cleaning Data _end.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Engineering WOrld\n",
    "\n",
    "## Python statistics essential training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as pp\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "billboard = pd.read_csv('billboard.csv', encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'file' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!file billboard.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "billboard = pd.read_csv('billboard.csv',encoding='latin-1')"
   ]
  },
  {
   "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>year</th>\n",
       "      <th>artist.inverted</th>\n",
       "      <th>track</th>\n",
       "      <th>time</th>\n",
       "      <th>genre</th>\n",
       "      <th>date.entered</th>\n",
       "      <th>date.peaked</th>\n",
       "      <th>x1st.week</th>\n",
       "      <th>x2nd.week</th>\n",
       "      <th>x3rd.week</th>\n",
       "      <th>...</th>\n",
       "      <th>x67th.week</th>\n",
       "      <th>x68th.week</th>\n",
       "      <th>x69th.week</th>\n",
       "      <th>x70th.week</th>\n",
       "      <th>x71st.week</th>\n",
       "      <th>x72nd.week</th>\n",
       "      <th>x73rd.week</th>\n",
       "      <th>x74th.week</th>\n",
       "      <th>x75th.week</th>\n",
       "      <th>x76th.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>Independent Women Part I</td>\n",
       "      <td>3:38</td>\n",
       "      <td>Rock</td>\n",
       "      <td>9/23/2000</td>\n",
       "      <td>11/18/2000</td>\n",
       "      <td>78</td>\n",
       "      <td>63.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "      <td>Rock</td>\n",
       "      <td>2/12/2000</td>\n",
       "      <td>4/8/2000</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>I Knew I Loved You</td>\n",
       "      <td>4:07</td>\n",
       "      <td>Rock</td>\n",
       "      <td>10/23/1999</td>\n",
       "      <td>1/29/2000</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>Rock</td>\n",
       "      <td>8/12/2000</td>\n",
       "      <td>9/16/2000</td>\n",
       "      <td>41</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>Rock</td>\n",
       "      <td>8/5/2000</td>\n",
       "      <td>10/14/2000</td>\n",
       "      <td>57</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      artist.inverted                                  track  time  \\\n",
       "0  2000      Destiny's Child               Independent Women Part I  3:38   \n",
       "1  2000              Santana                           Maria, Maria  4:18   \n",
       "2  2000        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3  2000              Madonna                                  Music  3:45   \n",
       "4  2000  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  genre date.entered date.peaked  x1st.week  x2nd.week  x3rd.week     ...      \\\n",
       "0  Rock    9/23/2000  11/18/2000         78       63.0       49.0     ...       \n",
       "1  Rock    2/12/2000    4/8/2000         15        8.0        6.0     ...       \n",
       "2  Rock   10/23/1999   1/29/2000         71       48.0       43.0     ...       \n",
       "3  Rock    8/12/2000   9/16/2000         41       23.0       18.0     ...       \n",
       "4  Rock     8/5/2000  10/14/2000         57       47.0       45.0     ...       \n",
       "\n",
       "   x67th.week  x68th.week  x69th.week  x70th.week  x71st.week  x72nd.week  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "   x73rd.week  x74th.week  x75th.week  x76th.week  \n",
       "0         NaN         NaN         NaN         NaN  \n",
       "1         NaN         NaN         NaN         NaN  \n",
       "2         NaN         NaN         NaN         NaN  \n",
       "3         NaN         NaN         NaN         NaN  \n",
       "4         NaN         NaN         NaN         NaN  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billboard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'artist.inverted', 'track', 'time', 'genre', 'date.entered',\n",
       "       'date.peaked', 'x1st.week', 'x2nd.week', 'x3rd.week', 'x4th.week',\n",
       "       'x5th.week', 'x6th.week', 'x7th.week', 'x8th.week', 'x9th.week',\n",
       "       'x10th.week', 'x11th.week', 'x12th.week', 'x13th.week', 'x14th.week',\n",
       "       'x15th.week', 'x16th.week', 'x17th.week', 'x18th.week', 'x19th.week',\n",
       "       'x20th.week', 'x21st.week', 'x22nd.week', 'x23rd.week', 'x24th.week',\n",
       "       'x25th.week', 'x26th.week', 'x27th.week', 'x28th.week', 'x29th.week',\n",
       "       'x30th.week', 'x31st.week', 'x32nd.week', 'x33rd.week', 'x34th.week',\n",
       "       'x35th.week', 'x36th.week', 'x37th.week', 'x38th.week', 'x39th.week',\n",
       "       'x40th.week', 'x41st.week', 'x42nd.week', 'x43rd.week', 'x44th.week',\n",
       "       'x45th.week', 'x46th.week', 'x47th.week', 'x48th.week', 'x49th.week',\n",
       "       'x50th.week', 'x51st.week', 'x52nd.week', 'x53rd.week', 'x54th.week',\n",
       "       'x55th.week', 'x56th.week', 'x57th.week', 'x58th.week', 'x59th.week',\n",
       "       'x60th.week', 'x61st.week', 'x62nd.week', 'x63rd.week', 'x64th.week',\n",
       "       'x65th.week', 'x66th.week', 'x67th.week', 'x68th.week', 'x69th.week',\n",
       "       'x70th.week', 'x71st.week', 'x72nd.week', 'x73rd.week', 'x74th.week',\n",
       "       'x75th.week', 'x76th.week'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "billboard.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a85aa81630>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot(billboard.loc[0,'x1st.week':'x76th.week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a85abada90>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot(range(1,77),billboard.loc[0,'x1st.week':'x76th.week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index, row in billboard.iterrows():\n",
