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@@ -0,0 +1,670 @@
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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:38:38.465675Z",
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+ "start_time": "2019-02-23T16:38:38.422180Z"
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+ }
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+ },
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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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+ "The autoreload extension is already loaded. To reload it, use:\n",
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+ " %reload_ext autoreload\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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+ ],
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+ "text/vnd.plotly.v1+html": [
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+ "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "text/html": [
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+ "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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+ ],
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+ "text/vnd.plotly.v1+html": [
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+ "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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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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+ "import numpy as np\n",
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+ "\n",
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+ "%load_ext autoreload\n",
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+ "%autoreload 2\n",
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+ "\n",
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+ "import sys\n",
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+ "sys.path.append('../..')\n",
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+ "\n",
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+ "# Options for pandas\n",
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+ "pd.options.display.max_columns = 20\n",
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+ "pd.options.display.max_rows = 10\n",
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+ "\n",
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+ "# Display all cell outputs\n",
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+ "from IPython.core.interactiveshell import InteractiveShell\n",
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+ "InteractiveShell.ast_node_interactivity = 'all'\n",
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+ "\n",
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+ "import plotly.plotly as py\n",
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+ "import plotly.graph_objs as go\n",
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+ "from plotly.offline import iplot, init_notebook_mode\n",
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+ "init_notebook_mode(connected=True)\n",
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+ "\n",
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+ "import cufflinks\n",
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+ "cf.go_offline(connected=True)\n",
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+ "cf.set_config_file(theme='pearl')\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": 2,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:39:00.607978Z",
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+ "start_time": "2019-02-23T16:39:00.567876Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "(1000, 100)"
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+ ]
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+ },
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+ "execution_count": 2,
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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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+ "df = pd.DataFrame(np.random.randn(1000, 100))\n",
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+ "df.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": 3,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:39:16.845392Z",
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+ "start_time": "2019-02-23T16:39:16.774748Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "(100, 100)"
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+ ]
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+ },
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+ "execution_count": 3,
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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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+ "corrs = df.corr()\n",
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+ "corrs.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": 9,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:42:26.032480Z",
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+ "start_time": "2019-02-23T16:42:25.998079Z"
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+ }
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+ },
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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([ 3, 8, 44, 45, 54, 96]), array([54, 96, 45, 44, 3, 8]))"
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+ ]
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+ },
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+ "execution_count": 9,
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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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+ "threshold = -0.1\n",
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+ "direction = 'less'\n",
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+ "\n",
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+ "if direction == 'greater':\n",
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+ " values_index = np.where(corrs > threshold)\n",
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+ "elif direction == 'less':\n",
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+ " values_index = np.where(corrs < threshold)\n",
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+ " \n",
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+ "values_index"
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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": 35,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:47:56.940313Z",
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+ "start_time": "2019-02-23T16:47:56.909882Z"
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+ }
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+ },
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+ "outputs": [],
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+ "source": [
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+ "rows_index = values_index[0]\n",
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+ "columns_index = values_index[1]\n",
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+ "\n",
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+ "pairs = list(map(tuple, set([frozenset((x, y)) for x, y in zip(rows_index, columns_index)])))\n",
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+ "\n",
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+ "from collections import Counter\n",
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+ "\n",
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+ "# Counter(pairs)"
