Spotify Playlist Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS210 - Homework #1: Spotify Playlist Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploratory Data Analysis is a fundamental process in the field of data science that helps us gain insights into a dataset by summarizing its main characteristics and identifying patterns and relationships within the data. In this assignment, we will be applying exploratory data analysis techniques to a Spotify playlist to better understand the characteristics of the songs in the playlist. Through this exercise, we will learn how to clean and transform data, visualize distributions, and uncover insights that can help us make informed decisions about the playlist's content and structure. Whether you are a music lover, aspiring data scientist, or simply interested in exploring the power of data, this assignment will provide you with a hands-on experience in exploratory data analysis that you can apply to any dataset.\n",
    "\n",
    "For this homework, you will use the provided file `Spotify_Playlist_Analysis.csv`.\n",
    "\n",
    "Below, you can find all the set of features, provided in the dataset:\n",
    "\n",
    "\n",
    "  1.   **Track Name:** The name of the track.\n",
    "  \n",
    "  2.   **Album Name:** The name of the album that the track belongs.\n",
    "\n",
    "  3.   **Artist Name(s):** The name of the artists who released the track.\n",
    "\n",
    "  4.   **Release Date:** The release date of the track.\n",
    "  \n",
    "  5.   **Duration (ms):** Numerical, the duration of the track in milliseconds.\n",
    "  \n",
    "  6.   **Popularity:** Numerical, the popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are.\n",
    "  \n",
    "  7.   **Genres:** The genres of the album that the track has.\n",
    "\n",
    "  8.   **Danceability:** Numerical, danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.\n",
    "\n",
    "  9.   **Energy** Numerical, Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.\n",
    "  \n",
    "  10.   **Key:** Numerical, the estimated overall key of the track. Integers map to pitches using standard Pitch Class notation . E.g. 0 = C, 1 = C#/Db, 2 = D, and so on. If no key was detected, the value is -1.\n",
    "  \n",
    "  11.   **Loudness:** Numerical, Overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typical range between -60 and 0 db.\n",
    "  \n",
    "  12.   **Mode:** Numerical, mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0.\n",
    "  \n",
    "  13.   **Speechiness:**  Numerical, speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.\n",
    "\n",
    "  14.   **Acousticness:**  Numerical, Confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.\n",
    "\n",
    "  15.   **Instrumentalness** Numerical, predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.\n",
    "  \n",
    "  16.   **Liveness:** Numerical, detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.\n",
    "  \n",
    "  17.   **Valence:** Numerical, Measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).\n",
    "  \n",
    "  18.   **Tempo:**  Numerical, Overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.\n",
    "\n"
   ]
  },
  {
   "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 seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1: Data Cleaning (15 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not possible to find a perfect dataset. During data collection, some values can be lost or some values can be duplicated. These lost values are called as missing values. In the DataFrame, missing values are usually labelled as `NaN`. Before starting to work on our dataset, it is very important to clean the dataset. For this, we need to look at how many missing values we have in the data set, how many duplicates there are.\n",
    "\n",
    "In this question;\n",
    "* First, print the shape (Number of rows and columns) of the dataset.\n",
    "* Secondly, you should show info of the dataset (#,column, non_null count, Dtype of columns) , this will also help to understand if dataframe has any missing values.\n",
    "* Lastly, you should check if dataframe has any duplicates, and you should drop the duplicates if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows  607\n",
      "Number of columns  18\n"
     ]
    }
   ],
   "source": [
    "#3pts\n",
    "\n",
    "#read the file.\n",
    "#print number of the rows and columns. \n",
    "playlist_df = pd.read_csv('Spotify_Playlist_Analysis.csv')\n",
    " \n",
    "row_count, column_count = playlist_df.shape\n",
    "\n",
    "print(\"Number of rows \", row_count)\n",
    "print(\"Number of columns \", column_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 607 entries, 0 to 606\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Track Name        607 non-null    object \n",
      " 1   Album Name        607 non-null    object \n",
      " 2   Artist Name(s)    607 non-null    object \n",
      " 3   Release Date      607 non-null    object \n",
      " 4   Duration (ms)     607 non-null    int64  \n",
      " 5   Popularity        607 non-null    int64  \n",
      " 6   Genres            603 non-null    object \n",
      " 7   Danceability      607 non-null    float64\n",
      " 8   Energy            607 non-null    float64\n",
      " 9   Key               607 non-null    int64  \n",
      " 10  Loudness          607 non-null    float64\n",
      " 11  Mode              607 non-null    int64  \n",
      " 12  Speechiness       607 non-null    float64\n",
      " 13  Acousticness      607 non-null    float64\n",
      " 14  Instrumentalness  607 non-null    float64\n",
      " 15  Liveness          607 non-null    float64\n",
      " 16  Valence           607 non-null    float64\n",
      " 17  Tempo             607 non-null    float64\n",
      "dtypes: float64(9), int64(4), object(5)\n",
      "memory usage: 85.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#2pts\n",
    "\n",
    "#show the info of the playlist_df.  \n",
    "playlist_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we control the result, number of non-null objects is equals to number of rows. This means there are no missing rows in our dataset, so don't need to do any operation here. Now let's filter the duplicate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Simple Man</td>\n",
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       "      <td>alternative metal,nu metal,post-grunge,rap metal</td>\n",
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       "      <th>542</th>\n",
       "      <td>we fell in love in october</td>\n",
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       "      <td>bedroom pop,indie pop,norwegian indie,pop</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.366</td>\n",
       "      <td>7</td>\n",
       "      <td>-12.808</td>\n",
