diff --git a/capstone_project.ipynb b/capstone_project.ipynb
index f2fc59e..f214cb4 100644
--- a/capstone_project.ipynb
+++ b/capstone_project.ipynb
@@ -5,7 +5,7 @@
    "id": "58dfeab5",
    "metadata": {},
    "source": [
-    "# Working title: working subtitle\n",
+    "# NYC Building Energy Ratings\n",
     "\n",
     "## initial remarks"
    ]
@@ -232,7 +232,7 @@
     }
    ],
    "source": [
-    "# Is this dataset citywide? or just Manhattan?\n",
+    "# Is this citywide or just Manhattan?\n",
     "\n",
     "df['BoroughName'].value_counts()"
    ]
@@ -556,7 +556,7 @@
    "outputs": [],
    "source": [
     "# Missing Address is not a big deal because the rest of the values are complete.\n",
-    "# But missing Building Class could be significant.\n",
+    "# But missing Building Class could be a problem.\n",
     "\n",
     "# The two offending rows also have Building Count = 0.\n",
     "# How is that possible, since they have Energy Star scores?\n",
@@ -622,7 +622,7 @@
     "# Other columns are less interesting:\n",
     "\n",
     "# Building_Count is the number of buildings in one Block.\n",
-    "# A Block can have more than one Lot, but a Lot only has one Block.\n",
+    "# Block can have more than one Lot, but Lot only has one Block.\n",
     "# Block, Lot and BBL are identifiers assigned by the city.\n",
     "\n",
     "# A good visual reference is the Digital Tax Map put out by the NYC Department of Finance:\n",
@@ -1855,91 +1855,6 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "1f4d8878",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compare apples to apples\n",
-    "\n",
-    "# df.groupby(['Bldg_Class'])['Energy_Score'].describe().sort_values(by = 'mean', ascending = False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "b0bba53f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df.groupby(['Bldg_Class'])['Energy_Score'].plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "9ae68067",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Too many building types. Select major ones.\n",
-    "\n",
-    "# df['Bldg_Class'].value_counts().head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "951c61d8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Look up D1 through W1\n",
-    "# see https://www1.nyc.gov/assets/finance/jump/hlpbldgcode.html\n",
-    "\n",
-    "# D1    ELEVATOR APARTMENTS: ELEVATOR APT; SEMI-FIREPROOF WITHOUT STORES\n",
-    "# D4    ELEVATOR APARTMENTS: ELEVATOR COOPERATIVE\n",
-    "# R0    CONDOMINIUMS: SPECIAL CONDOMINIUM BILLING LOT\n",
-    "# C1    WALK UP APARTMENTS: OVER SIX FAMILIES WITHOUT STORES\n",
-    "# D7    ELEVATOR APARTMENTS: ELEVATOR APT; SEMI-FIREPROOF WITH STORES\n",
-    "# W1    EDUCATIONAL FACILITIES: PUBLIC ELEMENTARY, JUNIOR OR SENIOR HIGH"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "376dadae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df[(df['Block'] == 702) & (df['Lot'] == 10)]    # 10 Hudson Yards"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "e6d23093",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df[(df['Block'] == ) & (df['Lot'] == )]    # 30 Hudson Yards: 500 W 33rd St"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "169570ed",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df[df['Address'].dropna().str.contains('WEST 33')].head()\n",
-    "\n",
-    "# temp = df.dropna()\n",
-    "# temp[temp['Address'].str.contains('WEST 33')]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
    "id": "73f50d5c",
    "metadata": {},
    "outputs": [
@@ -1961,20 +1876,20 @@
        "       'R3', 'C8', 'RS', 'Q2', 'V7', 'Q4', 'Y7'], dtype=object)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# There are many types of building\n",
+    "# There are many building types\n",
     "\n",
     "df['Bldg_Class'].unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "5449a9dd",
    "metadata": {},
    "outputs": [],
@@ -1990,12 +1905,14 @@
     "# O6\tOFFICE WITH COMM 7 - 19 STORIES\n",
     "# O7\tPROFESSIONAL BUILDINGS/STAND ALONE FUNERAL HOMES\n",
     "# O8\tOFFICE WITH APARTMENTS ONLY (NO COMM)\n",
-    "# O9\tMISCELLANEOUS AND OLD STYLE BANK BLDGS"
+    "# O9\tMISCELLANEOUS AND OLD STYLE BANK BLDGS\n",
+    "\n",
+    "# Building glossary: https://www1.nyc.gov/assets/finance/jump/hlpbldgcode.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 43,
    "id": "bc229011",
    "metadata": {},
    "outputs": [
@@ -2060,7 +1977,7 @@
        "1473  501 WEST 30 STREET  MANHATTAN  1007020010            58            C  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2077,20 +1994,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 44,
    "id": "a344e1a3",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -2109,12 +2016,13 @@
     "# What's the distribution of scores?\n",
     "\n",
     "office = df[df['Bldg_Class'] == 'O4']\n",
-    "office['Energy_Score'].plot(kind = 'hist')"
+    "office['Energy_Score'].plot(kind = 'hist')\n",
+    "plt.savefig('office.png')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 45,
    "id": "1dabd257",
    "metadata": {},
    "outputs": [
@@ -2130,7 +2038,7 @@
        "Name: Block, dtype: int64"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2143,25 +2051,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 46,
    "id": "9a9bcc23",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
+       "<Figure size 864x648 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -2173,12 +2071,23 @@
    "source": [
     "# Square Footage versus Energy Score\n",
     "\n",
-    "office.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
+    "office.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (12, 9))\n",
