diff --git a/.DS_Store b/.DS_Store
index eadb589..e196697 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/Finalized Notebook.ipynb b/Finalized Notebook.ipynb
new file mode 100644
index 0000000..0e74ebb
--- /dev/null
+++ b/Finalized Notebook.ipynb	
@@ -0,0 +1,3119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import matplotlib.dates as mdates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Position</th>\n",
+       "      <th>Track Name</th>\n",
+       "      <th>Artist</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>URL</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Reggaetón Lento (Bailemos)</td>\n",
+       "      <td>CNCO</td>\n",
+       "      <td>19272</td>\n",
+       "      <td>https://open.spotify.com/track/3AEZUABDXNtecAO...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chantaje</td>\n",
+       "      <td>Shakira</td>\n",
+       "      <td>19270</td>\n",
+       "      <td>https://open.spotify.com/track/6mICuAdrwEjh6Y6...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Otra Vez (feat. J Balvin)</td>\n",
+       "      <td>Zion &amp; Lennox</td>\n",
+       "      <td>15761</td>\n",
+       "      <td>https://open.spotify.com/track/3QwBODjSEzelZyV...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Vente Pa' Ca</td>\n",
+       "      <td>Ricky Martin</td>\n",
+       "      <td>14954</td>\n",
+       "      <td>https://open.spotify.com/track/7DM4BPaS7uofFul...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Safari</td>\n",
+       "      <td>J Balvin</td>\n",
+       "      <td>14269</td>\n",
+       "      <td>https://open.spotify.com/track/6rQSrBHf7HlZjtc...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\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",
+       "      <td>3441192</td>\n",
+       "      <td>196</td>\n",
+       "      <td>More Than You Know</td>\n",
+       "      <td>Axwell /\\ Ingrosso</td>\n",
+       "      <td>2126</td>\n",
+       "      <td>https://open.spotify.com/track/71bBFbfn2OBK5Qw...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441193</td>\n",
+       "      <td>197</td>\n",
+       "      <td>Issues</td>\n",
+       "      <td>Julia Michaels</td>\n",
+       "      <td>2117</td>\n",
+       "      <td>https://open.spotify.com/track/7vu0JkJh0ldukEY...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441194</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Tuo Fei Lun</td>\n",
+       "      <td>Eason Chan</td>\n",
+       "      <td>2112</td>\n",
+       "      <td>https://open.spotify.com/track/0pmGleExiQyd0Hx...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441195</td>\n",
+       "      <td>199</td>\n",
+       "      <td>面具</td>\n",
+       "      <td>Alfred Hui</td>\n",
+       "      <td>2102</td>\n",
+       "      <td>https://open.spotify.com/track/58SUsgamkW6vFFq...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441196</td>\n",
+       "      <td>200</td>\n",
+       "      <td>Let Her Go</td>\n",
+       "      <td>Passenger</td>\n",
+       "      <td>2088</td>\n",
+       "      <td>https://open.spotify.com/track/2jyjhRf6DVbMPU5...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3441197 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Position                  Track Name              Artist  Streams  \\\n",
+       "0               1  Reggaetón Lento (Bailemos)                CNCO    19272   \n",
+       "1               2                    Chantaje             Shakira    19270   \n",
+       "2               3   Otra Vez (feat. J Balvin)       Zion & Lennox    15761   \n",
+       "3               4                Vente Pa' Ca        Ricky Martin    14954   \n",
+       "4               5                      Safari            J Balvin    14269   \n",
+       "...           ...                         ...                 ...      ...   \n",
+       "3441192       196          More Than You Know  Axwell /\\ Ingrosso     2126   \n",
+       "3441193       197                      Issues      Julia Michaels     2117   \n",
+       "3441194       198                 Tuo Fei Lun          Eason Chan     2112   \n",
+       "3441195       199                          面具          Alfred Hui     2102   \n",
+       "3441196       200                  Let Her Go           Passenger     2088   \n",
+       "\n",
+       "                                                       URL        Date Region  \n",
+       "0        https://open.spotify.com/track/3AEZUABDXNtecAO...  2017-01-01     ec  \n",
+       "1        https://open.spotify.com/track/6mICuAdrwEjh6Y6...  2017-01-01     ec  \n",
+       "2        https://open.spotify.com/track/3QwBODjSEzelZyV...  2017-01-01     ec  \n",
+       "3        https://open.spotify.com/track/7DM4BPaS7uofFul...  2017-01-01     ec  \n",
+       "4        https://open.spotify.com/track/6rQSrBHf7HlZjtc...  2017-01-01     ec  \n",
+       "...                                                    ...         ...    ...  \n",
+       "3441192  https://open.spotify.com/track/71bBFbfn2OBK5Qw...  2018-01-09     hk  \n",
+       "3441193  https://open.spotify.com/track/7vu0JkJh0ldukEY...  2018-01-09     hk  \n",
+       "3441194  https://open.spotify.com/track/0pmGleExiQyd0Hx...  2018-01-09     hk  \n",
+       "3441195  https://open.spotify.com/track/58SUsgamkW6vFFq...  2018-01-09     hk  \n",
+       "3441196  https://open.spotify.com/track/2jyjhRf6DVbMPU5...  2018-01-09     hk  \n",
+       "\n",
+       "[3441197 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"../data.csv\", parse_dates = True)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Position</th>\n",
+       "      <th>Track Name</th>\n",
+       "      <th>Artist</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>URL</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Reggaetón Lento (Bailemos)</td>\n",
+       "      <td>CNCO</td>\n",
+       "      <td>19272</td>\n",
+       "      <td>https://open.spotify.com/track/3AEZUABDXNtecAO...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chantaje</td>\n",
+       "      <td>Shakira</td>\n",
+       "      <td>19270</td>\n",
+       "      <td>https://open.spotify.com/track/6mICuAdrwEjh6Y6...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Otra Vez (feat. J Balvin)</td>\n",
+       "      <td>Zion &amp; Lennox</td>\n",
+       "      <td>15761</td>\n",
+       "      <td>https://open.spotify.com/track/3QwBODjSEzelZyV...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Vente Pa' Ca</td>\n",
+       "      <td>Ricky Martin</td>\n",
+       "      <td>14954</td>\n",
+       "      <td>https://open.spotify.com/track/7DM4BPaS7uofFul...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Safari</td>\n",
+       "      <td>J Balvin</td>\n",
+       "      <td>14269</td>\n",
+       "      <td>https://open.spotify.com/track/6rQSrBHf7HlZjtc...</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "    </tr>\n",
+       "    <tr>\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",
+       "      <td>3441192</td>\n",
+       "      <td>196</td>\n",
+       "      <td>More Than You Know</td>\n",
+       "      <td>Axwell /\\ Ingrosso</td>\n",
+       "      <td>2126</td>\n",
+       "      <td>https://open.spotify.com/track/71bBFbfn2OBK5Qw...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441193</td>\n",
+       "      <td>197</td>\n",
+       "      <td>Issues</td>\n",
+       "      <td>Julia Michaels</td>\n",
+       "      <td>2117</td>\n",
+       "      <td>https://open.spotify.com/track/7vu0JkJh0ldukEY...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441194</td>\n",
+       "      <td>198</td>\n",
+       "      <td>Tuo Fei Lun</td>\n",
+       "      <td>Eason Chan</td>\n",
+       "      <td>2112</td>\n",
+       "      <td>https://open.spotify.com/track/0pmGleExiQyd0Hx...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441195</td>\n",
+       "      <td>199</td>\n",
+       "      <td>面具</td>\n",
+       "      <td>Alfred Hui</td>\n",
+       "      <td>2102</td>\n",
+       "      <td>https://open.spotify.com/track/58SUsgamkW6vFFq...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441196</td>\n",
+       "      <td>200</td>\n",
+       "      <td>Let Her Go</td>\n",
+       "      <td>Passenger</td>\n",
+       "      <td>2088</td>\n",
+       "      <td>https://open.spotify.com/track/2jyjhRf6DVbMPU5...</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3441197 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Position                  Track Name              Artist  Streams  \\\n",
+       "0               1  Reggaetón Lento (Bailemos)                CNCO    19272   \n",
+       "1               2                    Chantaje             Shakira    19270   \n",
+       "2               3   Otra Vez (feat. J Balvin)       Zion & Lennox    15761   \n",
+       "3               4                Vente Pa' Ca        Ricky Martin    14954   \n",
+       "4               5                      Safari            J Balvin    14269   \n",
+       "...           ...                         ...                 ...      ...   \n",
+       "3441192       196          More Than You Know  Axwell /\\ Ingrosso     2126   \n",
+       "3441193       197                      Issues      Julia Michaels     2117   \n",
+       "3441194       198                 Tuo Fei Lun          Eason Chan     2112   \n",
+       "3441195       199                          面具          Alfred Hui     2102   \n",
+       "3441196       200                  Let Her Go           Passenger     2088   \n",
+       "\n",
+       "                                                       URL       Date Region  \n",
+       "0        https://open.spotify.com/track/3AEZUABDXNtecAO... 2017-01-01     ec  \n",
+       "1        https://open.spotify.com/track/6mICuAdrwEjh6Y6... 2017-01-01     ec  \n",
+       "2        https://open.spotify.com/track/3QwBODjSEzelZyV... 2017-01-01     ec  \n",
+       "3        https://open.spotify.com/track/7DM4BPaS7uofFul... 2017-01-01     ec  \n",
+       "4        https://open.spotify.com/track/6rQSrBHf7HlZjtc... 2017-01-01     ec  \n",
+       "...                                                    ...        ...    ...  \n",
+       "3441192  https://open.spotify.com/track/71bBFbfn2OBK5Qw... 2018-01-09     hk  \n",
+       "3441193  https://open.spotify.com/track/7vu0JkJh0ldukEY... 2018-01-09     hk  \n",
+       "3441194  https://open.spotify.com/track/0pmGleExiQyd0Hx... 2018-01-09     hk  \n",
+       "3441195  https://open.spotify.com/track/58SUsgamkW6vFFq... 2018-01-09     hk  \n",
+       "3441196  https://open.spotify.com/track/2jyjhRf6DVbMPU5... 2018-01-09     hk  \n",
+       "\n",
+       "[3441197 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['Date'] = data['Date'].astype('datetime64[ns]')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_date(data, column):\n",