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       "      <td>0.0280</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>0.237</td>\n",
       "      <td>129.959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>Moaning Lisa Smile</td>\n",
       "      <td>My Love Is Cool (Deluxe Edition)</td>\n",
       "      <td>Wolf Alice</td>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>160747</td>\n",
       "      <td>48</td>\n",
       "      <td>indie pop,indie rock,modern alternative rock,m...</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.806</td>\n",
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       "      <th>544</th>\n",
       "      <td>zombie girl</td>\n",
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       "      <td>2020-10-23</td>\n",
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       "      <td>48</td>\n",
       "      <td>art pop,chamber pop,indie folk,indie pop,indie...</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.103</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.383</td>\n",
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      "text/plain": [
       "                     Track Name  \\\n",
       "540                  Simple Man   \n",
       "541             Battle Symphony   \n",
       "542  we fell in love in october   \n",
       "543          Moaning Lisa Smile   \n",
       "544                 zombie girl   \n",
       "\n",
       "                                            Album Name   Artist Name(s)  \\\n",
       "540  (Pronounced 'Leh-'Nérd 'Skin-'Nérd) [Expanded ...   Lynyrd Skynyrd   \n",
       "541                                     One More Light      Linkin Park   \n",
       "542            we fell in love in october / forget her      girl in red   \n",
       "543                   My Love Is Cool (Deluxe Edition)       Wolf Alice   \n",
       "544                                              songs  Adrianne Lenker   \n",
       "\n",
       "    Release Date  Duration (ms)  Popularity  \\\n",
       "540         1973         357933           0   \n",
       "541   2017-05-19         216281          62   \n",
       "542   2018-11-21         184153          32   \n",
       "543   2016-02-05         160747          48   \n",
       "544   2020-10-23         164224          48   \n",
       "\n",
       "                                                Genres  Danceability  Energy  \\\n",
       "540  album rock,blues rock,classic rock,country roc...         0.461   0.734   \n",
       "541   alternative metal,nu metal,post-grunge,rap metal         0.653   0.839   \n",
       "542          bedroom pop,indie pop,norwegian indie,pop         0.566   0.366   \n",
       "543  indie pop,indie rock,modern alternative rock,m...         0.470   0.806   \n",
       "544  art pop,chamber pop,indie folk,indie pop,indie...         0.538   0.103   \n",
       "\n",
       "     Key  Loudness  Mode  Speechiness  Acousticness  Instrumentalness  \\\n",
       "540    1    -9.379     1       0.0374      0.000790          0.005540   \n",
       "541   10    -5.077     1       0.0685      0.036900          0.000000   \n",
       "542    7   -12.808     1       0.0280      0.113000          0.181000   \n",
       "543    2    -2.920     1       0.0505      0.000575          0.056400   \n",
       "544    8   -19.380     1       0.0462      0.914000          0.000053   \n",
       "\n",
       "     Liveness  Valence    Tempo  \n",
       "540    0.2200    0.423  120.550  \n",
       "541    0.0680    0.669  149.034  \n",
       "542    0.1550    0.237  129.959  \n",
       "543    0.0917    0.339   98.046  \n",
       "544    0.1370    0.383  128.424  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5pts\n",
    "\n",
    "#filter the duplicate rows and show the head. \n",
    "\n",
    "duplicate = playlist_df[playlist_df.duplicated()]\n",
    " \n",
    "\n",
    "duplicate.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see above, there are some duplicate rows in our dataset. The duplicate values can be an issue in our analysis, so it's important to get rid of them. In below cell, first drop the duplicates then print the shape and see if the shape at the start changed or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows  594\n",
      "Number of columns  18\n"
     ]
    }
   ],
   "source": [
    "#5pts\n",
    "\n",
    "#drop the duplicates.\n",
    "#print number of the rows and columns after dropping duplicates.\n",
    "playlist_df = playlist_df.drop_duplicates()\n",
    "row_count, column_count = playlist_df.shape\n",
    "\n",
    "print(\"Number of rows \", row_count)\n",
    "print(\"Number of columns \", column_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, you may fail to drop the duplicates. Double-check is important in these kind of cases therefore in the cell below, filter the duplicate rows again. If you see anything, then you did something wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Loudness</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Speechiness</th>\n",
       "      <th>Acousticness</th>\n",
       "      <th>Instrumentalness</th>\n",
       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Track Name, Album Name, Artist Name(s), Release Date, Duration (ms), Popularity, Genres, Danceability, Energy, Key, Loudness, Mode, Speechiness, Acousticness, Instrumentalness, Liveness, Valence, Tempo]\n",
       "Index: []"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "#filter the duplicate rows.\n",
    "duplicate_check = playlist_df[playlist_df.duplicated()]\n",
    " \n",
    "\n",
    "duplicate_check.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2: Exploring the Dataset (40 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we complete the cleaning, we can finally start deep-diving into the dataset. First let's look at the 5 rows of our dataframe, and see the statistical summaries of columns with describe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crying Lightning</td>\n",
       "      <td>Humbug</td>\n",
       "      <td>Arctic Monkeys</td>\n",
       "      <td>2009-08-20</td>\n",
       "      <td>224826</td>\n",
       "      <td>67</td>\n",
       "      <td>garage rock,modern rock,permanent wave,rock,sh...</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.885</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.423</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0461</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.00041</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.670</td>\n",
       "      <td>106.719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ode To The Mets</td>\n",
       "      <td>The New Abnormal</td>\n",
       "      <td>The Strokes</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>351786</td>\n",
       "      <td>64</td>\n",
