1157 lines
157 KiB
Plaintext
1157 lines
157 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Spectral clustering [25 points]\n",
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"1. (10 points) For the following data (two moons), give one method that will successfully separate the two moons? Explain your rationale.\n",
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"3\n",
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"2. (15 points) Political blogs dataset. We will study a political blogs dataset first compiled for the paper Lada A. Adamic and Natalie Glance, “The political blogosphere and the 2004 US Election”, in Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem (2005). The dataset nodes.txt contains a graph with n = 1490 vertices (“nodes”) corresponding to political blogs. Each vertex has a 0-1 label (in the 3rd column) corresponding to the political orientation of that blog. We will consider this as the true label and try to reconstruct the true label from the graph using the spectral clustering on the graph. The dataset edges.txt contains edges between the vertices. You may remove isolated nodes (nodes that are not connected any other nodes).\n",
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"(a) (10 points) Assume the number of clusters to be estimated is k = 2. Using spectral clustering to find the 2 clusters. Compare the clustering results with the true labels. What is the false classification rate (the percentage of nodes that are classified incorrectly). It is required you implementing the algorithms yourself rather than calling from a package. (b) (5 points) You might observe the performance is not as good as you expected (given that there is no coding bugs). What do you think might be the reason for the not-so-good performance, due to the discrepancy from “theory” and “application”? Please write in your report.\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[ 267 1394]\n",
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" [ 267 483]\n",
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" [ 267 1051]\n",
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" ...\n",
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" [1133 1423]\n",
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" [1133 1408]\n",
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" [1133 1152]]\n",
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"[ 266 266 266 ... 1132 1132 1132]\n",
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"[1393 482 1050 ... 1422 1407 1151]\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"from os.path import abspath, exists\n",
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"from scipy import sparse\n",
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"from sklearn.cluster import KMeans\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"\n",
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"def import_graph():\n",
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" # read the graph from 'play_graph.txt'\n",
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" f_path = abspath(\"./data/edges.txt\")\n",
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" if exists(f_path):\n",
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" with open(f_path) as graph_file:\n",
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" lines = [line.split() for line in graph_file]\n",
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" return np.array(lines).astype(int)\n",
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"\n",
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"\n",
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"a = import_graph()\n",
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"i = a[:, 0]-1\n",
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"j = a[:, 1]-1\n",
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"\n",
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"#k=2\n",
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"n = max(max(i), max(j)) + 1\n",
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"print(a)\n",
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"print(i)\n",
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"print(j)\n",
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"#v = np.ones((a.shape[0], 1)).flatten()\n",
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"\n",
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"#A = sparse.coo_matrix((v, (i, j)), shape=(n, n))\n",
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"\n",
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"# A = (A + np.transpose(A))/2\n",
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"# D = np.diag(1/np.sqrt(np.sum(A, axis=1)).A1)\n",
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"# D = np.where(np.isfinite(D), D, 0)\n",
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"# L = D @ A @ D\n",
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"\n",
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"# v, x = np.linalg.eig(L)\n",
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"# x = x[:, 0:k].real\n",
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"# x = x/np.repeat(np.sqrt(np.sum(x*x, axis=1).reshape(-1, 1)), k, axis=1)\n",
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"\n",
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"# x = x[~np.any(np.isnan(x), axis=1)]\n",
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"\n",
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"# # # k-means\n",
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"# kmeans = KMeans(n_clusters=k).fit(x)\n",
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"# c_idx = kmeans.labels_\n",
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"# plt.scatter(x[:,0],x[:,1], c=c_idx, cmap='rainbow', alpha=0.7, edgecolors='b')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"array([[ 0, 1218],\n",
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" [ 1, 6]], dtype=int64)"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"unique, counts = np.unique(c_idx, return_counts=True)\n",
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"\n",
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"results = np.asarray((unique, counts)).T\n",
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"results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Label\n",
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"0.0 756\n",
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"1.0 732\n",
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"Name: Label, dtype: int64"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"nodes = pd.read_csv('./data/nodes.txt', sep='\\t',header=None,names= ['Idx','Blog_Url','Label','Blog_Name']).reset_index(drop=True)\n",
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"nodes.groupby('Label')['Label'].count()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"4 PCA: Food consumption in European area [25 points]\n",
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"The data food-consumption.csv contains 16 countries in the European area and their consumption for 20 food items, such as tea, jam, coffee, yoghurt, and others. There are some missing data entries: you may remove the rows “Sweden”, “Finland”, and “Spain”. The goal is to perform PCA analysis on the data, i.e., find a way to perform linear combinations of features across all 20 food-item consumptions, for each country. If we extract two principal components, that means we use two singular vectors that correspond to the largest singular values of the data matrix, in combining features.\n",
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"1. (5 points) Write down the set-up of PCA for this setting. Explain how the data matrix is set-up in this case (e.g., each dimension of the matrix correspond to what.) Explain in words how PCA is performed in this setting.\n",
