FinalProject_group023.ipynb

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   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7QJkBlICZmuU"
   },
   "source": [
    "# Names\n",
    "\n",
    "- Shinuo Xu: Data cleaning, Data visualization, Statistical analysis, Data processing, Ethics and privacy\n",
    "\n",
    "- Liza Megally: Conclusion & Discussion, Proof Read, Research, Data Visualization\n",
    "- Kriti Jain: Ethics & Privacy, Background and Prior Work\n",
    "- Maisha Maliha: Data Gathering, Web Scraping, Hypothesis, Data Visualization, Data Analysis, Regression Models, Predictive Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eDkvx2coZmuV"
   },
   "source": [
    "# Research Question\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k_3pL4vvZmuW"
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   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "Is there a relationship between the valence,tempo, danceability, and energy of Spotify’s most popular songs and depression rates in California in the last five years?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0U6PkFmvZmuX"
   },
   "source": [
    "# Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IBEkgS1kZmuX"
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   "source": [
    "Whether it is used for celebrations, form of expression or emotional outlets, music is embedded in many parts of out culture. There are factors that influence the type of music we listen to. By looking at prior research, we found that there is a relationship between what kind of music people listen to, and how they are feeling. Eric Lacourse states in his research article, “Heavy Metal Music and Adolescent Suicidal Risk,” [link](https://pdfs.semanticscholar.org/3b35/07371a5750934b135e538f783094bdc85ae2.pdf)that there was a big correlation between girls who listened to heavy metal music and sucidal risks, so we want to see if that could somehow translate over to depression rates and the type of music being heard. Moreover Peter J. Rentfrow in his article, “The Do Re Mi’s of Everyday life: The Structure and Personality Correlated of Music Preferences,” [link](https://pdfs.semanticscholar.org/1364/53addebb04b046e06a524c19fa4e891ea7ae.pdf)found out that there is a relationship between people's personality traits and the kind of music that they listen to. This relates to our topic where we see how people's behaviors and thought process affects the type of music they listen to. \n",
    "\n",
    "If they are a more depressed and sad emotional person, do they listen low tempo music or upbeat music? This article will help support our findings and gives a light into the correlation between personalities/their emotions and the kind of music they listen to, which will help prove the correlation between tempo and depression rates.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Hypothesis\n"
   ]
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   "source": [
    "\n",
    "We hypothesize that there is an inversely positive correlation between the two datasets whereas depression rates increase, the tempo, valence, energy and dance-ability of the top 100 songs listened to on Spotify should decrease. \n",
    "\n",
    "Mental health is important to one’s behavioral patterns and how they cope with life challenges and depression rates in the States is on the rise as more people are prioritizing their mental health and being more vocal about it. With that in mind we expect that depression to affect one’s mood and in return affect the type of music they listen to where they would be listening to low energy and slow songs. We expect that as there is an increase in depression rates, more people would be listening to more mellow and sad-like songs and hence these songs would be part of the most streamed songs on spotify. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IZNV3yDxZmua"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aLQEtVvLsERd"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "from scipy.stats import pearsonr, norm, ttest_ind\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ueFxG_zPZmue"
   },
   "source": [
    "# Data: Gathering "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
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    "colab_type": "code",
    "id": "95jg6mU1no-L",
    "outputId": "8f03c78e-7cd3-4d19-e1b6-ea05cfae3fbf"
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-5d6f4d88cca6>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    >> artists=[]\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#Code to get spotify data using spotify's api\n",
    "\n",
    "#Personalized token for each user \n",
    "token=util.prompt_for_user_token(username='xxxx',scope=scope,client_id=id,client_secret=secret,redirect_uri=uri)\n",
    "\n",
    "#Creating empty lists\n",
    ">> artists=[]\n",
    ">>> tracks=[]\n",
    ">>> track_ids=[]\n",
    ">>> popularity=[]\n",
    "\n",
    "#Searching the year with query '2014'\n",
    ">>> track_results_2014=sp.search(q='year:2014', type='track',limit=50, offset=50)\n",
    "\n",
    "#Looping through the search result varaible and adding to our empty lists\n",
    ">>> for i, t in enumerate(track_results_2014['tracks']['items']):\n",
    "\tartists.append(t['artists'][0]['name'])\n",
    "\ttracks.append(t['name'])\n",
    "\ttrack_ids.append(t['id'])\n",
    "\tpopularity.append(t['popularity'])\n",
    "\n",
    "  \n",
    "#Creating a data set with our lists \n",
    ">>> df_2014=pd.DataFrame({'Artist':artists,'Track Name':tracks,'Track Id':track_ids,'Popularity':popularity})\n",
    ">>> df_2014.head()\n",
    "                Artist  ... Popularity\n",
    "0        Bobby Shmurda  ...         71\n",
    "1         Taylor Swift  ...         67\n",
    "2        Ariana Grande  ...         75\n",
    "3  5 Seconds of Summer  ...         67\n",
    "4         Andy Grammer  ...         70\n",
    "\n",
    "[5 rows x 4 columns]\n",
    "\n",
    "#Creating new empty lists for our audio features \n",
    ">>> energy=[]\n",
    ">>> tempo=[]\n",
    ">>> danceability=[]\n",
    ">>> valence=[]\n",
    ">>> batch=df_2014['Track Id']\n",
    "\n",
