From 465ff7ef9c585ecfc81bbb17278e1848cd18082f Mon Sep 17 00:00:00 2001 From: alishashaw439 <43449528+alishashaw439@users.noreply.github.com> Date: Fri, 25 Jan 2019 21:58:10 +0530 Subject: [PATCH 1/3] assignment 3 --- Get_to_know_your_Data.ipynb | 2363 +++++++++++++++++++++++++++++++++++ 1 file changed, 2363 insertions(+) create mode 100644 Get_to_know_your_Data.ipynb diff --git a/Get_to_know_your_Data.ipynb b/Get_to_know_your_Data.ipynb new file mode 100644 index 0000000..9e4b51f --- /dev/null +++ b/Get_to_know_your_Data.ipynb @@ -0,0 +1,2363 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Get to know your Data.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "J82LU53m_OU0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Get to know your Data\n", + "\n", + "\n", + "#### Import necessary modules\n" + ] + }, + { + "metadata": { + "id": "ZyO1UXL8mtSj", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yXTzTowtnwGI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Loading CSV Data to a DataFrame" + ] + }, + { + "metadata": { + "id": "H1Bjlb5wm9f-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KE-k7b_Mn5iN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### See the top 10 rows\n" + ] + }, + { + "metadata": { + "id": "HY2Ps7xMn4ao", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "017b51e1-2440-46d8-a300-5a464d400d19" + }, + "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": 51 + }, + "outputId": "6c74195a-35dc-4b63-8729-d606bc9c714f" + }, + "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": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4ckCiGPhrC_t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Print all columns" + ] + }, + { + "metadata": { + "id": "S6jgMyRDrF2a", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "9e408c17-5b06-4b79-d8fb-8a3a52614066" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 7, + "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": "94120348-bd55-4d90-e6e0-950d1b95d96b" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 8, + "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": "406b3928-ea2e-4a52-c0ff-163932876154" + }, + "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": 9, + "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", + "142 5.8 2.7 5.1 1.9 virginica\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "13 4.3 3.0 1.1 0.1 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "137 6.4 3.1 5.5 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": "77750f2e-af1d-4d81-bf5d-e17537b82ed7" + }, + "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": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "142 5.8 2.7 5.1 1.9 virginica\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "13 4.3 3.0 1.1 0.1 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "137 6.4 3.1 5.5 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "142 5.8 27.0 5.1 1.9 virginica\n", + "17 5.1 35.0 1.4 0.3 setosa\n", + "13 4.3 30.0 1.1 0.1 setosa\n", + "11 4.8 34.0 1.6 0.2 setosa\n", + "137 6.4 31.0 5.5 1.8 virginica\n", + " sepal_length sepal_width petal_length petal_width species\n", + "142 5.8 2.7 5.1 1.9 virginica\n", + "17 5.1 3.5 1.4 0.3 setosa\n", + "13 4.3 3.0 1.1 0.1 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "137 6.4 3.1 5.5 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": "29cffb2e-8a0c-4559-bf62-a59341e05c31" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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175.13.51.40.3setosa
114.83.41.60.2setosa
465.13.81.60.2setosa
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856.03.44.51.6versicolor
185.73.81.70.3setosa
265.03.41.60.4setosa
205.43.41.70.2setosa
315.43.41.50.4setosa
1097.23.66.12.5virginica
165.43.91.30.4setosa
1486.23.45.42.3virginica
395.13.41.50.2setosa
1366.33.45.62.4virginica
64.63.41.40.3setosa
365.53.51.30.2setosa
485.33.71.50.2setosa
224.63.61.00.2setosa
1177.73.86.72.2virginica
435.03.51.60.6setosa
1317.93.86.42.0virginica
75.03.41.50.2setosa
275.23.51.50.2setosa
105.43.71.50.2setosa
244.83.41.90.2setosa
325.24.11.50.1setosa
195.13.81.50.3setosa
155.74.41.50.4setosa
405.03.51.30.3setosa
285.23.41.40.2setosa
05.13.51.40.2setosa
55.43.91.70.4setosa
445.13.81.90.4setosa
215.13.71.50.4setosa
145.84.01.20.2setosa
335.54.21.40.2setosa
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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": 12 + } + ] + }, + { + "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": "2457ff15-7d26-4c45-9826-0a68fbdeb3a0" + }, + "cell_type": "code", + "source": [ + "iris_df.sort_values(by='sepal_width')#, ascending = False)\n", + "#pass ascending = False for descending order" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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605.02.03.51.0versicolor
686.22.24.51.5versicolor
1196.02.25.01.5virginica
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535.52.34.01.3versicolor
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574.92.43.31.0versicolor
815.52.43.71.0versicolor
805.52.43.81.1versicolor
1135.72.55.02.0virginica
895.52.54.01.3versicolor
695.62.53.91.1versicolor
985.12.53.01.1versicolor
726.32.54.91.5versicolor
1466.32.55.01.9virginica
1064.92.54.51.7virginica
1086.72.55.81.8virginica
925.82.64.01.2versicolor
905.52.64.41.2versicolor
1346.12.65.61.4virginica
795.72.63.51.0versicolor
1187.72.66.92.3virginica
595.22.73.91.4versicolor
945.62.74.21.3versicolor
1116.42.75.31.9virginica
675.82.74.11.0versicolor
1425.82.75.11.9virginica
825.82.73.91.2versicolor
..................