+    "    data[column + \"_year\"] = data[column].apply(lambda x: x.year)\n",
+    "    data[column + \"_month\"] = data[column].apply(lambda x: x.month)\n",
+    "extract_date(data, \"Date\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Position      3441197\n",
+       "Track Name    3440540\n",
+       "Artist        3440540\n",
+       "Streams       3441197\n",
+       "URL           3441189\n",
+       "Date          3441197\n",
+       "Region        3441197\n",
+       "Date_year     3441197\n",
+       "Date_month    3441197\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Position      3440540\n",
+       "Track Name    3440540\n",
+       "Artist        3440540\n",
+       "Streams       3440540\n",
+       "URL           3440540\n",
+       "Date          3440540\n",
+       "Region        3440540\n",
+       "Date_year     3440540\n",
+       "Date_month    3440540\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.dropna(how='any')\n",
+    "data.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Rank</th>\n",
+       "      <th>Song Name</th>\n",
+       "      <th>Artist</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Date_year</th>\n",
+       "      <th>Date_month</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Reggaetón Lento (Bailemos)</td>\n",
+       "      <td>CNCO</td>\n",
+       "      <td>19272</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chantaje</td>\n",
+       "      <td>Shakira</td>\n",
+       "      <td>19270</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Otra Vez (feat. J Balvin)</td>\n",
+       "      <td>Zion &amp; Lennox</td>\n",
+       "      <td>15761</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Vente Pa' Ca</td>\n",
+       "      <td>Ricky Martin</td>\n",
+       "      <td>14954</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Safari</td>\n",
+       "      <td>J Balvin</td>\n",
+       "      <td>14269</td>\n",
+       "      <td>2017-01-01</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\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",
+       "      <td>3440997</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Rewrite The Stars</td>\n",
+       "      <td>Zac Efron</td>\n",
+       "      <td>13012</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3440998</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Wolves</td>\n",
+       "      <td>Selena Gomez</td>\n",
+       "      <td>12636</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3440999</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Havana</td>\n",
+       "      <td>Camila Cabello</td>\n",
+       "      <td>12608</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441000</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Finesse (Remix) [feat. Cardi B]</td>\n",
+       "      <td>Bruno Mars</td>\n",
+       "      <td>11309</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3441001</td>\n",
+       "      <td>5</td>\n",
+       "      <td>River (feat. Ed Sheeran)</td>\n",
+       "      <td>Eminem</td>\n",
+       "      <td>11208</td>\n",
+       "      <td>2018-01-09</td>\n",
+       "      <td>hk</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98240 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Rank                        Song Name          Artist  Streams  \\\n",
+       "0           1       Reggaetón Lento (Bailemos)            CNCO    19272   \n",
+       "1           2                         Chantaje         Shakira    19270   \n",
+       "2           3        Otra Vez (feat. J Balvin)   Zion & Lennox    15761   \n",
+       "3           4                     Vente Pa' Ca    Ricky Martin    14954   \n",
+       "4           5                           Safari        J Balvin    14269   \n",
+       "...       ...                              ...             ...      ...   \n",
+       "3440997     1                Rewrite The Stars       Zac Efron    13012   \n",
+       "3440998     2                           Wolves    Selena Gomez    12636   \n",
+       "3440999     3                           Havana  Camila Cabello    12608   \n",
+       "3441000     4  Finesse (Remix) [feat. Cardi B]      Bruno Mars    11309   \n",
+       "3441001     5         River (feat. Ed Sheeran)          Eminem    11208   \n",
+       "\n",
+       "              Date Region  Date_year  Date_month  \n",
+       "0       2017-01-01     ec       2017           1  \n",
+       "1       2017-01-01     ec       2017           1  \n",
+       "2       2017-01-01     ec       2017           1  \n",
+       "3       2017-01-01     ec       2017           1  \n",
+       "4       2017-01-01     ec       2017           1  \n",
+       "...            ...    ...        ...         ...  \n",
+       "3440997 2018-01-09     hk       2018           1  \n",
+       "3440998 2018-01-09     hk       2018           1  \n",
+       "3440999 2018-01-09     hk       2018           1  \n",
+       "3441000 2018-01-09     hk       2018           1  \n",
+       "3441001 2018-01-09     hk       2018           1  \n",
+       "\n",
+       "[98240 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "renamed_df = data.rename(columns={\"Position\": \"Rank\", \"Track Name\": \"Song Name\" })\n",
+    "renamed_df.head()\n",
+    "\n",
+    "updated_df = renamed_df[[\"Rank\",\"Song Name\", \"Artist\", \"Streams\", \"Date\", \"Region\", \"Date_year\", \"Date_month\"]]\n",
+    "updated_df\n",
+    "\n",
+    "position_df = (updated_df.loc[(data[\"Position\"]<6),:])\n",
+    "position_df = pd.DataFrame(updated_df.loc[(data[\"Position\"]<6),:])\n",
+    "position_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find top songs"
+   ]
+  },
+  {
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "      <th>Date_month</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Song Name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>rockstar</td>\n",
+       "      <td>4029</td>\n",
+       "      <td>896977091</td>\n",
+       "      <td>4647168</td>\n",
+       "      <td>25231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Havana</td>\n",
+       "      <td>5665</td>\n",
+       "      <td>527960542</td>\n",
+       "      <td>3967439</td>\n",
+       "      <td>21620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>New Rules</td>\n",
+       "      <td>3490</td>\n",
+       "      <td>364710843</td>\n",
+       "      <td>1893963</td>\n",
+       "      <td>9867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Wolves</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>145735336</td>\n",
+       "      <td>1811266</td>\n",
+       "      <td>10204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Dusk Till Dawn - Radio Edit</td>\n",
+       "      <td>3294</td>\n",
+       "      <td>142278571</td>\n",
+       "      <td>1950439</td>\n",
+       "      <td>10007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>We Wait for the Light</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4793</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Don't Look Down - Radio Edit</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3430</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Do You Still Want Me</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2714</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Розовое вино</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2214</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Nothing Without You</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2061</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>340 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Rank    Streams  Date_year  Date_month\n",
+       "Song Name                                                           \n",
+       "rockstar                      4029  896977091    4647168       25231\n",
+       "Havana                        5665  527960542    3967439       21620\n",
+       "New Rules                     3490  364710843    1893963        9867\n",
+       "Wolves                        2650  145735336    1811266       10204\n",
+       "Dusk Till Dawn - Radio Edit   3294  142278571    1950439       10007\n",
+       "...                            ...        ...        ...         ...\n",
+       "We Wait for the Light            2       4793       2017          10\n",
+       "Don't Look Down - Radio Edit     2       3430       2017          12\n",
+       "Do You Still Want Me             3       2714       2017          10\n",
+       "Розовое вино                     5       2214       2017          12\n",
+       "Nothing Without You              5       2061       2017          12\n",
+       "\n",
+       "[340 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change Date according to quarter\n",
+    "date = position_df.loc[(pd.to_datetime(position_df[\"Date\"]) > pd.to_datetime(\"2017-10-01\")) & \n",
+    "                       (pd.to_datetime(position_df[\"Date\"]) <= pd.to_datetime(\"2017-12-31\")),:]\n",
+    "\n",
+    "date\n",
+    "\n",
+    "date = date.groupby(\"Song Name\").sum()\n",
+    "date\n",
+    "\n",
+    "topstreams_date_global = date.sort_values(\"Streams\", ascending = False)\n",
+    "topstreams_date_global"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Streams</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>175147277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>202102455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>233465327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>146431398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>76270815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>34299896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>14326636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>1495722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>649873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>693444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>704119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1496486</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Streams\n",
+       "Date_month           \n",
+       "1           175147277\n",
+       "2           202102455\n",
+       "3           233465327\n",
+       "4           146431398\n",
+       "5            76270815\n",
+       "6            34299896\n",
+       "7            14326636\n",
+       "8             1495722\n",
+       "9              649873\n",
+       "10             693444\n",
+       "11             704119\n",
+       "12            1496486"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape_ofyou = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Shape of You\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "shape_ofyou\n",
+    "\n",
+    "global_shape= (shape_ofyou.loc[(shape_ofyou[\"Region\"]==\"global\"),:]).index\n",
