diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index dbe4405..86176b5 100755
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,12 +11,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Libraries\n",
-    "import numpy as np"
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n"
    ]
   },
   {
@@ -30,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,27 +58,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Method 1"
+    "plt.figure(figsize=(20,8))\n",
+    "plt.plot(x,y)\n",
+    "#plt.ylablel(\"Height cm\", size = 20, rotation=10)\n",
+    "#plt.xlabel(\"Age (years)\", size= 20)\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(10,5))\n",
+    "a = plt.plot(x,y)\n",
+    "b = plt.plot(x,z)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "\n",
     "#### Use plt.subplots(nrows=1, ncols=2) to create the plot."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 190,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Method 2"
+    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
+    "\n",
+    "axes[0].plot(x, y)\n",
+    "axes[1].plot(x, z)\n",
+    "plt.show"
    ]
   },
   {
@@ -88,11 +166,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "tuple indices must be integers or slices, not tuple",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[44], line 7\u001b[0m\n\u001b[1;32m      2\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m, figsize \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(x,y)\n\u001b[0;32m----> 7\u001b[0m \u001b[43max\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mplot(x, y, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mred\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      8\u001b[0m ax[\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mplot(x, z, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: tuple indices must be integers or slices, not tuple"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.title(\"what are subplots for?\", size = 20)\n",
+    "ax = plt.subplots(1,2, figsize = (10,5))\n",
+    "plt.plot(x,y)\n",
+    "\n",
+    "\n",
+    "ax[0, 0].plot(x, y, color='red')\n",
+    "ax[0, 1].plot(x, z, color='blue')\n",
+    "#ax[1, 0].plot(x, y, color='green')\n",
+    "#ax[1, 1].plot(x, y, color='purple')\n",
+    "\n",
+    "\n",
+    "#plt.title(\"Very Important Chart\", size= 20)\n",
+    "#plt.ylabel(\"Attention span\", size = 15, rotation=30)\n",
+    "#plt.xlabel(\"Number of sql mentions\", size=15)\n"
    ]
   },
   {
@@ -109,7 +232,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "plt.subplots(figsize=(10,5))\n",
+    "a = plt.plot(x,y)\n",
+    "b = plt.plot(x,z)\n",
+    "\n",
+    "\n",
+    "##### ???????"
    ]
   },
   {
@@ -121,11 +249,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x11cde3b80>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.subplots(figsize=(10,5))\n",
+    "a = plt.plot(x,y, label=\"x and y\")\n",
+    "b = plt.plot(x,z, label=\"x and z\")\n",
+    "plt.legend()      "
    ]
   },
   {
@@ -140,11 +294,246 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>Date</th>\n",
+       "      <th>Calorie burned</th>\n",
+       "      <th>Steps</th>\n",
+       "      <th>Distance</th>\n",
+       "      <th>Floors</th>\n",
+       "      <th>Minutes Sedentary</th>\n",
+       "      <th>Minutes Lightly Active</th>\n",
+       "      <th>Minutes Fairly Active</th>\n",
+       "      <th>Minutes Very Active</th>\n",
+       "      <th>Activity Calories</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Distance_miles</th>\n",
+       "      <th>Days</th>\n",
+       "      <th>Days_encoded</th>\n",
+       "      <th>Work_or_Weekend</th>\n",
+       "      <th>Hours Sleep</th>\n",
+       "      <th>Sleep efficiency</th>\n",
+       "      <th>Yesterday_sleep</th>\n",
+       "      <th>Yesterday_sleep_efficiency</th>\n",
+       "      <th>Months</th>\n",
+       "      <th>Months_encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-05-08</td>\n",
+       "      <td>1934</td>\n",
+       "      <td>905</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.355</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.403891</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-05-09</td>\n",
+       "      <td>3631</td>\n",
+       "      <td>18925</td>\n",
+       "      <td>14.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>611.000</td>\n",
+       "      <td>316</td>\n",
+       "      <td>61</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.767545</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-05-10</td>\n",
+       "      <td>3204</td>\n",
+       "      <td>14228</td>\n",
+       "      <td>10.57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>602.000</td>\n",
+       "      <td>226</td>\n",
+       "      <td>14</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.567891</td>\n",
+       "      <td>Sunday</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-05-11</td>\n",
+       "      <td>2673</td>\n",
+       "      <td>6756</td>\n",
+       "      <td>5.02</td>\n",
+       "      <td>8</td>\n",
+       "      <td>749.000</td>\n",
+       "      <td>190</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.119282</td>\n",
+       "      <td>Monday</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-05-12</td>\n",
+       "      <td>2495</td>\n",
+       "      <td>502</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>876.000</td>\n",
