correlation_matrix_in_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Correlation Matrices in Python with NumPy and Pandas\n",
    "This is the code for the blog post about how to create a [Python correlation matrix](https://www.marsja.se/correlation-matrix-python-numpy-pandas).\n",
    "\n",
    "Note, the data should be stored in a subfolder called 'SimData' (or you'll need to change the string with the location of the data file, below).\n",
    "\n",
    "### 1. NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.38322543, 0.4033913 , 0.43527164],\n",
       "       [0.38322543, 1.        , 0.94319646, 0.17686429],\n",
       "       [0.4033913 , 0.94319646, 1.        , 0.15407927],\n",
       "       [0.43527164, 0.17686429, 0.15407927, 1.        ]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "data = './SimData/correlationMatrixPython.csv'\n",
    "\n",
    "x = np.loadtxt(data, skiprows=1, delimiter=',',\n",
    "              unpack=True)\n",
    "\n",
    "np.corrcoef(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383225</td>\n",
       "      <td>0.403391</td>\n",
       "      <td>0.435272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.383225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.943196</td>\n",
       "      <td>0.176864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.403391</td>\n",
       "      <td>0.943196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.435272</td>\n",
       "      <td>0.176864</td>\n",
       "      <td>0.154079</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "a  1.000000  0.383225  0.403391  0.435272\n",
       "b  0.383225  1.000000  0.943196  0.176864\n",
       "c  0.403391  0.943196  1.000000  0.154079\n",
       "d  0.435272  0.176864  0.154079  1.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = './SimData/correlationMatrixPython.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Spearman with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.298602</td>\n",
       "      <td>0.338962</td>\n",
       "      <td>0.397516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.298602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.936370</td>\n",
       "      <td>0.131449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.338962</td>\n",
       "      <td>0.936370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.397516</td>\n",
       "      <td>0.131449</td>\n",
       "      <td>0.105359</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "a  1.000000  0.298602  0.338962  0.397516\n",
       "b  0.298602  1.000000  0.936370  0.131449\n",
       "c  0.338962  0.936370  1.000000  0.105359\n",
       "d  0.397516  0.131449  0.105359  1.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cormat = df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.383</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td></td>\n",
       "      <td>0.943</td>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>0.154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       b      c      d\n",
       "a  0.383  0.403  0.435\n",
       "b         0.943  0.177\n",
       "c                0.154"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def triang(cormat, triang='lower'):\n",
    "    \n",
    "    if triang == 'upper':\n",
    "        rstri = pd.DataFrame(np.triu(cormat.values),\n",
    "                             index=cormat.index,\n",
    "                             columns=cormat.columns).round(3)\n",
    "        rstri = rstri.iloc[:,1:]\n",
    "        rstri.drop(rstri.tail(1).index, inplace=True)\n",
    "        \n",
    "    if triang == 'lower':\n",
    "        rstri = pd.DataFrame(np.tril(cormat.values),\n",
    "                             index=cormat.index,\n",
    "                             columns=cormat.columns).round(3)\n",
    "        rstri = rstri.iloc[:,:-1]\n",
    "        rstri.drop(rstri.head(1).index, inplace=True) \n",
    " \n",
    "    rstri.replace(to_replace=[0,1], value='', inplace=True)\n",
    "    \n",
    "    return(rstri)\n",
    "\n",
    "triang(cormat, triang='upper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Creating the Heatmap from the Post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x297ded52c50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mask = np.zeros_like(cormat, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(cormat, mask=mask, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}