+    "global_shape\n",
+    "\n",
+    "shape_ofyou.drop(global_shape , inplace=True)\n",
+    "shape_ofyou\n",
+    "\n",
+    "shape_ofyou = shape_ofyou.groupby(\"Date_month\").sum()\n",
+    "shape_ofyou\n",
+    "\n",
+    "years_shape = pd.DataFrame(shape_ofyou[\"Streams\"])\n",
+    "years_shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>Streams</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>67281563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>27122370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>27677654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>1758446</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Streams\n",
+       "Date_month          \n",
+       "1           67281563\n",
+       "2           27122370\n",
+       "3           27677654\n",
+       "4            1758446"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "castle = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Castle on the Hill\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "castle = castle.sort_values(\"Date_month\", ascending = False)\n",
+    "castle\n",
+    "\n",
+    "global_castle = (castle.loc[(castle[\"Region\"]==\"global\"),:]).index\n",
+    "global_castle\n",
+    "\n",
+    "castle.drop(global_castle , inplace=True)\n",
+    "castle\n",
+    "\n",
+    "castle = castle.groupby(\"Date_month\").sum()\n",
+    "castle\n",
+    "\n",
+    "years_castle = pd.DataFrame(castle[\"Streams\"])\n",
+    "years_castle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Streams</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>125238551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>180614337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>21751850</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>91421</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Streams\n",
+       "Date_month           \n",
+       "1           125238551\n",
+       "2           180614337\n",
+       "3            21751850\n",
+       "4               91421"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fifty_shades = position_df.loc[(position_df[\"Song Name\"]==\"I Don’t Wanna Live Forever (Fifty Shades Darker) - From \\\"Fifty Shades Darker (Original Motion Picture Soundtrack)\\\"\") &\n",
+    "                               (position_df[\"Date_year\"]==2017),:]\n",
+    "fifty_shades\n",
+    "\n",
+    "global_fifty= (fifty_shades.loc[(fifty_shades[\"Region\"]==\"global\"),:]).index\n",
+    "global_fifty\n",
+    "\n",
+    "global_fifty.drop(global_fifty)\n",
+    "fifty_shades\n",
+    "\n",
+    "\n",
+    "fifty_shades = fifty_shades.sort_values(\"Date_month\", ascending = False)\n",
+    "fifty_shades\n",
+    "\n",
+    "fifty_shades = fifty_shades.groupby(\"Date_month\").sum()\n",
+    "fifty_shades\n",
+    "\n",
+    "years_fifty = pd.DataFrame(fifty_shades[\"Streams\"])\n",
+    "years_fifty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "from matplotlib.ticker import FormatStrFormatter\n",
+    "\n",
+    "x_axis = [\"Jan\", \"Feb\", \"Mar\", \"Apr\"]\n",
+    "y_axis_shape = [175147277,202102455, 233465327, 146431398]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "shape = ax.plot(x_axis, y_axis_shape, color=\"blue\", label=\"Shape of You by Ed Sheeran\" )\n",
+    "\n",
+    "fifty = ax.plot(x_axis,years_fifty.iloc[:,[0]], color=\"red\", label=\"Fifty Shades of Grey by ZAYN\" )\n",
+    "\n",
+    "castle = ax.plot(x_axis, years_castle, color=\"green\", label=\"Castle on the Hill by Ed Sheeran\" )\n",
+    "\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song from Jan-April\\n (Quarterly Report)\")\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Quarter1.png\", dpi= 300)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "QUARTER 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>327</td>\n",
+       "      <td>27606046</td>\n",
+       "      <td>500216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>689</td>\n",
+       "      <td>72246058</td>\n",
+       "      <td>1048840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>727</td>\n",
+       "      <td>67441671</td>\n",
+       "      <td>1198098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>1092</td>\n",
+       "      <td>49462833</td>\n",
+       "      <td>1125486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>1127</td>\n",
+       "      <td>46017353</td>\n",
+       "      <td>750324</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>379</td>\n",
+       "      <td>6262712</td>\n",
+       "      <td>199683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>68164</td>\n",
+       "      <td>16136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>36</td>\n",
+       "      <td>107683</td>\n",
+       "      <td>36306</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "1            327  27606046     500216\n",
+       "2            689  72246058    1048840\n",
+       "3            727  67441671    1198098\n",
+       "4           1092  49462833    1125486\n",
+       "5           1127  46017353     750324\n",
+       "6            379   6262712     199683\n",
+       "7             29     68164      16136\n",
+       "11            36    107683      36306"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "despacito = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Despacito (Featuring Daddy Yankee)\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "despacito = despacito.sort_values(\"Date_month\", ascending = False)\n",
+    "despacito\n",
+    "\n",
+    "global_despacito= (despacito.loc[(despacito[\"Region\"]==\"global\"),:]).index\n",
+    "global_despacito\n",
+    "\n",
+    "despacito.drop(global_despacito , inplace=True)\n",
+    "despacito\n",
+    "\n",
+    "despacito = despacito.groupby(\"Date_month\").sum()\n",
+    "despacito"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1963993</td>\n",
+       "      <td>6051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>828</td>\n",
+       "      <td>224458081</td>\n",
+       "      <td>740239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>893</td>\n",
+       "      <td>200800893</td>\n",
+       "      <td>611151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>632</td>\n",
+       "      <td>105073849</td>\n",
+       "      <td>348941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>60</td>\n",
+       "      <td>12692728</td>\n",
+       "      <td>24204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank    Streams  Date_year\n",
+       "Date_month                            \n",
+       "3              8    1963993       6051\n",
+       "4            828  224458081     740239\n",
+       "5            893  200800893     611151\n",
+       "6            632  105073849     348941\n",
+       "7             60   12692728      24204"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "humble = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"] == \"HUMBLE.\") & \n",
+    "                                     (position_df[\"Date_year\"]==2017),:])\n",
+    "humble = humble.sort_values(\"Date_month\", ascending = False)\n",
+    "\n",
+    "humble\n",
+    "\n",
+    "# global_humble= (humble.loc[humble[\"Region\"]==\"global\"),:]).index  <<--- NO GLOBAL DATA\n",
+    "# global_humble\n",
+    "\n",
+    "# humble.drop(global_humble , inplace=True)\n",
+    "# humble\n",
+    "\n",
+    "humble = humble.groupby(\"Date_month\").sum()\n",
+    "humble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>4</td>\n",
+       "      <td>52</td>\n",
+       "      <td>5582318</td>\n",
+       "      <td>32272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>2275</td>\n",
+       "      <td>134909468</td>\n",
+       "      <td>1619651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>2154</td>\n",
+       "      <td>95024527</td>\n",
+       "      <td>1274744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1160</td>\n",
+       "      <td>51613600</td>\n",
+       "      <td>609134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>202</td>\n",
+       "      <td>4874804</td>\n",
+       "      <td>114969</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank    Streams  Date_year\n",
+       "Date_month                            \n",
+       "4             52    5582318      32272\n",
+       "5           2275  134909468    1619651\n",
+       "6           2154   95024527    1274744\n",
+       "7           1160   51613600     609134\n",
+       "8            202    4874804     114969"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "the_one = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"] == \"I'm the One\") & \n",
+    "                                     (position_df[\"Date_year\"]==2017),:])\n",
+    "the_one = the_one.sort_values(\"Date_month\", ascending = False)\n",
+    "the_one\n",
+    "global_one= (the_one.loc[(the_one[\"Region\"]==\"global\"),:]).index\n",
+    "global_one\n",
+    "\n",
+    "the_one.drop(global_one , inplace=True)\n",
+    "the_one\n",
+    "\n",
+    "the_one = the_one.groupby(\"Date_month\").sum()\n",
+    "the_one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_axis = [ \"Mar\",\"Apr\", \"May\", \"June\", \"July\", \"August\"]\n",
+    "y_axis_despacito = [67441671, 61170701, 184182643, 170141881, 123781558, 0]\n",
+    "y_axis_humble = [1963993,1963993, 224458081, 200800893, 105073849, 0]\n",
+    "y_axis_one = [None,5582318, 134909468, 95024527, 51613600, 4874804]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,8))\n",
+    "desp = ax.plot(x_axis, y_axis_despacito, color=\"blue\", label=\"Despacito(Remix) by Luis Fonsi\" )\n",
+    "hum = ax.plot(x_axis, y_axis_humble, color = \"red\", label = \"HUMBLE. by Kendrick Lamar\")\n",
+    "one = ax.plot(x_axis, y_axis_one, color = \"green\", label = \"I'm the One by DJ Khaled\")\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song from March-August\\n (Quarterly Report)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Quarter2.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "QUARTER 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>86781</td>\n",
+       "      <td>6051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1085</td>\n",
+       "      <td>49086161</td>\n",
+       "      <td>1188013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>82650620</td>\n",
+       "      <td>2033136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>1716</td>\n",
+       "      <td>58681882</td>\n",
+       "      <td>1510733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>354</td>\n",
+       "      <td>5665852</td>\n",
+       "      <td>171445</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "6             11     86781       6051\n",
+       "7           1085  49086161    1188013\n",