114.83.41.60.2setosa
1486.23.45.42.3virginica
265.03.41.60.4setosa
856.03.44.51.6versicolor
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435.03.51.60.6setosa
05.13.51.40.2setosa
365.53.51.30.2setosa
45.03.61.40.2setosa
224.63.61.00.2setosa
1097.23.66.12.5virginica
215.13.71.50.4setosa
105.43.71.50.2setosa
485.33.71.50.2setosa
185.73.81.70.3setosa
445.13.81.90.4setosa
465.13.81.60.2setosa
195.13.81.50.3setosa
1317.93.86.42.0virginica
1177.73.86.72.2virginica
55.43.91.70.4setosa
165.43.91.30.4setosa
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
1425.82.75.11.9virginica
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1006.33.36.02.5virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "142 5.8 2.7 5.1 1.9 virginica\n", + "137 6.4 3.1 5.5 1.8 virginica\n", + "101 5.8 2.7 5.1 1.9 virginica\n", + "100 6.3 3.3 6.0 2.5 virginica\n", + "138 6.0 3.0 4.8 1.8 virginica" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 15 + } + ] + }, + { + "metadata": { + "id": "7tumfZ3DotPG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "1ad1d582-a6c2-417f-b4ec-466a65b8fe9e" + }, + "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": 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", + "17 5.1 3.5 1.4 0.3 setosa\n", + "13 4.3 3.0 1.1 0.1 setosa\n", + "11 4.8 3.4 1.6 0.2 setosa\n", + "46 5.1 3.8 1.6 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "cUYm5UqVpDPy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "e61a0167-36c7-43af-83ec-be5b50b3c3d8" + }, + "cell_type": "code", + "source": [ + "\n", + "\n", + "virginica = iris_df[iris_df['species'] == species[2]]\n", + "\n", + "virginica.head()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
626.02.24.01.0versicolor
535.52.34.01.3versicolor
856.03.44.51.6versicolor
836.02.75.11.6versicolor
695.62.53.91.1versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "62 6.0 2.2 4.0 1.0 versicolor\n", + "53 5.5 2.3 4.0 1.3 versicolor\n", + "85 6.0 3.4 4.5 1.6 versicolor\n", + "83 6.0 2.7 5.1 1.6 versicolor\n", + "69 5.6 2.5 3.9 1.1 versicolor" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "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": "084d2d93-f45d-4e11-a812-6d5e2b7abe81" + }, + "cell_type": "code", + "source": [ + "setosa.describe()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_width
count50.0000050.00000050.00000050.00000
mean6.588002.9740005.5520002.02600
std0.635880.3224970.5518950.27465
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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": 20 + } + ] + }, + { + "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": "774f1a34-8ca5-481d-edce-2a9023591e07" + }, + "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": 21, + "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": 21 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file From 22c337a7167b945fbfeb7df696155ee7d2d56763 Mon Sep 17 00:00:00 2001 From: alishashaw439 <43449528+alishashaw439@users.noreply.github.com> Date: Fri, 25 Jan 2019 22:04:18 +0530 Subject: [PATCH 2/3] assignment 3 --- Basic_Pandas.ipynb | 1037 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1037 insertions(+) create mode 100644 Basic_Pandas.ipynb diff --git a/Basic_Pandas.ipynb b/Basic_Pandas.ipynb new file mode 100644 index 0000000..fd0f917 --- /dev/null +++ b/Basic_Pandas.ipynb @@ -0,0 +1,1037 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Basic Pandas.ipynb", + "version": "0.3.2", + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "cGbE814_Xaf9", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas\n", + "\n", + "Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Python with Pandas is used in a wide range of fields including academic and commercial domains including finance, economics, Statistics, analytics, etc.In this tutorial, we will learn the various features of Python Pandas and how to use them in practice.\n", + "\n", + "\n", + "## Import pandas and numpy" + ] + }, + { + "metadata": { + "id": "irlVYeeAXPDL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BI2J-zdMbGwE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### This is your playground feel free to explore other functions on pandas\n", + "\n", + "#### Create Series from numpy array, list and dict\n", + "\n", + "Don't know what a series is?