    "#Getting the audio features \n",
    ">>> results_2015=sp.audio_features(batch)\n",
    "\n",
    "#Looping through the audio features and updating our list\n",
    ">>> for i,t in enumerate(results_2015):\n",
    "\tenergy.append(t['energy'])\n",
    "\ttempo.append(t['tempo'])\n",
    "\tdanceability.append(t['danceability'])\n",
    "\tvalence.append(t['valence'])\n",
    "\n",
    "#Adding the audio features lists to our existing data frame\n",
    ">>> df_2014=pd.DataFrame({'Artist':artists,'Track Name':tracks,'Track Id':track_ids,'Popularity':popularity, 'Energy':energy, 'Tempo':tempo, 'Danceability':danceability, 'Valence':valence})\n",
    ">>> df_2014.head()\n",
    "                Artist            Track Name  ... Danceability  Valence\n",
    "0        Bobby Shmurda             Hot N*gga  ...        0.794    0.180\n",
    "1         Taylor Swift          Shake It Off  ...        0.648    0.943\n",
    "2        Ariana Grande            Break Free  ...        0.687    0.284\n",
    "3  5 Seconds of Summer  She Looks So Perfect  ...        0.493    0.470\n",
    "4         Andy Grammer      Honey, I'm Good.  ...        0.752    0.595\n",
    "\n",
    "[5 rows x 8 columns]\n",
    "\n",
    "#Finally exporting dataframe as a csv file\n",
    ">>> df_2014.to_csv('ugh2014_2.csv')"
   ]
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   "source": [
    "The same process was repeated extensively for all the other years. As spotify only limits us to 50 songs at each search, we did the whole process twice to get top 100 songs from each year in two separate dataframes. Then, we used the concat function to merge the two dataframes and get one csv file of all the 100 song in each year. We did this for the year 2012 up to 2017. The code for all the years was not put in because it is exhaustive and repetitive. "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# **Data: Description**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFg0igmB-JYi"
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   "source": [
    "We got our [depression data](https://healthdata.gov/dataset/adult-depression-lghc-indicator/resource/57174298-f044-4534-ad8c-d039bc6d4837#%7B%7D) set from the official healthdata.gov website. It was a csv file with depression rates from the year 2012 to 2017. The data was divided into many criterias like depression rates among different genders, various race and ethnicity groups, different age groups, income levels, or education levels. Since, our question doesn’t look into the divided strata inside the depression rates, we only focused on the total depression rate for each year. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "szq-p6UG-UUU"
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   "source": [
    "Our **song data** came from the spotify api through web-scraping. We described the whole process above. The spotify data has song names, artists names, track-ID (which is the song code spotify uses to give us track analysis data), tempo, energy, and valence. Below is the description of each of these features taken from the official spotify api website. \n",
    "\n",
    "**Tempo: a float type variable, The overall estimated tempo of the section 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",
    "**Danceability: type-float, 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. The distribution of values for this feature look like this: \n",
    "[danceability distribution](https://https://developer.spotify.com/assets/audio/danceability.png)\n",
    "\n",
    "**Energy: type- float variable, 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. The distribution of values for this feature look like this:**[energy distribution](https://developer.spotify.com/assets/audio/energy.png) \n",
    "\n",
    "**Valence: type-float, A 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). The distribution of values for this feature look like this:**[valence distribtuion](https://developer.spotify.com/assets/audio/valence.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DJz5PtrJoOwN"
   },
   "source": [
    "# **Data: Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5-OMoZYIohZG"
   },
   "source": [
    "When cleaning **adult-depression-lghc-indicator-24.csv**, we first put all of the data into a dataframe. Then, we narrow our dataframe into a three column dataframe by dropping five columns: \"Strata\",\"Strata Name\", \"Weighted Frequency\",\"Lower 95% CL\" , \"Upper 95% CL\". \n",
    "\n",
    "* The reason we drop the \"Strata\",\"Strata Name\" is because that we do only care overall  percent of American who get depressed, but do not care about how they get depressed. \n",
    "* The reason we drop the \"Lower 95% CL\" , \"Upper 95% CL” is because that we do not need these two types of data to doing our data analysis. \n",
    "* The reason we drop the weighted frequency is because … \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ibaonuu7bV74"
   },
   "source": [
    "\n",
    "When cleaning **music data from 2013 to 2018** (whose name is data2013.csv, data2014.csv, …, data2018.csv), we drop four column from the original dataframe:   'Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist' .\n",
    "* The reason we drop them is because these information is useless for us to analyze the data . \n",
    "\n",
    "But we maintain the column “Track Name” due to the reason that, once we have a name, we can track our numeric data more easily. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zTEXtF0xblCd"
   },
   "source": [
    "What's more, we rearrange all the column in our dataframes for better readability after gaining the cleaned data\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "---\n",
    "\n",
    "\n",
    "We begin with reading the data of depression rate (adult-depression-lghc-indicator-24.csv). \n",
    "\n",
    "We named the origional dataframe that read from csv file as **df**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "height": 204
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    "id": "FvYSVBbisiiL",
    "outputId": "293c8bd7-8722-4711-a3a7-be7cdec7c0ce"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Strata</th>\n",
