }, + "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": 32, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "66 5.6 3.0 4.5 1.5 versicolor\n", + "95 5.7 3.0 4.2 1.2 versicolor\n", + "90 5.5 2.6 4.4 1.2 versicolor\n", + "78 6.0 2.9 4.5 1.5 versicolor\n", + "73 6.1 2.8 4.7 1.2 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 32 + } + ] + }, + { + "metadata": { + "id": "cUYm5UqVpDPy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "942c64a4-a1c5-4ffb-c7cd-9f30af52c586" + }, + "cell_type": "code", + "source": [ + "\n", + "\n", + "virginica = iris_df[iris_df['species'] == species[2]]\n", + "\n", + "virginica.head()" + ], + "execution_count": 33, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
1027.13.05.92.1virginica
1046.53.05.82.2virginica
1336.32.85.11.5virginica
1346.12.65.61.4virginica
1097.23.66.12.5virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "102 7.1 3.0 5.9 2.1 virginica\n", + "104 6.5 3.0 5.8 2.2 virginica\n", + "133 6.3 2.8 5.1 1.5 virginica\n", + "134 6.1 2.6 5.6 1.4 virginica\n", + "109 7.2 3.6 6.1 2.5 virginica" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 33 + } + ] + }, + { + "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": "5d4f7973-4e45-4175-fcbe-6fb68f166b0a" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 34, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count50.0000050.00000050.00000050.00000
mean5.006003.4180001.4640000.24400
std0.352490.3810240.1735110.10721
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"outputId": "9e890e0e-b21e-4850-f784-9cdcde5aaa08" + }, + "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'])#, color = 'red')#, bins = 10) # by default bins value is 10\n", + "plt.hist(versicolor['sepal_length'])#, color = 'blue')\n", + "plt.hist(virginica['sepal_length'])#, color = 'yellow')" + ], + "execution_count": 37, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 1., 0., 5., 5., 8., 9., 10., 5., 1., 6.]),\n", + " array([4.9, 5.2, 5.5, 5.8, 6.1, 6.4, 6.7, 7. , 7.3, 7.6, 7.9]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 37 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "KkbWbnHn5YKa", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 401 + }, + "outputId": "3fcae123-2093-46f9-c3fd-902d7b68b5ed" + }, + "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'], color = 'red', bins = 10)\n", + "plt.hist(versicolor['sepal_length'], color = 'blue')\n", + "plt.hist(virginica['sepal_length'], color = 'yellow')" + ], + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([ 1., 0., 5., 5., 8., 9., 10., 5., 1., 6.]),\n", + " array([4.9, 5.2, 5.5, 5.8, 6.1, 6.4, 6.7, 7. , 7.3, 7.6, 7.9]),\n", + " )" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 38 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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