From 77794d1cda4ec10697f116a9a377e574704b9f7c Mon Sep 17 00:00:00 2001 From: Silver-T Date: Thu, 3 May 2018 11:25:54 +1000 Subject: [PATCH] Still chipping away at week 8 --- wk7/wk7.ipynb | 755 ++++++++++++++++++++++++++++++++++++++++++++++++-- wk8/wk8.ipynb | 171 +++++++----- 2 files changed, 829 insertions(+), 97 deletions(-) diff --git a/wk7/wk7.ipynb b/wk7/wk7.ipynb index a7dd198..e381063 100644 --- a/wk7/wk7.ipynb +++ b/wk7/wk7.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -19,24 +19,13 @@ "Using matplotlib backend: TkAgg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] - }, - { - "ename": "ImportError", - "evalue": "No module named 'seaborn'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: No module named 'seaborn'" - ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "import pandas as pd\n", - "import seaborn as sn\n", + "#import seaborn as sn\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", @@ -46,19 +35,739 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'data' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackground_gradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Wistia'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" - ] + "data": { + "text/html": [ + "\n", + " \n", + "\n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ diff --git a/wk8/wk8.ipynb b/wk8/wk8.ipynb index d539460..a3325a8 100644 --- a/wk8/wk8.ipynb +++ b/wk8/wk8.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -77,97 +77,97 @@ " \n", "\n", - " \n", + "
\n", " \n", "\n", " \n", @@ -215,33 +215,33 @@ " \n", " \n", " \n", - " \n", @@ -249,33 +249,33 @@ " \n", " \n", " \n", - " \n", @@ -283,33 +283,33 @@ " \n", " \n", " \n", - " \n", @@ -317,31 +317,31 @@ " \n", " \n", " \n", - "
\n", " Distance from event center (km)\n", " \n", " \n", " \n", - " \n", " 1\n", " \n", " \n", " \n", - " \n", - " -0.104681\n", + " -0.40041\n", " \n", " \n", " \n", - " \n", - " -0.101477\n", + " -0.327719\n", " \n", " \n", " \n", - " \n", - " -0.375261\n", + " -0.50201\n", " \n", " \n", "
\n", " Min of ref. Days\n", " \n", " \n", " \n", - " \n", - " -0.104681\n", + " -0.40041\n", " \n", " \n", " \n", - " \n", " 1\n", " \n", " \n", " \n", - " \n", - " 0.990716\n", + " 0.735453\n", " \n", " \n", " \n", - " \n", - " 0.855398\n", + " 0.288805\n", " \n", " \n", "