       "      <th>Strata Name</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Weighted Frequency</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Lower 95% CL</th>\n",
       "      <th>Upper 95% CL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>Total</td>\n",
       "      <td>Total</td>\n",
       "      <td>1920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.74</td>\n",
       "      <td>11.11</td>\n",
       "      <td>12.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sex</td>\n",
       "      <td>Male</td>\n",
       "      <td>561</td>\n",
       "      <td>1116664.0</td>\n",
       "      <td>8.12</td>\n",
       "      <td>7.32</td>\n",
       "      <td>8.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>Sex</td>\n",
       "      <td>Female</td>\n",
       "      <td>1359</td>\n",
       "      <td>2163108.0</td>\n",
       "      <td>15.25</td>\n",
       "      <td>14.30</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>Race-Ethnicity</td>\n",
       "      <td>White</td>\n",
       "      <td>1314</td>\n",
       "      <td>1806371.0</td>\n",
       "      <td>14.57</td>\n",
       "      <td>13.67</td>\n",
       "      <td>15.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>Race-Ethnicity</td>\n",
       "      <td>Black</td>\n",
       "      <td>97</td>\n",
       "      <td>222022.0</td>\n",
       "      <td>13.54</td>\n",
       "      <td>10.44</td>\n",
       "      <td>16.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year          Strata Strata Name  Frequency  Weighted Frequency  Percent  \\\n",
       "0  2012           Total       Total       1920                 NaN    11.74   \n",
       "1  2012             Sex        Male        561           1116664.0     8.12   \n",
       "2  2012             Sex      Female       1359           2163108.0    15.25   \n",
       "3  2012  Race-Ethnicity       White       1314           1806371.0    14.57   \n",
       "4  2012  Race-Ethnicity       Black         97            222022.0    13.54   \n",
       "\n",
       "   Lower 95% CL  Upper 95% CL  \n",
       "0         11.11         12.37  \n",
       "1          7.32          8.92  \n",
       "2         14.30         16.20  \n",
       "3         13.67         15.46  \n",
       "4         10.44         16.65  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for depression rates\n",
    "df = pd.read_csv(\"adult-depression-lghc-indicator-24.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JR2YU0wLV_wW"
   },
   "source": [
    "After printing the head of the dataframe, we find that there are five column that need to be drop: \n",
    "*  Strata\n",
    "*  Strata Name\n",
    "*  Weighted Frequency\n",
    "*  Lower 95% CL\n",
    "*  Upper 95% CL\n",
    "\n",
    "We named this new dataframe as **df1**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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       "      <th>Year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1920</td>\n",
       "      <td>11.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>1689</td>\n",
       "      <td>13.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.30</td>\n",
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       "      <th>69</th>\n",
       "      <td>2015</td>\n",
       "      <td>1848</td>\n",
       "      <td>12.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2016</td>\n",
       "      <td>1645</td>\n",
       "      <td>13.77</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2017</td>\n",
       "      <td>1550</td>\n",
       "      <td>19.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year  Frequency  Percent\n",
       "0    2012       1920    11.74\n",
       "23   2013       1689    13.08\n",
       "46   2014       1309    13.30\n",
       "69   2015       1848    12.92\n",
       "92   2016       1645    13.77\n",
       "115  2017       1550    19.04"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating smaller dataframe with just total values\n",
    "df1 = df.loc[df['Strata'] == \"Total\"]\n",
    "df1 = df1.drop(columns=[\"Strata\",\"Strata Name\", \"Weighted Frequency\",\"Lower 95% CL\" , \"Upper 95% CL\"])\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JoXeTozhWPn7"
   },
   "source": [
    "Now, we have already got the df1 as the perfect dataframe on the depression rate.\n",
    "\n",
    "So we begin to read in the data from 2013-2018 about the popular music. We named each dataframe using the year that the data in.(data2013, data2014 ... data2018)\n",
    "\n",
    "Different from reading the depression rate file, we directly clean the data after reading data from each year to prevent the redundancy of code \n",
    "\n",
    "We have four columns that need to be dropped:\n",
    "* Unnamed: 0\n",
    "* Unnamed: 0.1\n",
    "* Track Id\n",
    "* Artist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "N9MGRABdtAlh",
    "outputId": "dd91da7e-7f9a-4dc1-becf-482c42eda4a1"
   },
   "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>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All of Me</td>\n",
       "      <td>84</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.264</td>\n",
       "      <td>0.331</td>\n",
       "      <td>119.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Play It Again</td>\n",
       "      <td>74</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.596</td>\n",
       "      <td>144.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wagon Wheel</td>\n",
       "      <td>75</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.663</td>\n",
       "      <td>0.592</td>\n",
       "      <td>148.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wake Me Up</td>\n",
       "      <td>83</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.784</td>\n",
       "      <td>0.588</td>\n",
       "      <td>124.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3005</td>\n",
       "      <td>74</td>\n",
       "      <td>0.472</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.636</td>\n",
       "      <td>166.077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Track Name  Popularity  Danceability  Energy  Valence    Tempo\n",
