{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lab explores the dog licensing data set:  [https://data.cityofnewyork.us/Health/NYC-Dog-Licensing-Dataset/nu7n-tubp/data](https://data.cityofnewyork.us/Health/NYC-Dog-Licensing-Dataset/nu7n-tubp/data)\n",
    "\n",
    "Filter this data set to only contain entries with the license expiring after today (Aug. 29) before downloading it as a CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the CSV file into a dataframe called `dogs`, and display the dataframe to make sure it was loaded correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>RowNumber</th>\n",
       "      <th>AnimalName</th>\n",
       "      <th>AnimalGender</th>\n",
       "      <th>AnimalBirthMonth</th>\n",
       "      <th>BreedName</th>\n",
       "      <th>Borough</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>LicenseIssuedDate</th>\n",
       "      <th>LicenseExpiredDate</th>\n",
       "      <th>Extract Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15470</td>\n",
       "      <td>FOTCH</td>\n",
       "      <td>M</td>\n",
       "      <td>2007</td>\n",
       "      <td>Labrador Retriever Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10464</td>\n",
       "      <td>05/31/2015</td>\n",
       "      <td>08/30/2019</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55224</td>\n",
       "      <td>SANGRIA</td>\n",
       "      <td>F</td>\n",
       "      <td>2010</td>\n",
       "      <td>Italian Greyhound</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10037</td>\n",
       "      <td>04/10/2016</td>\n",
       "      <td>08/30/2019</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75708</td>\n",
       "      <td>CARLUSHA</td>\n",
       "      <td>M</td>\n",
       "      <td>2003</td>\n",
       "      <td>Shih Tzu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11235</td>\n",
       "      <td>07/05/2016</td>\n",
       "      <td>08/30/2019</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76211</td>\n",
       "      <td>RUFUS</td>\n",
       "      <td>M</td>\n",
       "      <td>2002</td>\n",
       "      <td>Cocker Spaniel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10024</td>\n",
       "      <td>07/07/2016</td>\n",
       "      <td>08/30/2019</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89066</td>\n",
       "      <td>CHEWIE</td>\n",
       "      <td>M</td>\n",
       "      <td>2009</td>\n",
       "      <td>Golden Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10025</td>\n",
       "      <td>08/30/2016</td>\n",
       "      <td>08/30/2019</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber AnimalName AnimalGender  AnimalBirthMonth  \\\n",
       "0      15470      FOTCH            M              2007   \n",
       "1      55224    SANGRIA            F              2010   \n",
       "2      75708   CARLUSHA            M              2003   \n",
       "3      76211      RUFUS            M              2002   \n",
       "4      89066     CHEWIE            M              2009   \n",
       "\n",
       "                       BreedName  Borough  ZipCode LicenseIssuedDate  \\\n",
       "0  Labrador Retriever Crossbreed      NaN    10464        05/31/2015   \n",
       "1              Italian Greyhound      NaN    10037        04/10/2016   \n",
       "2                       Shih Tzu      NaN    11235        07/05/2016   \n",
       "3                 Cocker Spaniel      NaN    10024        07/07/2016   \n",
       "4               Golden Retriever      NaN    10025        08/30/2016   \n",
       "\n",
       "  LicenseExpiredDate  Extract Year  \n",
       "0         08/30/2019          2016  \n",
       "1         08/30/2019          2016  \n",
       "2         08/30/2019          2016  \n",
       "3         08/30/2019          2016  \n",
       "4         08/30/2019          2016  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs = pd.read_csv(\"NYC_Dog_Licensing_Dataset.csv\")\n",
    "dogs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two of the columns are dates, so make them into `datetime` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"LicenseIssuedDate\"] = pd.to_datetime(dogs[\"LicenseIssuedDate\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"LicenseExpiredDate\"] = pd.to_datetime(dogs[\"LicenseExpiredDate\"])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get an over-view of the dataset by calling the `describe()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>RowNumber</th>\n",
       "      <th>AnimalBirthMonth</th>\n",
       "      <th>Borough</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>Extract Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56120.000000</td>\n",
       "      <td>56120.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56120.000000</td>\n",
       "      <td>56120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>69507.152994</td>\n",
       "      <td>2012.165752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10655.356700</td>\n",
       "      <td>2017.634979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>40045.130293</td>\n",
       "      <td>20.427198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>893.995844</td>\n",
       "      <td>0.636891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6403.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31748.250000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10026.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>86828.500000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10462.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>104383.250000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11223.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>117371.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98363.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           RowNumber  AnimalBirthMonth  Borough       ZipCode  Extract Year\n",
       "count   56120.000000      56120.000000      0.0  56120.000000  56120.000000\n",
       "mean    69507.152994       2012.165752      NaN  10655.356700   2017.634979\n",
       "std     40045.130293         20.427198      NaN    893.995844      0.636891\n",
       "min         1.000000          1.000000      NaN   6403.000000   2016.000000\n",
       "25%     31748.250000       2010.000000      NaN  10026.000000   2017.000000\n",
       "50%     86828.500000       2013.000000      NaN  10462.000000   2018.000000\n",