       "      <td>alternative rock,garage rock,modern rock,perma...</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.617</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.423</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>0.3410</td>\n",
       "      <td>0.18100</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.201</td>\n",
       "      <td>92.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Buzzcut Season</td>\n",
       "      <td>Pure Heroine</td>\n",
       "      <td>Lorde</td>\n",
       "      <td>2013-09-27</td>\n",
       "      <td>246755</td>\n",
       "      <td>68</td>\n",
       "      <td>art pop,metropopolis,nz pop,pop</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.525</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0750</td>\n",
       "      <td>0.6060</td>\n",
       "      <td>0.30500</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.247</td>\n",
       "      <td>111.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I Know The End</td>\n",
       "      <td>Punisher</td>\n",
       "      <td>Phoebe Bridgers</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>344692</td>\n",
       "      <td>0</td>\n",
       "      <td>indie pop,la indie</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.282</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.01900</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.259</td>\n",
       "      <td>111.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Simulation Swarm</td>\n",
       "      <td>Dragon New Warm Mountain I Believe In You</td>\n",
       "      <td>Big Thief</td>\n",
       "      <td>2022-02-11</td>\n",
       "      <td>252733</td>\n",
       "      <td>62</td>\n",
       "      <td>art pop,brooklyn indie,indie pop,indie rock,sm...</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.578</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.103</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0728</td>\n",
       "      <td>0.4460</td>\n",
       "      <td>0.00240</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.670</td>\n",
       "      <td>105.204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Track Name                                 Album Name  \\\n",
       "0  Crying Lightning                                     Humbug   \n",
       "1   Ode To The Mets                           The New Abnormal   \n",
       "2    Buzzcut Season                               Pure Heroine   \n",
       "3    I Know The End                                   Punisher   \n",
       "4  Simulation Swarm  Dragon New Warm Mountain I Believe In You   \n",
       "\n",
       "    Artist Name(s) Release Date  Duration (ms)  Popularity  \\\n",
       "0   Arctic Monkeys   2009-08-20         224826          67   \n",
       "1      The Strokes   2020-04-10         351786          64   \n",
       "2            Lorde   2013-09-27         246755          68   \n",
       "3  Phoebe Bridgers   2020-06-18         344692           0   \n",
       "4        Big Thief   2022-02-11         252733          62   \n",
       "\n",
       "                                              Genres  Danceability  Energy  \\\n",
       "0  garage rock,modern rock,permanent wave,rock,sh...         0.498   0.885   \n",
       "1  alternative rock,garage rock,modern rock,perma...         0.426   0.617   \n",
       "2                    art pop,metropopolis,nz pop,pop         0.733   0.620   \n",
       "3                                 indie pop,la indie         0.328   0.320   \n",
       "4  art pop,brooklyn indie,indie pop,indie rock,sm...         0.703   0.578   \n",
       "\n",
       "   Key  Loudness  Mode  Speechiness  Acousticness  Instrumentalness  Liveness  \\\n",
       "0    4    -4.423     0       0.0461        0.0148           0.00041     0.239   \n",
       "1    1    -5.423     0       0.0289        0.3410           0.18100     0.101   \n",
       "2    1   -10.525     1       0.0750        0.6060           0.30500     0.117   \n",
       "3    1   -10.282     1       0.0350        0.3240           0.01900     0.234   \n",
       "4    1    -9.103     1       0.0728        0.4460           0.00240     0.102   \n",
       "\n",
       "   Valence    Tempo  \n",
       "0    0.670  106.719  \n",
       "1    0.201   92.002  \n",
       "2    0.247  111.039  \n",
       "3    0.259  111.453  \n",
       "4    0.670  105.204  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2pts\n",
    "\n",
    "#show the first 5 rows\n",
    "playlist_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Duration (ms)</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Key</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Speechiness</th>\n",
       "      <th>Acousticness</th>\n",
       "      <th>Instrumentalness</th>\n",
       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>594.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>242163.707071</td>\n",
       "      <td>47.897306</td>\n",
       "      <td>0.532816</td>\n",
       "      <td>0.609467</td>\n",
       "      <td>5.469697</td>\n",
       "      <td>-8.024519</td>\n",
       "      <td>0.675084</td>\n",
       "      <td>0.049382</td>\n",
       "      <td>0.251596</td>\n",
       "      <td>0.109972</td>\n",
       "      <td>0.170078</td>\n",
       "      <td>0.439241</td>\n",
       "      <td>124.301288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67305.655426</td>\n",
       "      <td>23.347754</td>\n",
       "      <td>0.147752</td>\n",
       "      <td>0.210018</td>\n",
       "      <td>3.496554</td>\n",
       "      <td>3.201484</td>\n",
       "      <td>0.468738</td>\n",
       "      <td>0.040821</td>\n",
       "      <td>0.296811</td>\n",
       "      <td>0.221824</td>\n",
       "      <td>0.126547</td>\n",
       "      <td>0.228532</td>\n",
       "      <td>27.708996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>96553.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.112000</td>\n",
       "      <td>0.052300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-23.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024300</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042600</td>\n",
       "      <td>0.039400</td>\n",
       "      <td>65.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>199164.250000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.429000</td>\n",
       "      <td>0.447250</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-9.511500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031050</td>\n",
       "      <td>0.013650</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.097675</td>\n",
       "      <td>0.253500</td>\n",
       "      <td>104.981000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>231626.500000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.535500</td>\n",