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"2. (5 points) Suppose we aim to find top k principal components. Write down the mathematical optimization problem involved for solving this problem. Explain the procedure to find the top k principal components in performing PCA.\n",
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"\n",
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"3. (7 points) Find the top two principal direction vectors (i.e., the eigenvectors of C) for the dataset and plot them (plot a value of the vector as a one-dimensional function). Describe do you see any pattern. You may either use a package or write your own code.\n",
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"4. (8 points) Now project each data point using the top two principal component vectors (thus now each data point will be represented using a two-dimensional vector). Draw a scatter plot of two-dimensional reduced representation for each country. What pattern can you observe? You may use use a package or write your own code.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 226,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Read in dataframe and filter out the following countries ['Sweden','Finland','Spain']\n",
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"import pandas as pd\n",
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"from sklearn.decomposition import PCA\n",
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"import numpy as np\n",
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"pd_food_cons = pd.read_csv('./data/food-consumption.csv')\n",
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"filter_countries = ['Sweden','Finland','Spain']\n",
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"pd_food_cons = pd_food_cons[~pd_food_cons.Country.isin(filter_countries)].reset_index()\n",
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"pd_food_cons = pd_food_cons.drop(['index'],axis=1)\n",
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"pd_food_cons = pd_food_cons.reset_index()\n",
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"\n",
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"#From the output we can see that the data is all scaled with the same units, meaning it's a good candidate\n",
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"#for measuring the covariance matrix instead of using the correlation matrix\n",
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"pd_pca = pd_food_cons.drop(['Country'],axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 211,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"(13, 21)\n",
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"(13, 2)\n"
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]
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}
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],
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"source": [
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"#Import pca library to find the top two principal direction vectors\n",
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"pca = PCA(n_components=2)\n",
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"X_pca = pca.fit_transform(pd_pca)\n",
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"print(pd_pca.shape)\n",
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"print(X_pca.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 271,
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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"text/plain": [
|
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"([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\n",
|
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" <a list of 21 Text major ticklabel objects>)"
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]
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},
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"execution_count": 271,
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"metadata": {},
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"output_type": "execute_result"
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},
|
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{
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"data": {
|
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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(pd_pca.columns,pca.components_[0])\n",
|
||
"plt.xticks(rotation=90)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 272,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\n",
|
||
" <a list of 21 Text major ticklabel objects>)"
|
||
]
|
||
},
|
||
"execution_count": 272,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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ysm1dHZ14nmIWTjeGNh1+/N6BYVZsbuX8RTW+hmHXVRyM1MkErMF36OwbpPXAQFSDDzCzbAoP3nAq151Wz09WNHLNff9k9/7kl46zZC+rmo3/Pp0ROgDFBbkU5+ckzaXjGvwyD8VPRjM/rPpVqlmxuZX+oRAX+ui/B5NtC5kTqWMNvkNThAXb8SjIzeGOK47m7mtPYP2uTt569z94ZnPGKUVYMoRVzR0U5gViTkhKBsmsbRurcFo4pYV51E4tTIumzvL1ewgW5HKyz+Jtri5+poioWYPv4C7CHuHR4LtcflwtSz9xBtOK83nPT57nvqffSMbwLFnOqm3tHDuzLG6lSz+pDiYv27a9OzbhtNEsmB5k454D0Xf0kVBIWb5hL2cvrPJdQ6usKJ/SwtyMEVFL/9mXIWxp6UYEZpfHnuZ+ZHWQP3/8DN68sJqvPbKBA/2ZsUBjyQz6h4ZZu6Mz7f57Fzf5Khm09wwkFIW0sCbIG3sPMJTCdbHVO/bT0tXPBQmKpY1FfUWxdelkGk37uplZNiVuGdriglyuPaUOVdi4u9Pn0VmymbU7OxkYDk0agx+P/95lQU2QgeEQTSk0kMvX7yEgcO6CZBn8zAnNtAbfYbyQTK8snmH8s+t2ZYbMqyUzcBds0x2S6VJdWkBX/1BSEgjbuhOc4U8/qKmTKp5cv5clc8qTJnBWX1HEjo7ejIjmswYfEyPth8GfWTaF0sJc1u20M3zLQVY1tzOzbAo1pYXpHgoAVSXJy7bt6BmMSQt/NEdWlyCSOk2dHR29rN/VmTR3Dphs2+GQsrMjPfkF4ViDD+zrHqCrbyhhgy8iLJ5Ryvpd1uBbDrKquYPjM8SdA0YiGfzPtnWF0xIx+IV5OcypKE7ZDH/5+j0AnO9jdu1o6ioyJzTTGnxiD8kcj4baUjbs7mQ4SWqEluxib2cfOzp6OWF25hj8gzN8fw1+vMJpo0ll9asn1+9lbmUx86r8y64djRuamQkFza3B56BoWqwhmZFomFFK32DIau1YAHg5w/z3kLxi5q6OTjzCaeEsrAnS1NpN32ByRQoP9A/xzzf2JdWdA1ATLCQ/N0DzvvTbBGvwMTP83IAws2xKwsda7FS5t24dC5j4+/ycAEfPLE33UEYoL8onJyC++/BdpcxEXDpgYvFDCm+0JDcef8WmFgaGQ0l15wAEAkJ9eVFKI4/GHEu6B5AJNLZ2U1dRRK4PSTHza0rIDQjrrMG3YPz3DbWlCZfL85NAQKgsyfddQM0VTkvU4IdXv0omT6zby9QpeSypT/7TV31FUUZk21qDjxOSWZG4OweM5MKR1SU2UsfC0HCI1ds7Mib+PpzqYCEtB3x26SQgnBbOnMpi8nKEjbuTN8MfDil/27iXcxdW+TLRi0ZdeTHNbT1pk352mfQGPxRSTyqZsdBgI3UswIbdXfQNhjLKf+9SFSzwfYbf4Rj8RBdt83ICzKsqSeoM/5Vt7bR1DyTdneNSX1FE7+Bw0gvIR2PSG/zdnX30DYaYW+Wjwa8tZW9XP60+z6As2cWq5naAjIrQcakOFvg/w3eE04rzE3dfLZweTGos/pPr95IbEM5ZUJW0PsKpy5BIHV8MvohcLCIbRWSziNwW4e/vFpHVzutZETnOj379wA3J9MulA2aGD3bhdrKzqrmDqmABs6YlHgzgN1XBAvYd6Pc1fDhR4bRwFtQE2dHRS1dfcmrvLl+/h1PmljN1SmIRRV7JFJnkhA2+iOQA9wCXAA3AtSLSMGq3RuAcVT0WuBO4L9F+/cINyfRzhu9G6lg//uRm1bYOTphd5mtBDb+oDhYQUtjn4yw/UeG0cNyF2017/ffjN+/r4fU9B1LmzgGYNa2IgMDWNIdm+jHDPwXYrKpbVHUAeAC4InwHVX1WVdudzX8Cs3zo1xeaWrspzAtQE/Qv7X1acT4zphbaSJ1JTHv3AI2t3Rnpvweocs53P5OvEs2yDWdEUycJbp0nnezaZMffh5OfG6C2bEr2z/CBmcC2sO3tzntj8SHgER/69YXG1m7mVBQTCPg7C7MSC5ObVdsc/30GRujAwdq2fi4itnUPMC3BpCuXmWVTKMrPSUrG7fINe5hfXUK9j25cL9RXFE0IH34kSxnRMSgib8YY/FvHPJjI9SKyUkRWtrS0+DC88fFDNC0SDTNKeaMl+dmClsxkVXMHOQHh2FlT0z2UiFQnweC3JyicFk4gIMyvCfoeqdPZN8jzW9pS6s5xqSsvTnu2rR8GfzswO2x7FrBz9E4icizwY+AKVd031sFU9T5VXaKqS6qqkruCPjQcormtJzkGv7aU4ZCmpSCzJf2sau5g0fQgRfmxl/pLBVVBfxUzQyGlw0cfPsDCmhLfY/Gf2tjCUEhT6s5xqa8oor1nkM4kLUR7wQ+D/yIwX0Tmikg+cA2wNHwHEakD/gC8V1Vf96FPX9je3stQSH0RTRuNlViYvAyHlFe2ZWbClUthXg6lhbm+zfBd4bQyn2b4YCJ1Wg/0+7qwvHz9HsqL89OytjInA+rbJmzwVXUI+ATwGLAeeFBV14rIjSJyo7PbF4AK4Aci8oqIrEy0Xz9o3OefaNpo6suLKMrPsZE6k5A3Wg5woH+IE2Zn5oKtS3VpoW+Ltn4Jp4VzsBiKP7P8oeEQf9vYwrkLq8jxec3OC3Xlxs40pdGt48vzpqouA5aNeu/esN8/DHzYj778pLHFCclMgsEPBFxtfOvSmWy8vDWzF2xdqkoKfDP4fgmnhROuqXPavIqEj/fS1nb29w5yYRr895AZuviTOtO2aV83wcJcX/2O4SyeEWTdrk5CVht/UrGquYOyorykTCT8pLrUv9q27d3+6OiEUxUsoKwoz7dInSfX7yE/J8BZKcquHU1JQS6VJfnZ7dLJZhpbuzmisjhpiTENM6ZyoH+I7e3pL21mSR2rtrVnbMJVOGaG3+eLoJcrnObnDF9EWFAT9C0Wf/n6vbzpiHJKCtK3kF5XXsTWtvS5dCa1wd/S0p2UBVuXhlon49Yu3E4aOvsG2bT3QMYmXIVTXVpA32CIA/1DCR9rRBrZ56flhTVGU8ddI4iXLS0H2NLazQVpcue41FcU2xl+OugbHGbn/t6kPnYvrAkSEGvwJxOrt+1HNfP992AkksGfbNv2nkHycwK+CKeFc9FR0+kbGuaCbz3Fn1/ZEffTyPL1ewE4Pw3hmOHUVxSxq7OP/qH05OdMWoNvtKmTs2DrMiU/h7mVxTZSZxLxcnM7InBcBipkjmYkFt8HmeT27gHKivJ8d2OdOb+Sh286i7ryIm554BU++NMX2dERu4v0yfV7WDQ9yKxpRb6OL1bqK4pQhW1t6XHzTlqD79acTfbCmpVYmFysam5nfnUJpYWpUWFMhJFsWx/i3Nt8TroKZ+H0IL//6OncflkDzze2ceG3nuKnzzR6Vvrs6Blg5db2tLtz4GBoZrpE1Ca9wU+mDx+MH39HRy/7e9KXXWdJDarqKGRmvv8ewmf4iWfbdvgonBaJnIDwgTPm8tgnz2bJnHK++NA63nnvs2zyEMHz940tDIc07e4cMDN8SF9o5qQ1+E2t3VSW5Cd9Jjaijb87c2f5zft6WLezk8HhULqHktU07euho2cwK/z3AFOn5JGfE/Bnht+dvBl+OLPLi/jZB07m2/96HI2t3Vx69z/4zpOvMzA09rn75Po9VJYUcNys9P9fKorzKc7PoTlNImqZKfSRArYkSTRtNA1h2vinHpF48ojftB7o57Lvr2B/r1l0WzC9hKNmTOXomaU01E5l8YzM1YPJNEYqXGVBhA6YsMeqYAEtfvjwewYpK0qNG0tEePsJszhrfhVfemgd33lyE8vW7OKuq47lxFHf/cBQiKdeb+HSo2f4rogbDyJCXUVx2lw6k/ZKbmzt5twUJGBUBQuoLMnP2Eidux7ZQM/AEF++8mi2tfWwdmcnj6/bzW9WGsVrESM9cVTtVI6qLR356Xf43UTg5eZ2SgpyObK6JN1D8UyVD6UOkyGc5oXKkgLuvvYErjyhlv/842tc9cNnue60Ofy/ixZS7MTav9jURlffUEa4c1zmVBQlRfbZC5PS4B/oH6Klq9/XKldjISIZu3C7sqmN3720nY+eO4/3nFo/8r6qsmt/H2t3drJ2535e29HJyqY2lr56UAS1dmohDY7xf8tRNRxVm5kywKlkVXMHx88uS4tOS7xUBwsS9ie7wmnJ9OGPx3mLanj8UxV889EN/Oy5Jp5Yt4evvP1ozl1YbbJrcwOcOb8yLWOLRF1FEcvX72U4pCk/VyalwU9GHdvxaJhRyv3PNDEwFCI/NzOWTYaGQ/znn16jdmohN5135CF/ExFqy6ZQWzaFCxsORja0dw+M3ATcn8s37OFH/9jC4/92dtpD3tJJz8AQG3Z38bFz56V7KDFRFSzgxaa2hI7RNpJ0lb7IpJKCXO644mguP76WW3+/hvff/yJXHl/Lyq3tnDGvIqPckvXlxQwMh9i1vzfl10zmfAsppDEJdWzHo6G2lIHhEG+0HBiRTU43P39uKxt2d3Hve070fDFMK87nzPmVh8yWtrX1cNF3nuY//vgaP/vAyRkvJ5As1mzfz3BIs2bB1qU6WEh7z2BCk5H2JMgqxMtJ9eX85eYzuedvb/DDv29mcFi58ZzMugnXh8kkp9rgZ8Z0M8W4Br++PHUzfMgcbfy9nX1864nXOWdBFRcdNT2hY80uL+L/XbSQp19v4Y+rdvg0wuxj1bYOAI7PkpBMFzc0szUBP357twk5TrUPfywKcnP41IULePims7jh7CO44vjadA/pEOrKndDMNETqTEqD39TaTe3UQqb4nAY+FnMri8nPDWRMxu1Xlq1nYDjEHZcf5cuM/H2nzeGEujK+9PC6hAxHNvPy1nbmVBRljNHzih+lDpMhnOYHC6cH+eyliwlmWBJcbdkU8nIkLbH4k9Lgb2ntTpk7ByA3J8Ci6cGMiMV/9o1W/vzKTm48Z55vSWc5AeHrVx1Ld/8Qdzy0zpdjZhNuwtXokMBsoLrULXWYyAw/OcJpE5WcgDB7WhHNaVDNnJQGv2lfN3NSXLF+8fRS1u3s9EWKNl4GhkJ84c9rmV0+xffFxQU1QT7+5iN56NWdLF+/x9djZzo7Onpp6erPOv89+FPbNlnCaROZuooiO8NPBe3dA3T0DKa8OEVDbSntPYPs9iGNPV7uf6aRzXsP8MXLjqIwz/+L82PnHsnCmiD/+afX6EpjoeZUs6rZ+O+zJeEqnMqSxF067d0DTCv2XzhtIlNfbgx+qieAk87gb0mRaNpoXG38dC3c7trfy3eXb+KCxTWcnyQRqfzcAHdddQy7O/v4+qMbktJHJrKquYPCvMBIDdZsIi8nQHlxfkIunbYk6+hMROoqijnQP5Swzn+sTDqD35Qmg7/IMQbpWri98+F1DIeU2y9rSGo/J9RN4wOnz+X//tnMC42JxXdnCy83t3PszDLycrLzcqoOJlbqsL3bGvxYqU9TpE52nqEJ0NjabRZNylMb/xoszKOuvCgtRc2ffr2FZWt284k3H5mSz/2ZixYwa9oUbvv9avoG01PoIVX0Dw2zbmcnJ9Rnn//epSqYWDHz9jTIKmQ7cyoPxuKnEl8MvohcLCIbRWSziNwW4e+LROQ5EekXkc/40We8NO7rZva0KWmZjTXMKE25pk7/0DC3L13L3Mpirj/niJT0WZSfy1fffgxbWru5e/mmlPSZLtbu7GRgOJQ1ksiRMAJqiS3apko4baIwa1oRIqmXSU7Y6olIDnAPcAnQAFwrIqP9Bm3AzcB/JdpfojQmuY7teCyeUUrTvm66fagh6pX7ntpCY2s3d1x+FAW5qYuiOHtBFVedOIv/eXoLa3fuT1m/qebggm32zvCrg4W0HOiPawExXcJp2U5hXg7TSwtTXtDcj2nuKcBmVd2iqgPAA8AV4Tuo6l5VfRFIa+iGqtK0LzWyyJFoqC1FFTbsTo1bZ1tbD9//22YuPWY6Z6dAGXQ0n3/bYqYV5XHr71czNEG19lc1tzOzbAo1pYXpHkrcVAULGBxWOuIo0pNu4bRspq489aGZfhj8mcC2sO3tznsZx96ufnoGhjkijQYfUlfU/I6H1pETED7/tuQu1I5FWVE+X7z8KF9b0xcAACAASURBVF7b0clPVjSmZQzJZlVzB8dn8eweEit16EaZ2Bl+7NSnIRbfD4MfKfg27uBSEbleRFaKyMqWlpYEhnU4W1pSU9ZwLGqnFlJamJuSSJ3l6/fw5Po93Hz+fGZMnZL0/sbircfM4MKGGr71xOsjEVIThT2dfezo6M3KDNtwEilm7gqnWR9+7NRXFNN6oD+lLl4/DP52YHbY9ixg5xj7RkVV71PVJaq6pKrKXzdE0770hGS6iAgNtcnXxu8bHOaLD63lyOoSPnjG3KT2FQ0R4c4rjiY/J8Bn/7AmrZnGfjMR/PcQPsOPfeG2LcOE07KJEdXMFIZm+mHwXwTmi8hcEckHrgGW+nBc32ls7SY/N0BtGme8i2eUsmF3J8Oh5Bm+H/xtM9vaeo2hzQD9/elTC/nspYt5bss+fvPitugNsoRV29rJzwlwVG1mSF7HS7Wz/pDIDN/68GPHVetNpVsnYWugqkPAJ4DHgPXAg6q6VkRuFJEbAURkuohsBz4F/KeIbBeRlF8lja3dzKkoSmtty4YZpfQNhkaeNvymqbWbe5/awhXH13LavMypoXvNybN509xyvrJsPXvSKC/hJ6u2dtBQW5rS6KdkUJyfw5S8nLhi8a1wWvzUjczwU+fq9GX6p6rLVHWBqs5T1a84792rqvc6v+9W1VmqWqqqZc7vKU85NQY/Pe4cl5GF2yT48VWV25euJT83wOcuXez78RMhEBDuuupYR8DttXQPJ2EGh0Os3tGR9e4cMG636tL4sm3begascFqcTJ2SR1lRHk3ZNMPPFoZDSvO+npTKIkfiyOoScgOSlEidx9bu5qnXW/i3CxeMPKZnEnMri/nkBQt4bO0eHlmzK93DSYiNu7voGwxl/YKtS1VJQVyKmR3dg1Y4LQHqy4tSmm07aQz+zo5eBoZDaQvJdCnIzeHI6hLfF257Bob40kPrWDQ9yHWn1UdvkCY+ctZcjqot5QtL17I/jrjvTGFVczuQ/Qu2LonM8K3/Pn7qKopTmnw1aQy+q5KZbpcOGLeO3y6du5dvZuf+Pr585dHkZrCIV25OgK9fdSxt3QN8ZVn2FktZ1dxBVbCAmWXpCwDwk+pgYdw+fGvw42dORRE7O/oYTFFiYuZaBp9pSnHh8vFomFHK3q5+38oBNrZ285MVW7j6pFksmVPuyzGTydEzp3L92Ufw4MrtPLO5Nd3DiYuXm9s5YXbZhHFlVAUL6OobilnszgqnJUZdeRHDIWVHe29K+stNSS8ZQGNrN8X5OVQ5BR/SSXhR87PmJ55r8PVHNpCXE+DfL16Y8LFSxS3nz+fR13ZzywOvcOaRFdSWTWHmtCnmp/MqLsjM07Ote4CmfT1cc0pduofiG1VhtW1jUVRt7zE+fEt81Dseh61tPSlJCM3MKyoJNDp1bDNhRrZ4xsFInUQN/otNbTy6djefunAB1cHMW6gdi8K8HO6+5gS+umw9K7e2s3v1LoZG5SZMnZLHzDL3JlB42A2hsqQgLSG2r2xz/PezJ4b/Hg4tdejV4A87wmnWpRM/bvLV1n3dQPL1riaVwT921tR0DwMwMcszphYmvHCrqnz5L+upKS3gI2elRvrYT46ZNZVfX38qYIzH3q4+dnb0sr29l50dfezo6GFnRx/b23t4fss+ukaloBfl5/DvFy3kutPnpPRGvqq5g5yAcEyGnE9+UB2MvdRhZ68VTkuU6mABhXmBlCVfTQqDPzAUYnt7D1ceX5vuoYzghzb+w6t38eq2Dr559bFMyfI46JyAMGPqFGZMncJJYwQZdfYNsrOjlx3tvezs6OXJ9Xv54kPreLGpnbuuOoZgYfJdC42t3Ty8eheLpgcpyp84l8/BGb53g+9m2VoffvyISEpVMyfOGTsOzW09hDR9ommRWDyjlL+/3kLf4HBcBcX7h4b5+qMbWDyjlHecOCsJI8w8SgvzKJ2ex6LpxiX27jfVc98/tvDNxzayflcnP3jPiSN/8xtV5VcvNPPlh9eTnxvgi5efkJR+0kVFcQEBiW2GPyKrYA1+QtRXFDsuneQzKaJ00lXHdjwaaksZDimb9hyIq/3Pn93K9vZePnfpYnLSKBWRTgIB4cZz5vGrD7+Jrv4hrrznGX730nbf+2np6ufDP1vJ5/74GifVT+OxT57NOWmoL5BMcgJCZUlBTHo6rnDaNKuUmRD15UU0t/WkRFhwUhj8xkw0+O7C7a7Yq0G1dw/wvb9u4tyFVZw5v9LvoWUdbzqigr/cfCYnzJ7GZ377Krf+zr9auo+v3c1F33maFZtbuf2yBn7+wVOYPjV7FsdjwdS29Z5ta4XT/KG+ooi+wVBCdYW9MikM/pbWbqYV5VGWQSdmXXkRxfk5cSVgfe+vmznQP8RnL8ksvZx0Uh0s5P8+/CY+8eYj+c3Kbbz9B88mpL9/oH+IW3+3mut/8RIzphby8E1n8oEz5qZVeC/ZVAcLYiqC0m6Ln/hCXUXqVDMnhcFvak1fWcOxCASERTNKWb8rtnKHTa3d/OKfTfzrybNZOD2YpNFlJzkB4TMXLeT+95/Mrv29XPa9FTz6WuyaPS9tbePS7/6D3760jY+dO48/fuwM5tdM/O+6KhijS8cRTivK8oCBdFPvhMEmS0E3nElh8Btb01e4fDwaZphiKLH47r7+qEmy+rcLFiRxZNnNmxdV8/BNZ3JEdQk3/t/L3PnwOk+p6wNDIb752Abeee9zKMpvbjiNf794UUbUFEgF1cFC9nUPeK7V0N49YIXTfGDmtCnkBCQlImoT/kzuGRhid2df2kXTIrF4Rild/UNs95hWvbKpjUde280NZ8/LSDXMTGLWtCJ+e8NpvP/0OfxkRSPX3PdPdu0f+3vevLeLd/zwGe752xtcfdIslt18FidngUyFn1SXFjAc0pE6tdFo7xm0/nsfyMsJMLNsCltTUPlqwhv8plbzJWbkDN/Rxl/rwY9/SJLV2ektW5gtmPDJo/j+u05gw65O3nr3Cp5+/dA6yaGQcv8zjbz17hXs7Ojj3vecxDeuPi4lMf2Zhis74nXhtr3b6uj4RX1FEc3WpZM46a5jOx4La4IEBE8JWH9Zs4tXtnXw6bcsnFAJP6ngbcfWsvSmM6kqKeC6+1/g20+8znBI2b2/j+vuf4E7HlrH6fMqePSTZ3Hx0dPTPdy0UV0aW7atlUb2j7ryopTM8Ce85WjMIFnk0UzJz2FuZXFUiQU3yWrR9CBXTZIkK7+ZV1XCnz5+Bp/70xq+u3wTz2xuZdPeAwwMhfjylUfz7jfVTXpfdFWJU9vWo8HvsMJpvlFfUURHzyD7ewaZmsS8hgk/w9/S0k1NaUHGKi821E6NGpr5i+e2sq2tl8+9dfImWfnBlPwc/vudx3HXO45h9Y79zKks5i83n8l7Tq2f9MYeYpvhu8Jp5XaG7wt1bkHzJBdDyUwr6CNN+zIvJDOcxTOCPPTqzjHv7B09A9y9fBPnLKjyRUp5siMiXHNKHZccPYOSwlx7Aw2jMC+HYGGuJ4PvCqdlUm5LNjOn0lXN7OHYWclTYZ3wM/zGDIzBD2dEG3935Fm+m2T1HxlWlDzbmVqUZ419BLxm21rhNH+pc2Lxm5Psx5/QBn9/zyBt3QOZbfBrD2rjj2brvm5+/lwT/7LEJllZUkN10FttWyuc5i9F+blUBQuSLqLmi8EXkYtFZKOIbBaR2yL8XUTkbufvq0XkRD/6jUbjvsxdsHWpDhZSWZIfceH2G49uJDcQ4FMX2iQrS2qo8ljb1hVOsz58/6hPgUxywgZfRHKAe4BLgAbgWhFpGLXbJcB853U98MNE+/WCq6VyRAbUsR2PxRG08V/a2s5f1uzihnOOsElWlpTheYbvJGeVWaVM36irKMoKl84pwGZV3aKqA8ADwBWj9rkC+Lka/gmUicgMH/oely2t3QSEmGp0poOG2lI27Tkwkv6vqnzlL+uoDhZw/dnZV8nKkr1UBQvoGRjmwKjqYqOxPnz/qS8vZtf+Pt+UXiPhh8GfCWwL297uvBfrPgCIyPUislJEVra0tETaxTONrd3MnDaFgtzMFndqmFHKwHCIN1qMNv4jr+3m5eYOPmOTrCwpxmupw7aeAfJzrXCan7iROtuSOMv3w+BHCnUYrb7kZR/zpup9qrpEVZdUVSUWhmhUMksSOkYqaAgraj4wFOKuR5wkq5NskpUltVQHneSrzvEjddq7B5hWZIXT/MSN1EmmH9+P6eN2YHbY9ixgZxz7+Iqq0tjazYl1yYtp9Yu5lcXk5wZYt7OT9p5Bmtt6+PkHT7Fhg5aU47W2rRVO8596Vxc/iTN8Pwz+i8B8EZkL7ACuAd41ap+lwCdE5AHgTcB+VY1dqDwGWg8McKB/KKNDMl1ycwIsmh7k+cY2mtt6OHtBFWdPsBJ6luzAq0vHCqf5z7SiPIIFuUkVUUvYpaOqQ8AngMeA9cCDqrpWRG4UkRud3ZYBW4DNwI+AjyXabzRGNHSywOCDceus2bGfrr5B/uPSRekejmWSUlaUR16ORJ3ht/UM2Bh8nxER6iqSK6Lmy4qgqi7DGPXw9+4N+12Bj/vRl1dGQjKzwIcPJjQT4J0nzWbR9NI0j8YyWRERqkqih2a6PnyLv9RXFMVcBS8WJmym7ZbWbvJyhNqy7IhhP29RNecvqubTb7FJVpb0UlVaOK68wnBI2d87aJOukkB9RTHb23s8Vx2LlQlr8Jtau6krLyI3Jzs+4uzyIn7y/pNtkpUl7USb4bvCadal4z/15UUMDis7O7xVwYuV7LCGcZDpomkWS6ZSXTq+wW9zdXTsDN936iqSK6I2IQ1+KKQZL4tssWQqVSUF7OseGLPwe4cVTksaI6GZSYrFn5AGf1dnH/1DoaxIurJYMg23EMq+A5GLmVvhtOQxvbSQ/JxA0gqhTEiD39jihmRmtoaOxZKJjGTbjrFw6wqn2fKG/pMTEGaXT2Frq53he8aVRc6WkEyLJZOoipJ81W59+EmlvqI4abH4E9Pgt3QzJS+HGufR1GKxeKc6iryCFU5LLnXlRSPrJH4zIQ1+075u5lQWW2EniyUOKkscg985xgy/2xQvt9dXcvjcWxfz7G3nJeXYE9Lgm5