\n", + "\n", + "[Series Doc](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.html)" + ] + }, + { + "metadata": { + "id": "GeEct691YGE3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "173f8e09-2fee-447a-ef53-2b09e89a8a2a" + }, + "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": 476 + }, + "outputId": "f8bb3250-2884-41c2-8840-b67ef585937d" + }, + "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": 476 + }, + "outputId": "a7d1bd3e-9739-4d69-e13b-290e54c56f23" + }, + "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": 476 + }, + "outputId": "7223ac0f-0691-4c4c-e852-f4c05165b3f2" + }, + "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": 289 + }, + "outputId": "9a067902-e839-49fa-b8f4-a8802fba11b3" + }, + "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", + "n=15\n", + "series3.head(n)" + ], + "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", + "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", + "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": "5a564994-6b8f-491f-ac14-5155b1b10be1" + }, + "cell_type": "code", + "source": [ + "data = {'alphabets': alphabets, 'alpha_numbers': numbers}\n", + "\n", + "df = pd.DataFrame(data)\n", + "\n", + "#Lets Change the column `values` to `alpha_numbers`\n", + "\n", + "df.columns = ['alphabets', 'alpha_numbers']\n", + "\n", + "df" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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alphabetsalpha_numbers
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11B
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33D
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1212M
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" + ], + "text/plain": [ + " alphabets alpha_numbers\n", + "0 0 A\n", + "1 1 B\n", + "2 2 C\n", + "3 3 D\n", + "4 4 E\n", + "5 5 F\n", + "6 6 G\n", + "7 7 H\n", + "8 8 I\n", + "9 9 J\n", + "10 10 K\n", + "11 11 L\n", + "12 12 M\n", + "13 13 N\n", + "14 14 O\n", + "15 15 P\n", + "16 16 Q\n", + "17 17 R\n", + "18 18 S\n", + "19 19 T\n", + "20 20 U\n", + "21 21 V\n", + "22 22 W\n", + "23 23 X\n", + "24 24 Y\n", + "25 25 Z" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "uaK_1EO9etGS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 140 + }, + "outputId": "46c72a77-a21f-4ec0-ab82-43159cc95daf" + }, + "cell_type": "code", + "source": [ + "# transpose\n", + "\n", + "df.T\n", + "\n", + "# there are many more operations which we can perform look at the documentation with the subsequent exercises we will learn more" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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0123456789...16171819202122232425
alphabets0123456789...16171819202122232425
alpha_numbersABCDEFGHIJ...QRSTUVWXYZ
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2 rows × 26 columns

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" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \\\n", + "alphabets 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 \n", + "alpha_numbers A B C D E F G H I J ... Q R S T U V W \n", + "\n", + " 23 24 25 \n", + "alphabets 23 24 25 \n", + "alpha_numbers X Y Z \n", + "\n", + "[2 rows x 26 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "ZYonoaW8gEAJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Extract Items from a series" + ] + }, + { + "metadata": { + "id": "tc1-KX_Bfe7U", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "272ea89a-f5ae-42df-def5-3e94808c6c8a" + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(list('abcdefghijklmnopqrstuvwxyz'))\n", + "pos = [0, 4, 8, 14, 20]\n", + "\n", + "vowels = ser.take(pos)\n", + "\n", + "df = pd.DataFrame(vowels)#, columns=['vowels'])\n", + "\n", + "df.columns = ['vowels']\n", + "\n", + "df.index = [0, 1, 2, 3, 4]\n", + "\n", + "df" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
vowels
0a
1e
2i
3o
4u