       "      <td>0.630500</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-7.595000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037600</td>\n",
       "      <td>0.111000</td>\n",
       "      <td>0.002225</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>0.416000</td>\n",
       "      <td>123.166000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>275446.250000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.644000</td>\n",
       "      <td>0.788750</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>-5.644500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052300</td>\n",
       "      <td>0.410750</td>\n",
       "      <td>0.076550</td>\n",
       "      <td>0.205750</td>\n",
       "      <td>0.594000</td>\n",
       "      <td>141.366500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>757440.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.888000</td>\n",
       "      <td>0.977000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>-0.734000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464000</td>\n",
       "      <td>0.986000</td>\n",
       "      <td>0.954000</td>\n",
       "      <td>0.922000</td>\n",
       "      <td>0.979000</td>\n",
       "      <td>206.165000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Duration (ms)  Popularity  Danceability      Energy         Key  \\\n",
       "count     594.000000  594.000000    594.000000  594.000000  594.000000   \n",
       "mean   242163.707071   47.897306      0.532816    0.609467    5.469697   \n",
       "std     67305.655426   23.347754      0.147752    0.210018    3.496554   \n",
       "min     96553.000000    0.000000      0.112000    0.052300    0.000000   \n",
       "25%    199164.250000   38.000000      0.429000    0.447250    2.000000   \n",
       "50%    231626.500000   53.000000      0.535500    0.630500    6.000000   \n",
       "75%    275446.250000   64.000000      0.644000    0.788750    9.000000   \n",
       "max    757440.000000   90.000000      0.888000    0.977000   11.000000   \n",
       "\n",
       "         Loudness        Mode  Speechiness  Acousticness  Instrumentalness  \\\n",
       "count  594.000000  594.000000   594.000000    594.000000        594.000000   \n",
       "mean    -8.024519    0.675084     0.049382      0.251596          0.109972   \n",
       "std      3.201484    0.468738     0.040821      0.296811          0.221824   \n",
       "min    -23.625000    0.000000     0.024300      0.000007          0.000000   \n",
       "25%     -9.511500    0.000000     0.031050      0.013650          0.000053   \n",
       "50%     -7.595000    1.000000     0.037600      0.111000          0.002225   \n",
       "75%     -5.644500    1.000000     0.052300      0.410750          0.076550   \n",
       "max     -0.734000    1.000000     0.464000      0.986000          0.954000   \n",
       "\n",
       "         Liveness     Valence       Tempo  \n",
       "count  594.000000  594.000000  594.000000  \n",
       "mean     0.170078    0.439241  124.301288  \n",
       "std      0.126547    0.228532   27.708996  \n",
       "min      0.042600    0.039400   65.740000  \n",
       "25%      0.097675    0.253500  104.981000  \n",
       "50%      0.118000    0.416000  123.166000  \n",
       "75%      0.205750    0.594000  141.366500  \n",
       "max      0.922000    0.979000  206.165000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3pts\n",
    "\n",
    "#show the statistical summary of columns.\n",
    "playlist_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's learn more about our dataset. There are various artists in playlists but some of them are more frequent than others. Find the most frequent 10 artists in the playlist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Arctic Monkeys     48\n",
       "Lorde              21\n",
       "Nilüfer Yanya      20\n",
       "Wolf Alice         18\n",
       "Phoebe Bridgers    13\n",
       "The Strokes        13\n",
       "Big Thief          12\n",
       "Alvvays            12\n",
       "alt-J              12\n",
       "girl in red        12\n",
       "Name: Artist Name(s), dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5pts\n",
    "\n",
    "#show the most frequent 10 artists in the playlist.\n",
    "playlist_df['Artist Name(s)'].value_counts().nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you completed the previous cell correctly, Arctic Monkeys should be the most frequent artist in the playlist. We also want to learn more about the genres in this playlist. The problem is most of the songs have more than one genre, so it's hard to use same method that you used above. Try to find a different approach, and show the most frequent genre in the playlist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#10pts\n",
    "\n",
    "#find the most frequent genre in the playlist.\n",
    "\n",
    "\n",
    "#print the most frequent genre name, and how many time it occurs.\n",
    "print(\"The most common genre is \" + 'YOUR VARIABLE '+ \" with \" + 'YOUR VARIABLE '+ ' times.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common genre is not surprising since Arctic Monkeys was at the top by far in our most frequent artist list. Our dataset contains lots of technical information about the music. Still, we can create different columns for better information source. In this question, we will apply a pretty basic feature engineering approach. Danceability, Energy and Valence are good indicators for the emotion that conveyed by the track. Create a new column called \"Positivity\" in our dataframe by taking the mean of three columns (Danceability, Energy, Valence) and show the statistical properties with describe again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    594.000000\n",
       "mean       0.527175\n",
       "std        0.141960\n",
       "min        0.164767\n",
       "25%        0.423750\n",
       "50%        0.538667\n",
       "75%        0.622333\n",
       "max        0.892667\n",
       "Name: Positivity, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5pts\n",
    "\n",
    "#create a new column\n",
    "#show the statistical properties\n",
    "\n",
    "columns = playlist_df.loc[:,[\"Danceability\",\"Energy\",\"Valence\"]]\n",
    "playlist_df['Positivity'] = columns.mean(axis=1)\n",
    "\n",
    "#playlist_df_final['Positivity'] = playlist_df_final[['Danceability', 'Energy', 'Valence']].mean(axis=1)\n",
    "playlist_df['Positivity'].describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are in a good shape now. Let's test our filtering skills. Filtering is a significant skill in data science because in many cases you will only need some part of the dataset.  In this question, we will filter a song called Crying Lightning by Arctic Monkeys. It's not a song that is arbitrarily chosen by the assistant, you can also take it as a recommendation. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Track Name</th>\n",