BM67+3WOIhPzfAtKI8Wg5E9uG3dQ/awidJJC8nkLSb6YQz+EPDIXZ09NqQTIslAaqCBWPO8Dt6rHBatjLhqmvk5gRY88W3MDicnNRki2UyUB0spOXA2D58V/vJkl1MuBk+QEFuDiUFE+5eZrGkjOpxZviuD9+SfUxIg2+xWBKjyilmboRuD+IKp1mlzOzEGnyLxXIYVcECBoZDdPYeWszcCqdlN9bgWyyWwzhY6vDQSB1XOM0u2mYn1uBbLJbDcOUVRmfburIKZdaHn5VYg2+xWA7DFVAbnW3b3mOF07IZa/AtFsthjOXSscJp2Y01+BaL5TCCBbkU5gUOc+lYH352Yw2+xWI5DBEx2baHuXSMcNqUPCuclo0kZPBFpFxEnhCRTc7PaWPs978isldEXkukP4vFkjqqg4URF22tcFr2kugM/zZguarOB5Y725H4KXBxgn1ZLJYUUh1hht/WPWhj8LOYRA3+FcDPnN9/BlwZaSdVfRpoS7Avi8WSQtxs23A6egZslm0Wk6jBr1HVXQDOz+rEh2SxWDKB6mAB+3sH6RscHnmvrWfAzvCzmKgKYyLyJDA9wp8+5/9wQESuB64HqKurS0YXFovFA+GlDmeXm/oSVjgtu4lq8FX1grH+JiJ7RGSGqu4SkRnA3kQHpKr3AfcBLFmyxGocWyxpYiTb9oAx+MMhpaPX+vCzmURdOkuB65zfrwP+nODxLBZLhjCSfOXIJHf2DqKK9eFnMYka/LuAC0VkE3Chs42I1IrIMncnEfk18BywUES2i8iHEuzXYrEkGbeYuVsIxSZdZT8JVQlR1X3A+RHe3wlcGrZ9bSL9WCyW1FNRUkBAoKXTyCuMyCpYH37WYjNtLRZLRHICQnnxwVh8VzjNGvzsxRp8i8UyJtVhsfhWOC37sQbfYrGMSbiejvXhZz/W4FssljEZPcMvsMJpWY01+BaLZUyqSwtoPdBPKKS09wwwzQqnZTXW4FssljGpKilgyDH2Vjgt+7EG32KxjEl1qcm23dvVT3vPAOV2wTarsQbfYrGMycFSh8bg2+Ll2Y01+BaLZUyqwwTUrHBa9mMNvsViGRN3hr+ns88Kp00ArMG3WCxjUpSfS0lBLpv3HkAVyq1wWlZjDb7FYhmX6mABG3Z3AdgZfpZjDb7FYhmXymABb+w9AFgdnWzHGnyLxTIu1cECBoZDgJVVyHaswbdYLOPiLtwClFkfflZjDb7FYhkXt9Qh2Bl+tmMNvsViGRc3Ft8Kp2U/1uBbLJZxcV065cVWOC3bsQbfYrGMS3WpMfhWViH7sQbfYrGMS1WJO8O3C7bZjjX4FotlXKYV5ZMbEBuDPwFIyOCLSLmIPCEim5yf0yLsM1tE/iYi60VkrYjckkifFosltQQCwpI50zh21tR0D8WSIInO8G8DlqvqfGC5sz2aIeDTqroYOBX4uIg0JNivxWJJIQ9cfxrXnz0v3cOwJEiiBv8K4GfO7z8Drhy9g6ruUtWXnd+7gPXAzAT7tVgsFkuMJGrwa1R1FxjDDlSPt7OIzAFOAJ5PsF+LxWKxxEhutB1E5ElgeoQ/fS6WjkSkBPg98ElV7Rxnv+uB6wHq6upi6cJisVgs4xDV4KvqBWP9TUT2iMgMVd0lIjOAvWPsl4cx9r9U1T9E6e8+4D6AJUuWaLTxWSwWi8Ubibp0lgLXOb9fB/x59A5iUvN+AqxX1W8l2J/FYrFY4iRRg38XcKGIbAIudLYRkVoRWebscwbwXuA8EXnFeV2aYL8Wi8ViiZGoLp3xUNV9wPkR3t8JXOr8vgKwAhwWi8WSZmymrcVisUwSRDVz10VFpAXYGmfzSqB1ErVNZ9/2M2dH23T2bT9z6trWq2pVxL+o6oR8ASsnU9tsHbf9zPb7sp/Z/8881su6dCwWi2WSYA2+xWKxTBImssG/b5K1TWff9jNnR9t09m0/c+rajklGL9paLBaLxT8m8gzfYrFYLGFYg2+xWCyTBGvwwxCR4nSPwZL5iEhARErTPQ5LZhGpml+mVfib8D58EclX1YEo+5wO/BgoUdU6ETkOuEFVP+axj3pgvqo+KSJTgFw1xV6itSsEPgQcBRS676vqBz20DQCnquqzXsboJyJSAXwRo5OkwArgS2qkNry0PxE402n7jDoFcpKNiJyBGXc9RlZEAFXVIzy0/RVwIzAMvARMBb6lqt+McQwBzHk2pkS4n4jI11X11mjvRWi3BCOBPvq7OjZpgz3Yty/ntnMt1qnqRg/7fmq8v6sH4UcReVlVTxz13ipVPWGcNu+I0u+46sKxMqFm+CLyd6fIirt9CvCih6bfBi4C9gGo6qvA2R77/AjwO+B/nLdmAX/yOORfYGoNXAQ85bSNeqNwxhgC/ttjPxERkWLn4kJEFojI5Y6UdTQewEhhXwVcDbQAv/HY5xcw1dEqMNmE94vIf6Zo3D8BvoW52ZwMLHF+eqHBMdJXAsuAOowooJfx/kpESp0nyHXARhH5fx77RUTOcJ8+ReQ9IvItZ5LhhQsjvHeJh3a/BO7H/I8vA97m/PSMiHxcRMrCtqeJSNRJlE/n9mXAK8CjzvbxIrJ0nCbBKK/x+rpWRB4CjhCRpWGvv+HYlHG4zHl9CHN+vtt5/Rh4T5S2sZOMbK50vTCGcwPwMeArwMvAiR7aPe/8XBX23qse+3wFyB/Vdo3Htqucn6udn3nAX2P4vHdgLkiJ8/t6CSjClJzcBvwRU7MgarsI73nKDMSUuCwM256Ckc5OxbifT+DcWuv8f34LnBPrOeL8fDfmhpPn/s89tl+NmWEf5/x+C/BUlDYfBdYA3U4b99UI/J+HPlfE+12N/tyj3lvlsa0f5/bUUdel5+88xr7qgXOB54Bzwl4nYp72vRzjYWBG2PYM4A9+jzUhtcxMQ1UfE5EbgScwOhQnqOpuD023OW4dFZF84GaMYfJCv6oOGNl/EJFcjKvCC4POzw4RORrYDczx2BbgU0AxMCwivRx87PbqXxZV7RGRDwHfU9VviMgqD+3+JiLXAA8621cDf/HYZxPGfdXnbBcAb3hs6xLTuB0XkjvubwJ/APrdv6s3l9L/OGN/FXjamWF7dcvkOU8gVwLfV9VBEYnFlzqkqioiVwDfVdWfiMh1Udr8CngE+BpwW9j7Xara5qHP20Xkx8ByDv2uYnExBERE1LFgIpKDmRx5IdFze0hV97vXZTRE5N+d8+h7RLh+VfXmsdqq6lYR2Q50q+pTHsc3mjnqlIt12AMsiPNYYzKhDL6IfB74F4w75ljg7yLyaVWNZoxuBL6LmTFuBx4HPu6x26dE5D+AKSJyIebp4iGPbe8TkWnA5zHFZEqAL3hsi6qO+6jpARGR0zAzzw8573k5J27AXJD/52wHgG7HDxrtouwH1orIE5gL60JghYjcDeNfWAmMe7R7YEnY7wqcF61DVb0buDvsra0i8mYPY4XEbhYAXSLyWYwL6SzHcEZzYamqNonIYeexiJR7MPofABY5/YTcY2Jull55DHhQRO512t6I42KJhg/n9msi8i4gR0TmYyZx460JuBO8lfF0pqrDItIjIlNVdX8ch/i7iDwG/BrzXV0D/C2esYzHhFq0FZHvArepaq+zXQ/8WFUj+TH96jOAMTpvwcxCHnP6TPoXK2b68m5grqreKSKzMY+FL3hsfzbwGczC6ddF5AhMzWEvRjfeMY87M1XVn3k4xjnAp0ntuGuArwK1qnqJiDQAp6nqT+I8Xq6qDnncdzrwLuBFVf2HiNQB56rqz8dp87Cqvk1EGjEGJHyqqxploVpE1qjqMV7GN84xApjJwflO/49jro1hD20TPbeLMIvO4dflnaraN27Dg+2DmO/pgJf9nTYPAqdiPAzd7vtez0tnAfcsZ/NpVf2j1749j3EiGXyIbWU+rM0C4IdAjaoeLSLHAper6peT1afTLiEjIiI/xMy+zlPVxc7TwuOq6mkhUkTeqaq/jfZehHYRF7RV9Wkv/fqFiBSranf0PUf2vwWzENkF/AjjY71NVR/30PYRp+3nVPU4x3W3yotR9ONmIYdGghUBOeohEixeRORHwLdVdV2y+ojSf0LndgL9Ho0JpijH3ChagPep6loPbSNOZrxMYlKG34sC6XxhVrs3Ao3O9vHAUg/tngJO4dAFntc89nl5PH06+z6CcUG96mzn4nHB19n/ZednzIvN4e2jvRdhn4fCXk8A+/G42IxZNNwy+hXj//k0TLRLs7N9HPADD+3c7/kijAvtOC+f12nzYoTv+rBFyST9nz+CiTZ7w9meDyz32PbsSC8P7dYDA865vRqzAOxp0RN40Pm5hkMXjFfHcIxEz+0FGD2ax4G/ui8P7Z4F3hy2fS7wbCznZzwvzJPBi8AB53sfBjr97mdC+fAxMdanAH8HUNVXRGSuh3ZFqvrCqAUeT4/bwO0R+pwzXoOwx/lKVX3Q8c+iqkMiEvVxN4xBx5/rLopVcdDfOl7/l2BKUM50fecOpXj43Kp6SHie87j9DY9jDvefFwLvxMymYuE7HDTaqOqrYz11jML9B18K3O+081p+s1tM/oH7XZ+KudF5IdH/88cx59jzTvtNIlLtsW14+Gehc5yXiL5ucXEM4xuNm2z0tgSOEde5HcZvgXsx4Y2xfNfFqjriO1fVv4vHhMww99khqIc8D+D7GL/9bzHXyPuAIz2NOAYmmsGPtDLvxWfVKiLzOHhyXQ3sGr/JuH1G4wWMOyERIwJmEfGPQLWIfAUTLeMlpn0nZnHqcszF79IF/FsM/btsB472sqMenpz1HRFZQQyL1c5xto36zr1c1C+JyOPAXOCzjp/WqxH5FOYGM09EngGqMN+3FxL9P8cdCRbvzVlVtzr7VxOWFOixz13hx4iTSOf252NoP6SqP4yj3y1O8McvnO33YJ5KvZDQZEZVN4tIjpo1jvtFxPekyolm8GNdmXf5OObxb5GI7MD8g9+dxD5dS5WIEUFVfykiL3FwUexKVY0aTqomsexVEfmlelw4PGTwh4auBTBurFc9tg3PRAxgLpJYIzLiDaP9kDPWLWrCOisw0ShRUdWXncXihZjveqOqDkZp5pLQ/5nEIsFG4+nmLCKXY6KbajFJdvWY7/goD227OLhQHH5j8hxaGe+5HcZDYpK8/sihYaXRopM+iMkBcKORnsb7OZLIZKbHOZdfEZFvYCacvku9TKhF21hX5kXkFlX9roicoarPOI9uAY1hMWxUnzh9fnmsPp022zEJOGCMXoEz3n5gWD2kcYcdKweoIezmrarNUdo8qKr/IiJriPwIOm76/KjFqSGgSVWf8Tje8FCzIUy44n9pbIvslZgw2gsw399jwC0RLjh3/0WqumHUzWYEHScOX0TOU9W/yhgp8OoxLt2Zlcdzs0goEmyMm3OTqo6bxSkir2LcPk+q6gliQlCvVdXrvY47EUTkF6r63mjvjdM+0qxcPbpX4mKMycxHVfU4D23rMbH3+Zin7KmYdanNvo5xIhn8WBGRV1T1eImggeGh7S9U9b3uTSPGtrswUUER/UCqeofH49yEWUPYg3FpeNI7EZEZqrpLxkjP9/Io7sxG3MSQmAxYqhGR+1T1+lE3GxdV1TH92SJyh6reLiL3j9F2TN2jsW4SYY191UkZYwxx3ZxFZKWqLnEM/wmqGhKRF1T1FI/9BjALtJ5cfRHaH3JNOhObNaraEM/xUkGikxmJM9ovFiaES0eMjsWYdy5VvXyMP60XkSagSkRWhx+S6IbzJMdgflBEfs4o4x3l0XGXqn5pnL975RZg4Vgz27FI1McqIudi9HCaMJ97tohcpx7CMkVkKuYm5S6yPoURXvPs0xaRWcD3OFS87RZV3T5Gkyecnx9S1S1ey51eJQAAG5NJREFU+wFQ1dudn54e60cxnvaM5ySmMZ7E9mPWYb48zpNNDnBhtNn8GHSISAnwD+CXIrIX74EMODeIV0WkLtoTZzjOwrbrvnKT0wQTufIjD+19eSKLB1X1moh3GGK0f/4LM8OfKyLHY66LsWxXfP1MhBm+41sFeAdGjMzNAL0WM6P5j3HaTsc8Ih/2xY5nEEXkZoxeyRHADmJIbJEoCnpecWYUF8bjh3fau75WMCdaHiY9fFwfq+NbfZc7ExGTx/BrVT3JQ5+/B17D3DDAZI8ep6rjzoZHHeMJjHRA+MLau3WMBDt3thjPk1zYMSKpKe7H6Aq9Es8xY+j7G5gnuF85b13j/OwEzhy9MDuq7WPAZRpFMTZCuyKM/IVgvt9SjF6RF1kG9xh/xYjTvcChiUhRjZiIfE1VPxvLmJ12cT+RJYqIFGD0f+ZwqIs16uTOuabOA/7u2gYRWR3taT1WJsQMXx39ChG5U1XDw/MeEpExZ50islxVzxeRx+KY7T6kqneLyA9V9aMxtj0/xv3HYgsmJfsvHLow5WkNQEelr4vIlZiwvWjkhT92qurr4k2tEmCeql4Vtn2HiMRqMKtUNfyC/qmIfHKc/fc5N8e5EkEx0eMsaonzchdL34qJm75RRH6rqmNGvjiLw7dzUBI6Jjlp4AxVPSNse42IPKOqZ4hItNl7E/CM87nDjW7Ec2TUJGDkbefnF0TkDUzy2XIP4/bkmhyDw85D93odr5Fj7APAI6r64Hj7RkISS8L8M84kgLDr0SPxRPvFzIQw+GFUicgR7mO7mBj8qnH2n+E8HVwmIr/mcLfMeKJavwNOIg6Bo1hmSVFodl75eBelGhNV/ZOI3BZ9T1aKyE84dIb90jj7h9MrImeq6goAMRr1vTEOtdUxdL92tq9lfBnat2LCYH9B/LK7FRjl1QMAInI75hw4G/PZxwt1fAAT7eHe6N6NkZO+wGPfJSLyJlV93un7FIzuEkR3s+x0XgE8REONngSE47iIjsZIJ0f1zWscQmJiakQUA5Vismvda7IUEzEUFced9AkOivvFwo8wuQv/4xxrtZhaCF4M/ixVjTd/Id4Iw5iYaAb/3zAzXtdPOwej5TEWX8AoCc7iYNSMSzRRrYBz0S+I9LjvdZadCO7irsQoMeAyys/pRhV48fF9FBPKejPmgnwa+IHHbj8K/Mzx5QvQBrzfY1uXD2ISVb7tjPdZxgmdc9wZ/xSR01W1Jca+XOowfmSXQaBeVXtFJNpsrlxV7wzb/rLzNOWVDwP/6/jUBePK+bCYqLKvRWogByNaOmINKhgLNfHhr4qJ/ImKmHyD7wGLMROSHKK7DG8APokx7uETrk7gnhiG+4SIfAZzYw1/sok22UokCfNZETlGVdfEME6XmzDRfv0Y191jeLvJxMSE8OGH4/jRFjmbG1Q16qOViHx+1AXppZ+FGLnbT2Iy+g7Ba6RNIohRjPwJ8VfqCneLuFEFP1LVvTGMoRwzs1kddedD25UCaByVn8QJo432np+IScZ5O+axHcyC7FLME8N9qjpm3oaI/BdmgTVcTvood0E4hjFMxVyzHR72XYcpdLIUIw8QS1CBL4jISg7PHp0/3ppaWNubVNXTjWWM9nGFZYrRTPoE8Ftn3edqzGL/mEVjwhbVczGyF1swhttr1FwO8Jiqen3ii5uJaPBP5/BFkzFVBcPaXc7ByJG/q+rDHvu7RFUfiWOoCSMiz2OMx9KwhZ7XNM5QuBj6/TtmkTsXUwCmBVOQY9wycU7buAXMwo4RqZRc3AuyMfR7EsYPL5gCIZ6kdB2/eDEHs3oDHJx1qodF8pgXAxMJKvALORjaObL4KCLPqurpHtq+L9L7Xq7lRBCjvHofcDrQjpOEGSWAI2J4s4uX9UFnjeW9Gp+0smcmlEtHRH4BzMMYITfVXoFxTxIR+RpmkeiXzlu3ODNGL1ECz4rIt0ggzDARND6JAQBE5GeYcMYOZ3sa8N8eIhmmqmqniHwYo0lzuxwa1joeH1ST7HYRUI1xxdyPEbmKNt7TMBdi1Sg3WinGXZA0nCeZRsLS7EUkTz3kH4znF/dIzIuB6uj3xxlU4Bdu9uirEnv2aLgqZiEm0OFlolzL4YhRvmzg0HrR0dpvVdULJIYkTD0oQxFJRsFrEmcfZjE+Lmllr0wog495bGzQ2B9b3gocr6aWpmsIVwFeDP7/YsIM/8XZfi/GgHkOM0yARCp1ARwb7h5Q1XYR8RIumisiMzCf+XMxjfjgTDMeAbN8zGJlLocuQHbiQapAjADXRzh8puwlVO9lYDZm1idAGbBLTHz6R1R13EVrZ73EjdL5h6p6rXsMCSwGptHYg7kWApj1nn/DrJVdNW4LB1W9KXzbcWf9YozdD8NZXzsXY/CXYdxbK4h+w2gUkUcxvv+/eu3PIZFz5C94rxoXP5pk2c9UvjC+whlxtFuNWVhzt8vxLuMaqW6nJ9lcHz5vJeapZA9G7+T/wj+Hh/avAtNGfe6osr0YUajVOJLEGLfB7z326c7mN2Hq0gaJUCM3yjHq4/y+ngW+jrlRXeW+PLa9F7gobPstmIX+U4lSKxezoP045mnmA5iqT/fEMO77gGNScU75dF5eAXw8bPt5DspiXx3nMfOIofYxRpo5wEFJ6hpMKHW0dlOc8+MPmDWt72NyHZJ6jjj752Mq9R0D5Cflf5Puk8PnE+1vmLvrY5jFqqV408O/FtgK/BSTENQIXOOxz+fCTwhM9udzKfq8Z3h5b5z278M8EdzpvDZg/IjJHHMA47cvc7YrME8asRyjCvgmZuYWi9Z53DdiIhRpd9+LdlxMAXQJ2w4Aa2Poex1xatOn4wU8A8wO/94xk4k6vOv4PxR2DT/s3CzuimEMLzg/X8K4/CSW79xpOw3zRDCcgnPkUmAbRmb9KUy49SV+/28mmkvni/E0UtVfOwuRJ2NOjFvVW/FzODTMEMwN5/3xjCMOvocxntHei4iq/tyJpDgP87nfoUmucKTGbfZy2PY+xo+hj8QvMY/cb8PUSb0Os3AcjYdF5FJVXRZjfwBtInIrJqYe4F+BdifCIprE8kaMsXMX72ZjDLdXxowQyVDyVXVb2PYKNVFBbeJRWx4jM+AyhPGtjyWdEYmVIlKGCQx4CVNYxGt5xHMw/99LMMl1/zJ+ixESOUe+hSm8stkZwzyMi8fXgJAJF6UTDyLydswMcb+zXYapGerZz5pImGGshC1efhITi+5SCrxdPajzOceZB2xX1X4x+jjHAj9XD2F/6UREXlLVk0ZFfzylqudEaedGyww4L89yvWIUOt1sWXCyZTGLqXU6jqqhiDzFQYkBnN+fA3rAc6YvMkqbXsfRqJHIGbMjePnM8SIim1U1YvEOEXlDVefFeLxKYJ/GaazEFCQqVQ+hw0445yuYENqlGlsJzfBzRDDnyB14O0ee1jCVAGdd6yk9VDkgYSbEDF9EVqjqmRFOcq8X9O0aVjBYVTucRZ+oBl9Evgp8Qw+NdPm0qnopRBIvCS1ehvF7YImIHImpDPQQJunj0rEaiElbv1rjSFv3ETcyZpeIvBWTSTorWiNNIFpGVVuBm0SkRA8vbB1NwjZcD10wBuFajK59VCQObXr3s4rIl4DdmAVPtzB4olFD0XheRD6iqoeInYnIDUSZZYtJ1roLk5B3J2bclZhEx/ep6qNeBiARpLCdCc5WHV976rh4J21h50gpEBp1nkQ8R+Rg8uNaEVmGudEoZp3sxXjGMR52hg8RRYpEZI16K1B9mBBaKmLCnX7qVXWrxJ9p64qK/TvQq6rfi/R5IrR7OpGZh8Sh4T+q/dswKo6zMS6sUuCLqjpuURBn1vRuYK6q3imm+tMMVY36qO9EQ/2Y+JPcjgfehXEPNAJ/UI+JRZKANr2IPK+qb4r2np84TyJ/woSQuu67kzB1H65U1T3jtF2JUcucilmsvkRV/ykiizACfZ5EB0XknxjX5mrMje5o5/cK4EYdlfchIv+uqt+QQ0t+jqAewiNF5BiMz98Nz2wFrlPV18ZpE0nkLaxbf8XeJsQM3wdWOrH092DurjfhXRsmR0QK1MnoFaNpXZCcYR5GrZjMwBIgZiOEqRt6LWbx1lVc9CKCFm/aOnKohr/r11SMO8kr7Y77bT/wZue4Z4zfBDDRMiGM8bwT49e9h0Njvsfi28RYR1eMENc1HNT6+Q1mkhWrjO6gqu4TkYCIBPT/t3fuQVdVZRh/HqUSHSTzbkooXrHEcSTSTCeEtMy8lVSiGE5pg4GWlzGnCW9d1FFJHcO0NHLQRkCsxsvIIOAgiRe+8BaKoOmMGmbBRJbi0x/v2n77O985e699Oefs8531m9kzfPucddbiXNZe+13v+zzSQpI/92y7ieQpsLiy3FiyeLxmRlapfSjJsei9C/mTJJ80x0HRZEzyUknL3Gs+75+9C8AybM6Q9Ix7rZEwjZzLYBk4tXUfUTqz7+++HjMBfF/OE9eFSaMirroon+x2fsreBe7EAxbX/Rms/P1xAD+BmRn7tL0AFqs7A6bx8giAC1o07j/DVrlPxc49naH9SJh36Dfc37vDql7T2q2pc7zk2eeLALYt+P9+0udco+fUvF89vu911rawi8siAHvGznm9TzWv8xDson49TDBuBoClnm2Hwwq31sE2tu8BMLwV38+in23tZ+rzGcee2zBdut5jJY293/fB9/vVqiOs8AHIwiE+KpH12l5JqzIdB7t1vEzSA2WOL6X/3JW2soycqbG/18AufGntds8yxhr+hmwG3h/A4pW277pwktzrbQ9/E/M8RW4nwVb4C10xz52o0bTx5DiYoui5sJDUUNiGcSqS1rr2ncIomvEJ0d8EJYuZ+iqSN6FvxswqmkxFv+po5jdRgou9T0ExA/SWECb8EpBtJHltJpVMrkpbFve03RJmzD1MZh24F8x5y0d/qIiGf9HN6l/ATK13IHmFa+O7uX4WbGX9cZgR+IOwH3lDZIkA81wq4vGwCXtHNxHNk4d+kLtAzZcJa72PXuMYL1hM373lSCpLImMSbFP8HPRmzJwHm+zrhdSurnPOl9tgtT+zAOwMS4aIVGRPL/C6pRM2bTsY9jXzJmwSamjmHWtXyNOW5F2wWOdpbhIZDCs2O9BjzD9u0Ke3umhss3qINe2XNZPUdl+YLgthRUBeUhQkt1d+aeX463wMloExQQleujVtcgtruZTQ8wHMVAsF9toJC6hP0vT494QthFZLesez3VawbKyjYRN/NLHKZzFDM8mZjr62nVlMcrwIK/wORpYG1lCWN6FdP0/bjLnOIyRNcBu+kGnCe4UqskzsCQwh+RRcNgTJ1GwI97xbAVwv6cbYuemSpnv0uZSWo30XTEYiV62CbGN7pjt8KSKsVUTfvSORtInkRpJDfS+SJAfB9u4mwwrkNgOwq8uiuVjpInnvwj6bj8DuQrOupIua5HjR1RM+zcghKW7nk4o1TTUGE/XONYO8t+sl5Dr/z63qo1j4CKSoOJK8QdLZjWKlSTHSOtyMjNkQjqNg5vPXqFc18SvwqNCWtBfNaerrAC6m6c3fKel3KU3LYCEsDfV92B5NFoewde7ziT6rr8JUKwc6WS+SV8HChLvLKWTS8umvdse0Rh2RPBpWKXsvzBVtY47xFjXJ8aKrQzokJyU9Lik1Xsr62uylmJR79J3rdr1orjPJ8bDY90hYGOmzAE6X9HBCm/WStmav4XwflMEOj2SPaqqJ652r0+5JmILiHTCtkmkAlmf9rNzd0DUwnfSmyTI3WHXuBhOg+6HHqhOsr+8+0W3mDlga/bYb/aZJvgBg79o7XBceel7SXgl9LYHl9j9TYLylmOSk9tPNE34RXDjjm7CqySWxh4bAxJaa715DLpc0On6BIbkiLZYefw7J5yTtF3vM62LlYo6fgcXCl7nwUtLzS7sIkpwHK+iJZ0McLClxRVTzPk0HMB5WeJVqBuJWeyfAVvgjYJu/v1eKLHIRSF4L+z6dW2fVuVFSknF77Wt567t3IyRXSarrT530WIn9R7Ifm2C/qUwmOb50dUgnwqXnXYj+ZglJm2pLYbfG26GvMfYGZBPGKkLe2/V4KmJteKDhCoD9y9WjvoaRHKZk0/faVMq+nWbzAJ4M0yiZ6/5ejARP2xj3xvqb7u50Ul26HD2wHPZLJT2aYaxF+DJqVp0y45nvwpRNUyd81rhlRbF8JbhlDQRc5thP0f833eji/qwLZ/bRyyc5EfZeNxUVN8nxIkz4RqS+eAw81RfdhufLAA5p+ugaMwV2u74vydfg7Ng82uXNdY4ubFvAzGZ6XJsDYEVghzVoB1iefGTCnQuXQXEWLItiJUyzKDWsESFz5toRvZW1j/lmygDYw3NDu0xUr0+3Kek7lsxuWQOE38Aquq+FpWF+C8nfvSkA5pKcDHuvBPueDIbd2TUVWqX4Ckn/dheZgwBcpwySI16oAtVf7T7gDDgQ0xiHKdX5tD0RZubxL1g++AYA61s8/q0ADGlhf3ciZsgB0ym5LaWNd5VkwmvcBTN5ORO22r4uY/uTYRfp22GaJ2uQ05CjRe/zPbDU19rzE+Hh8+Ce6115PZCO2G96ZezcEo92Y2HSKlMBHNnC8UaaP6Pcv6f5zkFZjrDCN3KpLzquBHCsPPO5y4TkagDLYHsIi2FGGa1gX0kroz8kPU0TB0si98o+xkg5QTuXYumlbx7jYgCjZVovUSjvIQB3lzC2ZlDGqnMpyU/FP68u4R2asusLJM+GGbnvkNZIpveT1dqwDN6TJJLHAZgh6da0pJI8hAnfuJxmYPID9KovnuvZ9o12TPaOkQDGAPgcgKtdlk2PpGbfgj5H8hbYaluwFWfae3BkCf1+EL6R9J5n6n+czaLJ3vEWbHOskkh6DcAY9oqQEcB9khZkeJnDAJzuagj+615DSqmmHgCcA7PQnApLPR4LC9VWlQ0kL4L9lg532UE+QoaZCFk6BSE5A8BO6JWDBQBImtuwUXl9D4Kt+I6A/bC3hYWlzmxyv1vAnL4itcjFAG6SZ1VigX43oTdzgbCV7kbAz/eA5FWw/YbZ7tQE2Pt1oUffRQzQ2wZzVlMHWgvJnWBZf8slLSE5DGbClGa6nq2fMOEX0xthfT1rtWIiILkRtnl5DUwrvdQy7IR+x8JSMfMUmLQF2u3ArrALZORItFgx45uU9kthobMnEBOokzSn/NGWCwv6D3QSNBmKhihbgd+AI0z46Fy9ERfvOwzAp2GWfUthk1iWW/48/f4WloP/FmwSXALzLX27mf0Whc4aMWfb1PqGKsIG/gMDNaRD8u8wRdbZsMyxPnE/ZSjwawUs7taXrb8w4ecvYHLP2wKmhb8/+ub7tuxW38XuvwiLW+4gaXCL+t0FVhF4HoBdJFV6T4jkjbBsoszWcSQvh2nQ5zFAbxskXwQwplV3f+3G3c2Mhxm9HAAzAp+tAlWwA4nKbli1mCJ6I7NgMfyjYGYXu8JSM5sOyTkuU2cGLDXzVADbtKDfiSRnwrJbxgG4AbZxXHU+D2AZydUk/0JyJc3LwIdpAP5I8h2S60luiNUvVJnc/gOdiKRNku6XNAl2F/oiTI77e20eWkNoTmaJwn+l9RVW+A31Rk7x2diK7grofHFJfggmzepb0JNnvKNhP+TdYBIDE2HVlGth3q6pVoMF+18HYDWAXwJYqIrrsrgq4Fe6cQPTpa/uA1vpZvUf6EhcdfExsFX+cFiF9a9d1lMlIXkHgIuavbdS6VvwViHpJQDjIr0RmNzABFiRThpRquA/SX4SwOuwL1kzmQlgnKTlNF/Vn8KKRQ6EXbh8zEByI2k7kvvDsnSucGXsf5V0ajP7LcA9MBXDl0nOkXRSaosa3KZvLgP0NvOKOz7sjgENydthhYD3AbhEKZLZFWJnAM+QfAx91T1L3WTu6gnfCVFNgbkYzYcV4UyBxaR7YJILadxMchuYeuS9MPmAHzVlwL1sHlvFTwBws8sWmUNyRZP7jt63YQA+Abu4DYW/VWA7iG/cpQqlNaCIAXrbUDn+A53EqbAJc28AU2O1Gk3ZBC2RlnxOXT3hw+LvbwN4FJZjfQFsFXS8JN+Jc4HLTlkMN5mQLOL56sPmJAdJeg9W0PSd2GOt+EwfiR03SHq1BX0WQQ3+nYUxkg6iGa9A0ts0W8lKwnL9BzoGSR21L0lyT1g6+KKa84fDqoNLpdsn/D1ipfq3AFgH82nNsuk6ByZ0FOduALnS/zyZDWCRi6X/B06e2X15mr5BF6X00VkMNru/EkgSi/Nd9RUxQG8HpwE4G8W8WgPN5zqYN0UtG91jx5bZWbdP+PFS/U0k1/hO9i4Vcn8AQ0meGHtoayQrThZG0hUkF8Difg+qd+d9M1gsv6m4vYpZMItButznSVWNl6ock5IiBujtYDVQvbzzQD+GS+qXKSbpcZLDy+6s2yf8UTWrvcGxlWDaym8fmF75R9H3KrwBFh5qKpKW1Tm3qtn9OvJaDHYsku4g+QR6DdCPb6OGkg9l+g8EmkfS4rD0epqunvCLrPwkzQcwn+Qhap0hRlXYKprsAUDSwy7DacDCYgbo7aCw/0CgJSwn+W1Jv4qfJHkGTMajVEIefkFIXgngclgs/X6YnvU5ao25dVtgTovBTobkq7A9ng8M0FnHz7gqVHlsgV5ohjzzYNIo0QR/MCx55ARJr5fZX0ftaFeUL0haDwvvvApLBzu/vUNqOpMBbA+zGJwLs3n0sRjsZN6E1R18jeSNTqm0yqvnKo8t4JD0hqRDYWmZa91xiaRDyp7sgS4P6ZREpFn9JZhmxz+YXae9I2BBi8EOh+7CfizNAH0RrP6gqpThPxBoES5EujD1iQUJK/zi/IHk87DbsAUuXa+puvBt5HbY/3MlTKztqvYOp6X0MUCHVTevbddg0mi2vEagMwkx/BJwlbbrXWrnlgC2bsbtWLshuTJWtzAIZgLeNXFi9jdAfzPp+YFA1QghnXLYD8BwNwlGlOpUUxGKWgx2LCRPht3RPAyLj19P8nxJVfXDDQT6EVb4BSE5C8AIACvQ64QkSVPbN6rmwIIWg50MyR4A41VjgC5pVHtHFgj4E1b4xTkYwEh1wZWzpIrVTqWjDNADgXqECb84T8MMUHwNUwKdyf0kH0BfA/SOcr8KBEJIpyAkF8J06B9DX4OJAalG2I04LfzcBuiBQFUIE35BSB5R73wQrRpYsIABeiBQFUJIpyBhYu8alpEcnccAPRCoCmGFnxOSG1BfC37AZ6x0IySfhSmkroVlKkWf8wHtHFcgkIUw4QcCCXSzAXpg4BEm/EAggbjqZF4D9ECgKoQ84kAgmTIM0AOBShAm/EAgmTIM0AOBShBCOoFAAjE5ibiUBBA25wMdSJjwA4FAoEsIIZ1AIBDoEsKEHwgEAl1CmPADgUCgSwgTfiAQCHQJYcIPBAKBLiFM+IFAINAl/B/32APFaeVy2wAAAABJRU5ErkJggg==\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(pd_pca.columns,pca.components_[1])\n",
|
||
"plt.xticks(rotation=90)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 213,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.colorbar.Colorbar at 0x19c0ce96588>"
|
||
]
|
||
},
|
||
"execution_count": 213,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(X_pca[:, 0], X_pca[:, 1], edgecolor='none',cmap=plt.cm.get_cmap('Accent', 10),alpha=0.5)\n",
|
||
"plt.xlabel('component 1')\n",
|
||
"plt.ylabel('component 2')\n",
|
||
"plt.colorbar()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 238,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>index</th>\n",
|
||
" <th>Country</th>\n",
|
||