+       "8           2017  82650620    2033136\n",
+       "9           1716  58681882    1510733\n",
+       "10           354   5665852     171445"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_gente = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Mi Gente\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "mi_gente = mi_gente.sort_values(\"Date_month\", ascending = False)\n",
+    "mi_gente\n",
+    "\n",
+    "global_gente= (mi_gente.loc[(mi_gente[\"Region\"]==\"global\"),:]).index\n",
+    "global_gente\n",
+    "\n",
+    "mi_gente.drop(global_gente , inplace=True)\n",
+    "mi_gente\n",
+    "\n",
+    "mi_gente = mi_gente.groupby(\"Date_month\").sum()\n",
+    "mi_gente"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>5</td>\n",
+       "      <td>142</td>\n",
+       "      <td>8861534</td>\n",
+       "      <td>70595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>880</td>\n",
+       "      <td>25112130</td>\n",
+       "      <td>455842</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1212</td>\n",
+       "      <td>57240047</td>\n",
+       "      <td>762426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>1158</td>\n",
+       "      <td>66213146</td>\n",
+       "      <td>770494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>521</td>\n",
+       "      <td>16362894</td>\n",
+       "      <td>308601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>101</td>\n",
+       "      <td>802526</td>\n",
+       "      <td>46391</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "5            142   8861534      70595\n",
+       "6            880  25112130     455842\n",
+       "7           1212  57240047     762426\n",
+       "8           1158  66213146     770494\n",
+       "9            521  16362894     308601\n",
+       "10           101    802526      46391"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unforgettable= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Unforgettable\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "unforgettable =unforgettable.sort_values(\"Date_month\", ascending = False)\n",
+    "unforgettable\n",
+    "\n",
+    "global_unforget= (unforgettable.loc[(unforgettable[\"Region\"]==\"global\"),:]).index\n",
+    "global_unforget\n",
+    "\n",
+    "unforgettable.drop(global_unforget , inplace=True)\n",
+    "unforgettable\n",
+    "\n",
+    "unforgettable= unforgettable.groupby(\"Date_month\").sum()\n",
+    "unforgettable"
+   ]
+  },
+  {
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>6</td>\n",
+       "      <td>645</td>\n",
+       "      <td>97209040</td>\n",
+       "      <td>453825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>1547</td>\n",
+       "      <td>219728644</td>\n",
+       "      <td>1125486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>796</td>\n",
+       "      <td>113618118</td>\n",
+       "      <td>486097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>29</td>\n",
+       "      <td>114707</td>\n",
+       "      <td>12102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank    Streams  Date_year\n",
+       "Date_month                            \n",
+       "6            645   97209040     453825\n",
+       "7           1547  219728644    1125486\n",
+       "8            796  113618118     486097\n",
+       "9             29     114707      12102"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wild_thoughts= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Wild Thoughts\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "wild_thoughts =wild_thoughts.sort_values(\"Date_month\", ascending = False)\n",
+    "wild_thoughts\n",
+    "\n",
+    "# global_wild= (wild_thoughts.loc[wild_thoughts[\"Region\"]==\"global\"),:]).index\n",
+    "# global_wild\n",
+    "\n",
+    "# wild_thoughts.drop(global_wild , inplace=True)\n",
+    "# wild_thoughts\n",
+    "\n",
+    "wild_thoughts= wild_thoughts.groupby(\"Date_month\").sum()\n",
+    "wild_thoughts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_axis = [ \"June\",\"July\", \"August\", \"Sept\",\"Oct\" ]\n",
+    "y_axis_gente = [86781,49086161, 82650620, 58681882, 5665852]\n",
+    "y_axis_unforget = [25112130,57240047,66213146,16362894, 802526 ]\n",
+    "y_axis_wild= [97209040, 219728644, 113618118, 114707,0]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,8))\n",
+    "gente = ax.plot(x_axis, y_axis_gente, color=\"blue\", label=\"Mi Gente by J Balvin\" )\n",
+    "unforgettable = ax.plot(x_axis, y_axis_unforget, color = \"red\", label = \"Unforgettable by French Montana\")\n",
+    "wild = ax.plot(x_axis, y_axis_wild, color = \"green\", label = \"Wild Thoughts by DJ Khaled\")\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song from June to October\\n (Quarterly Report)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Quarter3.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "QUARTER 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>459</td>\n",
+       "      <td>78018216</td>\n",
+       "      <td>629304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>1153</td>\n",
+       "      <td>168703080</td>\n",
+       "      <td>1639821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>1325</td>\n",
+       "      <td>135138889</td>\n",
+       "      <td>1514767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>100771939</td>\n",
+       "      <td>1359458</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank    Streams  Date_year\n",
+       "Date_month                            \n",
+       "9            459   78018216     629304\n",
+       "10          1153  168703080    1639821\n",
+       "11          1325  135138889    1514767\n",
+       "12          1488  100771939    1359458"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rockstar= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"rockstar\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "rockstar = rockstar.sort_values(\"Date_month\", ascending = False)\n",
+    "rockstar\n",
+    "\n",
+    "global_rock= (rockstar.loc[(rockstar[\"Region\"]==\"global\"),:]).index\n",
+    "global_rock\n",
+    "\n",
+    "rockstar.drop(global_rock , inplace=True)\n",
+    "rockstar\n",
+    "\n",
+    "rockstar= rockstar.groupby(\"Date_month\").sum()\n",
+    "rockstar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>104</td>\n",
+       "      <td>162654</td>\n",
+       "      <td>48408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>952</td>\n",
+       "      <td>11128461</td>\n",
+       "      <td>461893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>1768</td>\n",
+       "      <td>44532265</td>\n",
+       "      <td>1095231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>2168</td>\n",
+       "      <td>94294853</td>\n",
+       "      <td>1666042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1542</td>\n",
+       "      <td>37943423</td>\n",
+       "      <td>1044806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "8            104    162654      48408\n",
+       "9            952  11128461     461893\n",
+       "10          1768  44532265    1095231\n",
+       "11          2168  94294853    1666042\n",
+       "12          1542  37943423    1044806"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "havana= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Havana\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "havana = havana.sort_values(\"Date_month\", ascending = False)\n",
+    "havana\n",
+    "\n",
+    "global_havana= (havana.loc[(havana[\"Region\"]==\"global\"),:]).index\n",
+    "global_havana\n",
+    "\n",
+    "havana.drop(global_havana , inplace=True)\n",
+    "havana\n",
+    "\n",
+    "havana= havana.groupby(\"Date_month\").sum()\n",
+    "havana"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>7</td>\n",
+       "      <td>22</td>\n",
+       "      <td>166851</td>\n",
+       "      <td>10085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8</td>\n",
+       "      <td>771</td>\n",
+       "      <td>23971436</td>\n",
+       "      <td>518369</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9</td>\n",
+       "      <td>1627</td>\n",
+       "      <td>44419203</td>\n",
+       "      <td>1069010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10</td>\n",
+       "      <td>1915</td>\n",
+       "      <td>41465573</td>\n",
+       "      <td>1123469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>987</td>\n",
+       "      <td>21350953</td>\n",
+       "      <td>478029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>359</td>\n",
+       "      <td>6023547</td>\n",
+       "      <td>159343</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "7             22    166851      10085\n",
+       "8            771  23971436     518369\n",
+       "9           1627  44419203    1069010\n",
+       "10          1915  41465573    1123469\n",
+       "11           987  21350953     478029\n",
+       "12           359   6023547     159343"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_rules = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"New Rules\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "new_rules = new_rules.sort_values(\"Date_month\", ascending = False)\n",
+    "new_rules\n",
+    "\n",
+    "global_rules= (new_rules.loc[(new_rules[\"Region\"]==\"global\"),:]).index\n",
+    "global_rules\n",
+    "\n",
+    "new_rules.drop(global_rules , inplace=True)\n",
+    "new_rules\n",
+    "\n",
+    "new_rules= new_rules.groupby(\"Date_month\").sum()\n",
+    "new_rules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_axis = [ \"Sept\",\"Oct\", \"Nov\", \"Dec\" ]\n",
+    "y_axis_rock = [78018216,168703080,135138889, 100771939]\n",
+    "y_axis_havana = [11128461,44532265,94294853,37943423 ]\n",
+    "y_axis_rules= [44419203, 41465573,21350953, 6023547]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,8))\n",
+    "rock = ax.plot(x_axis, y_axis_rock, color=\"blue\", label=\"rockstar by Post Malone\" )\n",
+    "Havana = ax.plot(x_axis, y_axis_havana, color = \"red\", label = \"Havana by Camila Cabello\")\n",
+    "rules = ax.plot(x_axis, y_axis_rules, color = \"green\", label = \"New Rules by Dua Lipa\")\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song from September to December\\n (Quarterly Report)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Quarter4.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CHRISTMAS SPECIAL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>30</td>\n",
+       "      <td>252276</td>\n",
+       "      <td>16136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>1152</td>\n",
+       "      <td>54376604</td>\n",
+       "      <td>913701</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "11            30    252276      16136\n",