+       "      <td>171</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.317714</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.783333</td>\n",
+       "      <td>82.892057</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date  Calorie burned  Steps  Distance  Floors  Minutes Sedentary  \\\n",
+       "0  2015-05-08            1934    905      0.65       0              1.355   \n",
+       "1  2015-05-09            3631  18925     14.11       4            611.000   \n",
+       "2  2015-05-10            3204  14228     10.57       1            602.000   \n",
+       "3  2015-05-11            2673   6756      5.02       8            749.000   \n",
+       "4  2015-05-12            2495    502      3.73       1            876.000   \n",
+       "\n",
+       "   Minutes Lightly Active  Minutes Fairly Active  Minutes Very Active  \\\n",
+       "0                      46                      0                    0   \n",
+       "1                     316                     61                   60   \n",
+       "2                     226                     14                   77   \n",
+       "3                     190                     23                    4   \n",
+       "4                     171                      0                    0   \n",
+       "\n",
+       "   Activity Calories  ...  Distance_miles      Days  Days_encoded  \\\n",
+       "0               1680  ...        0.403891    Friday           4.0   \n",
+       "1               2248  ...        8.767545  Saturday           5.0   \n",
+       "2               1719  ...        6.567891    Sunday           6.0   \n",
+       "3               9620  ...        3.119282    Monday           0.0   \n",
+       "4               7360  ...        2.317714   Tuesday           1.0   \n",
+       "\n",
+       "   Work_or_Weekend  Hours Sleep Sleep efficiency  Yesterday_sleep  \\\n",
+       "0                1     6.400000        92.086331         0.000000   \n",
+       "1                0     7.566667        92.464358         6.400000   \n",
+       "2                0     6.450000        88.761468         7.566667   \n",
+       "3                1     5.183333        88.857143         6.450000   \n",
+       "4                1     6.783333        82.892057         5.183333   \n",
+       "\n",
+       "   Yesterday_sleep_efficiency  Months  Months_encoded  \n",
+       "0                    0.000000     May               5  \n",
+       "1                   92.086331     May               5  \n",
+       "2                   92.464358     May               5  \n",
+       "3                   88.761468     May               5  \n",
+       "4                   88.857143     May               5  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "fitbit = pd.read_csv(\"Fitbit2.csv\")\n",
+    "fitbit.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(367, 24)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitbit.shape"
    ]
   },
   {
@@ -167,15 +556,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date                           object\n",
+       "Calorie burned                  int64\n",
+       "Steps                           int64\n",
+       "Distance                      float64\n",
+       "Floors                          int64\n",
+       "Minutes Sedentary             float64\n",
+       "Minutes Lightly Active          int64\n",
+       "Minutes Fairly Active           int64\n",
+       "Minutes Very Active             int64\n",
+       "Activity Calories               int64\n",
+       "MinutesOfSleep                  int64\n",
+       "MinutesOfBeingAwake             int64\n",
+       "NumberOfAwakings                int64\n",
+       "LengthOfRestInMinutes           int64\n",
+       "Distance_miles                float64\n",
+       "Days                           object\n",
+       "Days_encoded                  float64\n",
+       "Work_or_Weekend                 int64\n",
+       "Hours Sleep                   float64\n",
+       "Sleep efficiency              float64\n",
+       "Yesterday_sleep               float64\n",
+       "Yesterday_sleep_efficiency    float64\n",
+       "Months                         object\n",
+       "Months_encoded                  int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitbit.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date                          datetime64[ns]\n",
+       "Calorie burned                         int64\n",
+       "Steps                                  int64\n",
+       "Distance                             float64\n",
+       "Floors                                 int64\n",
+       "Minutes Sedentary                    float64\n",
+       "Minutes Lightly Active                 int64\n",
+       "Minutes Fairly Active                  int64\n",
+       "Minutes Very Active                    int64\n",
+       "Activity Calories                      int64\n",
+       "MinutesOfSleep                         int64\n",
+       "MinutesOfBeingAwake                    int64\n",
+       "NumberOfAwakings                       int64\n",
+       "LengthOfRestInMinutes                  int64\n",
+       "Distance_miles                       float64\n",
+       "Days                                  object\n",
+       "Days_encoded                         float64\n",
+       "Work_or_Weekend                        int64\n",
+       "Hours Sleep                          float64\n",
+       "Sleep efficiency                     float64\n",
+       "Yesterday_sleep                      float64\n",