    "    pp.plot(range(1,77),row['x1st.week':'x76th.week'],color='C0',alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "bshort = billboard[['artist.inverted','track','time','date.entered','x1st.week','x2nd.week','x3rd.week']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>artist.inverted</th>\n",
       "      <th>track</th>\n",
       "      <th>time</th>\n",
       "      <th>date.entered</th>\n",
       "      <th>x1st.week</th>\n",
       "      <th>x2nd.week</th>\n",
       "      <th>x3rd.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>Independent Women Part I</td>\n",
       "      <td>3:38</td>\n",
       "      <td>9/23/2000</td>\n",
       "      <td>78</td>\n",
       "      <td>63.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "      <td>2/12/2000</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>I Knew I Loved You</td>\n",
       "      <td>4:07</td>\n",
       "      <td>10/23/1999</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>8/12/2000</td>\n",
       "      <td>41</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>8/5/2000</td>\n",
       "      <td>57</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       artist.inverted                                  track  time  \\\n",
       "0      Destiny's Child               Independent Women Part I  3:38   \n",
       "1              Santana                           Maria, Maria  4:18   \n",
       "2        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3              Madonna                                  Music  3:45   \n",
       "4  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  date.entered  x1st.week  x2nd.week  x3rd.week  \n",
       "0    9/23/2000         78       63.0       49.0  \n",
       "1    2/12/2000         15        8.0        6.0  \n",
       "2   10/23/1999         71       48.0       43.0  \n",
       "3    8/12/2000         41       23.0       18.0  \n",
       "4     8/5/2000         57       47.0       45.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bshort.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>artist</th>\n",
       "      <th>track</th>\n",
       "      <th>time</th>\n",
       "      <th>date.entered</th>\n",
       "      <th>wk1</th>\n",
       "      <th>wk2</th>\n",
       "      <th>wk3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Destiny's Child</td>\n",
       "      <td>Independent Women Part I</td>\n",
       "      <td>3:38</td>\n",
       "      <td>9/23/2000</td>\n",
       "      <td>78</td>\n",
       "      <td>63.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "      <td>2/12/2000</td>\n",
       "      <td>15</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>I Knew I Loved You</td>\n",
       "      <td>4:07</td>\n",
       "      <td>10/23/1999</td>\n",
       "      <td>71</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>8/12/2000</td>\n",
       "      <td>41</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>8/5/2000</td>\n",
       "      <td>57</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                artist                                  track  time  \\\n",
       "0      Destiny's Child               Independent Women Part I  3:38   \n",
       "1              Santana                           Maria, Maria  4:18   \n",
       "2        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3              Madonna                                  Music  3:45   \n",
       "4  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  date.entered  wk1   wk2   wk3  \n",
       "0    9/23/2000   78  63.0  49.0  \n",
       "1    2/12/2000   15   8.0   6.0  \n",
       "2   10/23/1999   71  48.0  43.0  \n",
       "3    8/12/2000   41  23.0  18.0  \n",
       "4     8/5/2000   57  47.0  45.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bshort.columns = ['artist','track','time','date.entered','wk1','wk2','wk3']\n",
    "bshort.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>9/23/2000</td>\n",
       "      <td>wk1</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "      <td>2/12/2000</td>\n",
       "      <td>wk1</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>I Knew I Loved You</td>\n",
       "      <td>4:07</td>\n",
       "      <td>10/23/1999</td>\n",
       "      <td>wk1</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>8/12/2000</td>\n",
       "      <td>wk1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>8/5/2000</td>\n",
       "      <td>wk1</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                artist                                  track  time  \\\n",
       "0      Destiny's Child               Independent Women Part I  3:38   \n",
       "1              Santana                           Maria, Maria  4:18   \n",
       "2        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3              Madonna                                  Music  3:45   \n",
       "4  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  date.entered week  rank  \n",
       "0    9/23/2000  wk1  78.0  \n",
       "1    2/12/2000  wk1  15.0  \n",
       "2   10/23/1999  wk1  71.0  \n",
       "3    8/12/2000  wk1  41.0  \n",
       "4     8/5/2000  wk1  57.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt = bshort.melt(['artist','track','time','date.entered'],['wk1','wk2','wk3'],'week','rank')\n",
    "bmelt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>track</th>\n",
       "      <th>time</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
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       "      <td>wk1</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
       "      <td>3:57</td>\n",
       "      <td>9/16/2000</td>\n",
       "      <td>wk2</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
       "      <td>3:57</td>\n",