+       "12          1152  54376604     913701"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "want_for_christmas= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"All I Want for Christmas Is You\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "want_for_christmas = want_for_christmas.sort_values(\"Date_month\", ascending = False)\n",
+    "want_for_christmas\n",
+    "\n",
+    "global_want= (want_for_christmas.loc[(want_for_christmas[\"Region\"]==\"global\"),:]).index\n",
+    "global_want\n",
+    "\n",
+    "want_for_christmas.drop(global_want , inplace=True)\n",
+    "want_for_christmas\n",
+    "\n",
+    "month_for_christmas= want_for_christmas.groupby(\"Date_month\").sum()\n",
+    "month_for_christmas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>Rank</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date_year</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date_month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>125827</td>\n",
+       "      <td>4034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12</td>\n",
+       "      <td>753</td>\n",
+       "      <td>32602497</td>\n",
+       "      <td>488114</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Rank   Streams  Date_year\n",
+       "Date_month                           \n",
+       "11             8    125827       4034\n",
+       "12           753  32602497     488114"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last_christmas= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Last Christmas\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "last_christmas = last_christmas.sort_values(\"Date_month\", ascending = False)\n",
+    "last_christmas\n",
+    "\n",
+    "global_last= (last_christmas.loc[(last_christmas[\"Region\"]==\"global\"),:]).index\n",
+    "global_last\n",
+    "\n",
+    "last_christmas.drop(global_last , inplace=True)\n",
+    "last_christmas\n",
+    "\n",
+    "month_christmas= last_christmas.groupby(\"Date_month\").sum()\n",
+    "month_christmas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# know_christmas= pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Do They Know It's Christmas? - 1984 Version\")&\n",
+    "#                                         (position_df[\"Date_year\"]==2017),:])\n",
+    "# know_christmas = know_christmas.sort_values(\"Date_month\", ascending = False)\n",
+    "# know_christmas\n",
+    "\n",
+    "# global_know= (know_christmas.loc[(know_christmas[\"Region\"]==\"global\"),:]).index\n",
+    "# global_know\n",
+    "\n",
+    "# know_christmas.drop(global_know , inplace=True)\n",
+    "# know_christmas\n",
+    "\n",
+    "# know_christmas= know_christmas.groupby(\"Date_month\").sum()\n",
+    "# know_christmas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x_axis = [\"Nov\", \"Dec\"]\n",
+    "\n",
+    "y_axis_know = [0, 6395145]\n",
+    "y_month_christmas = [125827,32602497 ]\n",
+    "y_axis_for_christmas = [252276,54376604 ]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8,8))\n",
+    "want = ax.plot(x_axis, y_axis_for_christmas, color = \"blue\", label = \"All I Want for Christmas is You by Mariah Carey\")\n",
+    "# last = ax.plot(x_axis, last_christmas, color = \"red\", label = \"Last Christmas by Wham!\")\n",
+    "last = ax.plot (x_axis, y_month_christmas, color = \"red\", label = \"Last Christmas by Wham!\")\n",
+    "know = ax.plot (x_axis, y_axis_know, color = \"green\", label = \"Do They Know It's Christmas? - 1984 Version by Band Aid\")\n",
+    "\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "# plt.ylim(100000, 50000000)\n",
+    "\n",
+    "# yticks = np.arange(0, 50000000, step=10000)\n",
+    "# start, end = ax.get_xlim()\n",
+    "# ax.xaxis.set_ticks(np.arange(start, end, 10000))\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# Of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song from November to December\\n (Christmas Edition)\")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Christmas.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Throughout the Year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "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": [
+    "x_axis = [\"Jan\",\"Feb\",\"Mar\", \"Apr\", \"May\",\"Jun\", \"Jul\", \"Aug\", \"Sept\", \"Oct\", \"Nov\", \"Dec\"]\n",
+    "\n",
+    "y_axis_fity = [67281563, 27122370, 27677654, 1758446, None, None, None, None, None, None, None, None,]\n",
+    "y_axis_castle = [67281563, 27122370, 27677654, 1758446,None, None, None, None, None, None, None, None]\n",
+    "\n",
+    "y_axis_despacito = [27606046, 72246058, 67441671, 49462833, 46017353, 6262712, 68164, 107683, None,None,None,None]\n",
+    "y_axis_humble = [None, None,1963993, 224458081, 200800893, 105073849, 1269728,None,None,None,None, None]\n",
+    "y_axis_one = [None, None, None,5582318, 134909468, 95024527, 51613600, 4874804, None,None,None,None]\n",
+    "\n",
+    "y_axis_gente = [None,None,None,None,None,86781,49086161, 82650620, 58681882, 5665852, None, None]\n",
+    "y_axis_unforget = [None,None,None,None,8861534,25112130,57240047,66213146,16362894, 802526,None,None]\n",
+    "y_axis_wild= [None,None,None,None,None,97209040, 219728644, 113618118, 114707,None,None,None]\n",
+    "\n",
+    "y_axis_rock = [None,None,None,None,None,None,None,None,78018216,168703080,135138889, 100771939]\n",
+    "y_axis_havana = [None,None,None,None,None,None,None,162654,11128461,44532265,94294853,37943423 ]\n",
+    "y_axis_rules= [None,None,None,None,None,None,None, 166851, 44419203, 41465573,21350953, 6023547]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(20,10))\n",
+    "shape = ax.plot(x_axis, years_shape, color=\"blue\", label=\"Shape of You by Ed Sheeran\" )\n",
+    "\n",
+    "fifty = ax.plot(x_axis,y_axis_fity, color=\"red\", label=\"Fifty Shades of Grey by ZAYN\" )\n",
+    "\n",
+    "castle = ax.plot(x_axis, y_axis_castle, color=\"green\", label=\"Castle on the Hill by Ed Sheeran\" )\n",
+    "\n",
+    "desp = ax.plot(x_axis, y_axis_despacito, color=\"purple\", label=\"Despacito(Remix) by Luis Fonsi\" )\n",
+    "hum = ax.plot(x_axis, y_axis_humble, color = \"black\", label = \"HUMBLE. by Kendrick Lamar\")\n",
+    "one = ax.plot(x_axis, y_axis_one, color = \"cyan\", label = \"I'm the One by DJ Khaled\")\n",
+    "\n",
+    "gente = ax.plot(x_axis, y_axis_gente, color=\"magenta\", label=\"Mi Gente by J Balvin\" )\n",
+    "unforgettable = ax.plot(x_axis, y_axis_unforget, color = \"olive\", label = \"Unforgettable by French Montana\")\n",
+    "wild = ax.plot(x_axis, y_axis_wild, color = \"gray\", label = \"Wild Thoughts by DJ Khaled\")\n",
+    "\n",
+    "rock = ax.plot(x_axis, y_axis_rock, color=\"orange\", label=\"rockstar by Post Malone\" )\n",
+    "Havana = ax.plot(x_axis, y_axis_havana, color = \"darkgoldenrod\", label = \"Havana by Camila Cabello\")\n",
+    "rules = ax.plot(x_axis, y_axis_rules, color = \"orangered\", label = \"New Rules by Dua Lipa\")\n",
+    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))\n",
+    "\n",
+    "plt.xlabel(\"Months in 2017\")\n",
+    "plt.ylabel(\"# of Total Streams\")\n",
+    "plt.title(\"Total # of Streams per Song\\n Throughout the Year\")\n",
+    "plt.legend()\n",
+    "plt.autoscale(enable=True, axis='both', tight=None)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Yearly.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Songs Sparced by Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>Rank</th>\n",
+       "      <th>Song Name</th>\n",
+       "      <th>Artist</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Date_year</th>\n",
+       "      <th>Date_month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1603</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Shape of You</td>\n",
+       "      <td>Ed Sheeran</td>\n",
+       "      <td>12200</td>\n",
+       "      <td>2017-01-09</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>75200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Shape of You</td>\n",
+       "      <td>Ed Sheeran</td>\n",
+       "      <td>113479</td>\n",
+       "      <td>2017-01-06</td>\n",
+       "      <td>fr</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>157004</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Shape of You</td>\n",
+       "      <td>Ed Sheeran</td>\n",
+       "      <td>142451</td>\n",
+       "      <td>2017-02-13</td>\n",
+       "      <td>ar</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>223601</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Shape of You</td>\n",
+       "      <td>Ed Sheeran</td>\n",
+       "      <td>94929</td>\n",
+       "      <td>2017-01-06</td>\n",
+       "      <td>fi</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>297801</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Shape of You</td>\n",
+       "      <td>Ed Sheeran</td>\n",
+       "      <td>135566</td>\n",
+       "      <td>2017-01-06</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Rank     Song Name      Artist  Streams       Date Region  Date_year  \\\n",
+       "1603       4  Shape of You  Ed Sheeran    12200 2017-01-09     ec       2017   \n",
+       "75200      1  Shape of You  Ed Sheeran   113479 2017-01-06     fr       2017   \n",
+       "157004     5  Shape of You  Ed Sheeran   142451 2017-02-13     ar       2017   \n",
+       "223601     2  Shape of You  Ed Sheeran    94929 2017-01-06     fi       2017   \n",
+       "297801     2  Shape of You  Ed Sheeran   135566 2017-01-06     no       2017   \n",
+       "\n",
+       "        Date_month  \n",
+       "1603             1  \n",
+       "75200            1  \n",
+       "157004           2  \n",
+       "223601           1  \n",
+       "297801           1  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_song = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Shape of You\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "one_song\n",
+    "\n",
+    "one_df = one_song.drop_duplicates(subset=[\"Song Name\", \"Region\"], keep = \"first\", inplace = False)\n",
+    "one_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>Rank</th>\n",
+       "      <th>Song Name</th>\n",
+       "      <th>Artist</th>\n",
+       "      <th>Streams</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>Date_year</th>\n",
+       "      <th>Date_month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>21200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Despacito - Remix</td>\n",
+       "      <td>Luis Fonsi</td>\n",
+       "      <td>21030</td>\n",
+       "      <td>2017-04-17</td>\n",
+       "      <td>ec</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>96804</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Despacito - Remix</td>\n",
+       "      <td>Luis Fonsi</td>\n",
+       "      <td>82032</td>\n",
+       "      <td>2017-04-24</td>\n",