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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": 36,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:47:57.429941Z",
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+ "start_time": "2019-02-23T16:47:57.397928Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[(3, 54), (8, 96), (44, 45)]"
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+ ]
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+ },
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+ "execution_count": 36,
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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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+ "pairs"
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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": 49,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T17:04:56.074717Z",
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+ "start_time": "2019-02-23T17:04:56.041811Z"
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+ }
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+ },
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+ "outputs": [],
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+ "source": [
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+ "subset_df = pd.DataFrame(dict(value=corrs.values[values_index], var1=corrs.index[values_index[0]],\n",
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+ " var2=corrs.columns[values_index[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": 58,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-24T20:36:52.221603Z",
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+ "start_time": "2019-02-24T20:36:52.182531Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>value</th>\n",
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+ " <th>var1</th>\n",
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+ " <th>var2</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>-0.111172</td>\n",
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+ " <td>3</td>\n",
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+ " <td>54</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>-0.117402</td>\n",
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+ " <td>8</td>\n",
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+ " <td>96</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>-0.104640</td>\n",
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+ " <td>44</td>\n",
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+ " <td>45</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>"
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+ ],
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+ "text/plain": [
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+ " value var1 var2\n",
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+ "0 -0.111172 3 54\n",
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+ "1 -0.117402 8 96\n",
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+ "2 -0.104640 44 45"
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+ ]
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+ },
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+ "execution_count": 58,
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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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+ "subset_df.iloc[:int(len(subset_df)/2)]"
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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": 53,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T17:05:53.369263Z",
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+ "start_time": "2019-02-23T17:05:53.337720Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "var1 var2\n",
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+ "3 54 1\n",
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+ "8 96 1\n",
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+ "44 45 1\n",
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+ "45 44 1\n",
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+ "54 3 1\n",
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+ "96 8 1\n",
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+ "dtype: int64"
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+ ]
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+ },
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+ "execution_count": 53,
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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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+ "subset_df.groupby(['var1', 'var2']).size()"
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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": 55,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-24T14:57:30.059725Z",
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+ "start_time": "2019-02-24T14:57:30.027029Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "Index(['value', 'variable1', 'var2'], dtype='object')"
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+ ]
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+ },
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+ "execution_count": 55,
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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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+ "subset_df.columns.str.replace('var1', 'variable1')"
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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": 40,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:50:24.548345Z",
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+ "start_time": "2019-02-23T16:50:24.513301Z"
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+ }
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+ },
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+ "outputs": [],
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+ "source": [
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+ "values = []; indices = []; columns = []\n",
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+ "\n",
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+ "for pair in pairs:\n",
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+ " indices.append(corrs.index[pair[0]])\n",
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+ " columns.append(corrs.columns[pair[1]])\n",
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+ " values.append(corrs.values[pair])\n",
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+ " \n",
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+ "subset_df = pd.DataFrame(dict(value=values, var1=indices, var2=columns))"
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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": 41,
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+ "metadata": {
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+ "ExecuteTime": {
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+ "end_time": "2019-02-23T16:50:25.388032Z",
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+ "start_time": "2019-02-23T16:50:25.352969Z"
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+ }
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+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>value</th>\n",
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+ " <th>var1</th>\n",
|
|
|
+ " <th>var2</th>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " </thead>\n",
|
|
|
+ " <tbody>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>-0.111172</td>\n",
|
|
|
+ " <td>3</td>\n",
|
|
|
+ " <td>54</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>1</th>\n",
|
|
|
+ " <td>-0.117402</td>\n",
|
|
|
+ " <td>8</td>\n",
|
|
|
+ " <td>96</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>2</th>\n",