       "75%    104383.250000       2015.000000      NaN  11223.000000   2018.000000\n",
       "max    117371.000000       2018.000000      NaN  98363.000000   2018.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.  Are the summary statistics (mean, standard deviation, min, etc.) meaningful for all of these columns?\n",
    "2.  Are we missing any data?\n",
    "\n",
    "Answers:\n",
    "1. No.  The only column they might be meaningful for is `Extract Year`.\n",
    "2. Yes, notice we have no borough information, despite it being a column.  Also, some of the columns are missing!\n",
    "\n",
    "To display columns with non-numeric data, we need to use the following command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>AnimalName</th>\n",
       "      <th>AnimalGender</th>\n",
       "      <th>BreedName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56047</td>\n",
       "      <td>56119</td>\n",
       "      <td>56120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8977</td>\n",
       "      <td>2</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>BELLA</td>\n",
       "      <td>M</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>636</td>\n",
       "      <td>30256</td>\n",
       "      <td>5833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AnimalName AnimalGender BreedName\n",
       "count       56047        56119     56120\n",
       "unique       8977            2       305\n",
       "top         BELLA            M   Unknown\n",
       "freq          636        30256      5833"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.describe(include = [\"O\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the most common dog name?\n",
    "\n",
    "To visualize the distribution of a qualitative or categorica column, we will make a bar chart (as in MAT 128).  Let's do this for the `AnimalGender` column.\n",
    "\n",
    "First get the value counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    30256\n",
       "F    25863\n",
       "Name: AnimalGender, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_counts = dogs['AnimalGender'].value_counts()\n",
    "gender_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there roughly 50% male and female dogs?\n",
    "\n",
    "Next we plot the counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6966ed4710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_counts.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a title and axis labels.  Remember to repeat your original plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# of dogs')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_counts.plot.bar()\n",
    "plt.title(\"Dog gender distribution\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylabel(\"# of dogs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the distribution of another categorial column, `BreedName`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6968962ba8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "breed_counts = dogs[\"BreedName\"].value_counts()\n",
    "breed_counts.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f696814ffd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dogs[\"BreedName\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this plot as readable as the gender one?\n",
    "\n",
    "We can fix this by just plotting the top 10 breeds using filtering.  First, look at the counts to see what the cut-off should be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown                                 5833\n",
       "Yorkshire Terrier                       2917\n",
       "Shih Tzu                                2802\n",
       "Chihuahua                               2337\n",
       "Labrador Retriever                      2025\n",
       "Maltese                                 1675\n",
       "Labrador Retriever Crossbreed           1563\n",
       "American Pit Bull Mix / Pit Bull Mix    1379\n",
       "Havanese                                1067\n",
       "American Pit Bull Terrier/Pit Bull      1021\n",
       "Pomeranian                               959\n",
       "Beagle                                   947\n",
       "Golden Retriever                         867\n",
       "Chihuahua Crossbreed                     848\n",
       "German Shepherd Dog                      821\n",
       "Shih Tzu Crossbreed                      816\n",
       "Jack Russell Terrier                     794\n",
       "French Bulldog                           766\n",
       "Terrier mix                              739\n",
       "Cavalier King Charles Spaniel            728\n",
       "Shiba Inu                                722\n",
       "Poodle Crossbreed                        666\n",
       "German Shepherd Crossbreed               654\n",
       "Boston Terrier                           610\n",
       "Cocker Spaniel                           595\n",
       "Poodle, Toy                              573\n",
       "Poodle, Miniature                        571\n",
       "Beagle Crossbreed                        564\n",
       "Labradoodle                              563\n",
       "Poodle                                   555\n",
       "                                        ... \n",
       "Mountain Feist                             2\n",
       "Mastiff, Tibetan                           2\n",
       "Barbet                                     2\n",
       "Wirehaired Vizsla                          2\n",
       "Welsh Springer Spaniel                     2\n",
       "Bearded Collie                             2\n",
       "Curly-Coated Retriever                     2\n",
       "Tibetan Mastiff                            2\n",
       "Jagdterrier                                2\n",
       "Spanish Water Dog                          2\n",
       "Estrela Mountain Dog                       2\n",
       "Standard Schnauzer                         2\n",
       "French Spaniel                             2\n",
       "Irish Red and White Setter                 1\n",
       "Beauceron                                  1\n",
       "Pyrenean Shepherd                          1\n",
       "Stabyhoun                                  1\n",
       "Hovawart                                   1\n",
       "Pumi                                       1\n",
       "Bracco Italiano                            1\n",