       "      <th>Album Name</th>\n",
       "      <th>Artist Name(s)</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Duration (ms)</th>\n",
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       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Positivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Crying Lightning</td>\n",
       "      <td>Humbug</td>\n",
       "      <td>Arctic Monkeys</td>\n",
       "      <td>2009-08-20</td>\n",
       "      <td>224826</td>\n",
       "      <td>67</td>\n",
       "      <td>garage rock,modern rock,permanent wave,rock,sh...</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.885</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.423</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0461</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.00041</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.67</td>\n",
       "      <td>106.719</td>\n",
       "      <td>0.684333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Track Name Album Name  Artist Name(s) Release Date  Duration (ms)  \\\n",
       "0  Crying Lightning     Humbug  Arctic Monkeys   2009-08-20         224826   \n",
       "\n",
       "   Popularity                                             Genres  \\\n",
       "0          67  garage rock,modern rock,permanent wave,rock,sh...   \n",
       "\n",
       "   Danceability  Energy  Key  Loudness  Mode  Speechiness  Acousticness  \\\n",
       "0         0.498   0.885    4    -4.423     0       0.0461        0.0148   \n",
       "\n",
       "   Instrumentalness  Liveness  Valence    Tempo  Positivity  \n",
       "0           0.00041     0.239     0.67  106.719    0.684333  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3pts\n",
    "\n",
    "#filter the crying lightning\n",
    "crying_ligthing = playlist_df.loc[playlist_df['Track Name'] == \"Crying Lightning\"]\n",
    "crying_ligthing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we filtered Crying Lightning, let's learn more about some columns of the dataset. In this question, create a new dataframe called \"energetic_df\" by sorting the energy column of \"playlist_df\". Sorting must be descending. Then show the most energetic three songs in the playlist with their information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most energetic three songs in the playlist is:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>Track Name</th>\n",
       "      <th>Album Name</th>\n",
       "      <th>Artist Name(s)</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Duration (ms)</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Key</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Speechiness</th>\n",
       "      <th>Acousticness</th>\n",
       "      <th>Instrumentalness</th>\n",
       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Positivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Sugar/Tzu</td>\n",
       "      <td>Hellfire</td>\n",
       "      <td>black midi</td>\n",
       "      <td>2022-07-15</td>\n",
       "      <td>230687</td>\n",
       "      <td>48</td>\n",
       "      <td>crank wave,indie rock,noise rock,uk noise rock...</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.977</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.418</td>\n",
       "      <td>1</td>\n",
       "      <td>0.378</td>\n",
       "      <td>0.105000</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.4720</td>\n",
       "      <td>0.101</td>\n",
       "      <td>87.678</td>\n",
       "      <td>0.451000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Brianstorm</td>\n",
       "      <td>Favourite Worst Nightmare</td>\n",
       "      <td>Arctic Monkeys</td>\n",
       "      <td>2007-04-22</td>\n",
       "      <td>172866</td>\n",
       "      <td>68</td>\n",
       "      <td>garage rock,modern rock,permanent wave,rock,sh...</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.974</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.706</td>\n",
       "      <td>1</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>0.001550</td>\n",
       "      <td>0.0871</td>\n",
       "      <td>0.463</td>\n",
       "      <td>165.182</td>\n",
       "      <td>0.619000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>By the Way</td>\n",
       "      <td>By the Way (Deluxe Edition)</td>\n",
       "      <td>Red Hot Chili Peppers</td>\n",
       "      <td>2002-07-09</td>\n",
       "      <td>216933</td>\n",
       "      <td>72</td>\n",
       "      <td>alternative rock,funk metal,funk rock,permanen...</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.938</td>\n",
       "      <td>1</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.026400</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.198</td>\n",
       "      <td>122.444</td>\n",
       "      <td>0.539667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Track Name                   Album Name         Artist Name(s)  \\\n",
       "234   Sugar/Tzu                     Hellfire             black midi   \n",
       "188  Brianstorm    Favourite Worst Nightmare         Arctic Monkeys   \n",
       "326  By the Way  By the Way (Deluxe Edition)  Red Hot Chili Peppers   \n",
       "\n",
       "    Release Date  Duration (ms)  Popularity  \\\n",
       "234   2022-07-15         230687          48   \n",
       "188   2007-04-22         172866          68   \n",
       "326   2002-07-09         216933          72   \n",
       "\n",
       "                                                Genres  Danceability  Energy  \\\n",
       "234  crank wave,indie rock,noise rock,uk noise rock...         0.275   0.977   \n",
       "188  garage rock,modern rock,permanent wave,rock,sh...         0.420   0.974   \n",
       "326  alternative rock,funk metal,funk rock,permanen...         0.451   0.970   \n",
       "\n",
       "     Key  Loudness  Mode  Speechiness  Acousticness  Instrumentalness  \\\n",
       "234    0    -4.418     1        0.378      0.105000          0.000011   \n",
       "188    1    -4.706     1        0.191      0.000081          0.001550   \n",
       "326    0    -4.938     1        0.107      0.026400          0.003550   \n",
       "\n",
       "     Liveness  Valence    Tempo  Positivity  \n",
       "234    0.4720    0.101   87.678    0.451000  \n",
       "188    0.0871    0.463  165.182    0.619000  \n",
       "326    0.1020    0.198  122.444    0.539667  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6pts\n",
    "\n",
    "#create energetic df\n",
    "print(\"The most energetic three songs in the playlist is:\")\n",
    "#your code\n",
    "\n",
    "energetic_df = playlist_df.sort_values(\"Energy\", ascending = False)\n",