+       "Yesterday_sleep_efficiency           float64\n",
+       "Months                                object\n",
+       "Months_encoded                         int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitbit[\"Date\"] = fitbit[\"Date\"].astype('datetime64[ns]')\n",
+    "fitbit.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Date\n",
+       "April        13079.033333\n",
+       "August       11584.774194\n",
+       "December      8470.064516\n",
+       "February      8922.866667\n",
+       "January       9892.064516\n",
+       "July          9193.322581\n",
+       "June         12536.166667\n",
+       "March        10312.935484\n",
+       "May          10620.096774\n",
+       "November      9467.466667\n",
+       "October       8272.451613\n",
+       "September     9191.333333\n",
+       "Name: Steps, dtype: float64"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# MEDIAN STEPS BY MONTH_ENCODED\n",
-    "# your code here\n",
+    "mean_steps_month = fitbit.groupby(fitbit['Date'].dt.strftime('%B'))['Steps'].mean()\n",
+    "mean_steps_month\n",
     "\n",
-    "# MEDIAN STEPS BY WORK_OR_WEEKEND\n",
-    "# your code here"
+    "\n",
+    "\n",
+    "# MEDIAN STEPS BY WORK_OR_WEEKEND        #### not working\n",
+    "\n",
+    "#fitbit[\"DayOfWeek\"] = fitbit[\"Date\"].dt.weekday\n",
+    "#fitbit[\"Weekend\"] = fitbit[\"DayOfWeek\"] >= 5\n",
+    "\n",
+    "#fitbit.groupby([\"Weekend\"]).agg({fitbit[\"Steps\"]})\n"
    ]
   },
   {
@@ -191,11 +700,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 93,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "activity = [\"Minutes Lightly Active\", \"Minutes Very Active\", \"Minutes Sedentary\"]\n",
+    "\n",
+    "for x in activity:\n",
+    "    plt.plot(fitbit[x], fitbit[\"Steps\"], \"ro\")\n",
+    "    plt.ylabel(\"Steps\", size = 15, rotation=30)\n",
+    "    plt.xlabel(x, size=15)\n",
+    "    plt.show()\n",
+    "\n",
+    "# plt.plot(fitbit[\"Minutes Lightly Active\"], fitbit[\"Steps\"])"
    ]
   },
   {
@@ -211,13 +759,157 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1       0.0       3   \n",
+       "1            2       1.0       1   \n",
+       "2            3       1.0       3   \n",
+       "3            4       1.0       1   \n",
+       "4            5       0.0       3   \n",
+       "\n",
+       "                                                Name  Gender   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500    U0        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250    U0        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500    U0        S  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "titanic = pd.read_csv(\"titanic.csv\")\n",
+    "titanic.head()"
    ]
   },
   {
@@ -229,11 +921,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      int64\n",
+       "Survived       float64\n",
+       "Pclass           int64\n",
+       "Name            object\n",
+       "Gender          object\n",
+       "Age            float64\n",
+       "SibSp            int64\n",
+       "Parch            int64\n",
+       "Ticket          object\n",
+       "Fare           float64\n",
+       "Cabin           object\n",
+       "Embarked        object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "titanic.dtypes"
    ]
   },
   {
@@ -246,22 +961,349 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>1305</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>29.513190</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>1306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>108.9000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>38.500000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>1308</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>29.513190</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>1309</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>25.315435</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22.3583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      PassengerId  Survived  Pclass        Age  SibSp  Parch      Fare\n",
+       "0               1       0.0       3  22.000000      1      0    7.2500\n",
+       "1               2       1.0       1  38.000000      1      0   71.2833\n",
+       "2               3       1.0       3  26.000000      0      0    7.9250\n",
+       "3               4       1.0       1  35.000000      1      0   53.1000\n",
+       "4               5       0.0       3  35.000000      0      0    8.0500\n",
+       "...           ...       ...     ...        ...    ...    ...       ...\n",
+       "1304         1305       NaN       3  29.513190      0      0    8.0500\n",
+       "1305         1306       NaN       1  39.000000      0      0  108.9000\n",
+       "1306         1307       NaN       3  38.500000      0      0    7.2500\n",
+       "1307         1308       NaN       3  29.513190      0      0    8.0500\n",
+       "1308         1309       NaN       3  25.315435      1      1   22.3583\n",
+       "\n",
+       "[1309 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# NUMERICAL VARIABLES\n",
-    "# your code here"
+    "\n",
+    "titanic.select_dtypes(include=\"number\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>Name</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>Spector, Mr. Woolf</td>\n",
+       "      <td>male</td>\n",
+       "      <td>A.5. 3236</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>PC 17758</td>\n",