       "Sussex Spaniel                             1\n",
       "Tosa                                       1\n",
       "Perro de Presa Canario                     1\n",
       "English Setter Crossbreed                  1\n",
       "Eurasier                                   1\n",
       "Dandie Dinmont Terrier                     1\n",
       "Kromfohrlander                             1\n",
       "Kai Ken                                    1\n",
       "Cesky Terrier                              1\n",
       "Baladi                                     1\n",
       "Name: BreedName, Length: 305, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breed_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like 1000 would be a good cut-off.  We can do the filter one of two ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6968882b00>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_filter = breed_counts >=1000\n",
    "breed_counts[top_filter].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f696811ddd8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "breed_counts.head(15).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "breed_counts[breed_counts >= 1000].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the most popular breed of dog?\n",
    "\n",
    "In Lab 1, we only filtered by a single criteria, but we can filter by multiple criteria using `&` (and) and `|` (or).  For example, if we wanted to count the number of dog licenses issues in August to a Chihuahua:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aug_chihuahua_filter = (dogs[\"BreedName\"] == \"Chihuahua\") & (dogs[\"LicenseIssuedDate\"].dt.month == 8)\n",
    "len(dogs[aug_chihuahua_filter])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or if we wanted to count the number of dogs named BELLA or dogs whose license expires in 2020:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15505"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bella_2020_filter = (dogs[\"AnimalName\"] == \"BELLA\") | (dogs[\"LicenseExpiredDate\"].dt.year == 2020)\n",
    "len(dogs[bella_2020_filter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs_filtered = dogs[bella_2020_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>RowNumber</th>\n",
       "      <th>AnimalName</th>\n",
       "      <th>AnimalGender</th>\n",
       "      <th>AnimalBirthMonth</th>\n",
       "      <th>BreedName</th>\n",
       "      <th>Borough</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>LicenseIssuedDate</th>\n",
       "      <th>LicenseExpiredDate</th>\n",
       "      <th>Extract Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>84424</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2005</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11414</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>84645</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2006</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11379</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>84647</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2006</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11417</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>87307</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2007</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11428</td>\n",
       "      <td>2018-07-25</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>88204</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2007</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10462</td>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>88465</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2004</td>\n",
       "      <td>Beagle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11379</td>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>93382</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2009</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11223</td>\n",
       "      <td>2018-08-24</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>94367</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Morkie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10454</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>94529</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Poodle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11228</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>99599</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>Poodle, Toy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11228</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>2019-08-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>89265</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2010</td>\n",
       "      <td>American Staffordshire Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10314</td>\n",
       "      <td>2018-08-02</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>91916</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Jack Russell Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11228</td>\n",
       "      <td>2018-08-16</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>94771</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10459</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>94814</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Chihuahua</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10075</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>97215</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11237</td>\n",
       "      <td>2018-09-12</td>\n",
       "      <td>2019-08-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>84546</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2006</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11216</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>94867</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>M</td>\n",
       "      <td>2017</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001</td>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>84545</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2010</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10301</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>95072</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10467</td>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585</th>\n",