+       "      <td>fr</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>169600</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Despacito - Remix</td>\n",
+       "      <td>Luis Fonsi</td>\n",
+       "      <td>259697</td>\n",
+       "      <td>2017-04-17</td>\n",
+       "      <td>ar</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>244201</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Despacito - Remix</td>\n",
+       "      <td>Luis Fonsi</td>\n",
+       "      <td>67826</td>\n",
+       "      <td>2017-04-19</td>\n",
+       "      <td>fi</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>318403</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Despacito - Remix</td>\n",
+       "      <td>Luis Fonsi</td>\n",
+       "      <td>122097</td>\n",
+       "      <td>2017-04-19</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Rank          Song Name      Artist  Streams       Date Region  \\\n",
+       "21200      1  Despacito - Remix  Luis Fonsi    21030 2017-04-17     ec   \n",
+       "96804      5  Despacito - Remix  Luis Fonsi    82032 2017-04-24     fr   \n",
+       "169600     1  Despacito - Remix  Luis Fonsi   259697 2017-04-17     ar   \n",
+       "244201     2  Despacito - Remix  Luis Fonsi    67826 2017-04-19     fi   \n",
+       "318403     4  Despacito - Remix  Luis Fonsi   122097 2017-04-19     no   \n",
+       "\n",
+       "        Date_year  Date_month  \n",
+       "21200        2017           4  \n",
+       "96804        2017           4  \n",
+       "169600       2017           4  \n",
+       "244201       2017           4  \n",
+       "318403       2017           4  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "despacito = pd.DataFrame(position_df.loc[(position_df[\"Song Name\"]==\"Despacito - Remix\")&\n",
+    "                                        (position_df[\"Date_year\"]==2017),:])\n",
+    "despacito\n",
+    "\n",
+    "despacito_one = despacito.drop_duplicates(subset=[\"Song Name\", \"Region\"], keep = \"first\", inplace = False)\n",
+    "despacito_one.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mansi/opt/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
+      "\n",
+      "To register the converters:\n",
+      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
+      "\t>>> register_matplotlib_converters()\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "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": [
+    "dates = one_df[\"Date\"]\n",
+    "dates\n",
+    "\n",
+    "\n",
+    "names = one_df[\"Region\"]\n",
+    "# Choose some nice levels\n",
+    "levels = np.tile([5, 4, 3, 2, 1, -1],\n",
+    "                 int(np.ceil(len(dates))))[:len(dates)]\n",
+    "\n",
+    "\n",
+    "# Create figure and plot a stem plot with the date\n",
+    "fig, ax = plt.subplots(figsize=(20,10), constrained_layout=False)\n",
+    "ax.set(title=\"Regions with 'Shape of You' as Top Song by Date\")\n",
+    "\n",
+    "markerline, stemline, baseline = ax.stem(dates, levels,\n",
+    "                                         linefmt=\"C3-\", basefmt=\"k-\",\n",
+    "                                         use_line_collection=True)\n",
+    "\n",
+    "plt.setp(markerline, mec=\"k\", mfc=\"w\", zorder=3)\n",
+    "\n",
+    "# Shift the markers to the baseline by replacing the y-data by zeros.\n",
+    "markerline.set_ydata(np.zeros(len(dates)))\n",
+    "\n",
+    "# annotate lines\n",
+    "vert = np.array(['top', 'bottom'])[(levels > 0).astype(int)]\n",
+    "for d, l, r, va in zip(dates,levels,names, vert):\n",
+    "    ax.annotate(r, xy=(d, l), xytext=(-3, np.sign(l)*3),\n",
+    "                textcoords=\"offset points\", va=va, ha=\"right\")\n",
+    "\n",
+    "# format xaxis with 4 month intervals\n",
+    "# ax.get_xaxis().set_major_locator(mdates.MonthLocator(interval=1))\n",
+    "# ax.get_xaxis().set_major_formatter(mdates.DateFormatter(\"%b %Y\"))\n",
+    "plt.setp(ax.get_xticklabels(), rotation=30, ha=\"right\")\n",
+    "# \n",
+    "# remove y axis and spines\n",
+    "ax.get_yaxis().set_visible(True)\n",
+    "for spine in [\"left\", \"top\", \"right\"]:\n",
+    "    ax.spines[spine].set_visible(False)\n",
+    "\n",
+    "ax.margins(y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Geography_Shape_of_you.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "des_dates = despacito_one[\"Date\"]\n",
+    "\n",
+    "\n",
+    "\n",
+    "des_names = despacito_one[\"Region\"]\n",
+    "# Choose some nice levels\n",
+    "levels = np.tile([5, 4, 3, 2, 1, -1],\n",
+    "                 int(np.ceil(len(des_dates))))[:len(des_dates)]\n",
+    "\n",
+    "\n",
+    "# Create figure and plot a stem plot with the date\n",
+    "fig, ax = plt.subplots(figsize=(20,10), constrained_layout=False)\n",
+    "ax.set(title=\"Regions with 'Despacito' as Top Song by Date\")\n",
+    "\n",
+    "markerline, stemline, baseline = ax.stem(des_dates, levels,\n",
+    "                                         linefmt=\"C3-\", basefmt=\"k-\",\n",
+    "                                         use_line_collection=True)\n",
+    "\n",
+    "plt.setp(markerline, mec=\"k\", mfc=\"w\", zorder=3)\n",
+    "\n",
+    "# Shift the markers to the baseline by replacing the y-data by zeros.\n",
+    "markerline.set_ydata(np.zeros(len(des_dates)))\n",
+    "\n",
+    "# annotate lines\n",
+    "vert = np.array(['top', 'bottom'])[(levels > 0).astype(int)]\n",
+    "for d, l, r, va in zip(des_dates,levels,des_names, vert):\n",
+    "    ax.annotate(r, xy=(d, l), xytext=(-3, np.sign(l)*3),\n",
+    "                textcoords=\"offset points\", va=va, ha=\"right\")\n",
+    "\n",
+    "# format xaxis with 4 month intervals\n",
+    "# ax.get_xaxis().set_major_locator(mdates.MonthLocator(interval=1))\n",
+    "# ax.get_xaxis().set_major_formatter(mdates.DateFormatter(\"%b %Y\"))\n",
+    "plt.setp(ax.get_xticklabels(), rotation=30, ha=\"right\")\n",
+    "# \n",
+    "# remove y axis and spines\n",
+    "ax.get_yaxis().set_visible(True)\n",
+    "for spine in [\"left\", \"top\", \"right\"]:\n",
+    "    ax.spines[spine].set_visible(False)\n",
+    "\n",
+    "ax.margins(y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/Geography_Despacito.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regions = ['Austria', 'Sweden', 'New Zealand', 'Estonia', 'Iceland',\n",
+    "       'Australia', 'Denmark', 'Lithuania', 'United States', 'France',\n",
+    "       'Global', 'Luxembourg', 'Latvia', 'Germany', 'Greece', 'Ireland',\n",
+    "        'Portugal', 'Switzerland', 'Great Britain', 'Canada']\n",
+    "\n",
+    "day_count = [1,9,20,22,27,37,40,47,47,48,49,65,65,66,91,97,129,126,128,141]\n",
+    "fig, ax = plt.subplots(figsize=(10,10), constrained_layout=False)\n",
+    "x_axis = np.arange(0, len(regions))\n",
+    "plt.title('Number of Days Unforgettable Stayed Top 5 in Each Region')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Days in Top 5')\n",
+    "\n",
+    "plt.xlim(-.75, len(regions)-.25)\n",
+    "plt.ylim(0, max(day_count) + 5)\n",
+    "plt.bar(x_axis, day_count, facecolor=\"red\", alpha=0.75, align=\"center\")\n",
+    "plt.xticks(x_axis, regions)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig(\"./Images/bar_graph.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Images/Christmas.png b/Images/Christmas.png
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diff --git a/Images/Geography_Despacito.png b/Images/Geography_Despacito.png
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diff --git a/Images/Geography_Shape_of_you.png b/Images/Geography_Shape_of_you.png
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diff --git a/Images/Quarter1.png b/Images/Quarter1.png
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diff --git a/Images/Quarter2.png b/Images/Quarter2.png
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diff --git a/Images/Quarter3.png b/Images/Quarter3.png
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diff --git a/Images/Quarter4.png b/Images/Quarter4.png
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diff --git a/Images/Yearly.png b/Images/Yearly.png
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diff --git a/Images/bar_graph.png b/Images/bar_graph.png
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diff --git a/Song Data.ipynb b/Song Data.ipynb
deleted file mode 100644
index 460a672..0000000
--- a/Song Data.ipynb	
+++ /dev/null
@@ -1,1105 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "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>Position</th>\n",
-       "      <th>Track Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>URL</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>https://open.spotify.com/track/3AEZUABDXNtecAO...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>19270</td>\n",
-       "      <td>https://open.spotify.com/track/6mICuAdrwEjh6Y6...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Otra Vez (feat. J Balvin)</td>\n",
-       "      <td>Zion &amp; Lennox</td>\n",
-       "      <td>15761</td>\n",
-       "      <td>https://open.spotify.com/track/3QwBODjSEzelZyV...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>14954</td>\n",
-       "      <td>https://open.spotify.com/track/7DM4BPaS7uofFul...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>J Balvin</td>\n",
-       "      <td>14269</td>\n",
-       "      <td>https://open.spotify.com/track/6rQSrBHf7HlZjtc...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\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",
-       "      <td>3441192</td>\n",
-       "      <td>196</td>\n",
-       "      <td>More Than You Know</td>\n",
-       "      <td>Axwell /\\ Ingrosso</td>\n",
-       "      <td>2126</td>\n",
-       "      <td>https://open.spotify.com/track/71bBFbfn2OBK5Qw...</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441193</td>\n",
-       "      <td>197</td>\n",
-       "      <td>Issues</td>\n",
-       "      <td>Julia Michaels</td>\n",
-       "      <td>2117</td>\n",
-       "      <td>https://open.spotify.com/track/7vu0JkJh0ldukEY...</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441194</td>\n",
-       "      <td>198</td>\n",
-       "      <td>Tuo Fei Lun</td>\n",
-       "      <td>Eason Chan</td>\n",
-       "      <td>2112</td>\n",
-       "      <td>https://open.spotify.com/track/0pmGleExiQyd0Hx...</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441195</td>\n",
-       "      <td>199</td>\n",
-       "      <td>面具</td>\n",
-       "      <td>Alfred Hui</td>\n",
-       "      <td>2102</td>\n",
-       "      <td>https://open.spotify.com/track/58SUsgamkW6vFFq...</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441196</td>\n",
-       "      <td>200</td>\n",
-       "      <td>Let Her Go</td>\n",
-       "      <td>Passenger</td>\n",
-       "      <td>2088</td>\n",
