diff --git a/ Runtime-and-CoreCount-ReqMemCPU.ipynb b/ Runtime-and-CoreCount-ReqMemCPU.ipynb
deleted file mode 100644
index 2060d1efa2e997d8aa8bb3431950dd5ad4d02a90..0000000000000000000000000000000000000000
--- a/ Runtime-and-CoreCount-ReqMemCPU.ipynb	
+++ /dev/null
@@ -1,329 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Notebook Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "import sqlite3\n",
-    "import slurm2sql\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "import seaborn as sns\n",
-    "import seaborn as sb\n",
-    "import plotly.express as px\n",
-    "import matplotlib.ticker as ticker\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from RC_styles import rc_styles as style"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import KMeans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "# creates database of info from March 2020 using sqlite 3\n",
-    "db = sqlite3.connect('/data/rc/rc-team/slurm-since-March.sqlite3')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "# df is starting database\n",
-    "df = pd.read_sql('SELECT * FROM slurm', db)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# voluntary\n",
-    "\n",
-    "# for displaying all available column options\n",
-    "pd.set_option('display.max_columns', None)\n",
-    "df.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "# converts units in ReqMemCPU column from bytes to gigs\n",
-    "df['ReqMemCPU'] = df['ReqMemCPU'].div(1024**3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "# converts Elapsed time to hours (from seconds)\n",
-    "df['Elapsed'] = df['Elapsed'].div(3600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# must run\n",
-    "\n",
-    "# df_completed is dataframe of all completed jobs\n",
-    "df_completed = df[df.State.str.contains('COMPLETED')]\n",
-    "#df_completed.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ReqMemCPU,Corecount,Runtime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "UpperlimitGB = 50"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_1 = df_completed.loc[:,['ReqMemCPU', 'Elapsed', 'AllocCPUS']]\n",
-    "df_1.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_1['ReqMemCPU'] = df_1['ReqMemCPU'].apply(np.ceil)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_1['Elapsed'] = df_1['Elapsed'].apply(np.ceil)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_1_sorted = df_1.sort_values(by='AllocCPUS', ascending=True)\n",
-    "df_1_sorted.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_runtime = df_1_sorted[(df_1_sorted['ReqMemCPU'] <= UpperlimitGB)]\n",
-    "df_runtime.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "style.default_axes_and_ticks()\n",
-    "style.figsize()\n",
-    "\n",
-    "runtime_graph = sns.scatterplot(x=\"ReqMemCPU\", y=\"AllocCPUS\",data=df_runtime)\n",
-    "                              #hue=\"AllocCPUS\")\n",
-    "                              #, size=\"AllocCPUS\")\n",
-    "\n",
-    "#plt.title('Average Requested RAM per CPU by User for all Users Running %i Jobs or less'%UpperlimitJobCount)\n",
-    "\n",
-    "plt.xlabel('ReqMemCPU')\n",
-    "plt.ylabel('AllocCPUS')\n",
-    "#plt.yscale(\"log\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "style.default_axes_and_ticks()\n",
-    "style.figsize()\n",
-    "\n",
-    "g = sns.PairGrid(df_runtime, y_vars=[\"Elapsed\"], x_vars=[\"ReqMemCPU\", \"AllocCPUS\"], height=4)\n",
-    "g.map(sns.regplot, color=\"blue\")\n",
-    "#g.set(ylim=(-1, 11), yticks=[0, 5, 10]);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "style.default_axes_and_ticks()\n",
-    "style.figsize()\n",
-    "\n",
-    "\n",
-    "g = sb.PairGrid(df_runtime)\n",
-    "g.map(plt.scatter);\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_runtime_cluster = df_1_sorted[(df_1_sorted['ReqMemCPU'] <= UpperlimitGB)]\n",
-    "#df_runtime_graph_cluster.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Sum_of_squared_distances = []\n",
-    "K = range(1,10)\n",
-    "for k in K:\n",
-    "    km = KMeans(n_clusters=k)\n",
-    "    km = km.fit(df_runtime_cluster)\n",
-    "    Sum_of_squared_distances.append(km.inertia_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.plot(K, Sum_of_squared_distances, 'bx-')\n",
-    "plt.xlabel('k')\n",
-    "plt.ylabel('Sum_of_squared_distances')\n",
-    "plt.title('Elbow Method For Optimal k')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans = KMeans(n_clusters=3, random_state=111)\n",
-    "kmeans.fit(df_runtime_cluster)\n",
-    "print(kmeans.cluster_centers_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "runtime_cluster_graph = plt.scatter(df_runtime_cluster['ReqMemCPU'],df_runtime_cluster['Elapsed'], c=kmeans.labels_, cmap='rainbow')\n",
-    "plt.scatter(kmeans.cluster_centers_[:,0] ,kmeans.cluster_centers_[:,1], color='grey')\n",
-    "#plt.yscale(\"log\")\n",
-    "plt.xlabel('ReqMemCPU')\n",
-    "plt.ylabel('Runtime')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python",
-   "pygments_lexer": "ipython3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}