+       "      <td>C105</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
+       "      <td>male</td>\n",
+       "      <td>SOTON/O.Q. 3101262</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>Ware, Mr. Frederick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>359309</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Peter, Master. Michael J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2668</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   Name  Gender  \\\n",
+       "0                               Braund, Mr. Owen Harris    male   \n",
+       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
+       "2                                Heikkinen, Miss. Laina  female   \n",
+       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
+       "4                              Allen, Mr. William Henry    male   \n",
+       "...                                                 ...     ...   \n",
+       "1304                                 Spector, Mr. Woolf    male   \n",
+       "1305                       Oliva y Ocana, Dona. Fermina  female   \n",
+       "1306                       Saether, Mr. Simon Sivertsen    male   \n",
+       "1307                                Ware, Mr. Frederick    male   \n",
+       "1308                           Peter, Master. Michael J    male   \n",
+       "\n",
+       "                  Ticket Cabin Embarked  \n",
+       "0              A/5 21171    U0        S  \n",
+       "1               PC 17599   C85        C  \n",
+       "2       STON/O2. 3101282    U0        S  \n",
+       "3                 113803  C123        S  \n",
+       "4                 373450    U0        S  \n",
+       "...                  ...   ...      ...  \n",
+       "1304           A.5. 3236    U0        S  \n",
+       "1305            PC 17758  C105        C  \n",
+       "1306  SOTON/O.Q. 3101262    U0        S  \n",
+       "1307              359309    U0        S  \n",
+       "1308                2668    U0        C  \n",
+       "\n",
+       "[1309 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# CATEGORICAL VARIABLES\n",
-    "# your code here"
+    "\n",
+    "titanic.select_dtypes(exclude=\"number\")"
    ]
   },
   {
@@ -274,11 +1316,198 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x600 with 0 Axes>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1200x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "\n",
+    "##### HELP!\n",
+    "\n",
+    "\n",
+    "plt.plot(\"titanic\")\n",
+    "plt.figure(figsize= (12,6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1       0.0       3   \n",
+       "1            2       1.0       1   \n",
+       "2            3       1.0       3   \n",
+       "3            4       1.0       1   \n",
+       "4            5       0.0       3   \n",
+       "\n",
+       "                                                Name  Gender   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500    U0        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250    U0        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500    U0        S  "
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.head()\n"
    ]
   },
   {
@@ -290,11 +1519,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1661959f0>]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "\n",
+    "plt.plot(titanic[\"PassengerId\"], titanic[\"Age\"], \"bp\")"
    ]
   },
   {
@@ -306,13 +1557,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 126,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.figure(figsize = (20,8))\n",
+    "plt.hist(titanic[\"Age\"], 10)\n",
+    "plt.hist(titanic[\"Age\"], 20)\n",
+    "plt.hist(titanic[\"Age\"], 50)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "##### Do not think i get it"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'label' must be of length 'x'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[121], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtitanic\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAge\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m21\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/matplotlib/pyplot.py:2772\u001b[0m, in \u001b[0;36mpie\u001b[0;34m(x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch, data)\u001b[0m\n\u001b[1;32m   2765\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mpie)\n\u001b[1;32m   2766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpie\u001b[39m(\n\u001b[1;32m   2767\u001b[0m         x, explode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, colors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, autopct\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2770\u001b[0m         textprops\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, center\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m), frame\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m   2771\u001b[0m         rotatelabels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, hatch\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 