-       "      <td>https://open.spotify.com/track/2jyjhRf6DVbMPU5...</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3441197 rows × 7 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Position                  Track Name              Artist  Streams  \\\n",
-       "0               1  Reggaetón Lento (Bailemos)                CNCO    19272   \n",
-       "1               2                    Chantaje             Shakira    19270   \n",
-       "2               3   Otra Vez (feat. J Balvin)       Zion & Lennox    15761   \n",
-       "3               4                Vente Pa' Ca        Ricky Martin    14954   \n",
-       "4               5                      Safari            J Balvin    14269   \n",
-       "...           ...                         ...                 ...      ...   \n",
-       "3441192       196          More Than You Know  Axwell /\\ Ingrosso     2126   \n",
-       "3441193       197                      Issues      Julia Michaels     2117   \n",
-       "3441194       198                 Tuo Fei Lun          Eason Chan     2112   \n",
-       "3441195       199                          面具          Alfred Hui     2102   \n",
-       "3441196       200                  Let Her Go           Passenger     2088   \n",
-       "\n",
-       "                                                       URL        Date Region  \n",
-       "0        https://open.spotify.com/track/3AEZUABDXNtecAO...  2017-01-01     ec  \n",
-       "1        https://open.spotify.com/track/6mICuAdrwEjh6Y6...  2017-01-01     ec  \n",
-       "2        https://open.spotify.com/track/3QwBODjSEzelZyV...  2017-01-01     ec  \n",
-       "3        https://open.spotify.com/track/7DM4BPaS7uofFul...  2017-01-01     ec  \n",
-       "4        https://open.spotify.com/track/6rQSrBHf7HlZjtc...  2017-01-01     ec  \n",
-       "...                                                    ...         ...    ...  \n",
-       "3441192  https://open.spotify.com/track/71bBFbfn2OBK5Qw...  2018-01-09     hk  \n",
-       "3441193  https://open.spotify.com/track/7vu0JkJh0ldukEY...  2018-01-09     hk  \n",
-       "3441194  https://open.spotify.com/track/0pmGleExiQyd0Hx...  2018-01-09     hk  \n",
-       "3441195  https://open.spotify.com/track/58SUsgamkW6vFFq...  2018-01-09     hk  \n",
-       "3441196  https://open.spotify.com/track/2jyjhRf6DVbMPU5...  2018-01-09     hk  \n",
-       "\n",
-       "[3441197 rows x 7 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = pd.read_csv(\"./data.csv\")\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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>Rank</th>\n",
-       "      <th>Song Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>URL</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>https://open.spotify.com/track/3AEZUABDXNtecAO...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>19270</td>\n",
-       "      <td>https://open.spotify.com/track/6mICuAdrwEjh6Y6...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Otra Vez (feat. J Balvin)</td>\n",
-       "      <td>Zion &amp; Lennox</td>\n",
-       "      <td>15761</td>\n",
-       "      <td>https://open.spotify.com/track/3QwBODjSEzelZyV...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>14954</td>\n",
-       "      <td>https://open.spotify.com/track/7DM4BPaS7uofFul...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>J Balvin</td>\n",
-       "      <td>14269</td>\n",
-       "      <td>https://open.spotify.com/track/6rQSrBHf7HlZjtc...</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Rank                   Song Name         Artist  Streams  \\\n",
-       "0     1  Reggaetón Lento (Bailemos)           CNCO    19272   \n",
-       "1     2                    Chantaje        Shakira    19270   \n",
-       "2     3   Otra Vez (feat. J Balvin)  Zion & Lennox    15761   \n",
-       "3     4                Vente Pa' Ca   Ricky Martin    14954   \n",
-       "4     5                      Safari       J Balvin    14269   \n",
-       "\n",
-       "                                                 URL        Date Region  \n",
-       "0  https://open.spotify.com/track/3AEZUABDXNtecAO...  2017-01-01     ec  \n",
-       "1  https://open.spotify.com/track/6mICuAdrwEjh6Y6...  2017-01-01     ec  \n",
-       "2  https://open.spotify.com/track/3QwBODjSEzelZyV...  2017-01-01     ec  \n",
-       "3  https://open.spotify.com/track/7DM4BPaS7uofFul...  2017-01-01     ec  \n",
-       "4  https://open.spotify.com/track/6rQSrBHf7HlZjtc...  2017-01-01     ec  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "renamed_df = data.rename(columns={\"Position\": \"Rank\", \"Track Name\": \"Song Name\" })\n",
-    "renamed_df.head()\n",
-    "\n",
-    "# # position = (data[\"Position\"]<6)\n",
-    "# position = data.loc[(data[\"Position\"]<6),:]\n",
-    "# position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>Rank</th>\n",
-       "      <th>Song Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>19270</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Otra Vez (feat. J Balvin)</td>\n",
-       "      <td>Zion &amp; Lennox</td>\n",
-       "      <td>15761</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>14954</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>J Balvin</td>\n",
-       "      <td>14269</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\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",
-       "      <td>3441192</td>\n",
-       "      <td>196</td>\n",
-       "      <td>More Than You Know</td>\n",
-       "      <td>Axwell /\\ Ingrosso</td>\n",
-       "      <td>2126</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441193</td>\n",
-       "      <td>197</td>\n",
-       "      <td>Issues</td>\n",
-       "      <td>Julia Michaels</td>\n",
-       "      <td>2117</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441194</td>\n",
-       "      <td>198</td>\n",
-       "      <td>Tuo Fei Lun</td>\n",
-       "      <td>Eason Chan</td>\n",
-       "      <td>2112</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441195</td>\n",
-       "      <td>199</td>\n",
-       "      <td>面具</td>\n",
-       "      <td>Alfred Hui</td>\n",
-       "      <td>2102</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441196</td>\n",
-       "      <td>200</td>\n",
-       "      <td>Let Her Go</td>\n",
-       "      <td>Passenger</td>\n",
-       "      <td>2088</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3441197 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Rank                   Song Name              Artist  Streams  \\\n",
-       "0           1  Reggaetón Lento (Bailemos)                CNCO    19272   \n",
-       "1           2                    Chantaje             Shakira    19270   \n",
-       "2           3   Otra Vez (feat. J Balvin)       Zion & Lennox    15761   \n",
-       "3           4                Vente Pa' Ca        Ricky Martin    14954   \n",
-       "4           5                      Safari            J Balvin    14269   \n",
-       "...       ...                         ...                 ...      ...   \n",
-       "3441192   196          More Than You Know  Axwell /\\ Ingrosso     2126   \n",
-       "3441193   197                      Issues      Julia Michaels     2117   \n",
-       "3441194   198                 Tuo Fei Lun          Eason Chan     2112   \n",
-       "3441195   199                          面具          Alfred Hui     2102   \n",
-       "3441196   200                  Let Her Go           Passenger     2088   \n",
-       "\n",
-       "               Date Region  \n",
-       "0        2017-01-01     ec  \n",
-       "1        2017-01-01     ec  \n",
-       "2        2017-01-01     ec  \n",
-       "3        2017-01-01     ec  \n",
-       "4        2017-01-01     ec  \n",
-       "...             ...    ...  \n",
-       "3441192  2018-01-09     hk  \n",
-       "3441193  2018-01-09     hk  \n",
-       "3441194  2018-01-09     hk  \n",
-       "3441195  2018-01-09     hk  \n",
-       "3441196  2018-01-09     hk  \n",
-       "\n",
-       "[3441197 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "updated_df = renamed_df[[\"Rank\",\"Song Name\", \"Artist\", \"Streams\", \"Date\", \"Region\"]]\n",
-    "updated_df\n",
-    "# position = (data[\"Position\"]<6)\n",
-    "# position = renamed_df.loc[(data[\"Position\"]<6),:]\n",
-    "# position"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Rank</th>\n",
-       "      <th>Song Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>19270</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Otra Vez (feat. J Balvin)</td>\n",
-       "      <td>Zion &amp; Lennox</td>\n",
-       "      <td>15761</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>14954</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>J Balvin</td>\n",
-       "      <td>14269</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\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",
-       "      <td>3440997</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Rewrite The Stars</td>\n",
-       "      <td>Zac Efron</td>\n",
-       "      <td>13012</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3440998</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Wolves</td>\n",
-       "      <td>Selena Gomez</td>\n",
-       "      <td>12636</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3440999</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Havana</td>\n",
-       "      <td>Camila Cabello</td>\n",
-       "      <td>12608</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Finesse (Remix) [feat. Cardi B]</td>\n",
-       "      <td>Bruno Mars</td>\n",
-       "      <td>11309</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3441001</td>\n",
-       "      <td>5</td>\n",
-       "      <td>River (feat. Ed Sheeran)</td>\n",
-       "      <td>Eminem</td>\n",
-       "      <td>11208</td>\n",
-       "      <td>2018-01-09</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>98369 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Rank                        Song Name          Artist  Streams  \\\n",
-       "0           1       Reggaetón Lento (Bailemos)            CNCO    19272   \n",
-       "1           2                         Chantaje         Shakira    19270   \n",
-       "2           3        Otra Vez (feat. J Balvin)   Zion & Lennox    15761   \n",
-       "3           4                     Vente Pa' Ca    Ricky Martin    14954   \n",
-       "4           5                           Safari        J Balvin    14269   \n",
-       "...       ...                              ...             ...      ...   \n",
-       "3440997     1                Rewrite The Stars       Zac Efron    13012   \n",
-       "3440998     2                           Wolves    Selena Gomez    12636   \n",