\n", " Max of ref. Days\n", " \n", " \n", " \n", - " \n", - " -0.101477\n", + " -0.327719\n", " \n", " \n", " \n", - " \n", - " 0.990716\n", + " 0.735453\n", " \n", " \n", " \n", - " \n", " 1\n", " \n", " \n", " \n", - " \n", - " 0.869914\n", + " 0.412141\n", " \n", " \n", "
\n", " Decrease on Day 62\n", " \n", " \n", " \n", - " \n", - " -0.375261\n", + " -0.50201\n", " \n", " \n", " \n", - " \n", - " 0.855398\n", + " 0.288805\n", " \n", " \n", " \n", - " \n", - " 0.869914\n", + " 0.412141\n", " \n", " \n", " \n", - " \n", " 1\n", " \n", @@ -353,16 +353,16 @@ " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cust_data.corr().style.background_gradient(cmap='Wistia')" + "spend_data.corr().style.background_gradient(cmap='Wistia')" ] }, { @@ -374,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -383,7 +383,7 @@ "[]" ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -391,7 +391,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -418,7 +418,7 @@ "[]" ] }, - "execution_count": 29, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -426,7 +426,7 @@ "data": { "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -453,7 +453,7 @@ "[]" ] }, - "execution_count": 30, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -461,7 +461,7 @@ "data": { "image/png": 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UZLC+d5N0eTUrLc8pYda4PGMxvS4i3g/8ISLOJZmbeq/WhmXWmNHmjvCcEmb55UkQlVOupyXtTDLExtQa65sVznNKmDUuTxvE1ZJ6gfnAnSQ9mC5qaVRmDaq0M7gXk9n41Z0PYrOVkxFct4mI9a0LKT/PB2FmnaJM3a7zzgeRtxfT64C+yvqSiIhLG4rQzKxLdGq36zy9mL4HfBE4GHh1+qibeczMLFGr23WZ5bmC6Af2jrHURZmZ2Uad2u06Ty+me4C/aHUgZmYTVad2ux71CkLSz0h6LG1HMsXo7Ww+WN/bWx+emVnnmzt7xmZtENAZ3a5rVTF9sW1RmJlNYJ3a7bpWghgEdoqIW6oLJR0MrG50x5JWAk+RTEL0bET0S3oxcAVJj6mVwHsi4g+N7svMrGjHzJpW+oQwUq02iK8AT2aUr0+XNcNhETGzqj/uWcD1EbEncH362szMClDrCmKniFg2sjAilknqa1E8RwOHps+/C/wS+FSL9mUdrkw3HplNRLWuIHprLGtG03sA10paLGlOWrZTRFSqr34P7DTyTZLmSBqQNLB27domhGGdqHLj0eC6IYJNNx4tXDJYdGhmE0atBDEg6e9GFkr6W2BxE/Z9cETsD7wZ+Iik11cvTO+7eN69FxFxYUT0R0T/lClTmhCGdaJOvfHIrJPUqmI6HfiJpBPYlBD6ga2AdzS644gYTH+ukfQT4ADgUUlTI2K1pKnAmkb3YxNTp954ZNZJRr2CiIhHI+J1wLkkPYpWAudGxGsj4veN7FTSCyVtV3kOHElyQ95VwInpaicCP21kPzZxdeqNR2adJM+c1DcCNzZ5vzuRXJ1UYvh+RPybpDuAH0o6BXgYeE+T92sTRKfeeGTWSXKN5tpsEfEQ8MqM8seBN7Y/Ius0nXrjkVknKSRBmDVDJ954ZNZJ8gzWZ2ZmXcgJwszMMjlBmJlZJicIMzPL5ARhZmaZnCDMzCyTE4SZmWVygjAzs0xOEGZmlsl3UpuZdYh2T5LlBGFm1gEqk2RVBqisTJIFtCxJuIrJzKwDFDFJlhOEmVkHKGKSLCcIM7MOUMQkWU4QZmYdYO7sGfRMnrRZWasnyXIjtZlZByhikiwnCDOzDtHuSbLaXsUkaVdJN0q6V9JySR9Py8+RNCjprvTxlnbHZmZmmxRxBfEs8ImIuFPSdsBiSdely86PiC8WEJOZmY3Q9gQREauB1enzpyTdB3hiYTOzkim0F5OkPmAW8Ku06DRJSyVdIulFhQVmNgYLlwxy0Lwb2O2sn3PQvBtYuGSw6JDMmqKwBCFpW+BK4PSIeBL4JrAHMJPkCuNLo7xvjqQBSQNr165tW7xmWSrDHwyuGyLYNPyBk4RNBIUkCEmTSZLDZRGxACAiHo2I4Yh4DvgWcEDWeyPiwojoj4j+KVOmtC9oswxFDH9g1i5F9GIScDFwX0R8uap8atVq7wDuaXdsZmNVxPAHZu1SRC+mg4D3Acsk3ZWWfRo4XtJMIICVwAcLiM1sTHbu7WEwIxm0cvgDs3YpohfTfwLKWPSLdsXQ7jHVbeKaO3vGZkMwQ+uHPzBrl667k7qIMdVt4ipi+AOzdum6BFGrUdH/1DYeeYc/8JWrdZquSxBuVLQi+MrVOlHXDfddxJjqZu4Oa52o664g3KhoRfCVa2cqslqwDFWSXZcg3KhoRXB32M5TZLVgWaokuy5BQPvHVLfGlOFMqlHdfuXaiX/DIju0lKUzTVcmCOscZTmTalQ3X7l26t+wyGrBslRJOkFYqTX7TKrWmWyrz3K79cq1LGfDY1VktWBZqiS7rheTdZZmnknVGnnVo7K2TlnOhsdq7uwZ9EyetFlZu6oFi9x3NV9BWKk180yqXlfTTjzLbVQ72gbKcjY8VkVWC5alStIJwkqtmY274zmTLftZbiPa1TZQdAN9I0mwyGrBMlRJOkGMohN7XUxEzTyTqncm24lnuY1oV9tAkWfDndpAXhZOEBn8oSqXZp1J1TuT7bZuqO1sG2jX2fDIE7un//xs6asOy3wy6gSRoVN7XbRSmT/EeeU5k+3033EsOrVtYDRZJ3ajKUvVYdlPRp0gMnRqr4tWKfuHeCxqncmWoc63nZrZNtDME4jxbivrxG40ZUmCZT8ZdYLIMNHOrBpV9g+xjU+z2gaaeQKRta25P7qbc3+2nHVPb6gZY94TuDJVHZb9ZNQJIkPRvS7Kpuwf4jKoPuvdoWcyEnW/0MqgGVdN5/5sedNOILJORjY8F/zh6Q1A7eTT+4LJG9er9oLJW/CiF25dyqrD0U5Gt5BYuGSw8DhLlyAkHQX8MzAJuCgi5rU7hrL0QS6LVl5RTYS2jZFnveuGNn1Jla06Ls/xHsvfZOGSwcwvZRjfCUSe94yWfCKy199qy0ncctbhmcuK/vxlnYwCDEeU4nNTqgQhaRLwf4EjgFXAHZKuioh72x1LnjOroj9c7dKqK6qJ0rZRr+67LNVxeY73WP8mteazGM8JxGgnIyNlJZL1Q9mJarTyMnz+Kvv5xA/vZnhEhhv5uSni+6ZsQ20cADwQEQ9FxJ+BHwBHFxxTpm4amuGYWdM479j9mNbbg4BpvT2cd+x+DX84J8okOnnOestQHZfneI/1b1Lr9xrPCUTWEBNZspLPWCcDK8vn75hZ03hulMufyvEt6vumbAliGvC7qter0rLSKcuHq12OmTWNW846nN/Meyu3nHV4U85cJkrbRp4z5TJ0cMhzvMf6Nxnt9+rtmTyuz8jIk5HenslMnqTN1hnt6nWs4xeV6fNXL7kV9X1TtgRRl6Q5kgYkDaxdu7awOMr04epUE2X613pnvWXp4JDneI/1bzLal/I5b99nnFFufjJy1+eOZP67Xpnr6nWsV7pl+vzVS25Ffd+Uqg0CGAR2rXq9S1