       "      <td>95145</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>German Shepherd Dog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10458</td>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>97310</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2011</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10312</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>84690</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2011</td>\n",
       "      <td>American Pit Bull Terrier/Pit Bull</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11234</td>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1879</th>\n",
       "      <td>95858</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2006</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10021</td>\n",
       "      <td>2018-09-06</td>\n",
       "      <td>2019-09-04</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>89867</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Shih Tzu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10460</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>99835</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10033</td>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>2019-09-05</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>97447</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2007</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10309</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2385</th>\n",
       "      <td>84691</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2011</td>\n",
       "      <td>Maltese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11367</td>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>95206</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>American Pit Bull Terrier/Pit Bull</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11212</td>\n",
       "      <td>2018-09-03</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2454</th>\n",
       "      <td>95469</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Dachshund</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10027</td>\n",
       "      <td>2018-09-04</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>96061</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Labrador Retriever Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10035</td>\n",
       "      <td>2018-09-07</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>2018</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54007</th>\n",
       "      <td>59141</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Shih Tzu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10473</td>\n",
       "      <td>2018-03-09</td>\n",
       "      <td>2023-03-09</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54076</th>\n",
       "      <td>61227</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2015</td>\n",
       "      <td>Chihuahua Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10022</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2023-03-19</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54079</th>\n",
       "      <td>61235</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Lowchen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10454</td>\n",
       "      <td>2018-03-19</td>\n",
       "      <td>2023-03-19</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54091</th>\n",
       "      <td>61670</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2008</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10011</td>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>2023-03-21</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54141</th>\n",
       "      <td>52711</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10472</td>\n",
       "      <td>2018-02-06</td>\n",
       "      <td>2023-03-28</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54289</th>\n",
       "      <td>66565</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2014</td>\n",
       "      <td>Beagle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10462</td>\n",
       "      <td>2018-04-15</td>\n",
       "      <td>2023-04-15</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54411</th>\n",
       "      <td>69352</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Labrador Retriever Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11211</td>\n",
       "      <td>2018-04-29</td>\n",
       "      <td>2023-04-29</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54461</th>\n",
       "      <td>70662</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11377</td>\n",
       "      <td>2018-05-04</td>\n",
       "      <td>2023-05-04</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54604</th>\n",
       "      <td>74029</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Poodle Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10001</td>\n",
       "      <td>2018-05-21</td>\n",
       "      <td>2023-05-21</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54625</th>\n",
       "      <td>74533</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Havanese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10003</td>\n",
       "      <td>2018-05-23</td>\n",
       "      <td>2023-05-23</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54680</th>\n",
       "      <td>75848</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2015</td>\n",
       "      <td>Cane Corso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10003</td>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>2023-05-31</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54704</th>\n",
       "      <td>64762</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2006</td>\n",
       "      <td>Dachshund</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002</td>\n",
       "      <td>2018-04-07</td>\n",
       "      <td>2023-06-04</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54772</th>\n",
       "      <td>82929</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Shih Tzu Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11236</td>\n",
       "      <td>2018-07-05</td>\n",
       "      <td>2023-06-14</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54773</th>\n",
       "      <td>89137</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2010</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11692</td>\n",
       "      <td>2018-08-02</td>\n",
       "      <td>2023-06-14</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55102</th>\n",