+    "plt.savefig('scatter.png')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 47,
+   "id": "a5c7986c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# I can't tell from the plot whether there's any relationship between those two variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "id": "3b33f87e",
    "metadata": {},
    "outputs": [
@@ -2188,7 +2097,7 @@
        "<AxesSubplot:ylabel='Frequency'>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2206,7 +2115,6 @@
     }
    ],
    "source": [
-    "# The relationship certainly is not linear.\n",
     "# How about smaller office buildings?\n",
     "\n",
     "office_small = df[df['Bldg_Class'] == 'O2']\n",
@@ -2215,7 +2123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 49,
    "id": "41db8282",
    "metadata": {},
    "outputs": [
@@ -2231,7 +2139,7 @@
        "Name: Block, dtype: int64"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2244,7 +2152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 50,
    "id": "490d052a",
    "metadata": {},
    "outputs": [
@@ -2254,7 +2162,7 @@
        "<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2279,20 +2187,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 51,
    "id": "a75608c7",
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": "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\n",
@@ -2310,12 +2208,13 @@
     "# Something different: hotels\n",
     "\n",
     "hotels = df[df['Bldg_Class'] == 'H2']\n",
-    "hotels['Energy_Score'].plot(kind = 'hist')"
+    "hotels['Energy_Score'].plot(kind = 'hist')\n",
+    "plt.savefig('hotels.png')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 52,
    "id": "a6c9fbd4",
    "metadata": {},
    "outputs": [
@@ -2331,7 +2230,7 @@
        "Name: Block, dtype: int64"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2344,7 +2243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 53,
    "id": "5d0ea74d",
    "metadata": {},
    "outputs": [
@@ -2354,7 +2253,7 @@
        "<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2379,13 +2278,268 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 54,
    "id": "e12fed9d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Again, not linear.\n",
-    "# It's obvious that hotels score worse than office buildings."
+    "# Again, no apparent connection between score and building size.\n",
+    "# But it's obvious that hotels score worse than office buildings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "9719cc6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Lastly, let's take a look at residential buildings.\n",
+    "# This time we'll zoom out and consider two building classes:\n",
+    "# Walk up apartments and elevator apartments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "ca8d4f3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "walk_up = df[df['Bldg_Class'].str.contains('C')]\n",
+    "elevator = df[df['Bldg_Class'].str.contains('C')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "182f44b0",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "walk_up['Energy_Score'].plot(kind = 'hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "81203621",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Letter_Score\n",
+       "A     403\n",
+       "B     302\n",
+       "C     388\n",
+       "D    1909\n",
+       "F     285\n",
+       "Name: Block, dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "walk_up.groupby(['Letter_Score'])['Block'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5d84db66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Many Ds and Fs, and the rest are evenly distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "727698e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "walk_up.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "31162192",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elevator['Energy_Score'].plot(kind = 'hist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "c95f189b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Letter_Score\n",
+       "A     403\n",
+       "B     302\n",
+       "C     388\n",
+       "D    1909\n",
+       "F     285\n",
+       "Name: Block, dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elevator.groupby(['Letter_Score'])['Block'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "bc9de94c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remarkably similar to walk up apartments, with many Ds and Fs, and the rest are evenly distributed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "4bc3c3d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Sq_Footage', ylabel='Energy_Score'>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "elevator.plot(kind = 'scatter', x = 'Sq_Footage', y = 'Energy_Score', figsize = (20, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29a22a46",
+   "metadata": {},
+   "source": [
+    "## Findings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "87a96d0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# All scatter plots suggest that the relationship between a building's size and its energy rating\n",
+    "# is not simple. I had hoped the plots would show either a positive or negative relationship between square footage\n",
+    "# and energy rating.\n",
+    "\n",
+    "# The analysis elaborated here cannot answer the research question. However, some trends were uncovered in the process.\n",
+    "# Hotels scored worse overall than office buildings. And two kinds of apartments, walk ups and elevator buildings, had\n",
+    "# very similar distributions of letter grades."
    ]
   }
  ],