|
|
|
+ " <td>-0.104640</td>\n",
|
|
|
+ " <td>44</td>\n",
|
|
|
+ " <td>45</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " </tbody>\n",
|
|
|
+ "</table>\n",
|
|
|
+ "</div>"
|
|
|
+ ],
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|
|
+ "text/plain": [
|
|
|
+ " value var1 var2\n",
|
|
|
+ "0 -0.111172 3 54\n",
|
|
|
+ "1 -0.117402 8 96\n",
|
|
|
+ "2 -0.104640 44 45"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 41,
|
|
|
+ "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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|
|
+ "subset_df"
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|
|
+ ]
|
|
|
+ },
|
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|
+ {
|
|
|
+ "cell_type": "code",
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|
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+ "execution_count": 42,
|
|
|
+ "metadata": {
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+ "ExecuteTime": {
|
|
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+ "end_time": "2019-02-23T16:50:35.252569Z",
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+ "start_time": "2019-02-23T16:50:35.217497Z"
|
|
|
+ }
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|
+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "-0.11117190190235929"
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+ ]
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+ },
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+ "execution_count": 42,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "corrs.loc[3, 54]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
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+ "execution_count": 43,
|
|
|
+ "metadata": {
|
|
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+ "ExecuteTime": {
|
|
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+ "end_time": "2019-02-23T16:50:55.128251Z",
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+ "start_time": "2019-02-23T16:50:55.096675Z"
|
|
|
+ }
|
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|
+ },
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "-0.11117190190235929"
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+ ]
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+ },
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+ "execution_count": 43,
|
|
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+ "metadata": {},
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+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "corrs.loc[54, 3]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
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+ "execution_count": 44,
|
|
|
+ "metadata": {
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|
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+ "ExecuteTime": {
|
|
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+ "end_time": "2019-02-23T16:51:03.603309Z",
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+ "start_time": "2019-02-23T16:51:03.569575Z"
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|
|
+ }
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|
+ },
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+ "outputs": [
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|
+ {
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+ "data": {
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+ "text/plain": [
|
|
|
+ "-0.11740191658722447"
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|
|
+ ]
|
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|
+ },
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|
|
+ "execution_count": 44,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "corrs.loc[96, 8]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 45,
|
|
|
+ "metadata": {
|
|
|
+ "ExecuteTime": {
|
|
|
+ "end_time": "2019-02-23T16:51:12.504120Z",
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+ "start_time": "2019-02-23T16:51:12.472562Z"
|
|
|
+ }
|
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|
+ },
|
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|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
|
+ "-0.10463995106844964"
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|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 45,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "corrs.loc[44, 45]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 32,
|
|
|
+ "metadata": {
|
|
|
+ "ExecuteTime": {
|
|
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+ "end_time": "2019-02-23T16:47:20.066448Z",
|
|
|
+ "start_time": "2019-02-23T16:47:20.032246Z"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "outputs": [
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|
|
+ {
|
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|
+ "data": {
|
|
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+ "text/plain": [
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+ "-0.11117190190235929"
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+ ]
|
|
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+ },
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|
+ "execution_count": 32,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "corrs.values[(3, 54)]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 14,
|
|
|
+ "metadata": {
|
|
|
+ "ExecuteTime": {
|
|
|
+ "end_time": "2019-02-23T16:43:58.225875Z",
|
|
|
+ "start_time": "2019-02-23T16:43:58.190963Z"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "data": {
|
|
|
+ "text/plain": [
|
|
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+ "[(3, 54), (8, 96), (44, 45)]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ "execution_count": 14,
|
|
|
+ "metadata": {},
|
|
|
+ "output_type": "execute_result"
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "[tuple(x) for x in set(map(frozenset, pairs))]"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": null,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [],
|
|
|
+ "source": []
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "metadata": {
|
|
|
+ "hide_input": false,
|
|
|
+ "kernelspec": {
|
|
|
+ "display_name": "Python 3",
|
|
|
+ "language": "python",
|
|
|
+ "name": "python3"
|
|
|
+ },
|
|
|
+ "language_info": {
|
|
|
+ "codemirror_mode": {
|
|
|
+ "name": "ipython",
|
|
|
+ "version": 3
|
|
|
+ },
|
|
|
+ "file_extension": ".py",
|
|
|
+ "mimetype": "text/x-python",
|
|
|
+ "name": "python",
|
|
|
+ "nbconvert_exporter": "python",
|
|
|
+ "pygments_lexer": "ipython3",
|
|
|
+ "version": "3.6.5"
|
|
|
+ },
|
|
|
+ "toc": {
|
|
|
+ "base_numbering": 1,
|
|
|
+ "nav_menu": {},
|
|
|
+ "number_sections": true,
|
|
|
+ "sideBar": true,
|
|
|
+ "skip_h1_title": false,
|
|
|
+ "title_cell": "Table of Contents",
|
|
|
+ "title_sidebar": "Contents",
|
|
|
+ "toc_cell": false,
|
|
|
+ "toc_position": {},
|
|
|
+ "toc_section_display": true,
|
|
|
+ "toc_window_display": false
|
|
|
+ },
|
|
|
+ "varInspector": {
|
|
|
+ "cols": {
|
|
|
+ "lenName": 16,
|
|
|
+ "lenType": 16,
|
|
|
+ "lenVar": 40
|
|
|
+ },
|
|
|
+ "kernels_config": {
|
|
|
+ "python": {
|
|
|
+ "delete_cmd_postfix": "",
|
|
|
+ "delete_cmd_prefix": "del ",
|
|
|
+ "library": "var_list.py",
|
|
|
+ "varRefreshCmd": "print(var_dic_list())"
|
|
|
+ },
|
|
|
+ "r": {
|
|
|
+ "delete_cmd_postfix": ") ",
|
|
|
+ "delete_cmd_prefix": "rm(",
|
|
|
+ "library": "var_list.r",
|
|
|
+ "varRefreshCmd": "cat(var_dic_list()) "
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "types_to_exclude": [
|
|
|
+ "module",
|
|
|
+ "function",
|
|
|
+ "builtin_function_or_method",
|
|
|
+ "instance",
|
|
|
+ "_Feature"
|
|
|
+ ],
|
|
|
+ "window_display": false
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
|
+ "nbformat_minor": 2
|
|
|
+}
|
WillKoehrsen