       "      <td>88256</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Cockapoo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10308</td>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55203</th>\n",
       "      <td>90864</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bull Terrier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11105</td>\n",
       "      <td>2018-08-10</td>\n",
       "      <td>2023-08-10</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55370</th>\n",
       "      <td>113750</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bichon Frise Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11357</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>2023-09-05</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55473</th>\n",
       "      <td>89015</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2016</td>\n",
       "      <td>Labradoodle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10010</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2023-09-19</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55476</th>\n",
       "      <td>98568</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Cavalier King Charles Spaniel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11103</td>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>2023-09-19</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55512</th>\n",
       "      <td>99592</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Havanese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10024</td>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>2023-09-24</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55520</th>\n",
       "      <td>99958</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Labrador Retriever</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11004</td>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>2023-09-26</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55622</th>\n",
       "      <td>111984</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2017</td>\n",
       "      <td>Shih Tzu Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10025</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>2023-10-16</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55660</th>\n",
       "      <td>104529</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2012</td>\n",
       "      <td>Shiba Inu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10023</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>2023-10-20</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55666</th>\n",
       "      <td>104568</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2015</td>\n",
       "      <td>German Shepherd Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10002</td>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>2023-10-20</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55680</th>\n",
       "      <td>104656</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Yorkshire Terrier Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10309</td>\n",
       "      <td>2018-10-21</td>\n",
       "      <td>2023-10-21</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55759</th>\n",
       "      <td>107410</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Maltipoo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10034</td>\n",
       "      <td>2018-11-04</td>\n",
       "      <td>2023-11-04</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55789</th>\n",
       "      <td>108268</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2018</td>\n",
       "      <td>Yorkshire Terrier Crossbreed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11375</td>\n",
       "      <td>2018-11-08</td>\n",
       "      <td>2023-11-08</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56019</th>\n",
       "      <td>116128</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2013</td>\n",
       "      <td>Boxer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10024</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56050</th>\n",
       "      <td>116550</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2014</td>\n",
       "      <td>Shih Tzu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10022</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56089</th>\n",
       "      <td>92449</td>\n",
       "      <td>BELLA</td>\n",
       "      <td>F</td>\n",
       "      <td>2012</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11201</td>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>2024-08-23</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15505 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       RowNumber AnimalName AnimalGender  AnimalBirthMonth  \\\n",
       "193        84424      BELLA            F              2005   \n",
       "215        84645      BELLA            F              2006   \n",
       "217        84647      BELLA            F              2006   \n",
       "290        87307      BELLA            F              2007   \n",
       "332        88204      BELLA            F              2007   \n",
       "354        88465      BELLA            F              2004   \n",
       "499        93382      BELLA            F              2009   \n",
       "525        94367      BELLA            F              2018   \n",
       "590        94529      BELLA            F              2018   \n",
       "665        99599      BELLA            F              2003   \n",
       "855        89265      BELLA            F              2010   \n",
       "873        91916      BELLA            F              2016   \n",
       "964        94771      BELLA            F              2017   \n",
       "976        94814      BELLA            F              2016   \n",
       "991        97215      BELLA            F              2016   \n",
       "1081       84546      BELLA            F              2006   \n",
       "1149       94867      BELLA            M              2017   \n",
       "1300       84545      BELLA            F              2010   \n",
       "1400       95072      BELLA            F              2018   \n",
       "1585       95145      BELLA            F              2018   \n",
       "1661       97310      BELLA            F              2011   \n",
       "1753       84690      BELLA            F              2011   \n",
       "1879       95858      BELLA            F              2006   \n",