       "0      All of Me          84         0.422   0.264    0.331  119.930\n",
       "1  Play It Again          74         0.510   0.898    0.596  144.056\n",
       "2    Wagon Wheel          75         0.529   0.663    0.592  148.102\n",
       "3     Wake Me Up          83         0.518   0.784    0.588  124.102\n",
       "4           3005          74         0.472   0.448    0.636  166.077"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2013\n",
    "df_2013=pd.read_csv('data2013.csv')\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2013=df_2013.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2013 = df_2013[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "5oBP3Yc585Pc",
    "outputId": "82e8e058-52be-4107-aa66-e152b34943a0"
   },
   "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",
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       "    }\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>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Role Modelz</td>\n",
       "      <td>82</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.521</td>\n",
       "      <td>0.458</td>\n",
       "      <td>100.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wet Dreamz</td>\n",
       "      <td>78</td>\n",
       "      <td>0.504</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.584</td>\n",
       "      <td>175.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Riptide</td>\n",
       "      <td>77</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.510</td>\n",
       "      <td>101.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stay With Me</td>\n",
       "      <td>83</td>\n",
       "      <td>0.418</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.184</td>\n",
       "      <td>84.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Take Me to Church</td>\n",
       "      <td>75</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.437</td>\n",
       "      <td>128.945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Track Name  Popularity  Danceability  Energy  Valence    Tempo\n",
       "0     No Role Modelz          82         0.696   0.521    0.458  100.000\n",
       "1         Wet Dreamz          78         0.504   0.705    0.584  175.483\n",
       "2            Riptide          77         0.484   0.731    0.510  101.654\n",
       "3       Stay With Me          83         0.418   0.420    0.184   84.094\n",
       "4  Take Me to Church          75         0.566   0.664    0.437  128.945"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2014\n",
    "df_2014=pd.read_csv('data2014.csv')\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2014=df_2014.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2014 = df_2014[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "yE7IT8OYsuoL",
    "outputId": "7492dc34-cfce-43f6-c3a0-7cdff415951b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Track Name</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Less I Know The Better</td>\n",
       "      <td>82</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.74000</td>\n",
       "      <td>0.785</td>\n",
       "      <td>116.879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Exchange</td>\n",
       "      <td>78</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.43300</td>\n",
       "      <td>0.274</td>\n",
       "      <td>160.140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brown Noise - Loopable with No Fade</td>\n",
       "      <td>76</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Die A Happy Man</td>\n",
       "      <td>75</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.38300</td>\n",
       "      <td>0.381</td>\n",
       "      <td>83.096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tennessee Whiskey</td>\n",
       "      <td>76</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>0.512</td>\n",
       "      <td>48.718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Track Name  Popularity  Danceability   Energy  \\\n",
       "0           The Less I Know The Better          82         0.640  0.74000   \n",
       "1                             Exchange          78         0.530  0.43300   \n",
       "2  Brown Noise - Loopable with No Fade          76         0.000  0.00002   \n",
       "3                      Die A Happy Man          75         0.660  0.38300   \n",
       "4                    Tennessee Whiskey          76         0.392  0.37000   \n",
       "\n",
       "   Valence    Tempo  \n",
       "0    0.785  116.879  \n",
       "1    0.274  160.140  \n",
       "2    0.000    0.000  \n",
       "3    0.381   83.096  \n",
       "4    0.512   48.718  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2015\n",
    "df_2015=pd.read_csv('data2015.csv')\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2015=df_2015.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2015 = df_2015[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "1QM2pUMes6nI",
    "outputId": "b8181fe7-ee97-466a-d1ac-9ee6e8eed472"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</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>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I Fall Apart</td>\n",
       "      <td>85</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.291</td>\n",
       "      <td>143.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Congratulations</td>\n",
       "      <td>85</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.492</td>\n",
       "      <td>123.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>goosebumps</td>\n",
       "      <td>83</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.430</td>\n",
       "      <td>130.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Closer</td>\n",
       "      <td>84</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.524</td>\n",
       "      <td>0.661</td>\n",
       "      <td>95.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What Ifs</td>\n",
       "      <td>77</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.687</td>\n",
       "      <td>125.976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Track Name  Popularity  Danceability  Energy  Valence    Tempo\n",