" <th>Real coffee</th>\n",
|
||
" <th>Instant coffee</th>\n",
|
||
" <th>Tea</th>\n",
|
||
" <th>Sweetener</th>\n",
|
||
" <th>Biscuits</th>\n",
|
||
" <th>Powder soup</th>\n",
|
||
" <th>Tin soup</th>\n",
|
||
" <th>Potatoes</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>Apples</th>\n",
|
||
" <th>Oranges</th>\n",
|
||
" <th>Tinned fruit</th>\n",
|
||
" <th>Jam</th>\n",
|
||
" <th>Garlic</th>\n",
|
||
" <th>Butter</th>\n",
|
||
" <th>Margarine</th>\n",
|
||
" <th>Olive oil</th>\n",
|
||
" <th>Yoghurt</th>\n",
|
||
" <th>Crisp bread</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>77.140672</td>\n",
|
||
" <td>51.311557</td>\n",
|
||
" <td>84.115615</td>\n",
|
||
" <td>20.362509</td>\n",
|
||
" <td>67.123155</td>\n",
|
||
" <td>55.860068</td>\n",
|
||
" <td>25.259726</td>\n",
|
||
" <td>12.498288</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>76.306639</td>\n",
|
||
" <td>75.355540</td>\n",
|
||
" <td>54.603670</td>\n",
|
||
" <td>61.555633</td>\n",
|
||
" <td>38.592378</td>\n",
|
||
" <td>80.751330</td>\n",
|
||
" <td>76.935866</td>\n",
|
||
" <td>52.498715</td>\n",
|
||
" <td>26.830397</td>\n",
|
||
" <td>22.405815</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Italy</td>\n",
|
||
" <td>83.370973</td>\n",
|
||
" <td>20.149000</td>\n",
|
||
" <td>62.383124</td>\n",
|
||
" <td>5.279843</td>\n",
|
||
" <td>38.460989</td>\n",
|
||
" <td>39.761029</td>\n",
|
||
" <td>-7.234498</td>\n",
|
||
" <td>7.175140</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>52.628464</td>\n",
|
||
" <td>61.717583</td>\n",
|
||
" <td>11.087290</td>\n",
|
||
" <td>28.829175</td>\n",
|
||
" <td>80.513344</td>\n",
|
||
" <td>69.655407</td>\n",
|
||
" <td>54.482070</td>\n",
|
||
" <td>74.536031</td>\n",
|
||
" <td>15.734314</td>\n",
|
||
" <td>14.201416</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>France</td>\n",
|
||
" <td>91.314118</td>\n",
|
||
" <td>46.545480</td>\n",
|
||
" <td>73.243190</td>\n",
|
||
" <td>16.113204</td>\n",
|
||
" <td>59.771771</td>\n",
|
||
" <td>53.994259</td>\n",
|
||
" <td>12.716526</td>\n",
|
||
" <td>12.396357</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>76.415948</td>\n",
|
||
" <td>80.828703</td>\n",
|
||
" <td>47.341570</td>\n",
|
||
" <td>38.975773</td>\n",
|
||
" <td>76.213958</td>\n",
|
||
" <td>82.280313</td>\n",
|
||
" <td>71.911040</td>\n",
|
||
" <td>74.704311</td>\n",
|
||
" <td>34.222180</td>\n",
|
||
" <td>16.921918</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Holland</td>\n",
|
||
" <td>70.636217</td>\n",
|
||
" <td>61.419891</td>\n",
|
||
" <td>93.725044</td>\n",
|
||
" <td>25.913708</td>\n",
|
||
" <td>77.426873</td>\n",
|
||
" <td>60.879919</td>\n",
|
||
" <td>38.368412</td>\n",
|
||
" <td>13.983449</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>82.722033</td>\n",
|
||
" <td>77.215857</td>\n",
|
||
" <td>68.924983</td>\n",
|
||
" <td>78.131347</td>\n",
|
||
" <td>14.409982</td>\n",
|
||
" <td>83.256518</td>\n",
|
||
" <td>84.758753</td>\n",
|
||
" <td>38.962789</td>\n",
|
||
" <td>27.347371</td>\n",
|
||
" <td>26.501329</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>Belgium</td>\n",
|
||
" <td>80.339445</td>\n",
|
||
" <td>44.169785</td>\n",
|
||
" <td>78.123854</td>\n",
|
||
" <td>16.645688</td>\n",
|
||
" <td>60.156884</td>\n",
|
||
" <td>52.250414</td>\n",
|
||
" <td>16.798283</td>\n",
|
||
" <td>11.373741</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>71.379859</td>\n",
|
||
" <td>73.242564</td>\n",
|
||
" <td>44.549417</td>\n",
|
||
" <td>51.701291</td>\n",
|
||
" <td>52.380403</td>\n",
|
||
" <td>78.640521</td>\n",
|
||
" <td>71.576874</td>\n",
|
||
" <td>60.072195</td>\n",
|
||
" <td>25.504890</td>\n",
|
||
" <td>19.959050</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>Luxembourg</td>\n",
|
||
" <td>102.709515</td>\n",
|
||
" <td>63.762044</td>\n",
|
||
" <td>76.778044</td>\n",
|
||
" <td>22.265911</td>\n",
|
||
" <td>72.276242</td>\n",
|
||
" <td>63.557951</td>\n",
|
||
" <td>22.169089</td>\n",
|
||
" <td>16.136577</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>93.684554</td>\n",
|
||
" <td>96.846822</td>\n",
|
||
" <td>70.702535</td>\n",
|
||
" <td>37.332326</td>\n",
|
||
" <td>88.080820</td>\n",
|
||
" <td>92.031117</td>\n",
|
||
" <td>82.530989</td>\n",
|
||
" <td>83.667983</td>\n",
|
||
" <td>50.552940</td>\n",
|
||
" <td>16.708323</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>6</td>\n",
|
||
" <td>England</td>\n",
|
||
" <td>66.331374</td>\n",
|
||
" <td>70.127339</td>\n",
|
||
" <td>101.261503</td>\n",
|
||
" <td>30.504839</td>\n",
|
||
" <td>86.011217</td>\n",
|
||
" <td>65.262648</td>\n",
|
||
" <td>48.916736</td>\n",
|
||
" <td>15.332721</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>88.614668</td>\n",
|
||
" <td>79.560603</td>\n",
|
||
" <td>81.201982</td>\n",
|
||
" <td>90.684163</td>\n",
|
||
" <td>-3.356406</td>\n",
|
||
" <td>85.731295</td>\n",
|
||
" <td>91.341292</td>\n",
|
||
" <td>29.152264</td>\n",
|
||
" <td>28.685168</td>\n",
|
||
" <td>29.613986</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>7</td>\n",
|
||
" <td>Portugal</td>\n",
|
||
" <td>82.920839</td>\n",
|
||
" <td>14.584869</td>\n",
|
||
" <td>59.394933</td>\n",
|
||
" <td>2.816397</td>\n",
|
||
" <td>33.694068</td>\n",
|
||
" <td>36.816025</td>\n",
|
||
" <td>-12.141279</td>\n",
|
||
" <td>6.140506</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>47.959744</td>\n",
|
||
" <td>58.389089</td>\n",
|
||
" <td>3.389056</td>\n",
|
||
" <td>25.062982</td>\n",
|
||
" <td>84.309627</td>\n",
|
||
" <td>67.292920</td>\n",
|
||
" <td>50.660910</td>\n",
|
||
" <td>76.244670</td>\n",
|
||
" <td>12.678860</td>\n",
|
||
" <td>13.236350</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>8</td>\n",
|
||
" <td>Austria</td>\n",
|
||
" <td>66.168411</td>\n",
|
||
" <td>19.004152</td>\n",
|
||
" <td>71.537628</td>\n",
|
||
" <td>7.286976</td>\n",
|
||
" <td>41.320972</td>\n",
|
||
" <td>38.383202</td>\n",
|
||
" <td>1.557407</td>\n",
|
||
" <td>6.040531</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>46.839604</td>\n",
|
||
" <td>51.249955</td>\n",
|
||
" <td>10.288394</td>\n",
|
||
" <td>50.799150</td>\n",
|
||
" <td>40.902625</td>\n",
|
||
" <td>64.994226</td>\n",
|
||
" <td>55.754500</td>\n",
|
||
" <td>50.511298</td>\n",
|
||
" <td>3.342935</td>\n",
|
||
" <td>19.476107</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>9</td>\n",
|
||
" <td>Switzerland</td>\n",
|
||
" <td>83.973419</td>\n",
|
||
" <td>47.463460</td>\n",
|
||
" <td>77.969940</td>\n",
|
||
" <td>17.609123</td>\n",
|
||
" <td>62.222719</td>\n",
|
||
" <td>54.145620</td>\n",
|
||
" <td>17.773745</td>\n",
|
||
" <td>12.167602</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>75.093776</td>\n",
|
||
" <td>77.138268</td>\n",
|
||
" <td>48.951858</td>\n",
|
||
" <td>49.454113</td>\n",
|
||
" <td>58.082975</td>\n",
|
||
" <td>80.860716</td>\n",
|
||
" <td>73.433624</td>\n",
|
||
" <td>63.858425</td>\n",
|
||
" <td>29.628653</td>\n",
|
||
" <td>19.453091</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>Denmark</td>\n",
|
||
" <td>67.484934</td>\n",
|
||
" <td>48.073435</td>\n",
|
||
" <td>87.740202</td>\n",
|
||
" <td>20.309119</td>\n",
|
||
" <td>66.457674</td>\n",
|
||
" <td>53.722403</td>\n",
|
||
" <td>27.785420</td>\n",
|
||
" <td>11.390117</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>70.938788</td>\n",
|
||
" <td>68.047497</td>\n",
|
||
" <td>50.554593</td>\n",
|
||
" <td>71.851344</td>\n",
|
||
" <td>18.625580</td>\n",
|
||
" <td>77.084223</td>\n",
|
||
" <td>75.842341</td>\n",
|
||
" <td>40.109901</td>\n",
|
||
" <td>18.594222</td>\n",
|
||
" <td>24.849135</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>11</td>\n",
|
||
" <td>Norway</td>\n",
|
||
" <td>64.112648</td>\n",
|
||
" <td>36.571221</td>\n",
|
||
" <td>82.957177</td>\n",
|
||
" <td>15.575438</td>\n",
|
||
" <td>57.151582</td>\n",
|
||
" <td>47.524313</td>\n",
|
||
" <td>19.040861</td>\n",
|
||
" <td>9.119772</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>60.598533</td>\n",