2772\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpie\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2773\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexplode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexplode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2774\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautopct\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautopct\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpctdistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpctdistance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshadow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshadow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2775\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabeldistance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabeldistance\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstartangle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstartangle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2776\u001b[0m \u001b[43m        \u001b[49m\u001b[43mradius\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mradius\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcounterclock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcounterclock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2777\u001b[0m \u001b[43m        \u001b[49m\u001b[43mwedgeprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwedgeprops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtextprops\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtextprops\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcenter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcenter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2778\u001b[0m \u001b[43m        \u001b[49m\u001b[43mframe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrotatelabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrotatelabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnormalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2779\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1444\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1445\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1446\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/matplotlib/axes/_axes.py:3209\u001b[0m, in \u001b[0;36mAxes.pie\u001b[0;34m(self, x, explode, labels, colors, autopct, pctdistance, shadow, labeldistance, startangle, radius, counterclock, wedgeprops, textprops, center, frame, rotatelabels, normalize, hatch)\u001b[0m\n\u001b[1;32m   3207\u001b[0m     explode \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(x)\n\u001b[1;32m   3208\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(x) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(labels):\n\u001b[0;32m-> 3209\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(x) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(explode):\n\u001b[1;32m   3211\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mexplode\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be of length \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mValueError\u001b[0m: 'label' must be of length 'x'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -357,12 +1661,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2813061a0>]"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Method 1 - matplotlib\n",
-    "# your code here"
+    "\n",
+    "male = titanic.groupby(\"Gender\").agg({\"Gender\":\"count\"})\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "plt.ylim(0,900)\n",
+    "plt.plot(titanic[\"Gender\"].value_counts(), \"bo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "male      843\n",
+       "female    466\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic[\"Gender\"].value_counts()"
    ]
   },
   {
@@ -384,13 +1736,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 156,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3, 1, 2])"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "titanic[\"Pclass\"].unique()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'numpy.ndarray'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[165], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m classes \u001b[38;5;241m=\u001b[39m titanic\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39magg({titanic[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPclass\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39munique()})\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
+     ]
+    }
+   ],
+   "source": [
+    "classes = titanic.groupby(\"Pclass\").agg({titanic[\"Pclass\"].unique()})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axis.XTick at 0x283305b40>,\n",
+       "  <matplotlib.axis.XTick at 0x283305b10>,\n",
+       "  <matplotlib.axis.XTick at 0x283305a20>],\n",
+       " [Text(1, 0, 'First Class'),\n",
+       "  Text(2, 0, 'Second Class'),\n",
+       "  Text(4, 0, 'Third Class')])"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "classes = titanic.groupby(\"Pclass\").agg({\"Pclass\": \"sum\"})\n",
+    "\n",
+    "plt.plot(classes)\n",
+    "plt.xlim(0,2.5)\n",
+    "plt.xticks([1,2,4],[\"First Class\", \"Second Class\", \"Third Class\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -400,11 +1830,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "stats = titanic[\"Age\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2835ac520>]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(stats)\n"
    ]
   },
   {
@@ -414,7 +1874,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "it should not include the count with the other values.\n",
+    "it would be nice to have a bar chart\n",
     "\"\"\""
    ]
   },
@@ -449,7 +1910,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "## an histogram??\n"
    ]
   },
   {
@@ -571,7 +2032,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -585,7 +2046,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,