       "0     I Fall Apart          85         0.556   0.538    0.291  143.950\n",
       "1  Congratulations          85         0.630   0.804    0.492  123.146\n",
       "2       goosebumps          83         0.841   0.728    0.430  130.049\n",
       "3           Closer          84         0.748   0.524    0.661   95.010\n",
       "4         What Ifs          77         0.612   0.799    0.687  125.976"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2016\n",
    "df_2016=pd.read_csv('data2016.csv')\n",
    "df_2016.columns\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2016=df_2016.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2016 = df_2016[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "JL05vqIksutG",
    "outputId": "fcba8af2-30f0-42f8-ca69-d4eb699c2555"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Track Name</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jocelyn Flores</td>\n",
       "      <td>86</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.437</td>\n",
       "      <td>134.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>idontwannabeyouanymore</td>\n",
       "      <td>86</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.247</td>\n",
       "      <td>170.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ric Flair Drip (&amp; Metro Boomin)</td>\n",
       "      <td>83</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.428</td>\n",
       "      <td>0.333</td>\n",
       "      <td>100.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fuck Love (feat. Trippie Redd)</td>\n",
       "      <td>84</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.329</td>\n",
       "      <td>131.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Young Dumb &amp; Broke</td>\n",
       "      <td>84</td>\n",
       "      <td>0.798</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.394</td>\n",
       "      <td>136.949</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Track Name  Popularity  Danceability  Energy  Valence  \\\n",
       "0                   Jocelyn Flores          86         0.872   0.391    0.437   \n",
       "1           idontwannabeyouanymore          86         0.483   0.412    0.247   \n",
       "2  Ric Flair Drip (& Metro Boomin)          83         0.880   0.428    0.333   \n",
       "3   Fuck Love (feat. Trippie Redd)          84         0.797   0.533    0.329   \n",
       "4               Young Dumb & Broke          84         0.798   0.539    0.394   \n",
       "\n",
       "     Tempo  \n",
       "0  134.021  \n",
       "1  170.163  \n",
       "2  100.007  \n",
       "3  131.036  \n",
       "4  136.949  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2017\n",
    "df_2017=pd.read_csv('data2017.csv')\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2017=df_2017.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2017 = df_2017[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "vIDQEGZftFV4",
    "outputId": "c5c4c142-9e6d-462c-b20a-5fa6c3a550fb"
   },
   "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>Popularity</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sunflower - Spider-Man: Into the Spider-Verse</td>\n",
       "      <td>95</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.913</td>\n",
       "      <td>89.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Swervin (feat. 6ix9ine)</td>\n",
       "      <td>92</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.434</td>\n",
       "      <td>93.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wow.</td>\n",
       "      <td>94</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.539</td>\n",
       "      <td>0.385</td>\n",
       "      <td>99.947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Going Bad (feat. Drake)</td>\n",
       "      <td>90</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.544</td>\n",
       "      <td>86.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Murder On My Mind</td>\n",
       "      <td>90</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.740</td>\n",
       "      <td>115.007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Track Name  Popularity  Danceability  \\\n",
       "0  Sunflower - Spider-Man: Into the Spider-Verse          95         0.760   \n",
       "1                        Swervin (feat. 6ix9ine)          92         0.581   \n",
       "2                                           Wow.          94         0.833   \n",
       "3                        Going Bad (feat. Drake)          90         0.889   \n",
       "4                              Murder On My Mind          90         0.759   \n",
       "\n",
       "   Energy  Valence    Tempo  \n",
       "0   0.479    0.913   89.911  \n",
       "1   0.662    0.434   93.023  \n",
       "2   0.539    0.385   99.947  \n",
       "3   0.496    0.544   86.003  \n",
       "4   0.730    0.740  115.007  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading in data for 2018\n",
    "df_2018=pd.read_csv('data2018.csv')\n",
    "\n",
    "#Getting Read of the columns we don't need\n",
    "df_2018=df_2018.drop(columns=['Unnamed: 0','Unnamed: 0.1','Track Id' , 'Artist'])\n",
    "df_2018 = df_2018[['Track Name' ,  'Popularity' ,'Danceability' , 'Energy' , 'Valence','Tempo' ]]\n",
    "df_2018.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1pahNcg0WheG"
   },
   "source": [
    "Now, we have clean all of our data successfully. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1QJWpTD7Zmui"
   },
   "source": [
    "# Data: Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wOJavkBcZmui"
   },
   "source": [
    "Now, all the data has been settled down. We begin to doing data visualizaton to understand the relationship between our two variables: the characteristic of popular music and the depression rate\n",
    "\n",
    "We plot several different kinds of plots and partition them into four parts.\n",
    "\n",
    "Firstly, we want to get the general sense about our data. Thus, we just explore our existed data in depth by ploting the following plot:\n",
    "* Part One: The change of depression rate between male and female in 2013-2018\n",