    "energetic_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply the same method to an another column. In this question, create a new dataframe called \"positivity_df\" by sorting the positivity column of \"playlist_df\". Sorting must be descending. Then show the most positive three songs in the playlist with their information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most positive three songs in the playlist is:\n"
     ]
    },
    {
     "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>Track Name</th>\n",
       "      <th>Album Name</th>\n",
       "      <th>Artist Name(s)</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Duration (ms)</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Key</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Speechiness</th>\n",
       "      <th>Acousticness</th>\n",
       "      <th>Instrumentalness</th>\n",
       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Positivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>chase me</td>\n",
       "      <td>PAINLESS</td>\n",
       "      <td>Nilüfer Yanya</td>\n",
       "      <td>2022-03-04</td>\n",
       "      <td>210373</td>\n",
       "      <td>31</td>\n",
       "      <td>art pop,indie rock,uk alternative pop</td>\n",
       "      <td>0.867</td>\n",
       "      <td>0.832</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.383</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0745</td>\n",
       "      <td>0.979</td>\n",
       "      <td>119.993</td>\n",
       "      <td>0.892667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Ocean Man</td>\n",
       "      <td>The SpongeBob SquarePants Movie-Music From The...</td>\n",
       "      <td>Ween</td>\n",
       "      <td>2004-11-09</td>\n",
       "      <td>126946</td>\n",
       "      <td>38</td>\n",
       "      <td>alternative rock</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.912</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.130</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.551</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.0982</td>\n",
       "      <td>0.973</td>\n",
       "      <td>122.782</td>\n",
       "      <td>0.868333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Got My Mind Set On You - Remastered 2004</td>\n",
       "      <td>Cloud Nine</td>\n",
       "      <td>George Harrison</td>\n",
       "      <td>1987-11-02</td>\n",
       "      <td>234653</td>\n",
       "      <td>63</td>\n",
       "      <td>album rock,beatlesque,classic rock,folk rock,m...</td>\n",
       "      <td>0.767</td>\n",
       "      <td>0.854</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.947</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.4850</td>\n",
       "      <td>0.963</td>\n",
       "      <td>149.206</td>\n",
       "      <td>0.861333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Track Name  \\\n",
       "559                                  chase me   \n",
       "255                                 Ocean Man   \n",
       "536  Got My Mind Set On You - Remastered 2004   \n",
       "\n",
       "                                            Album Name   Artist Name(s)  \\\n",
       "559                                           PAINLESS    Nilüfer Yanya   \n",
       "255  The SpongeBob SquarePants Movie-Music From The...             Ween   \n",
       "536                                         Cloud Nine  George Harrison   \n",
       "\n",
       "    Release Date  Duration (ms)  Popularity  \\\n",
       "559   2022-03-04         210373          31   \n",
       "255   2004-11-09         126946          38   \n",
       "536   1987-11-02         234653          63   \n",
       "\n",
       "                                                Genres  Danceability  Energy  \\\n",
       "559              art pop,indie rock,uk alternative pop         0.867   0.832   \n",
       "255                                   alternative rock         0.720   0.912   \n",
       "536  album rock,beatlesque,classic rock,folk rock,m...         0.767   0.854   \n",
       "\n",
       "     Key  Loudness  Mode  Speechiness  Acousticness  Instrumentalness  \\\n",
       "559   11    -8.383     0       0.0453         0.113          0.000000   \n",
       "255    4    -6.130     1       0.0363         0.551          0.001220   \n",
       "536    4    -6.947     1       0.0426         0.465          0.000002   \n",
       "\n",
       "     Liveness  Valence    Tempo  Positivity  \n",
       "559    0.0745    0.979  119.993    0.892667  \n",
       "255    0.0982    0.973  122.782    0.868333  \n",
       "536    0.4850    0.963  149.206    0.861333  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#6pts\n",
    "\n",
    "#create positivity df\n",
    "print(\"The most positive three songs in the playlist is:\")\n",
    "#your code\n",
    "positivity_df = playlist_df.sort_values(\"Positivity\", ascending = False)\n",
    "positivity_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q3: Visualization and Analysis (45 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We learned many things about our dataset. Now let's create some visualizations using tools like Matplotlib, Seaborn etc. To warm up let's start with some univariate analysis. \n",
    "\n",
    "For the first question:\n",
    "\n",
    "- Create a histogram for Danceability, with bin size 15.\n",
    "- Set title as 'Danceability'\n",
    "- Set label for y as 'Frequencies'\n",
    "- Set label for x as 'Danceability'\n",
    "- Set boundaries for y axis as 0 to 100.\n",
    "- Set boundaries for x axis as 0 to 1.\n",
    "- After visualization, show the statistical properties of Danceability with describe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    " # create a histogram with the information above (3pts)\n",
    "plt.hist(playlist_df['Danceability'],\n",
    "        bins=15)\n",
    "plt.title(\"Danceability\")\n",
    "plt.xlabel(\"Danceability\")\n",
    "plt.ylabel(\"Frequencies\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(0,100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    594.000000\n",
       "mean       0.532816\n",
       "std        0.147752\n",
       "min        0.112000\n",
       "25%        0.429000\n",
       "50%        0.535500\n",
       "75%        0.644000\n",
       "max        0.888000\n",
       "Name: Danceability, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the statistical properties (2pts)\n",
    "playlist_df['Danceability'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we analyze the results, we see that the distribution of the 'Danceability' of the tracks in our data set is close to normal distribution. The mean is 0.532816 with a standard deviation 0.147752. The danceability level vary from 0.112000 to 0.888000 (both inclusive). The median is 0.535500.\n",
    "\n",
    "For the second question:\n",
    "\n",
    "- Create a histogram for Valence, with bin size 15.\n",
    "- Set title as 'Valence Level'\n",
    "- Set label for y as 'Frequencies'\n",
    "- Set label for x as 'Valence'\n",
    "- Set boundaries for y axis as 0 to 100.\n",
    "- Set boundaries for x axis as 0 to 1.\n",
    "- After visualization, show the statistical properties of Valence with describe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "# create a histogram with the information above (3pts)\n",
    "plt.hist(playlist_df['Valence'],\n",
    "        bins=15)\n",
    "plt.title(\"Valence Level\")\n",
    "plt.xlabel(\"Valence\")\n",
    "plt.ylabel(\"Frequencies\")\n",
    "plt.xlim(0,1)\n",
    "plt.ylim(0,100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    594.000000\n",
       "mean       0.439241\n",
       "std        0.228532\n",
       "min        0.039400\n",
       "25%        0.253500\n",
       "50%        0.416000\n",
       "75%        0.594000\n",
       "max        0.979000\n",
       "Name: Valence, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the statistical properties (2pts)\n",
    "playlist_df['Valence'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we analyze the results, we see that the distribution of the 'Valence' of the tracks in our data set is right-skewed. The mean is 0.439241 with a standard deviation 0.0.228532. The valence level vary from 0.039400 to 0.979000 (both inclusive). The median is 0.416000.\n",
    "\n",
    "Now let's do some bi-variate analysis. If we want to plot the relationship between Energy and Loudness, which plot would be the appropriate choice?\n",
    "\n",
    "*Question*: Which plot would be the appropriate choice?\n",
    "\n",
    "*Answer* : **EDIT THIS MARKDOWN, PLEASE EXPLAIN YOUR CHOICE** **(3pts)**\n",
    "\n",
    "Then;\n",
    "\n",
    "- Create the plot you chose for Energy and Loudness.\n",
    "- Set title as 'Loudness vs. Energy'\n",
    "- Set label for y as 'Loudness (dB)'\n",
    "- Set label for x as 'Energy'\n",
    "- Set boundaries for y axis as -25 to 0.\n",
    "- Set boundaries for x axis as 0 to 1.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-25.0, 0.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # create the plot (7pts)\n",
    "ax = sns.scatterplot(x=\"Energy\", y=\"Loudness\", data=playlist_df);\n",
    "ax.set_title(\"Loudness vs. Energy\")\n",
    "ax.set_xlabel(\"Energy\")\n",
    "ax.set_ylabel(\"Loudness (dB)\")\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_ylim(-25, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question : Interpretation of the graph above (5pts)\n",
    "\n",
    "What can we understand from the graph above? Shortly write your comments about the graph. You can edit this cell.\n",
    "\n",
    "# ANSWER\n",
    "\n",
    "These two features are positively correlated which signifies that both variables move in the same direction.\n",
    "\n",
    "\n",
    "# End of the Answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create more sophisticated plot. In this question you will also make some operations on the dataset again.\n",
    "\n",
    "**Objectives**\n",
    "\n",
    "- Create a new column called `Emotion` that contains positivity information in groups as string. \n",
    "- The emotions will be: Sad (if positivity value is smaller or equal than 0.45), Neutral (if positivity value is between 0.45 and 0.57 (inclusive)), and Happy (if positivity value is bigger than 0.57)\n",
    "- Then create the new dataframe called 'new_artist_df'.\n",
    "- Filter the songs of 'Phoebe Bridgers', 'The Strokes', 'alt-J' and 'girl in red'.\n",
    "- Sort 'new_artist_df' by positivity in ascending order.\n",
    "- Show the first 5 rows.\n",
    "\n",
    "\n"
   ]
  },
  {
   "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>Track Name</th>\n",
       "      <th>Album Name</th>\n",
       "      <th>Artist Name(s)</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Duration (ms)</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Key</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Mode</th>\n",
       "      <th>Speechiness</th>\n",
       "      <th>Acousticness</th>\n",
       "      <th>Instrumentalness</th>\n",
       "      <th>Liveness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Positivity</th>\n",
       "      <th>Emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ode To The Mets</td>\n",
       "      <td>The New Abnormal</td>\n",
       "      <td>The Strokes</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>351786</td>\n",
       "      <td>64</td>\n",
       "      <td>alternative rock,garage rock,modern rock,perma...</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.617</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.423</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0289</td>\n",
       "      <td>0.341000</td>\n",
       "      <td>0.1810</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.201</td>\n",
       "      <td>92.002</td>\n",
       "      <td>0.414667</td>\n",
       "      <td>Sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I Know The End</td>\n",
       "      <td>Punisher</td>\n",
       "      <td>Phoebe Bridgers</td>\n",
       "      <td>2020-06-18</td>\n",
       "      <td>344692</td>\n",
       "      <td>0</td>\n",
       "      <td>indie pop,la indie</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.282</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.324000</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.2340</td>\n",
       "      <td>0.259</td>\n",
       "      <td>111.453</td>\n",
       "      <td>0.302333</td>\n",
       "      <td>Sad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>.</td>\n",
       "      <td>if i could make it go quiet</td>\n",
       "      <td>girl in red</td>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>166240</td>\n",
       "      <td>11</td>\n",
       "      <td>bedroom pop,indie pop,norwegian indie,pop</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.907</td>\n",
       "      <td>10</td>\n",
       "      <td>-7.362</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2910</td>\n",
       "      <td>0.083800</td>\n",
       "      <td>0.8320</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>0.317</td>\n",
       "      <td>151.988</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Hard To Explain</td>\n",
       "      <td>Is This It</td>\n",
       "      <td>The Strokes</td>\n",
       "      <td>2001-07-30</td>\n",
       "      <td>224426</td>\n",
       "      <td>60</td>\n",
       "      <td>alternative rock,garage rock,modern rock,perma...</td>\n",
       "      <td>0.406</td>\n",
       "      <td>0.682</td>\n",
       "      <td>7</td>\n",
       "      <td>-6.281</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.8490</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>0.623</td>\n",
       "      <td>82.495</td>\n",