|
||
" <td>59.770707</td>\n",
|
||
" <td>34.752782</td>\n",
|
||
" <td>67.311775</td>\n",
|
||
" <td>20.708953</td>\n",
|
||
" <td>71.604644</td>\n",
|
||
" <td>68.237003</td>\n",
|
||
" <td>40.163261</td>\n",
|
||
" <td>10.599304</td>\n",
|
||
" <td>23.635235</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>Ireland</td>\n",
|
||
" <td>55.497436</td>\n",
|
||
" <td>39.817766</td>\n",
|
||
" <td>89.769746</td>\n",
|
||
" <td>18.317245</td>\n",
|
||
" <td>61.925854</td>\n",
|
||
" <td>48.842151</td>\n",
|
||
" <td>26.989571</td>\n",
|
||
" <td>9.245200</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>60.817389</td>\n",
|
||
" <td>56.636811</td>\n",
|
||
" <td>39.651871</td>\n",
|
||
" <td>81.310927</td>\n",
|
||
" <td>-2.464239</td>\n",
|
||
" <td>70.816770</td>\n",
|
||
" <td>71.534739</td>\n",
|
||
" <td>26.518156</td>\n",
|
||
" <td>6.278767</td>\n",
|
||
" <td>27.038245</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Germany</td>\n",
|
||
" <td>90.000000</td>\n",
|
||
" <td>49.000000</td>\n",
|
||
" <td>88.000000</td>\n",
|
||
" <td>19.000000</td>\n",
|
||
" <td>57.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>19.000000</td>\n",
|
||
" <td>21.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>81.000000</td>\n",
|
||
" <td>75.000000</td>\n",
|
||
" <td>44.000000</td>\n",
|
||
" <td>71.000000</td>\n",
|
||
" <td>22.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>85.000000</td>\n",
|
||
" <td>74.000000</td>\n",
|
||
" <td>30.000000</td>\n",
|
||
" <td>26.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Italy</td>\n",
|
||
" <td>82.000000</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>60.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>55.000000</td>\n",
|
||
" <td>41.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>67.000000</td>\n",
|
||
" <td>71.000000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" <td>46.000000</td>\n",
|
||
" <td>80.000000</td>\n",
|
||
" <td>66.000000</td>\n",
|
||
" <td>24.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>18.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>France</td>\n",
|
||
" <td>88.000000</td>\n",
|
||
" <td>42.000000</td>\n",
|
||
" <td>63.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>76.000000</td>\n",
|
||
" <td>53.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>23.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>87.000000</td>\n",
|
||
" <td>84.000000</td>\n",
|
||
" <td>40.000000</td>\n",
|
||
" <td>45.000000</td>\n",
|
||
" <td>88.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>47.000000</td>\n",
|
||
" <td>36.000000</td>\n",
|
||
" <td>57.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Holland</td>\n",
|
||
" <td>96.000000</td>\n",
|
||
" <td>62.000000</td>\n",
|
||
" <td>98.000000</td>\n",
|
||
" <td>32.000000</td>\n",
|
||
" <td>62.000000</td>\n",
|
||
" <td>67.000000</td>\n",
|
||
" <td>43.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>83.000000</td>\n",
|
||
" <td>89.000000</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>81.000000</td>\n",
|
||
" <td>15.000000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>97.000000</td>\n",
|
||
" <td>13.000000</td>\n",
|
||
" <td>53.000000</td>\n",
|
||
" <td>15.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>Belgium</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>38.000000</td>\n",
|
||
" <td>48.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>74.000000</td>\n",
|
||
" <td>37.000000</td>\n",
|
||
" <td>23.000000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>76.000000</td>\n",
|
||
" <td>76.000000</td>\n",
|
||
" <td>42.000000</td>\n",
|
||
" <td>57.000000</td>\n",
|
||
" <td>29.000000</td>\n",
|
||
" <td>84.000000</td>\n",
|
||
" <td>80.000000</td>\n",
|
||
" <td>83.000000</td>\n",
|
||
" <td>20.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>5</td>\n",
|
||
" <td>Luxembourg</td>\n",
|
||
" <td>97.000000</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>86.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" <td>79.000000</td>\n",
|
||
" <td>73.000000</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>7.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>85.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>83.000000</td>\n",
|
||
" <td>20.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>84.000000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>24.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>6</td>\n",
|
||
" <td>England</td>\n",
|
||
" <td>27.000000</td>\n",
|
||
" <td>86.000000</td>\n",
|
||
" <td>99.000000</td>\n",
|
||
" <td>22.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>55.000000</td>\n",
|
||
" <td>76.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>76.000000</td>\n",
|
||
" <td>68.000000</td>\n",
|
||
" <td>89.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>95.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>57.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>7</td>\n",
|
||
" <td>Portugal</td>\n",
|
||
" <td>72.000000</td>\n",
|
||
" <td>26.000000</td>\n",
|
||
" <td>77.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>22.000000</td>\n",
|
||
" <td>34.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>22.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>8.000000</td>\n",
|
||
" <td>16.000000</td>\n",
|
||
" <td>89.000000</td>\n",
|
||
" <td>65.000000</td>\n",
|
||
" <td>78.000000</td>\n",
|
||
" <td>92.000000</td>\n",
|
||
" <td>6.000000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>8</td>\n",
|
||
" <td>Austria</td>\n",
|
||
" <td>55.000000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>15.000000</td>\n",
|
||
" <td>29.000000</td>\n",
|
||
" <td>33.000000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>49.000000</td>\n",
|
||
" <td>42.000000</td>\n",
|
||
" <td>14.000000</td>\n",
|
||
" <td>41.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>72.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" <td>13.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>9</td>\n",
|
||
" <td>Switzerland</td>\n",
|
||
" <td>73.000000</td>\n",
|
||
" <td>72.000000</td>\n",
|
||
" <td>85.000000</td>\n",
|
||
" <td>25.000000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>69.000000</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>79.000000</td>\n",
|
||
" <td>70.000000</td>\n",
|
||
" <td>46.000000</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>82.000000</td>\n",
|
||
" <td>48.000000</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>48.000000</td>\n",
|
||
" <td>30.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>10</td>\n",
|
||
" <td>Denmark</td>\n",
|
||
" <td>96.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>92.000000</td>\n",
|
||
" <td>35.000000</td>\n",
|
||
" <td>66.000000</td>\n",
|
||
" <td>32.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>81.000000</td>\n",
|
||
" <td>72.000000</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>64.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>92.000000</td>\n",
|
||
" <td>91.000000</td>\n",
|
||
" <td>30.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>34.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>11</td>\n",
|
||
" <td>Norway</td>\n",
|
||
" <td>92.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>83.000000</td>\n",
|
||
" <td>13.000000</td>\n",
|
||
" <td>62.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>17.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>72.000000</td>\n",
|
||
" <td>34.000000</td>\n",
|
||
" <td>51.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>63.000000</td>\n",
|
||
" <td>94.000000</td>\n",
|
||
" <td>28.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>62.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>12</td>\n",
|
||
" <td>Ireland</td>\n",
|
||
" <td>30.000000</td>\n",
|
||
" <td>52.000000</td>\n",
|
||
" <td>99.000000</td>\n",
|
||
" <td>11.000000</td>\n",
|
||
" <td>80.000000</td>\n",
|
||
" <td>75.000000</td>\n",
|
||