    "* Part Two: The trend of change in music in 2013-2018\n",
    "\n",
    "Then, in Part Three and Four, we begin to find the relationship between music and depression rate by ploting the average of them and see whether there is any relationship. \n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V8K1JTL3p-wg"
   },
   "source": [
    "## Part One: Change of Depression Rate in 2013-2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z9-dUoLYXTtL"
   },
   "source": [
    "We first create a bar plot to see the change of depression rate between 2013-2017. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 575
    },
    "colab_type": "code",
    "id": "N7_NyWntZmuj",
    "outputId": "2e4ff80c-8bd5-48c3-e1d0-7125481f0980"
   },
   "outputs": [
    {
     "data": {
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c98nmzZv11Vdf5Wr/+eeflZWVpQMHDqhp06a6//779cwzz2j06NH65JNP1KlTJ91zzz0KCwtzvPeu55tvvpF05TW+VoUKFdS4cWNt2bJFP/zwg8LDwyVJQ4cO1fr167VgwQK9+OKLkuS466Zy5cqKiYnJdUxHjx51+d5PTk6WdOW9f/V6Ut7v44I4cOCAunXrpkuXLun999/P9d69nmt/l29W/fr1ZYxRVlaWjh8/rv/85z+aPHmytm3bpo8++kgVK1aUdOXuqmvvqAsJCclzfqisrCwNGjRI27dvV79+/W74zr7COM5FixbpoYce0p///GdNmjRJNWvW1NGjR/W3v/1NY8aM0ebNm/XBBx/c9H5wfYQbFJqMjAydPXtWklS5cmVJV241laT169dr/fr1ea6blpaWa1nVqlVd9q1WrZqkKx/IebVdKzU1VZcvX871Ae6qjsDAQEVGRmrr1q167rnnlJiYqIULF0qS6tWrpylTpmjAgAGSroStzz77TNOmTVNiYqImTJggSSpXrpyGDBmimTNnys/PL8/95RxDXreG5yw/f/58rraAgIBcy3JuUS3onEM1a9bUkSNHJEm//vqr1q9fr5EjR6pv375KSkpSWFiYU////Oc/6t27t3x8fNSxY0eFhoaqbNmyKlWqlDZt2qTNmzfr4sWLbu8/533y0ksv5dsv531Ss2ZN7dixQ1OnTtXHH3+s5cuXS7oS7saPH+/yVuBr3chr37NnT5UvX16LFi3SzJkzVbp0aa1evVqpqamKi4tzukU455hchWlXx3S1vN7H7kpOTla7du109uxZvf/++7r//vtz9fH395fk+vdIuvI+uLpfYSldurSCg4P1+OOPq2rVqhowYIAmT56sN954Q9KVcHPt72l0dLTLcJOVlaUHH3xQS5cuVd++fbVo0aJcIeV6x5mz/GaP88CBAxo+fLgaNWqkhQsXqlSpK9PI1a9fXwsXLtT+/fu1dOlSbdq0ifmebgEm8UOh2bZtmy5fvqyqVasqJCRE0v/9h/Haa6/JXDkN6vIxf/78XNvLa+KxkydPOm37ann99eXv768KFSrkW4Mxxmkk6O6779bq1at17tw5bd++XZMmTdKpU6f0wAMP6NNPP3X0q1Chgv7+978rJSVFycnJevvtt1W/fn298cYbeuSRR/J9zXKOIeeYrnXixIk8j7WolC9fXr169dJ7772nX3/9VYMHD3b8FZ9j0qRJ8vLy0s6dO7VixQq98sormjZtmqZOnap69eoVeJ9XfwDl9/O5ejSvQYMGWrJkiVJTU7Vz5049//zzys7O1uOPP6533nnH7X0W5LUvU6aM+vbtqxMnTjjC+oIFCyRJQ4YMcbn9lStX5ntMOXNHXe1mRhG+//57RUdH68yZM1q6dKl69erlsl/Oz+nAgQMu23NGlopy3qouXbpIktNIzdSpU3O9Rq7mRrp8+bIGDBig999/Xw888ID+/e9/u5x/pmzZsqpevbrS0tIcP9OrFdZxrlu3TpcuXVJ0dLQj2OQoVaqUoqKiJElff/31Te0H7iHcoFBkZ2frueeekyQ98MADjuUtW7aUJG3durXA29y8eXOuZVlZWdq2bZskqXHjxm5vq2XLljp37py+++67Atfh7e2tVq1aadq0aZo1a5Yk5Tlzb+3atTVixAht3rxZfn5+153hN+cYcoLhtXJmXG3SpEmB675ZMTEx6ty5s77++mv9+9//dmr78ccfFRYWpgYNGjgtz87Odvx8rlWqVKk8R5Ru5n3i4eGhpk2basKECVq8eLEkacWKFdddL+e1d/XBef78ee3evVs+Pj65jjFnBGHBggU6c+aM1q5dq0aNGikiIsKp380c043673//q7Zt2+rs2bNatmyZunfvnmffli1bqkyZMtq+fbsuXLjg1Jadna1169ZJktq1a1dk9R4/flyS3J7AL0dmZqZ69+6tpUuXavDgwVq4cKFKly6dZ/+cU4+uZgleu3atU58blTNSmXOq/Vo5y905XY1CUKRX9MAylM8FxadOnXJM4hccHJzrLpo2bdqYUqVKmXfeecfl+nv37nWa+M+du6XatWvntDznguJrJ+LKkXOb+t133+1yzpS0tDSn+Vm2bNmSa54TY3LfQXLo0CGXFykfP37ceHp6Ot2BkdfdUjkXqb700ktOy7/44gtTunRpU6FCBad5SK6e58YV5XPh7rXyu1vKGGO++uorI12Z/OzqCynr1atnypUr5/RaZmdnm8mTJzt+dlff5m2MMZUrV85zP99//73x9PQ0derUMfv378/VfvHiRbNlyxbH8y+//NLpDrscS5cuNZJM37598znqKw4fPmw8PT2Nv79/rrtYxowZYySZkSNHuly3Tp06pkyZMmbatGlGkklISMjVJzMz04SGhpoyZcrkuuU7x+eff+40QeLV89wU1K5du0xgYKApU6aM+fjjj91aJ+d27/j4eKfl+U3idzV3Lij+4osvXE4CeeHCBXPvvfcaSU5TNlxPRkaG45bvESNGuHWR7o1M4net611Q/OWXXxpJpkyZMmbPnj1Obbt27TI+Pj7GZrPleVMDChfX3KBAci6MzM7Odnz9wrZt25SZmanIyEi99957qlSpktM6//73v9W+fXuNGDFCs2bNUosWLRQQECC73a69e/fq22+/VVJSkqpUqeK0Xrdu3dSzZ0/17NlTtWvX1p49e7RmzRpVrFhRc+bMKVDdHTp00PPPP6+nn35aderUUUxMjO644w6lpaXp6NGj2rx5s1q3bu34y+6VV17RunXr1LZtW9WqVUt+fn767rvvtHbtWlWoUEGxsbGSpD179qhnz55q2rSp7rrrLt1+++06ffq0Vq5cqUuXLjmuwcnPm2++qXvuuUdPPvmk1q1bp2bNmiklJUVLly5VqVKlNH/+fJUrV65Ax1tYmjVrpu7du2vlypV655139PDDD0uSnnjiCY0aNUqNGzdWr1695Onpqe3bt2vfvn3q1q2bPvzww1zb6tChg95//31169ZNTZs2lYeHh6KiohQVFaX69etr3rx5Gj58uO6880517txZdevW1aVLl3Ts2DFt3bpVlStX1g8//CDpyntq9uzZio6OVu3atVWhQgUdPHhQH374oby9vR1fJZGfkJAQvfrqqxo9erSaNGmivn37qnLlytq8ebOSkpJUv359vfDCCy7XHTx4sCZNmqS//e1v8vDwcBqtzOHp6anly5erU6dO+tOf/qRWrVopIiJCvr6+SklJ0VdffaVDhw7pxIkT8vX1LciPJZdz586pQ4cOOnv2rDp06KCkpCTHBc1Xi4uLc7pWa8aMGdq0aZMSEhK0e/duRUZG6vvvv3d8/cLs2bNzbWPFihWOkbGcU3pJSUmOEa1KlSrp5ZdfdvSfOXOmNm3apOjoaAUHBzuOf+3atTp//rxatWqlp59+2u1jHTVqlNasWaNKlSqpevXqmjZtWq4+bdu2dbqupVWrVoqPj1dCQoIaNWrk+PqFJUuWOL5+IedUeo4zZ844XZx85swZSdKIESMcpw0nTpyo+vXrS7pyEfiwYcM0f/58NW/eXD179nRcy7ZixQplZmYqLi5Od955p9vHiptQ3OkKfwyS88yyXl5eJjAw0DRp0sSMHDnSrF27Nt+/oH799Vfz3HPPmSZNmpiyZcsaHx8fExISYmJiYszcuXNNWlqao+/VIxMffvihadmypfH19TX+/v7mz3/+s8u/7K83cpNj69atpk+fPua2225zzKobHh5unnjiCadZeD/55BMzdOhQ06BBA1O+fHnj6+tr6tata8aOHet0C3pKSop5+umnTatWrUzVqlWNl5eXqV69uuncubNZs2aN077zm6HYbrebUaNGmeDgYOPp6WkCAwNN9+7dXc5ieytHboy5cju/zWYz1atXd5rvZv78+SY8PNz4+vqawMBA06NHD7N37948Rx9OnTplBgwYYKpUqWJKlSplpNwzFO/du9cMGTLEBAcHGy8vL1OhQgVz5513mtjYWLNhwwZHvy+++MKMGjXKNGrUyFSoUMH4+PiY0NBQM3ToULcn8MvxySefmI4dO5qAgADj5eVlQkNDzZNPPuly1uocR48edRyDq681uPa4J0yYYO68807HjMC1a9c2vXr1MgsXLnQaCbjRkZucn+P1Hq5+P1JTU81jjz3meO9Vq1bNDBs2zKSkpLjcV06NeT2ufS+tXr3aPPDAA6ZOnTqmfPnyxsPDw1SuXNl06NDBzJ0797q3Vl8rOjr6usd57fsqx7/+9S/TrFkz4+vra/z8/ExUVFSu0eEc7rym1/6csrOzzfz58010dLQJCAhwjLy2b98+1zxfKFp8cSZKnH/961+Ov4Dyuv0TAIC8cEExAACwlFsebjIyMtSjRw/VrVtXERER6ty5s2N+jZ9//lmdO3dWnTp1dNddd+V514UkvfPOO6pTp45CQ0MVGxvr8k4TAADwv6dYRm5iY2O1f/9+7d69W127dnVcnDlx4kS1bNlSycnJmj9/vgYOHOgytBw+fFiTJk3Stm3b9OOPP+rkyZNuzWsBAACsr9ivudm5c6f69++vH3/8UX5+fjp8+LBjdtvIyEi9+OKLuWZzfOmll3TkyBHHlfxr1qzRiy++6HK+CgAA8L+l2K+5mTVrlrp166bU1FRlZ2c7go105VbNY8eO5Vrn2LFjTjPJ5tUPAAD87ynWcDNjxgwlJyc7Zra9dsrx/AaVru6bX7+EhAQFBQU5HgkJCTdZNQAAKMmKbRK/l19+WcuXL9enn34qX19fxyRWp0+fdozeHD161OW3CgcHBzsuQs6vnyTFx8crPj7e8dzb25uAAwDAH8zp06fd/kLeYrnmJiEhQe+9954+/fRTVahQwbF86NChCgkJ0dSpU/XVV1+pV69eOnToUK7vHTl06JBat26tXbt2qUqVKurevbtiYmI0atSo6+47KChIdru90I8JAAAUnYJ8ft/ycGO321WjRg3VqlXLMaW8t7e3vvzyS506dUqDBg3S4cOH5eXlpTlz5ji+BXjkyJG6//77df/990uS/vnPf+qFF15Qdna22rdvr3/84x/y9PS87v4JNwAA/PGU6HBT3Ag3AAD88RTk87vY75YCAAAoTIQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKYQbAABgKR7FXQAAAFaTdDC1uEsoFneHBhZ3CZIYuQEAABZDuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZSLOHmscceU0hIiGw2m7799ltJ0vnz5xUREeF41K1bVx4eHjp79myu9Tdt2iRfX1