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" + ], + "text/plain": [ + " vowels\n", + "0 a\n", + "1 e\n", + "2 i\n", + "3 o\n", + "4 u" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "cmDxwtDNjWpO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Change the first character of each word to upper case in each word of ser" + ] + }, + { + "metadata": { + "id": "5KagP9PpgV2F", + "colab_type": "code", + "outputId": "e060304c-2815-4818-a398-754afc1b543b", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "ser = pd.Series(['we', 'are', 'learning', 'pandas'])\n", + "\n", + "ser.map(lambda x : x.title())\n", + "\n", + "titles = [i.title() for i in ser]\n", + "\n", + "titles" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['We', 'Are', 'Learning', 'Pandas']" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "qn47ee-MkZN8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Reindexing" + ] + }, + { + "metadata": { + "id": "h5R0JL2NjuFS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "b778bf49-6013-4e15-d872-5dcb80235c12" + }, + "cell_type": "code", + "source": [ + "my_index = [1, 2, 3, 4, 5]\n", + "\n", + "df1 = pd.DataFrame({'upper values': ['A', 'B', 'C', 'D', 'E'],\n", + " 'lower values': ['a', 'b', 'c', 'd', 'e']},\n", + " index = my_index)\n", + "\n", + "df1" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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2168 2169 \n", + "156 2196 2197 \n", + "158 2224 2225 \n", + "160 2252 2253 \n", + "162 2280 2281 \n", + "164 2308 2309 \n", + "166 2336 2337 \n", + "168 2364 2365 \n", + "170 2392 2393 \n", + "172 2420 2421 \n", + "174 2448 2449 \n", + "176 2476 2477 \n", + "\n", + "[89 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 9 + } + ] + }, + { + "metadata": { + "id": "o6Cs6T1Rjz71", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) Alcohol \n", + "2) Malic acid \n", + "3) Ash \n", + "4) Alcalinity of ash \n", + "5) Magnesium \n", + "6) Total phenols \n", + "7) Flavanoids \n", + "8) Nonflavanoid phenols \n", + "9) Proanthocyanins \n", + "10)Color intensity \n", + "11)Hue \n", + "12)OD280/OD315 of diluted wines \n", + "13)Proline " + ] + }, + { + "metadata": { + "id": "my8HB4V4j779", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df = pd.DataFrame(np.arange(2301).reshape(177,13),columns=[\"Alcohol\",\"Malic acid\",\"Ash\",\"Alcalinity of ash\",\"Magnesium\",\"Total phenols\",\"Flavanoids\",\"Nonflavanoid phenols\",\"Proanthocyanins\",\"Color intensity\",\"hue\",\"OD280/OD315 of diluted wines\",\"Proline\"])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Zqi7hwWpkNbH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Set the values of the first 3 rows from alcohol as NaN\n", + "\n", + "Hint- Use iloc to select 3 rows of wine_df" + ] + }, + { + "metadata": { + "id": "buyT4vX4kPMl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df.iloc[0:5,0] = np.nan" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RQMNI2UHkP3o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "metadata": { + "id": "xunmCjaEmDwZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "30f05af5-03a9-4a57-d421-4ac3acc65da3" + }, + "cell_type": "code", + "source": [ + "random = np.random.randint(0,10,10)\n", + "print(random)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 0 4 7 0 6 5 3 7 1]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "hELUakyXmFSu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "metadata": { + "id": "zMgaNnNHmP01", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df.columns = [\"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.iloc[:,0:1] = np.nan" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "PHyK_vRsmRwV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### How many missing values do we have? \n", + "\n", + "Hint: you can use isnull() and sum()" + ] + }, + { + "metadata": { + "id": "EnOYhmEqmfKp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "null_data = wine_df[wine_df.isnull().any(axis=1)]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "As7IC6Ktms8-", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df = wine_df.dropna(how='any',axis = 0)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DlpG8drhmz7W", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "metadata": { + "id": "mD40T0Cnm5SA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1969 + }, + "outputId": "09722c06-f33c-4ab2-f1cc-8bd93b54e308" + }, + "cell_type": "code", + "source": [ + "wine=pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\")\n", + "wine_df=pd.DataFrame(wine)\n", + "wine_df.iloc[:3,0:1]=np.nan\n", + "wine_df=wine_df.dropna(how=\"any\",axis=0)\n", + "wine_df" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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