" <td>18.000000</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>57.000000</td>\n",
|
||
" <td>52.000000</td>\n",
|
||
" <td>46.000000</td>\n",
|
||
" <td>89.000000</td>\n",
|
||
" <td>5.000000</td>\n",
|
||
" <td>97.000000</td>\n",
|
||
" <td>25.000000</td>\n",
|
||
" <td>31.000000</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>9.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>26 rows × 22 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" index Country Real coffee Instant coffee Tea Sweetener \\\n",
|
||
"0 0 Germany 77.140672 51.311557 84.115615 20.362509 \n",
|
||
"1 1 Italy 83.370973 20.149000 62.383124 5.279843 \n",
|
||
"2 2 France 91.314118 46.545480 73.243190 16.113204 \n",
|
||
"3 3 Holland 70.636217 61.419891 93.725044 25.913708 \n",
|
||
"4 4 Belgium 80.339445 44.169785 78.123854 16.645688 \n",
|
||
"5 5 Luxembourg 102.709515 63.762044 76.778044 22.265911 \n",
|
||
"6 6 England 66.331374 70.127339 101.261503 30.504839 \n",
|
||
"7 7 Portugal 82.920839 14.584869 59.394933 2.816397 \n",
|
||
"8 8 Austria 66.168411 19.004152 71.537628 7.286976 \n",
|
||
"9 9 Switzerland 83.973419 47.463460 77.969940 17.609123 \n",
|
||
"10 10 Denmark 67.484934 48.073435 87.740202 20.309119 \n",
|
||
"11 11 Norway 64.112648 36.571221 82.957177 15.575438 \n",
|
||
"12 12 Ireland 55.497436 39.817766 89.769746 18.317245 \n",
|
||
"13 0 Germany 90.000000 49.000000 88.000000 19.000000 \n",
|
||
"14 1 Italy 82.000000 10.000000 60.000000 2.000000 \n",
|
||
"15 2 France 88.000000 42.000000 63.000000 4.000000 \n",
|
||
"16 3 Holland 96.000000 62.000000 98.000000 32.000000 \n",
|
||
"17 4 Belgium 94.000000 38.000000 48.000000 11.000000 \n",
|
||
"18 5 Luxembourg 97.000000 61.000000 86.000000 28.000000 \n",
|
||
"19 6 England 27.000000 86.000000 99.000000 22.000000 \n",
|
||
"20 7 Portugal 72.000000 26.000000 77.000000 2.000000 \n",
|
||
"21 8 Austria 55.000000 31.000000 61.000000 15.000000 \n",
|
||
"22 9 Switzerland 73.000000 72.000000 85.000000 25.000000 \n",
|
||
"23 10 Denmark 96.000000 17.000000 92.000000 35.000000 \n",
|
||
"24 11 Norway 92.000000 17.000000 83.000000 13.000000 \n",
|
||
"25 12 Ireland 30.000000 52.000000 99.000000 11.000000 \n",
|
||
"\n",
|
||
" Biscuits Powder soup Tin soup Potatoes ... Apples Oranges \\\n",
|
||
"0 67.123155 55.860068 25.259726 12.498288 ... 76.306639 75.355540 \n",
|
||
"1 38.460989 39.761029 -7.234498 7.175140 ... 52.628464 61.717583 \n",
|
||
"2 59.771771 53.994259 12.716526 12.396357 ... 76.415948 80.828703 \n",
|
||
"3 77.426873 60.879919 38.368412 13.983449 ... 82.722033 77.215857 \n",
|
||
"4 60.156884 52.250414 16.798283 11.373741 ... 71.379859 73.242564 \n",
|
||
"5 72.276242 63.557951 22.169089 16.136577 ... 93.684554 96.846822 \n",
|
||
"6 86.011217 65.262648 48.916736 15.332721 ... 88.614668 79.560603 \n",
|
||
"7 33.694068 36.816025 -12.141279 6.140506 ... 47.959744 58.389089 \n",
|
||
"8 41.320972 38.383202 1.557407 6.040531 ... 46.839604 51.249955 \n",
|
||
"9 62.222719 54.145620 17.773745 12.167602 ... 75.093776 77.138268 \n",
|
||
"10 66.457674 53.722403 27.785420 11.390117 ... 70.938788 68.047497 \n",
|
||
"11 57.151582 47.524313 19.040861 9.119772 ... 60.598533 59.770707 \n",
|
||
"12 61.925854 48.842151 26.989571 9.245200 ... 60.817389 56.636811 \n",
|
||
"13 57.000000 51.000000 19.000000 21.000000 ... 81.000000 75.000000 \n",
|
||
"14 55.000000 41.000000 3.000000 2.000000 ... 67.000000 71.000000 \n",
|
||
"15 76.000000 53.000000 11.000000 23.000000 ... 87.000000 84.000000 \n",
|
||
"16 62.000000 67.000000 43.000000 7.000000 ... 83.000000 89.000000 \n",
|
||
"17 74.000000 37.000000 23.000000 9.000000 ... 76.000000 76.000000 \n",
|
||
"18 79.000000 73.000000 12.000000 7.000000 ... 85.000000 94.000000 \n",
|
||
"19 91.000000 55.000000 76.000000 17.000000 ... 76.000000 68.000000 \n",
|
||
"20 22.000000 34.000000 1.000000 5.000000 ... 22.000000 51.000000 \n",
|
||
"21 29.000000 33.000000 1.000000 5.000000 ... 49.000000 42.000000 \n",
|
||
"22 31.000000 69.000000 10.000000 17.000000 ... 79.000000 70.000000 \n",
|
||
"23 66.000000 32.000000 17.000000 11.000000 ... 81.000000 72.000000 \n",
|
||
"24 62.000000 51.000000 4.000000 17.000000 ... 61.000000 72.000000 \n",
|
||
"25 80.000000 75.000000 18.000000 2.000000 ... 57.000000 52.000000 \n",
|
||
"\n",
|
||
" Tinned fruit Jam Garlic Butter Margarine Olive oil \\\n",
|
||
"0 54.603670 61.555633 38.592378 80.751330 76.935866 52.498715 \n",
|
||
"1 11.087290 28.829175 80.513344 69.655407 54.482070 74.536031 \n",
|
||
"2 47.341570 38.975773 76.213958 82.280313 71.911040 74.704311 \n",
|
||
"3 68.924983 78.131347 14.409982 83.256518 84.758753 38.962789 \n",
|
||
"4 44.549417 51.701291 52.380403 78.640521 71.576874 60.072195 \n",
|
||
"5 70.702535 37.332326 88.080820 92.031117 82.530989 83.667983 \n",
|
||
"6 81.201982 90.684163 -3.356406 85.731295 91.341292 29.152264 \n",
|
||
"7 3.389056 25.062982 84.309627 67.292920 50.660910 76.244670 \n",
|
||
"8 10.288394 50.799150 40.902625 64.994226 55.754500 50.511298 \n",
|
||
"9 48.951858 49.454113 58.082975 80.860716 73.433624 63.858425 \n",
|
||
"10 50.554593 71.851344 18.625580 77.084223 75.842341 40.109901 \n",
|
||
"11 34.752782 67.311775 20.708953 71.604644 68.237003 40.163261 \n",
|
||
"12 39.651871 81.310927 -2.464239 70.816770 71.534739 26.518156 \n",
|
||
"13 44.000000 71.000000 22.000000 91.000000 85.000000 74.000000 \n",
|
||
"14 9.000000 46.000000 80.000000 66.000000 24.000000 94.000000 \n",
|
||
"15 40.000000 45.000000 88.000000 94.000000 47.000000 36.000000 \n",
|
||
"16 61.000000 81.000000 15.000000 31.000000 97.000000 13.000000 \n",
|
||
"17 42.000000 57.000000 29.000000 84.000000 80.000000 83.000000 \n",
|
||
"18 83.000000 20.000000 91.000000 94.000000 94.000000 84.000000 \n",
|
||
"19 89.000000 91.000000 11.000000 95.000000 94.000000 57.000000 \n",
|
||
"20 8.000000 16.000000 89.000000 65.000000 78.000000 92.000000 \n",
|
||
"21 14.000000 41.000000 51.000000 51.000000 72.000000 28.000000 \n",
|
||
"22 46.000000 61.000000 64.000000 82.000000 48.000000 61.000000 \n",
|
||
"23 50.000000 64.000000 11.000000 92.000000 91.000000 30.000000 \n",
|
||
"24 34.000000 51.000000 11.000000 63.000000 94.000000 28.000000 \n",
|
||
"25 46.000000 89.000000 5.000000 97.000000 25.000000 31.000000 \n",
|
||
"\n",
|
||
" Yoghurt Crisp bread \n",
|
||
"0 26.830397 22.405815 \n",
|
||
"1 15.734314 14.201416 \n",
|
||
"2 34.222180 16.921918 \n",
|
||
"3 27.347371 26.501329 \n",
|
||
"4 25.504890 19.959050 \n",
|
||
"5 50.552940 16.708323 \n",
|
||
"6 28.685168 29.613986 \n",
|
||
"7 12.678860 13.236350 \n",
|
||
"8 3.342935 19.476107 \n",
|
||
"9 29.628653 19.453091 \n",
|
||
"10 18.594222 24.849135 \n",
|
||
"11 10.599304 23.635235 \n",
|
||
"12 6.278767 27.038245 \n",
|
||
"13 30.000000 26.000000 \n",
|
||
"14 5.000000 18.000000 \n",
|
||
"15 57.000000 3.000000 \n",
|
||
"16 53.000000 15.000000 \n",
|
||
"17 20.000000 5.000000 \n",
|
||
"18 31.000000 24.000000 \n",
|
||
"19 11.000000 28.000000 \n",
|
||
"20 6.000000 9.000000 \n",
|
||
"21 13.000000 11.000000 \n",
|
||
"22 48.000000 30.000000 \n",
|
||
"23 11.000000 34.000000 \n",
|
||
"24 2.000000 62.000000 \n",
|
||
"25 3.000000 9.000000 \n",
|
||
"\n",
|
||
"[26 rows x 22 columns]"
|
||
]
|
||
},
|
||
"execution_count": 238,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"X_concat_df = pd.concat([X_final_df,pd_food_cons])\n",
|
||
"X_concat_df = X_concat_df.drop(['index'],axis=1)\n",
|
||
"X_concat_df.reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 267,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
|
||
" <a list of 20 Text major ticklabel objects>)"
|
||
]
|
||
},
|
||
"execution_count": 267,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"germany_transformed = np.array(X_concat_df.loc[X_concat_df['Country'] == 'Germany'].iloc[:, 1:])\n",
|
||
"plt.plot(X_concat_df.columns[1:],germany_transformed[0])\n",
|
||
"plt.plot(X_concat_df.columns[1:],germany_transformed[1])\n",
|
||
"plt.xticks(rotation=90)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|