       "1973       89867      BELLA            F              2016   \n",
       "2138       99835      BELLA            F              2017   \n",
       "2315       97447      BELLA            F              2007   \n",
       "2385       84691      BELLA            F              2011   \n",
       "2450       95206      BELLA            F              2016   \n",
       "2454       95469      BELLA            F              2016   \n",
       "2470       96061      BELLA            F              2017   \n",
       "...          ...        ...          ...               ...   \n",
       "54007      59141      BELLA            F              2017   \n",
       "54076      61227      BELLA            F              2015   \n",
       "54079      61235      BELLA            F              2017   \n",
       "54091      61670      BELLA            F              2008   \n",
       "54141      52711      BELLA            F              2017   \n",
       "54289      66565      BELLA            F              2014   \n",
       "54411      69352      BELLA            F              2017   \n",
       "54461      70662      BELLA            F              2017   \n",
       "54604      74029      BELLA            F              2013   \n",
       "54625      74533      BELLA            F              2018   \n",
       "54680      75848      BELLA            F              2015   \n",
       "54704      64762      BELLA            F              2006   \n",
       "54772      82929      BELLA            F              2017   \n",
       "54773      89137      BELLA            F              2010   \n",
       "55102      88256      BELLA            F              2017   \n",
       "55203      90864      BELLA            F              2017   \n",
       "55370     113750      BELLA            F              2017   \n",
       "55473      89015      BELLA            F              2016   \n",
       "55476      98568      BELLA            F              2018   \n",
       "55512      99592      BELLA            F              2017   \n",
       "55520      99958      BELLA            F              2018   \n",
       "55622     111984      BELLA            F              2017   \n",
       "55660     104529      BELLA            F              2012   \n",
       "55666     104568      BELLA            F              2015   \n",
       "55680     104656      BELLA            F              2013   \n",
       "55759     107410      BELLA            F              2018   \n",
       "55789     108268      BELLA            F              2018   \n",
       "56019     116128      BELLA            F              2013   \n",
       "56050     116550      BELLA            F              2014   \n",
       "56089      92449      BELLA            F              2012   \n",
       "\n",
       "                                BreedName  Borough  ZipCode LicenseIssuedDate  \\\n",
       "193                               Unknown      NaN    11414        2018-07-13   \n",
       "215                               Unknown      NaN    11379        2018-07-13   \n",
       "217                               Unknown      NaN    11417        2018-07-13   \n",
       "290                               Unknown      NaN    11428        2018-07-25   \n",
       "332                     Yorkshire Terrier      NaN    10462        2018-07-30   \n",
       "354                                Beagle      NaN    11379        2018-07-30   \n",
       "499                               Unknown      NaN    11223        2018-08-24   \n",
       "525                                Morkie      NaN    10454        2018-08-30   \n",
       "590                                Poodle      NaN    11228        2018-08-30   \n",
       "665                           Poodle, Toy      NaN    11228        2018-09-24   \n",
       "855        American Staffordshire Terrier      NaN    10314        2018-08-02   \n",
       "873                  Jack Russell Terrier      NaN    11228        2018-08-16   \n",
       "964                               Unknown      NaN    10459        2018-08-31   \n",
       "976                             Chihuahua      NaN    10075        2018-08-31   \n",
       "991                               Unknown      NaN    11237        2018-09-12   \n",
       "1081                              Maltese      NaN    11216        2018-07-13   \n",
       "1149                              Unknown      NaN    10001        2018-09-01   \n",
       "1300                    Yorkshire Terrier      NaN    10301        2018-07-13   \n",
       "1400                    Yorkshire Terrier      NaN    10467        2018-09-02   \n",
       "1585                  German Shepherd Dog      NaN    10458        2018-09-03   \n",
       "1661                              Unknown      NaN    10312        2018-09-13   \n",
       "1753   American Pit Bull Terrier/Pit Bull      NaN    11234        2018-07-14   \n",
       "1879                              Unknown      NaN    10021        2018-09-06   \n",
       "1973                             Shih Tzu      NaN    10460        2018-08-05   \n",
       "2138                              Maltese      NaN    10033        2018-09-25   \n",
       "2315                   Labrador Retriever      NaN    10309        2018-09-13   \n",
       "2385                              Maltese      NaN    11367        2018-07-14   \n",
       "2450   American Pit Bull Terrier/Pit Bull      NaN    11212        2018-09-03   \n",
       "2454                            Dachshund      NaN    10027        2018-09-04   \n",
       "2470        Labrador Retriever Crossbreed      NaN    10035        2018-09-07   \n",
       "...                                   ...      ...      ...               ...   \n",
       "54007                            Shih Tzu      NaN    10473        2018-03-09   \n",
       "54076                Chihuahua Crossbreed      NaN    10022        2018-03-19   \n",
       "54079                             Lowchen      NaN    10454        2018-03-19   \n",
       "54091                  Labrador Retriever      NaN    10011        2018-03-21   \n",
       "54141                             Unknown      NaN    10472        2018-02-06   \n",
       "54289                              Beagle      NaN    10462        2018-04-15   \n",
       "54411       Labrador Retriever Crossbreed      NaN    11211        2018-04-29   \n",
