diff --git a/20-concurrency/primes/stats-procs.ipynb b/20-concurrency/primes/stats-procs.ipynb
index ce8cae7..96d8f13 100644
--- a/20-concurrency/primes/stats-procs.ipynb
+++ b/20-concurrency/primes/stats-procs.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +36,7 @@
        "  10.94])"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
        "10.39"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -344,13 +344,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
     "\n",
     "df=pd.DataFrame(\n",
     "    {'procs': range(1,21),\n",
@@ -359,26 +361,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 40,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "Text(0.5, 0, 'processes')"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x360 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -388,10 +380,24 @@
     }
    ],
    "source": [
+    "\n",
+    "fig = plt.figure(figsize = [10,5])\n",
+    "\n",
     "plt.plot('procs', 'median', data=df, marker='+')\n",
+    "plt.axhline(10.39, color = 'red')\n",
+    "\n",
+    "\n",
+    "ax = plt.gca()\n",
+    "ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
+    "\n",
+    "plt.xlabel('processes')\n",
     "plt.ylim([0, 50])\n",
     "plt.ylabel('time (s)')\n",
-    "plt.xlabel('processes')\n"
+    "\n",
+    "plt.savefig('figure.png', dpi = 300)\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -405,6 +411,21 @@
     "3. Como exibir uma linha horizontal na altura do valor mínimo, que é 10.39?\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "para resolver o problema dos ticks usei o\n",
+    "\n",
+    "`ax = plt.gca()`\n",
+    "`ax.xaxis.set_major_locator(mticker.MultipleLocator(1))` \n",
+    "o MultipleLocator define quais os passos que eu quero dar pela lista, no caso de 1 em 1. A única coisa necessária aqui é importar o mticker do matplotlib.\n",
+    "\n",
+    "Para o tamanho da figura `fig = plt.figure(figsize = [10,5])` funciona bem, mas existem métodos mais robustos. \n",
+    "\n",
+    "para a linha horizontal usei o `plt.axhline(10.39, color = 'red')`"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -429,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,