       "      <td>0.570333</td>\n",
       "      <td>Happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Not The Same Anymore</td>\n",
       "      <td>The New Abnormal</td>\n",
       "      <td>The Strokes</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>337333</td>\n",
       "      <td>56</td>\n",
       "      <td>alternative rock,garage rock,modern rock,perma...</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.613</td>\n",
       "      <td>9</td>\n",
       "      <td>-5.900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.384000</td>\n",
       "      <td>0.0123</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.282</td>\n",
       "      <td>177.375</td>\n",
       "      <td>0.416000</td>\n",
       "      <td>Sad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Track Name                   Album Name   Artist Name(s)  \\\n",
       "1        Ode To The Mets             The New Abnormal      The Strokes   \n",
       "3         I Know The End                     Punisher  Phoebe Bridgers   \n",
       "19                     .  if i could make it go quiet      girl in red   \n",
       "42       Hard To Explain                   Is This It      The Strokes   \n",
       "48  Not The Same Anymore             The New Abnormal      The Strokes   \n",
       "\n",
       "   Release Date  Duration (ms)  Popularity  \\\n",
       "1    2020-04-10         351786          64   \n",
       "3    2020-06-18         344692           0   \n",
       "19   2021-04-30         166240          11   \n",
       "42   2001-07-30         224426          60   \n",
       "48   2020-04-10         337333          56   \n",
       "\n",
       "                                               Genres  Danceability  Energy  \\\n",
       "1   alternative rock,garage rock,modern rock,perma...         0.426   0.617   \n",
       "3                                  indie pop,la indie         0.328   0.320   \n",
       "19          bedroom pop,indie pop,norwegian indie,pop         0.451   0.907   \n",
       "42  alternative rock,garage rock,modern rock,perma...         0.406   0.682   \n",
       "48  alternative rock,garage rock,modern rock,perma...         0.353   0.613   \n",
       "\n",
       "    Key  Loudness  Mode  Speechiness  Acousticness  Instrumentalness  \\\n",
       "1     1    -5.423     0       0.0289      0.341000            0.1810   \n",
       "3     1   -10.282     1       0.0350      0.324000            0.0190   \n",
       "19   10    -7.362     1       0.2910      0.083800            0.8320   \n",
       "42    7    -6.281     1       0.0584      0.000413            0.8490   \n",
       "48    9    -5.900     1       0.0310      0.384000            0.0123   \n",
       "\n",
       "    Liveness  Valence    Tempo  Positivity  Emotion  \n",
       "1     0.1010    0.201   92.002    0.414667      Sad  \n",
       "3     0.2340    0.259  111.453    0.302333      Sad  \n",
       "19    0.1370    0.317  151.988    0.558333  Neutral  \n",
       "42    0.0999    0.623   82.495    0.570333    Happy  \n",
       "48    0.1030    0.282  177.375    0.416000      Sad  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10pts - tasks above\n",
    "\n",
    "#your code\n",
    "\n",
    "def f(row):\n",
    "    if row['Positivity'] <= 0.45:\n",
    "        val = \"Sad\"\n",
    "    elif row['Positivity'] > 0.45 and row['Positivity'] <= 0.57 :\n",
    "        val = \"Neutral\"\n",
    "    else:\n",
    "        val = \"Happy\"\n",
    "    return val\n",
    "\n",
    "new_artist_df = playlist_df\n",
    "\n",
    "new_artist_df['Emotion'] = playlist_df.apply(f, axis=1)\n",
    "\n",
    "artists = ['Phoebe Bridgers', 'girl in red', 'alt-J', 'The Strokes']\n",
    "\n",
    "new_artist_df = new_artist_df.loc[new_artist_df[\"Artist Name(s)\"].isin(artists)]\n",
    "\n",
    "new_artist_df.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's time to create a visualization:\n",
    "\n",
    "- Plot the frequencies of each emotion by the artist. Use 'new_artist_df' as the data source.\n",
    "- Set the figure size as (8,6)\n",
    "- Set title as 'Artists' Emotional State'\n",
    "- Set x-tick label as 'Sad', 'Neutral' and 'Happy'.\n",
    "- Set y axis boundaries as (0,10)\n",
    "- Set x label as 'Emotions'.\n",
    "- Set y label as 'Frequencies'.\n",
    "- Set legend as ['Phoebe Bridgers', 'girl in red', 'alt-J', 'The Strokes']. Title should be artists.\n",
    "\n",
    "The result should be same as the figure below. (Colors may change, ignore them.)\n",
    "\n",
    "<img src=\"https://i.hizliresim.com/2sjj3hd.jpg\" alt=\"barplot-210\" border=\"0\" width=\"500\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 10pts - tasks above\n",
    "#your code\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "x = np.arange(len(new_artist_df.Emotion.unique()))\n",
    "\n",
    "\n",
    "bar_width = 0.2\n",
    "\n",
    "b1 = ax.bar(x, len(new_artist_df[(new_artist_df[\"Artist Name(s)\"] == \"Phoebe Bridgers\")]),\n",
    "            width=bar_width)\n",
    "b2 = ax.bar(x + bar_width, len(new_artist_df[(new_artist_df[\"Artist Name(s)\"] == \"girl in red\")]) ,\n",
    "            width=bar_width)\n",
    "b3 = ax.bar(x + 2*bar_width, len(new_artist_df[(new_artist_df[\"Artist Name(s)\"] == \"alt-J\")]) ,\n",
    "            width=bar_width)\n",
    "b4 = ax.bar(x + 3*bar_width, len(new_artist_df[(new_artist_df[\"Artist Name(s)\"] == \"The Strokes\")]) ,\n",
    "            width=bar_width)\n",
    "\n",
    "\n",
    "ax.set_xticks((x + 12*(bar_width)) / 3)\n",
    "ax.set_xticklabels(new_artist_df.Emotion.unique())\n",
    "\n",
    "# Add legend.\n",
    "ax.legend()\n",
    "\n",
    "# Axis styling.\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_color('#DDDDDD')\n",
    "ax.tick_params(bottom=False, left=False)\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(True, color='#EEEEEE')\n",
    "ax.xaxis.grid(False)\n",
    "\n",
    "# Add axis and chart labels.\n",
    "ax.set_xlabel('Emotions', labelpad=15)\n",
    "ax.set_ylabel('Frequencies', labelpad=15)\n",
    "ax.set_title('Artists Emotional State', pad=15)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations! You've reached the end of your assignment. In this assignment, we dealt with data questions about albums, artists, and many music related data over Spotify data. I hope it has been a fun process.\n",
    "\n",
    "**If you want to listen to the playlist we analyzed:**\n",
    "https://open.spotify.com/playlist/5TPzjEydJcgbNj7xLwKQVG?si=669fdd7512c44d96\n",
    "\n",
    "See you later!\n",
    "    \n",
    "<img src=\"https://i.imgur.com/KAoQCQg.png\" alt=\"barplot-210\" border=\"0\" width=\"300\">\n",
    "\n",
    "***dall-e prompt***: an oil painting of data science student does homework in pain\n",
    "\n"
   ]
  }
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