-       "3440999     3                           Havana  Camila Cabello    12608   \n",
-       "3441000     4  Finesse (Remix) [feat. Cardi B]      Bruno Mars    11309   \n",
-       "3441001     5         River (feat. Ed Sheeran)          Eminem    11208   \n",
-       "\n",
-       "               Date Region  \n",
-       "0        2017-01-01     ec  \n",
-       "1        2017-01-01     ec  \n",
-       "2        2017-01-01     ec  \n",
-       "3        2017-01-01     ec  \n",
-       "4        2017-01-01     ec  \n",
-       "...             ...    ...  \n",
-       "3440997  2018-01-09     hk  \n",
-       "3440998  2018-01-09     hk  \n",
-       "3440999  2018-01-09     hk  \n",
-       "3441000  2018-01-09     hk  \n",
-       "3441001  2018-01-09     hk  \n",
-       "\n",
-       "[98369 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# position_df = (updated_df[\"Rank\"]<6)\n",
-    "position_df = (updated_df.loc[(data[\"Position\"]<6),:])\n",
-    "position_df\n",
-    "# position = renamed_df.loc[(data[\"Position\"]<6),:]\n",
-    "# position\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "scrolled": true
-   },
-   "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>Rank</th>\n",
-       "      <th>Song Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>19270</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Otra Vez (feat. J Balvin)</td>\n",
-       "      <td>Zion &amp; Lennox</td>\n",
-       "      <td>15761</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>14954</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Safari</td>\n",
-       "      <td>J Balvin</td>\n",
-       "      <td>14269</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\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",
-       "      <td>3366997</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Closer</td>\n",
-       "      <td>The Chainsmokers</td>\n",
-       "      <td>12831</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3366998</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Starboy</td>\n",
-       "      <td>The Weeknd</td>\n",
-       "      <td>9626</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3366999</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Let Me Love You</td>\n",
-       "      <td>DJ Snake</td>\n",
-       "      <td>9499</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3367000</td>\n",
-       "      <td>4</td>\n",
-       "      <td>I Don’t Wanna Live Forever (Fifty Shades Darke...</td>\n",
-       "      <td>ZAYN</td>\n",
-       "      <td>8210</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3367001</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Don't Wanna Know</td>\n",
-       "      <td>Maroon 5</td>\n",
-       "      <td>7606</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>266 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Rank                                          Song Name  \\\n",
-       "0           1                         Reggaetón Lento (Bailemos)   \n",
-       "1           2                                           Chantaje   \n",
-       "2           3                          Otra Vez (feat. J Balvin)   \n",
-       "3           4                                       Vente Pa' Ca   \n",
-       "4           5                                             Safari   \n",
-       "...       ...                                                ...   \n",
-       "3366997     1                                             Closer   \n",
-       "3366998     2                                            Starboy   \n",
-       "3366999     3                                    Let Me Love You   \n",
-       "3367000     4  I Don’t Wanna Live Forever (Fifty Shades Darke...   \n",
-       "3367001     5                                   Don't Wanna Know   \n",
-       "\n",
-       "                   Artist  Streams        Date Region  \n",
-       "0                    CNCO    19272  2017-01-01     ec  \n",
-       "1                 Shakira    19270  2017-01-01     ec  \n",
-       "2           Zion & Lennox    15761  2017-01-01     ec  \n",
-       "3            Ricky Martin    14954  2017-01-01     ec  \n",
-       "4                J Balvin    14269  2017-01-01     ec  \n",
-       "...                   ...      ...         ...    ...  \n",
-       "3366997  The Chainsmokers    12831  2017-01-01     hk  \n",
-       "3366998        The Weeknd     9626  2017-01-01     hk  \n",
-       "3366999          DJ Snake     9499  2017-01-01     hk  \n",
-       "3367000              ZAYN     8210  2017-01-01     hk  \n",
-       "3367001          Maroon 5     7606  2017-01-01     hk  \n",
-       "\n",
-       "[266 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jan_2017 = position_df.loc[(position[\"Date\"] ==\"2017-01-01\"),:]\n",
-    "jan_2017\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "scrolled": true
-   },
-   "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>Rank</th>\n",
-       "      <th>Song Name</th>\n",
-       "      <th>Artist</th>\n",
-       "      <th>Streams</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Region</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Reggaetón Lento (Bailemos)</td>\n",
-       "      <td>CNCO</td>\n",
-       "      <td>19272</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ec</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>74200</td>\n",
-       "      <td>1</td>\n",
-       "      <td>One Dance</td>\n",
-       "      <td>Drake</td>\n",
-       "      <td>89403</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>fr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>148400</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Chantaje</td>\n",
-       "      <td>Shakira</td>\n",
-       "      <td>253019</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>ar</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>222600</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Hukutaan</td>\n",
-       "      <td>Arttu Lindeman</td>\n",
-       "      <td>60220</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>fi</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>296800</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Alone</td>\n",
-       "      <td>Alan Walker</td>\n",
-       "      <td>103886</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>no</td>\n",
-       "    </tr>\n",
-       "    <tr>\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",
-       "      <td>3190447</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Vente Pa' Ca</td>\n",
-       "      <td>Ricky Martin</td>\n",
-       "      <td>6873</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>py</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3246244</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Call On Me - Ryan Riback Extended Remix</td>\n",
-       "      <td>Starley</td>\n",
-       "      <td>83729</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>au</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3320444</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Black Beatles</td>\n",
-       "      <td>Rae Sremmurd</td>\n",
-       "      <td>1442</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>lv</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3339809</td>\n",
-       "      <td>5</td>\n",
-       "      <td>I Don’t Wanna Live Forever (Fifty Shades Darke...</td>\n",
-       "      <td>ZAYN</td>\n",
-       "      <td>2045</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>gr</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3367001</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Don't Wanna Know</td>\n",
-       "      <td>Maroon 5</td>\n",
-       "      <td>7606</td>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>hk</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>266 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Rank                                          Song Name  \\\n",
-       "0           1                         Reggaetón Lento (Bailemos)   \n",
-       "74200       1                                          One Dance   \n",
-       "148400      1                                           Chantaje   \n",
-       "222600      1                                           Hukutaan   \n",
-       "296800      1                                              Alone   \n",
-       "...       ...                                                ...   \n",
-       "3190447     5                                       Vente Pa' Ca   \n",
-       "3246244     5            Call On Me - Ryan Riback Extended Remix   \n",
-       "3320444     5                                      Black Beatles   \n",
-       "3339809     5  I Don’t Wanna Live Forever (Fifty Shades Darke...   \n",
-       "3367001     5                                   Don't Wanna Know   \n",
-       "\n",
-       "                 Artist  Streams        Date Region  \n",
-       "0                  CNCO    19272  2017-01-01     ec  \n",
-       "74200             Drake    89403  2017-01-01     fr  \n",
-       "148400          Shakira   253019  2017-01-01     ar  \n",
-       "222600   Arttu Lindeman    60220  2017-01-01     fi  \n",
-       "296800      Alan Walker   103886  2017-01-01     no  \n",
-       "...                 ...      ...         ...    ...  \n",
-       "3190447    Ricky Martin     6873  2017-01-01     py  \n",
-       "3246244         Starley    83729  2017-01-01     au  \n",
-       "3320444    Rae Sremmurd     1442  2017-01-01     lv  \n",
-       "3339809            ZAYN     2045  2017-01-01     gr  \n",
-       "3367001        Maroon 5     7606  2017-01-01     hk  \n",
-       "\n",
-       "[266 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# streams = jan_2017.sort_values(\"Streams\",ascending=False)\n",
-    "# streams.head(10)\n",
-    "streams = jan_2017.sort_values([\"Rank\", \"Date\"])\n",
-    "streams"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrameGroupBy' object has no attribute 'counts'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-6a79ccb732dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstreams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Rank\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrank\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcounts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         raise AttributeError(\n\u001b[0;32m--> 566\u001b[0;31m             \u001b[0;34m\"%r object has no attribute %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m         )\n\u001b[1;32m    568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrameGroupBy' object has no attribute 'counts'"
-     ]
-    }
-   ],
-   "source": [
-    "rank = streams.groupby(\"Rank\")\n",
-    "rank.counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}