+n/r///vutPgwAAFACeRTHTnv37q2nnnpKrVu3diwLCAjQ7t27Hc9ffvllbd68WRUrVnS5jbCwMO3cubPIawUAAH8sxRJuoqKirttn/vz5eu65525BNQAAwEpK5DU3SUlJSk1NVdeuXfPss3//fjVp0kTNmzfXnDlzbmF1AACgJCuWkZvrmTdvngYPHiwPD9flNWnSRHa7Xf7+/rLb7YqJiVGlSpXUt2/fXH0TEhKUkJDgeJ6WllZkdQMAgOJX4kZu0tPTtWTJEg0fPjzPPuXLl5e/v78kKSgoSAMGDNDWrVtd9o2Pj5fdbnc8/Pz8iqRuAABQMpS4cLN06VI1atRI9evXz7PPiRMnlJ2dLUm6cOGCVq9ercaNG9+qEgEAQAlWLOFm9OjRCgoKkt1u17333qvatWs72t555x2NGDEi1zojR47UqlWrJEnLli1Tw4YNFR4erpYtW6pjx44aNmzYLasfAACUXDZjjCnuIm6lnFAFAEBRSTqYWtz+MTldAAAgAElEQVQlFIu7QwOLbNsF+fwucaelAAAAbgbhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWEqxhJvHHntMISEhstls+vbbbx3LQ0JCVL9+fUVERCgiIkJLlizJcxvTp09XaGioQkNDNWnSpFtRNgAA+APwKI6d9u7dW0899ZRat26dqy0xMVF33XVXvutv2bJFixcv1t69e+Xh4aF77rlHrVu3VqdOnYqqZAAA8AdRLCM3UVFRCgoKuuH1lyxZoqFDh6ps2bLy9vbW8OHDtXjx4kKsEAAA/FGVuGtuBg4cqIYNG2rkyJE6ffq0yz7Hjh1TzZo1Hc9DQkJ07NixW1UiAAAowUpUuNmyZYv27Nmjb775RoGBgRoyZEiefW02m+Pfxpg8+yUkJCgoKMjxSEtLK9SaAQBAyVKiwk1wcLAkydPTU3Fxcdq6dWue/Y4cOeJ4fvToUce614qPj5fdbnc8/Pz8Cr1uAABQcpSYcJOenq7z5887ni9evFiNGzd22bdPnz5asGCB0tPTdfHiRc2bN0/9+/e/VaUCAIASrFjCzejRoxUUFCS73a57771XtWvX1qlTp9SuXTs1atRIDRs21ObNm/Xuu+861omJidHOnTslSW3btlXfvn3VsGFDNWjQQPfdd586d+5cHIcCAABKGJvJ74IVC8oJVQAAFJWkg6nFXUKxuDs0sMi2XZDP7xJzWgoAAKAwEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAICleBR3AQCAkivpYGpxl1Bs7g4NLO4ScIMYuQEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJbC1y8Af0D/q1PiMx0+AHcwcgMAACyFcAMAACyFcAMAACyFcAMAACyFcAMAACyFu6VQrLjrBwBQ2Bi5AQAAlkK4AQAAlkK4AQAAlsI1NwD+Z3CNF/C/gZEbAABgKYQbAABgKYQbAABgKW6HmzVr1mjRokWO5ykpKWrfvr2qVaumwYMHKz09vUgKBAAAKAi3w83UqVN14sQJx/O4uDjt379fAwcO1CeffKIpU6YUSYEAAAAF4Xa4SU5OVqNGjSRJ6enp+uijj5SQkKBXXnlFM2fO1PLly93e6WOPPaaQkBDZbDZ9++23kqSMjAz16NFDdevWVUREhDp37qwjR464XH/Tpk3y9fVVRESE4/H777+7vX8AAGBdboebzMxMeXt7S5KSkpKUlZWlLl26SJLq1KnjNKpzPb1799a2bdtUs2ZNp+WxsbHav3+/du/era5duyo2NjbPbYSFhWn37t2OR5kyZdzePwAAsC63w03t2rX10UcfSZLeffddRUZGqnz58pKkkydPKiAgwO2dRkVFKSgoyGmZj4+PYmJiZLPZJEktW7bUoUOH3N4mAACAVIBwEx8fr1deeUWVKlXSe++9p8cff9zRtnHjRscpq8Iya9YsdevWLc/2/fv3q0mTJmrevLnmzJlTqPsGAAB/XG7PUDxkyBDVqlVLO3bsUNOmTdW2bVtHW5UqVdS1a9dCK2rGjBlKTk7Wm2++6bK9SZMmstvt8vf3l91uV0xMjCpVqqS+ffvm6puQkKCEhATH87S0tEKrEwAAlDwF+vqFNm3aqE2bNrmWT506tbDq0csvv6zly5fr008/la+vr8s+OafDJCkoKEgDBgzQ1q1bXYab+Ph4xcfHO/UHAADWVaBJ/C5duqS33npLI0eO1H333afk5GRJUmJiovbv33/TxSQkJGjx4sVav359vtfwnDhxQtnZ2ZKkCxcuaPXq1WrcuPFN7x8AAPzxuR1ujhw5ogYNGiguLk7//e9/tWHDBl24cEGStGHDBr344otu73T06NEKCgqS3W7Xvffeq9q1a8tut2vcuHE6f/682rVrp4iICLVo0cKxzsiRI7Vq1SpJ0rJly9SwYUOFh4erZcuW6tixo4YNG+b2/gEAgHW5fVoqLi5O5cqV06FDh1SpUiV5eXk52tq2batnn33W7Z3Onj1bs2fPzrXcGJPnOm+//bbj32PGjNGYMWPc3h8AAPjf4Xa42bBhgxYtWqRq1aopKyvLqe22227T8ePHC704AACAgnL7tJTNZlOpUq67nzlzJs+LfwEAAG4lt8PN3Xffrbfeestl26JFixQVFVVoRQEAANwot09L/e1vf1N0dLRatmypPn36yGazKTExUX/961+1YcMGJSUlFWWdAAAAbnF75CYyMlKbN2+Wj4+PJk6cKGOMXnzxRZ0/f16bNm1Sw4YNi7JOAAAAtxRoEr/IyEht2rRJGRkZOnv2rAICArjWBgAAlChuj9wMHz5chw8flnTlSy5vv/12R7A5evSohg8fXjQVAgAAFIDb4eZf//qXTp8+7bLtzJkzWrBgQaEVBQAAcKMK9PULNpvN5fLvv/9egYGBhVIQAADAzcj3mpurZxK22Wzq16+ffHx8nPpkZGTo2LFj6tevX9FVCQAA4KZ8w01QUJDj+51++OEHhYWFqXLlyk59vLy81KBBA40YMaLoqgQAAHBTvuGme/fu6t69u+P55MmTdccddxR5UQAAADfK7VvB58+fX5R1AAAAFIoCzXNz9uxZffDBBzpw4IAyMjKc2mw2m8tv+v5fkXQwtbhLKDZ3h3IxOQCg5HA73Bw4cECtWrVSZmam0tPTValSJZ07d06XL19WhQoV5O/vX5R1AgAAuMXtW8HHjRunyMhInTp1SsYYrV27Vr///rsWLFigsmXLKjExsSjrBAAAcIvbIzc7d+7U3Llz5e3tLUm6dOmSSpcurUGDBunMmTOKi4vTli1biqxQAAAAd7g9cpOenq6AgACVKlVKAQEBOnnypKMtPDxcX3/9dZEUCAAAUBBuh5vQ0FD99NNPkqQ777xT8+fPlzFGkrRkyZJc898AAAAUB7dPS/3pT3/Shg0b1L9/fz3zzDPq3r27KlasKC8vL505c0YJCQlFWScAAIBb3A4306dPd/y7S5cu+vzzz7VixQr99ttv6tSpkzp16lQkBQIAABREgea5uVqzZs3UrFmzwqwFAADgphXoW8Hz8tFHH6l169aFsSkAAICbct2RmwsXLmjt2rU6evSoatWqpe7du8vD48pqy5Yt0/Tp07Vnzx6+cwoAAJQI+Yabffv2qUuXLrLb7Y47oyIjI7Vy5UoNGDBAmzdvVo0aNTR37lwNGzbslhQMAACQn3xPSz399NO6ePGi3nvvPe3bt09r1qxRRkaGmjVrpm3btmnmzJlKTk7WQw895BjNAQAAKE75JpJt27bp+eefV//+/SVJ9evXV5UqVdSsWTPNmDFDTz311C0pEgAAwF35jtycP39ed911l9OynOfR0dFFVxUAAMANyjfcGGNynW4qXbq0JMnHx6foqgIAALhB171Q5oUXXlCVKlUcz3MuLJ4xY4bTVy7YbDbNnj27CEoEAABwX77hJjg42OUXYtasWVM7d+50Wmaz2Qq3MgAAgBuQb7g5cuTILSoDAACgcBTKDMUAAAAlBeEGAABYCuEGAABYCuEGAABYCuEGAABYCuEGAABYSoG/7fLEiRNKSUlRRkZGrraoqKhCKQoAAOBGuR1ujh49qgcffFCff/65pP+bqdhms8kYI5vNpqysrKKpEgAAwE1uh5vY2FgdOnRIc+bMUVhYmLy8vIqyLgAAgBvidrhJSkrS22+/rb59+xZlPQAAADfF7QuKK1euLF9f36KsBQAA4Ka5HW6eeeYZ/f3vf1dmZmZR1gMAAHBT3D4ttXPnTh08eFB33HGHoqKiVKFCBad2m82m2bNnF3qBAAAABeF2uPn4449ls9nk5eWlL774Ile7zWYr1MIAAABuhNvh5vDhw0VZBwAAQKFghmIAAGApBZqh+MyZM3r99de1ZcsWnT17VhUrVlR0dLTGjBmjSpUqFVWNAAAAbnN75ObgwYMKDw/X888/L0kKCwuTJM2cOVPh4eE6dOhQ0VQIAABQAG6P3Dz55JMqV66ckpKSFBwc7FiekpKi++67T08++aSWLVtWJEUCAAC4y+2Rm88++0zTpk1zCjaSVKNGDU2dOlUbNmxwe6ePPfaYQkJCZLPZ9O233zqWJycnq1WrVqpbt64iIyO1b9++PLcxffp0hYaGKjQ0VJMmTXJ73wAAwNrcDjeXLl1SmTJlXLaVKVNGly9fdnunvXv31rZt21SzZk2n5Q8//LBiY2N14MABPfXUUxoxYoTL9bds2aLFixdr79692rdvn9auXatPPvnE7f0DAADrcjvcNG/eXAkJCbp06ZLT8szMTCUkJKh58+Zu7zQqKkpBQUFOy37++Wd98803evDBByVJvXr10uHDh3XkyJFc6y9ZskRDhw5V2bJl5e3treHDh2vx4sVu7x8AAFiX29fczJgxQx06dFBISIh69OihatWq6eTJk1qxYoXOnTtXoNNSrqSkpOj222+Xh8eVkmw2m4KDg3Xs2DGFhIQ49T127Jiio6Mdz0NCQpSYmHhT+wcAANbgdrhp1aqVPv/8c02fPl2JiYmOW8HbtGmjZ599VhERETddzLWzHBtj3OqbX7+EhAQlJCQ4nqelpd1EhQAAoKQr0Dw3jRs3LrI7omrUqCG73a7Lly/Lw8NDxhilpKTkuoBZkoKDg51OVx09etRlP0mKj49XfHy84/m1p8MAAIC1lJgZiqtUqaLGjRtr0aJFkqRly5YpJCQk1ykpSerTp48WLFig9PR0Xbx4UfPmzVP//v1vccUAAKAkynfkJiYmRq+++qrq1q2rmJiYfDdks9n00UcfubXT0aNHa+XKlTp58qTuvfde+fn56ccff9TcuXM1dOhQzZgxQ+XLl9eCBQucapk2bZqaNWumtm3bqm/fvmrYsKEkqX///urcubNb+wYAANaWb7j5/ffflZ2dLUn67bffCu2bv2fPnq3Zs2fnWl6vXj0lJSW5XGfNmjVOzydPnqzJkycXSj0AAMA68g03GzdudPx706ZNRV0LAADATSsx19wAAAAUBrfDzZo1axwX+0pX5qVp3769qlWrpsGDBys9Pb1ICgQAACgIt8PN1KlTdeLECcfzuLg47d+/XwMHDtQnn3yiKVOmFEmBAAAABeF2uElOTlajRo0kSenp6froo4+UkJCgV155RTNnztTy5cuLrEgAAAB3uR1uMjMz5e3tLUlKSkpSVlaWunTpIkmqU6eO06gOAABAcXE73NSuXdsxj827776ryMhIlS9fXpJ08uRJBQQEFE2FAAAABeD21y/Ex8dr2LBhmj9/vs6dO+f0LdwbN250nLICAAAoTm6HmyFDhqhWrVrasWOHmjZtqrZt2zraqlSpoq5duxZFfQAAAAVSoC/ObNOmjdq0aZNr+dSpUwurHgAAgJvi9jU3X331ldatW+d4fu7cOQ0fPlwtW7bU5MmTHV/TAAAAUJzcDjfjxo1z+t6ncePGadmyZapevboSEhI0Y8aMIikQAACgINwON/v27VOzZs0kSRcvXtTSpUuVkJCgZcuWaebMmU6zFwMAABQXt8NNenq6ypUrJ+nKKarffvtN3bt3lySFh4fr2LFjRVMhAABAAbgdboKDg/X5559LkhITE9WoUSNVqlRJkpSamio/P7+iqRAAAKAA3L5b6qGHHtLTTz+txMRE7d69W6+//rqjLSkpSWFhYUVSIAAAQEG4HW7Gjx