{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Case 01 ##\n", "\n", "Group of countries according to cultures.\n", "\n", "The Hofstede model allows characterizing the culture supported on studies on national values.\n", "\n", "Those values are:\n", "\n", "* PDI - Power distance\n", "* IDV - Individualism\n", "* MAS - Motivation - Aspiration\n", "* UAI - Uncertainty avoidance\n", "* LTOWVS - Long Term Orientation\n", "* IVR - Indulgencs vs. Restraing\n", "\n", "This study identifies the value of a specific country. Is it possible identifying groups of countries with similar cultures?\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "file='https://raw.githubusercontent.com/masterfloss/data/main/culture2015.csv'\n", "df=pd.read_csv(file, sep=\";\")" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ctrcountrypdiidvmasuailtowvsivr
0AFEAfrica East642741523240
1AFWAfrica West77204654978
2ALBAlbania#NULL!#NULL!#NULL!#NULL!6115
3ALGAlgeria#NULL!#NULL!#NULL!#NULL!2632
4ANDAndorra#NULL!#NULL!#NULL!#NULL!#NULL!65
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" ], "text/plain": [ " ctr country pdi idv mas uai ltowvs ivr\n", "0 AFE Africa East 64 27 41 52 32 40\n", "1 AFW Africa West 77 20 46 54 9 78\n", "2 ALB Albania #NULL! #NULL! #NULL! #NULL! 61 15\n", "3 ALG Algeria #NULL! #NULL! #NULL! #NULL! 26 32\n", "4 AND Andorra #NULL! #NULL! #NULL! #NULL! #NULL! 65" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ctr object\n", "country object\n", "pdi object\n", "idv object\n", "mas object\n", "uai object\n", "ltowvs object\n", "ivr object\n", "dtype: object" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "#df.iloc[:,2]=pd.to_numeric(df.iloc[:,2], errors='coerce')\n", "\n", "for i in range(2,8):\n", " df.iloc[:,i]=pd.to_numeric(df.iloc[:,i], errors='coerce')\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ctrcountrypdiidvmasuailtowvsivr
0AFEAfrica East64.027.041.052.032.040.0
1AFWAfrica West77.020.046.054.09.078.0
5ARAArab countries80.038.053.068.023.034.0
6ARGArgentina49.046.056.086.020.062.0
8AULAustralia38.090.061.051.021.071.0
...........................
102TURTurkey66.037.045.085.046.049.0
103USAU.S.A.40.091.062.046.026.068.0
106URUUruguay61.036.038.0100.026.053.0
107VENVenezuela81.012.073.076.016.0100.0
108VIEVietnam70.020.040.030.057.035.0
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65 rows × 8 columns