       "      <td>9/16/2000</td>\n",
       "      <td>wk3</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      artist track  time date.entered week  rank\n",
       "66   Profyle  Liar  3:57    9/16/2000  wk1  52.0\n",
       "383  Profyle  Liar  3:57    9/16/2000  wk2  32.0\n",
       "700  Profyle  Liar  3:57    9/16/2000  wk3  25.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt.query('track == \"Liar\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmelt['week']= bmelt['week'].apply(lambda s: int(s[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>track</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Savage Garden</td>\n",
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       "      <td>10/23/1999</td>\n",
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       "      <td>71.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>8/12/2000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>8/5/2000</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                artist                                  track  time  \\\n",
       "0      Destiny's Child               Independent Women Part I  3:38   \n",
       "1              Santana                           Maria, Maria  4:18   \n",
       "2        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3              Madonna                                  Music  3:45   \n",
       "4  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  date.entered  week  rank  \n",
       "0    9/23/2000     1  78.0  \n",
       "1    2/12/2000     1  15.0  \n",
       "2   10/23/1999     1  71.0  \n",
       "3    8/12/2000     1  41.0  \n",
       "4     8/5/2000     1  57.0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmelt['date.entered'] = pd.to_datetime(bmelt['date.entered'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2000-09-30 00:00:00')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt['date.entered'][0] + pd.Timedelta('7 days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmelt['date'] = bmelt['date.entered'] + pd.Timedelta('7 days') * (bmelt['week'] - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>78.0</td>\n",
       "      <td>2000-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "      <td>2000-02-12</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2000-02-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Savage Garden</td>\n",
       "      <td>I Knew I Loved You</td>\n",
       "      <td>4:07</td>\n",
       "      <td>1999-10-23</td>\n",
       "      <td>1</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1999-10-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Madonna</td>\n",
       "      <td>Music</td>\n",
       "      <td>3:45</td>\n",
       "      <td>2000-08-12</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2000-08-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aguilera, Christina</td>\n",
       "      <td>Come On Over Baby (All I Want Is You)</td>\n",
       "      <td>3:38</td>\n",
       "      <td>2000-08-05</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2000-08-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                artist                                  track  time  \\\n",
       "0      Destiny's Child               Independent Women Part I  3:38   \n",
       "1              Santana                           Maria, Maria  4:18   \n",
       "2        Savage Garden                     I Knew I Loved You  4:07   \n",
       "3              Madonna                                  Music  3:45   \n",
       "4  Aguilera, Christina  Come On Over Baby (All I Want Is You)  3:38   \n",
       "\n",
       "  date.entered  week  rank       date  \n",
       "0   2000-09-23     1  78.0 2000-09-23  \n",
       "1   2000-02-12     1  15.0 2000-02-12  \n",
       "2   1999-10-23     1  71.0 1999-10-23  \n",
       "3   2000-08-12     1  41.0 2000-08-12  \n",
       "4   2000-08-05     1  57.0 2000-08-05  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmelt.drop(['date.entered'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist</th>\n",
       "      <th>track</th>\n",
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       "      <th>week</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
       "      <td>3:57</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2000-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
       "      <td>3:57</td>\n",
       "      <td>2</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2000-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>Profyle</td>\n",
       "      <td>Liar</td>\n",
       "      <td>3:57</td>\n",
       "      <td>3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2000-09-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      artist track  time  week  rank       date\n",
       "66   Profyle  Liar  3:57     1  52.0 2000-09-16\n",
       "383  Profyle  Liar  3:57     2  32.0 2000-09-23\n",
       "700  Profyle  Liar  3:57     3  25.0 2000-09-30"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmelt.query('track == \"Liar\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "bfinal = bmelt[['artist', 'track', 'time', 'date', 'week', 'rank']]\n",
    "bfinal.sort_values(['artist', 'track'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>246</th>\n",
       "      <td>2 Pac</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
       "      <td>4:22</td>\n",
       "      <td>2000-02-26</td>\n",
       "      <td>1</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>2 Pac</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
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       "      <td>2000-03-04</td>\n",
       "      <td>2</td>\n",
       "      <td>82.0</td>\n",
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       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>2 Pac</td>\n",