       "54461                  Labrador Retriever      NaN    11377        2018-05-04   \n",
       "54604                   Poodle Crossbreed      NaN    10001        2018-05-21   \n",
       "54625                            Havanese      NaN    10003        2018-05-23   \n",
       "54680                          Cane Corso      NaN    10003        2018-05-31   \n",
       "54704                           Dachshund      NaN    10002        2018-04-07   \n",
       "54772                 Shih Tzu Crossbreed      NaN    11236        2018-07-05   \n",
       "54773                             Unknown      NaN    11692        2018-08-02   \n",
       "55102                            Cockapoo      NaN    10308        2018-07-30   \n",
       "55203                        Bull Terrier      NaN    11105        2018-08-10   \n",
       "55370             Bichon Frise Crossbreed      NaN    11357        2018-12-10   \n",
       "55473                         Labradoodle      NaN    10010        2018-08-01   \n",
       "55476       Cavalier King Charles Spaniel      NaN    11103        2018-09-19   \n",
       "55512                            Havanese      NaN    10024        2018-09-24   \n",
       "55520                  Labrador Retriever      NaN    11004        2018-09-26   \n",
       "55622                 Shih Tzu Crossbreed      NaN    10025        2018-11-30   \n",
       "55660                           Shiba Inu      NaN    10023        2018-10-20   \n",
       "55666          German Shepherd Crossbreed      NaN    10002        2018-10-20   \n",
       "55680        Yorkshire Terrier Crossbreed      NaN    10309        2018-10-21   \n",
       "55759                            Maltipoo      NaN    10034        2018-11-04   \n",
       "55789        Yorkshire Terrier Crossbreed      NaN    11375        2018-11-08   \n",
       "56019                               Boxer      NaN    10024        2018-12-22   \n",
       "56050                            Shih Tzu      NaN    10022        2018-12-26   \n",
       "56089                             Unknown      NaN    11201        2018-08-19   \n",
       "\n",
       "      LicenseExpiredDate  Extract Year  \n",
       "193           2019-08-30          2018  \n",
       "215           2019-08-30          2018  \n",
       "217           2019-08-30          2018  \n",
       "290           2019-08-30          2018  \n",
       "332           2019-08-30          2018  \n",
       "354           2019-08-30          2018  \n",
       "499           2019-08-30          2018  \n",
       "525           2019-08-30          2018  \n",
       "590           2019-08-30          2018  \n",
       "665           2019-08-30          2018  \n",
       "855           2019-08-31          2018  \n",
       "873           2019-08-31          2018  \n",
       "964           2019-08-31          2018  \n",
       "976           2019-08-31          2018  \n",
       "991           2019-08-31          2018  \n",
       "1081          2019-09-01          2018  \n",
       "1149          2019-09-01          2018  \n",
       "1300          2019-09-02          2018  \n",
       "1400          2019-09-02          2018  \n",
       "1585          2019-09-03          2018  \n",
       "1661          2019-09-03          2018  \n",
       "1753          2019-09-04          2018  \n",
       "1879          2019-09-04          2018  \n",
       "1973          2019-09-05          2018  \n",
       "2138          2019-09-05          2018  \n",
       "2315          2019-09-06          2018  \n",
       "2385          2019-09-07          2018  \n",
       "2450          2019-09-07          2018  \n",
       "2454          2019-09-07          2018  \n",
       "2470          2019-09-07          2018  \n",
       "...                  ...           ...  \n",
       "54007         2023-03-09          2018  \n",
       "54076         2023-03-19          2018  \n",
       "54079         2023-03-19          2018  \n",
       "54091         2023-03-21          2018  \n",
       "54141         2023-03-28          2018  \n",
       "54289         2023-04-15          2018  \n",
       "54411         2023-04-29          2018  \n",
       "54461         2023-05-04          2018  \n",
       "54604         2023-05-21          2018  \n",
       "54625         2023-05-23          2018  \n",
       "54680         2023-05-31          2018  \n",
       "54704         2023-06-04          2018  \n",
       "54772         2023-06-14          2018  \n",
       "54773         2023-06-14          2018  \n",
       "55102         2023-07-30          2018  \n",
       "55203         2023-08-10          2018  \n",
       "55370         2023-09-05          2018  \n",
       "55473         2023-09-19          2018  \n",
       "55476         2023-09-19          2018  \n",
       "55512         2023-09-24          2018  \n",
       "55520         2023-09-26          2018  \n",
       "55622         2023-10-16          2018  \n",
       "55660         2023-10-20          2018  \n",
       "55666         2023-10-20          2018  \n",
       "55680         2023-10-21          2018  \n",
       "55759         2023-11-04          2018  \n",
       "55789         2023-11-08          2018  \n",
       "56019         2023-12-22          2018  \n",
       "56050         2024-01-17          2018  \n",
       "56089         2024-08-23          2018  \n",
       "\n",
       "[15505 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a partner or by yourself, answer the following questions:\n",
    "\n",
    "1.  How many dogs are registered in the Lehman zip code (10468)? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.  Plot a bar chart of the number of dog licenses in the top 10 zip codes?  Which zip code has the most dog licenses and where is it located (type a zip code into Google maps to see the area)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.  How many female Labrador Retreivers are licensed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.  How many licenses are due to expired by the end of September?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Ask and answer your own question(s) about this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Challenge question:  Can you find the oldest dog in the dataset?"
   ]
  }
 ],
 "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": "3.4.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}