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" ], "text/plain": [ " ctr country pdi idv mas uai ltowvs ivr\n", "0 AFE Africa East 64.0 27.0 41.0 52.0 32.0 40.0\n", "1 AFW Africa West 77.0 20.0 46.0 54.0 9.0 78.0\n", "5 ARA Arab countries 80.0 38.0 53.0 68.0 23.0 34.0\n", "6 ARG Argentina 49.0 46.0 56.0 86.0 20.0 62.0\n", "8 AUL Australia 38.0 90.0 61.0 51.0 21.0 71.0\n", ".. ... ... ... ... ... ... ... ...\n", "102 TUR Turkey 66.0 37.0 45.0 85.0 46.0 49.0\n", "103 USA U.S.A. 40.0 91.0 62.0 46.0 26.0 68.0\n", "106 URU Uruguay 61.0 36.0 38.0 100.0 26.0 53.0\n", "107 VEN Venezuela 81.0 12.0 73.0 76.0 16.0 100.0\n", "108 VIE Vietnam 70.0 20.0 40.0 30.0 57.0 35.0\n", "\n", "[65 rows x 8 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=df.dropna()\n", "df" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "df=df.reset_index()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "df1=df.iloc[:,3:9]\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "wcss = []\n", "for i in range(1, 11):\n", " model = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n", " model.fit(df1)\n", " wcss.append(model.inertia_)\n", "plt.plot(range(1, 11), wcss)\n", "plt.title('Elbow Method')\n", "plt.xlabel('Number of clusters')\n", "plt.ylabel('WCSS')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[32.09090909 77.81818182 39.63636364 43.45454545 38.09090909 67.63636364]\n", " [69.1 35.55 42.3 85.7 41.1 40.3 ]\n", " [76.14285714 25.21428571 54.78571429 46.57142857 61.21428571 32.21428571]\n", " [66.85714286 22.28571429 58. 75.28571429 16.42857143 84.14285714]\n", " [45.07692308 65.07692308 56.15384615 73.38461538 72. 39. ]]\n" ] } ], "source": [ "kmeans = KMeans(n_clusters=5).fit(df1)\n", "centroids = kmeans.cluster_centers_\n", "print(centroids)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "pred_y =kmeans.predict(df1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "df2=pred_y.shape" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(65, 9)" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df2=pd.DataFrame(pred_y, columns=['groups'])" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " groups\n", "0 2\n", "1 3\n", "2 1\n", "3 3\n", "4 0\n", ".. ...\n", "60 1\n", "61 0\n", "62 1\n", "63 3\n", "64 2\n", "\n", "[65 rows x 1 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "XY2=df.merge(df2,left_index=True, right_index=True)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexctrcountrypdiidvmasuailtowvsivrgroups
00AFEAfrica East64.027.041.052.032.040.02
11AFWAfrica West77.020.046.054.09.078.03
25ARAArab countries80.038.053.068.023.034.01
36ARGArgentina49.046.056.086.020.062.03
48AULAustralia38.090.061.051.021.071.00
.................................
60102TURTurkey66.037.045.085.046.049.01
61103USAU.S.A.40.091.062.046.026.068.00
62106URUUruguay61.036.038.0100.026.053.01
63107VENVenezuela81.012.073.076.016.0100.03
64108VIEVietnam70.020.040.030.057.035.02
\n", "

65 rows × 10 columns

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" ], "text/plain": [ " index ctr country pdi idv mas uai ltowvs ivr groups\n", "0 0 AFE Africa East 64.0 27.0 41.0 52.0 32.0 40.0 2\n", "1 1 AFW Africa West 77.0 20.0 46.0 54.0 9.0 78.0 3\n", "2 5 ARA Arab countries 80.0 38.0 53.0 68.0 23.0 34.0 1\n", "3 6 ARG Argentina 49.0 46.0 56.0 86.0 20.0 62.0 3\n", "4 8 AUL Australia 38.0 90.0 61.0 51.0 21.0 71.0 0\n", ".. ... ... ... ... ... ... ... ... ... ...\n", "60 102 TUR Turkey 66.0 37.0 45.0 85.0 46.0 49.0 1\n", "61 103 USA U.S.A. 40.0 91.0 62.0 46.0 26.0 68.0 0\n", "62 106 URU Uruguay 61.0 36.0 38.0 100.0 26.0 53.0 1\n", "63 107 VEN Venezuela 81.0 12.0 73.0 76.0 16.0 100.0 3\n", "64 108 VIE Vietnam 70.0 20.0 40.0 30.0 57.0 35.0 2\n", "\n", "[65 rows x 10 columns]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "XY2" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexctrcountrypdiidvmasuailtowvsivrgroups
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indexctrcountrypdiidvmasuailtowvsivrgroups
59AUTAustria11.055.079.070.060.063.04
713BELBelgium65.075.054.094.082.057.04
1528CZECzech Rep57.058.057.074.070.029.04
1835ESTEstonia40.060.030.060.082.016.04
2037FRAFrance68.071.043.086.063.048.04
2139GERGermany35.067.066.065.083.040.04
2546HUNHungary46.080.088.082.058.031.04
3054ITAItaly50.076.070.075.061.030.04
3156JPNJapan54.046.095.092.088.042.04
3360LATLatvia44.070.09.063.069.013.04
3461LITLithuania42.060.019.065.082.016.04
3562LUXLuxembourg40.060.050.070.064.056.04
5695SWISwitzerland34.068.070.058.074.066.04
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