       "      <td>Baby Don't Cry (Keep Ya Head Up II)</td>\n",
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       "      <td>3</td>\n",
       "      <td>72.0</td>\n",
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       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2Ge+her</td>\n",
       "      <td>The Hardest Part Of Breaking Up (Is Getting Ba...</td>\n",
       "      <td>3:15</td>\n",
       "      <td>2000-09-09</td>\n",
       "      <td>2</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      artist                                              track  time  \\\n",
       "246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "563    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "880    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "287  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...  3:15   \n",
       "604  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...  3:15   \n",
       "\n",
       "          date  week  rank  \n",
       "246 2000-02-26     1  87.0  \n",
       "563 2000-03-04     2  82.0  \n",
       "880 2000-03-11     3  72.0  \n",
       "287 2000-09-02     1  91.0  \n",
       "604 2000-09-09     2  87.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfinal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "           artist                                              track  time\n",
       "246         2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22\n",
       "287       2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...  3:15\n",
       "24   3 Doors Down                                         Kryptonite  3:53\n",
       "193  3 Doors Down                                              Loser  4:24\n",
       "69       504 Boyz                                      Wobble Wobble  3:35"
      ]
     },
     "execution_count": 69,
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   "source": [
    "tracks = bfinal[['artist', 'track', 'time']].drop_duplicates()\n",
    "tracks.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    id        artist                                              track  time\n",
       "0  246         2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22\n",
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       "3  193  3 Doors Down                                              Loser  4:24\n",
       "4   69      504 Boyz                                      Wobble Wobble  3:35"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracks.index.name = 'id'\n",
    "tracksid = tracks.reset_index()\n",
    "tracksid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    id   artist                                              track time_x  \\\n",
       "0  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)   4:22   \n",
       "1  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)   4:22   \n",
       "2  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)   4:22   \n",
       "3  287  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...   3:15   \n",
       "4  287  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...   3:15   \n",
       "\n",
       "  time_y       date  week  rank  \n",
       "0   4:22 2000-02-26     1  87.0  \n",
       "1   4:22 2000-03-04     2  82.0  \n",
       "2   4:22 2000-03-11     3  72.0  \n",
       "3   3:15 2000-09-02     1  91.0  \n",
       "4   3:15 2000-09-09     2  87.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(tracksid,bfinal,on=['track','artist']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    id   artist                                              track  time  \\\n",
       "0  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "1  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "2  246    2 Pac                Baby Don't Cry (Keep Ya Head Up II)  4:22   \n",
       "3  287  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...  3:15   \n",
       "4  287  2Ge+her  The Hardest Part Of Breaking Up (Is Getting Ba...  3:15   \n",
       "\n",
       "        date  week  rank  \n",
       "0 2000-02-26     1  87.0  \n",
       "1 2000-03-04     2  82.0  \n",
       "2 2000-03-11     3  72.0  \n",
       "3 2000-09-02     1  91.0  \n",
       "4 2000-09-09     2  87.0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(tracksid,bfinal,on=['track','artist', 'time']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    id       date  week  rank\n",
       "0  246 2000-02-26     1  87.0\n",
       "1  246 2000-03-04     2  82.0\n",
       "2  246 2000-03-11     3  72.0\n",
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       "4  287 2000-09-09     2  87.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
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   ],
   "source": [
    "pd.merge(tracksid,bfinal,on=['track','artist', 'time']).drop(['artist', 'track', 'time'], axis=1)\n",
    "tidy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        1\n",
       "date    2000-02-12 00:00:00\n",
       "week                      1\n",
       "rank                     15\n",
       "Name: 756, dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidy.loc[tidy[tidy.week ==1]['rank'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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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>id</th>\n",
       "      <th>artist</th>\n",
       "      <th>track</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1</td>\n",
       "      <td>Santana</td>\n",
       "      <td>Maria, Maria</td>\n",
       "      <td>4:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id   artist         track  time\n",
       "252   1  Santana  Maria, Maria  4:18"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracksid.query('id == 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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