q2UURcCFwI0N/fP0qzVOu5K2zjJkpvsZGdGsraiynP8R7r36QdHTrG0tNqLOuW6fNX7zgW9X2jGK3pvwCStgT+G3gjSWK4A/jriFietX5/f38MDAy0McJNRjZwQfLhakbdfDfplob+smh2L6ZO1ym/a7O/byQtjoj+uuuVKUEASHoL8BWSbq6XRMQXRlu3yAQBnfPhMrPO18zvm45NEGNRdIIwM+tEeRNExzVSm5lZezhBmJlZJicIMzPL5ARhZmaZnCDMzCxTR/dikrQWeLjoOEaxI/BY0UHU4Pga4/gaV/YYJ3J8L42IKfVW6ugEUWaSBvJ0IyuK42uM42tc2WN0fK5iMjOzUThBmJlZJieI1rmw6ADqcHyNcXyNK3uMXR+f2yDMzCyTryDMzCyTE0QDJB0laYWkBySdlbH8JElrJd2VPv62zfFdImmNpHtGWS5JX03jXypp/5LFd6ik9VXH7x/aHN+ukm6UdK+k5ZI+nrFOYccwZ3yFHUNJ20i6XdLdaXznZqyztaQr0uP3K0l97YpvDDEW+n+cxjBJ0hJJV2csa90xjAg/xvEgGY78QWB3YCvgbmDvEeucBHy9wBhfD+wP3DPK8rcA1wACDgR+VbL4DgWuLvD4TQX2T59vRzJXyci/cWHHMGd8hR3D9Jhsmz6fDPwKOHDEOh8GLkifHwdcUcIYC/0/TmM4E/h+1t+ylcfQVxDjdwDwQEQ8FBF/Bn4AHF1wTJuJiJuBJ2qscjRwaSRuA3olTW1PdLniK1RErI6IO9PnTwH38fw50gs7hjnjK0x6TP6YvpycPkY2eh4NfDd9/mPgjZJEm+SMsVCSdgHeClw0yiotO4ZOEOM3Dfhd1etVZP9zvjOtevixpF0zlhcp7+9QpNeml//XSBr/RMcNSi/bZ5GcYVYrxTGsER8UeAzTqpG7gDXAdREx6vGLiGeB9cBLShYjFPt//BXgk8Bzoyxv2TF0gmitnwF9EfEK4Do2ZXnL506SIQFeCXwNWFhEEJK2Ba4ETo+IJ4uIoZY68RV6DCNiOCJmkswvf4Ckfdu5/zxyxFjY/7GktwFrImJxu/ZZzQli/AaB6jOJXdKyjSLi8Yh4Jn15EfCqNsWWV93foUgR8WTl8j8ifgFMlrRjO2OQNJnky/eyiFiQsUqhx7BefGU4hum+1wE3AkeNWLTx+KVz0u8APN7e6BKjxVjw//FBwNslrSSpxj5c0r+OWKdlx9AJYvzuAPaUtJukrUgah66qXmFEXfTbSeqIy+Qq4P1pT5wDgfURsbrooCok/UWlLlXSASSf17Z9eaT7vhi4LyK+PMpqhR3DPPEVeQwlTZHUmz7vAY4Afj1itauAE9Pn7wJuiLS1tSwxFvl/HBFnR8QuEdFH8h1zQ0T8zYjVWnYMt2zGRrpRRDwr6TRgEUmPpksiYrmkzwMDEXEV8DFJbweeJWmMPamdMUq6nKQXy46SVgGfI2mEIyIuAH5B0gvnAeBp4AMli+9dwIckPQsMAce188uD5OztfcCytI4a4NPA9KoYizyGeeIr8hhOBb4raRJJYvphRFw94n/kYuB7kh4g+R85rk2xjSXGQv+Ps7TrGPpOajMzy+QqJjMzy+QEYWZmmZwgzMwskxOEmZllcoIwM7NMThBmZpbJCcIySRpOhzZeno7j8wlJW6TL+iV9tcZ7+yT9dfuifd7+PybpPkmXFRVDM0jqlfThTtlHOk7R7unzP9Zbv8Z2TpN0cjNissY4QdhohiJiZkTsQ3J36ZtJbmQjIgYi4mM13tsHFJYgSIY/PiIiTqguTIch6CS9JL9LqfaR3jW+xYiyfYBJEfFQE2K6BPhoE7ZjDXKCsLoiYg0wBzgt/XI4VOnEJZLeoE0TqSyRtB0wDzgkLTsjvaL4D0l3po/Xpe89VNIv0zPPX0u6rGpYiFdL+q/06uV2SdspGXVzvqQ7lIys+cGRsUq6gGSOjmvSfZ8j6XuSbiG523QbSd+WtCyN97D0fSdJWijpOkkr07PYM9N1bpP04ox9TZF0ZRrPHZIOkrRF+v7eqvXul7RT1vrp8nOUTJ70S0kPSaok33nAHulxnJ+x//enx+FuSd8bLaax7kPS3KpjfG5a1qdkcqxLgXvYfPwpgBOAn2bEuKOkWyW9Nf173yTpp2kM8ySdkP59l0naI/28PQ2sVDI0iBWpWRNL+DGxHsAfM8rWATtRNQkNyUiXB6XPtyUZvmXj8rT8BcA26fM9SYYIIF1vPckAd1sAtwIHk0zA9BDw6nS97dPtzgE+m5ZtDQwAu2XEuRLYMX1+DrAY6Elff4JkWBSAlwG/BbYhGT7hAZKJd6akcZ2arnc+yUipI/fzfeDg9Pl0kjGRAP4Z+ED6/DXAv9dZ/xzgv9LfaUeSsZImk1yJjTaZ0j4kEwRVfs8XN2MfwJHAhSQT6WwBXE0ysVMfyXDTB44Sz03AftWfH5LPyq9IruYqf+91JMNbbE0yyNy56bKPA1+pev9ngE8U/X/Q7Y9Ou+S28rkF+LKS+v4FEbFKz5+rZDLwdUkzgWFgr6plt0fEKgAl4wn1kXw5r46IOyAZkTRdfiTwCknvSt+7A0nC+U2dGK+KiKH0+cEkw14TEb+W9HBVPDdGMvHOU5LWkyQ/gGXAKzK2+yZg76rfd3slQ29fAfwD8G3SGb7qrA/w80hGDH1G0hqSL9daDgd+FBGPpb9LZeKlRvdxZPpYkr7eluQY/xZ4OJJJkbJMBdZWvZ4MXA98JCJuqiq/I9LBDCU9CFybli8DDqtabw1JArcCOUFYLkoaH4dJ/nFfXimPiHmSfk4yYN0tkmZnvP0M4FHglSRnpf+vatkzVc+Hqf2ZFPDRiFg0xvD/lHO96lieq3r93ChxbUFyRl39+yDpVuAvJU0BjgH+d531R+673nGopdF9CDgvIv5lxPv7qH0ch0iuxCqeJblym01ydVGR9xhvk27TCuQ2CKsr/aK7gGRe3hixbI+IWBYR/4dkCPSXAU+RVNVU7EByRfAcyeijk+rscgUwVdKr031sp6SBeRHJyKST0/K9JL1wjL/Of5DUlyNpL5JqmBVj3EbFtVQ1pqZXSKTH6CfAl0mqeB6vtX4NI49jtRuAd0t6SbqtShtJo/tYBJxcueqQNE13GrNBAAABJUlEQVTS/6izDUiGwP7LqtcBnAy8TNKncrx/pL1I2jqsQE4QNpqetOFyOfDvJF8852asd7qkeyQtBTYA1wBLgeG08fQM4BvAiZLuJkkgNc/oI5nj+73A19L3XEdyRnkRcC9wp6R7gH9h7Gfa3wC2kLSMpOrnpNg0GcxYfQzoTxtz7wVOrVp2BfA3bKpeqrf+86SJ5Zb0+M4fsWw58AXgpvQYVeaDaGgfEXEtSTvGrekx+jGjJ6lqPydpY6je9jBwPMkkN2PtjXUQyd/dCuThvs2sYUom27mRpMPCcIPbmgWcGRHva0pwNm5OEGbWFGn7030R8dsGt3MEcH9ErGxKYDZuThBmZpbJbRBmZpbJCcLMzDI5QZiZWSYnCDMzy+QEYWZmmf4/JMoqW9sf6NgAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -486,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -511,7 +511,30 @@ { "cell_type": "markdown", "metadata": {}, - "source": [] + "source": [ + "**** \\*\\*\\*\\* Just need to do some T-Tests as in week 7 \\*\\*\\*\\* ****" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T score: 9.767587, P score: 0.000000\n" + ] + } + ], + "source": [ + "cust_increase = cust_data.loc[cust_data[\"Decrease on Day 62\"] >= cust_change_mean][\"Decrease on Day 62\"]\n", + "cust_decrease = cust_data.loc[cust_data[\"Decrease on Day 62\"] < cust_change_mean][\"Decrease on Day 62\"]\n", + "t, p = stats.ttest_ind(cust_increase, cust_decrease)\n", + "p = p/2 # Because 1 tailed T-Test\n", + "print(\"T score: %f, P score: %f\" % (t, p))" + ] }, { "cell_type": "markdown",