+vqlWraseOHRo7dqyGDBniaPvll180fPjwIikQAACgIAo0z82gQYM0aNCgXMvnzp1baAUBAADcDLevucmxbt06TZ8+XbGxsY6LiLdv384XZwIAgBLB7ZGb1NRU9ezZU9u2bVOVKlV0+vRpjRo1SsHBwZo7d678/f2drsMBAAAoDm6P3DzxxBOy2+3atWuX7Ha7jDGOto4dO2rDhg1FUiAAAEBBuD1ys3r1as2ZM0fh4eHKyspyaqtRowbz3AAAgBLB7ZGbixcvKiAgwGXbr7/+Kg+PAl2bDAAAUCTcDjeNGzfWkiVLXLatWrVKLVq0KLSiAAAAbpTbwy3PPvusunbtqvT0dPXr1082m01JSUlasGCBFi5cqPXr1xdlnQAAAG5xe+Smc+fOSkxM1Ndff60+ffrIGKOxY8dq5cqVWrp0qaKiooqyTgAAALcU6EKZHj16qEePHkpOTtbp06cVGBioevXqFVVtAAAABebWyE1GRoY6dOigjRs3SpLq1KmjVq1aEWwAAECJ41a48fHx0TfffOM0tw0AAEBJ5PY1NzExMfr444+LshYAAICb5vY1Nw888IAeeeQR/fLLL/rTn/6kqlWrymazOfWJjIws9AIBAAAKwu1w061bN0nSP//5T/3zn/90CjbGGNlstlwzFwMAANxqboebnIuJAQAASjK3w010dHRR1gEAAFAoCvyFUMnJydqxY4d++ukn3XbbbYqMjFTdunWLojYAAIACczvc/Pbbbxo1apQWL16srKwslSpVStnZ2SpVqpQGDBiguXPnytfXtyhrBQAAuC63bwV//PHHtWzZMs2aNUunTp3S5cuXderUKc2aNUv/+c9/9PjjjxdlnQAAAG5xO9wsXbpUM2fO1COPPKLKlStLkipXrqxHH31Uzz33nJYuXVpkRQIAALjL7XBTunRp1a9f32Vb/fr1Vbp06UIrCgAA4Ea5HW769++vd99912Xbu+++q759+xZaUQAAADfK7QuKmzdvrsmTJ6tVq1bq1auXqlSpop9//lmJiYk6fvy4pk2bpg8++MDRn7ADAACKg9vhZvjw4ZIku92uL774Is92SbLZbIQbAABQLNwON4cPHy7KOgAAAAqF2+GmZs2aRVkHAABAoXD7guIc69at0/Tp0xUbG6tjx45JkrZv364TJ04UenEAAAAF5fbITWpqqnr27Klt27apSpUqOn36tEaNGqXg4GDNnTtX/v7+ev3114uyVgAAgOtye+TmiSeekN1u165du2S322WMcbR17NhRGzZsKJICAQAACsLtkZvVq1drzpw5Cg8PV1ZWllNbjRo1HKeoAAAAipPbIzcXL15UQECAy7Zff/1VHh4F/oJxAACAQud2uGncuLGWLFnism3VqlVq0aJFoRUFAABwo9webnn22WfVtWtXpaenq1+/frLZbEpKStKCBQu0cOFCrV+/vijrBAAAcIvbIzedO3dWYmKivv76a/Xp00fGGI0dO1YrV67U0qVLFRUVVZR1AgAAuKVAF8r06NFDPXr00IEDB3TmzBkFBgaqXr16hVbM+fPn1bZtW8fz3377TYcOHdLPP/+sihUrOpZv2rRJMTExqlu3rmNZUlKSypQpU2i1AACAP6Ybugq4bt26TsGisAQEBGj37t2O5y+//LI2b97sFGxyhIWFaefOnYVeAwAA+GNz67TUl19+qUGDBik0NFRly5aVn5+fateurSFDhmjHjh1FVtz8+fM1YsSIIts+AACwnuuGm9dee0333HOPEhMTVb16dXXv3l3dunVTtWrV9MEHH6hVq1aaNWtWoReWlJSk1NRUde3a1WX7/v371aRJEzVv3lxz5swp9P0DAIA/pnxPS+3evVvjxo1Tv3799Prrr+c6PZSamqqxY8dq3LhxioqKUkRERKEVNm/ePA0ePNjl/DlNmjSR3W6Xv7+/7Ha7YmJiVKlSJfXt2zdX34SEBCUkJDiep6WlFVqNAACg5Ml35OYf//iHIiIitGjRIpfXvQQGBmrRokUKDw/Xm2++WWhFpaena8mSJRo+fLjL9vLly8vf31+SFBQUpAEDBmjr1q0u+8bHx8tutzsefn5+hVYnAAAoefINN9u3b9ewYcNks9ny3kCpUho2bJi2bdtWaEUtXbpUjRo1Uv369V22nzhxQtnZ2ZKkCxcuaPXq1WrcuHGh7R8AAPxx5RtuUlJSFBYWdt2NhIWFKSUlpdCKeuedd3JdSDxy5EitWrVKkrRs2TI1bNhQ4eHhatmypTp27Khhw4YV2v4BAMAfV77X3KSlpals2bLX3Yivr2+hXsvi6hTT22+/7fj3mDFjNGbMmELbHwAAsI58R26MMfmekgIAAChprjuJX1RU1HUDjjGm0AoCAAC4GfmGmylTptyqOgAAAAoF4QYAAFiK298KDgAA8EdAuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZS4sJNSEiI6tevr4iICEVERGjJkiUu+02fPl2hoaEKDQ3VpEmTbnGVAACgpPIo7gJcSUxM1F133ZVn+5YtW7R48WLt3btXHh4euueee9S6dWt16tTpFlYJAABKohI3cuOOJUuWaOjQoSpbtqy8vb01fPhwLV68uLjLAgAAJUCJDDcDBw5Uw4YNNXLkSJ0+fTpX+7Fjx1SzZk3H85CQEB07duxWlggAAEqoEhdutmzZoj179uibb75RYGCghgwZ4rKfzWZz/NsYk+f2EhISFBQU5HikpaUVes0AAKDkKHHhJjg4WJLk6empuLg4bd261WWfI0eOOJ4fPXrUsd614uPjZbfbHQ8/P78iqRsAAJQMJSrcpKen6/z5847nixcvVuPGjXP169OnjxYsWKD09HRdvHhR8+bNU//+/W9lqQAAoIQqUeHm1KlTateunRo1aqSGDRtq8+bNevfddyVJMTEx2rlzpySpbdu26tu3rxo2bKgGDRrovvvuU+fOnYuzdAAAUEKUqFvBa9WqpV27drlsW7NmjdPzyZMna/LkybeiLAAA8AdSokZuAAAAbhbhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWEqJCjcZGRnq0aOH6tatq4iICHXu3FlHjhzJ1W/Tpk3y9fVVRESE4/H777/f+oIBAECJ41HcBVwrNjZWXbp0kc1m0xtvvKHY2FitW7cuV7+wsDDt3LmzGCoEAAAlWYkaufHx8VFMTIxsNpskqWXLljp06FAxVwUAAP5ISlS4udasWbPUrVs3l2379+9XkyZN1Lx5c82ZM+cWVwYAAEqqEndaKseMGTOUnJysN998M1dbkyZNZLfb5e/vL7vdrpiYGFWqVEl9+/bN1TchIUEJCQmO52lpaUVaNwAAKF4lcuTm5Zdf1vLly7V27Vr5+vrmai9fvrz8/f0lSUFBQRowYIC2bt3qclvx8fGy2+2Oh5+fX5HWDgAAileJCzcJCQlavHix1q9fr4CAAJd9Tpw4oezsbEnShQsXtHr1ajVu3PhWlgkAAEqoEhVu7Ha7xo0bp/Pnz6tdu3aKiIhQixYtJEkjR47UqlWrJEnLli1Tw4YNFR4erpYtW6pjx44aNmxYcZYOAABKiBJ1zU1QUJCMMS7b3n77bce/x4wZozFjxtyqsgAAwB9IiRq5AQAAuFmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAAYCmEGwAA/l97dx9TVf3HAfx9HFxAWLUgMYEb4+ES3SsPM+1CrsgJgVJzMtTItoSiZK2WG82MQDZjtAzLIkEGyqIxNGlNUkKY9sBg2AOjRS2cIKDiRWepxeVBvr8/HPfnDa2kc+45nN6vjenlfowMY/EAAAwWSURBVDnn8/14uPftPefeL+kKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREemK5sJNT08P4uPjYTKZsGTJEnR3d99w3LZt2xAaGorQ0FC8/vrrLq6SiIiItEpz4ea5555DdnY2fvnlF7zyyivIysqaNubLL79EbW0turq60N3djcOHD+Pzzz9XoVoiIiLSGk2FG5vNhu+++w7r168HAKSlpaG3txd9fX1O4+rq6vD000/D29sbHh4eyMzMRG1trQoVExERkdZoKtwMDAxgwYIFcHNzAwBIkgSj0Yj+/n6ncf39/bjnnnsct4ODg6eNISIiov8mN7UL+DNJkpxuCyH+dtzNxgBASUkJSkpKHLeHhoYQGBj4L6vUlitXrsDHx0ftMmYd9m1m2LeZYd9uHXs2M3rt2/Dw8D8eq6lwExQUhMHBQUxMTMDNzQ1CCAwMDMBoNDqNMxqNTqeqTp06NW3MlE2bNmHTpk1Klq26wMBADA4Oql3GrMO+zQz7NjPs261jz2aGfdPYaal58+YhNjYWNTU1AIADBw4gODgYwcHBTuPS09NRXV2N33//HaOjo6iqqsK6detUqJiIiIi0RlPhBgDKy8tRXl4Ok8mE4uJiVFZWAgBWrFiBb775BgCQkJCANWvWYOHChYiMjERSUhKSk5PVLJuIiIg0QlOnpQAgIiICbW1t075/6NAhp9v5+fnIz893VVmapvfTbkph32aGfZsZ9u3WsWczw74Bkvirq3GJiIiIZhnNnZYiIiIi+jcYboiIiEhXGG40xm63Y9WqVTCZTIiJiUFycrLjbe82mw3JyckIDw+HxWLB119/7fi5oqIiREREYM6cOWhoaHDaZmZmJiIiIhATE4OHHnoInZ2drpySSyjRtw0bNiAqKgoxMTFYvHgxWlpaXDkll1Cib1Oqq6shSdJN75/NlOhbQkICQkJCEBMTg5iYGOzYscOVU1KcEj0TQmDr1q0wmUywWCxISEhw4YxcQ4m+xcfHO44zi8UCSZLQ1dXlymkpT5CmjIyMiM8++0xMTk4KIYR47733RGJiohBCiA0bNoiCggIhhBAdHR3CaDSK8fFxIYQQ7e3t4sSJE+Lhhx8WBw8edNrmp59+6hh38OBBER4e7qLZuI4Sfbt48aLj799//73w9fV1bF8vlOibEEIMDAyIuLg4YbVab3j/bKdE327WS71QomfvvPOOWL16tRgdHRVCCHHmzBkXzcZ1lPodnbJ//35hsViUnYQKGG407vjx4yI0NFQIIYS3t7ew2WyO+xYvXiyOHj3qNP7vDuTh4WFhMBjE1atXFalXK+Tu29GjR4Wfn5/uws2fydW3lJQU0d7ervsn7Cly9O2/0qspcvQsICBA9PT0KF6rlsj92JaSkiJ27NihSK1q4mkpjdu5cycee+wxXLhwAZOTk7jrrrsc981kTa13330XK1aswJw5+v6nl6tvmzdvRmhoKFavXo39+/dPWx5Eb+To265du2A2m/HAAw8oWaqmyHW85ebmYuHChVi7di1OnjypVLma8G97dunSJQwPD+OTTz6B1WqF1WpFXV2d0mWrTs7nhNOnT+PYsWOOxar1RHOfc0P/V1RUhJ6eHpSVlWFkZOQfr7t1MzU1Ndi3bx+++uorOcvUHDn7VlxcjOLiYjQ3NyM3Nxetra0wGAxyl6wJcvStt7cXFRUVaG1tVapMzZHrePvwww8RFBQEIQRKS0uRmpqK7u5uJUpWnRw9Gx8fx9jYGEZGRtDe3o7+/n7ExcXBbDbDYrEoVbqq5H5O2Lt3L1JTU+Hn5ydnmZqg7/++z2Lbt29HfX09Dh8+jLlz58LX1xeA88Jhf7Wm1p/V1dWhsLAQR44cwbx58xSpWQvk7tuU5cuX4/Lly/jhhx9krVcr5OpbW1sbzpw5g8jISAQHB6O9vR1ZWVmoqKhQtH61yHm8BQUFAbi2KPALL7yAkydP4sKFC8oUriK5eubr6wsfHx/Hqw5GoxEPPvig45Ps9UbuxzYhBPbs2YOsrCxF6lUbw40GlZSUoLa2FkeOHMEdd9zh+H56ejpKS0sBAMePH8fQ0BCWLl36t9vbt28f8vLy0NzcfMtP6rOJnH2bmJhAT0+P43ZHRwdsNhtCQkKUKV5FcvYtIyMDQ0ND6OvrQ19fH6xWKyorK/Hss88qOgc1yH28nTt3znH7wIED8Pf3dzyB6YXcj21PPPEEGhsbAQAXL15ER0cHoqKilCleRXL3DQC++OILjI2NITExUZGaVafe5T50IwMDAwKACAkJEdHR0SI6OlosWbJECCHE0NCQSExMFGFhYeK+++4Tx44dc/xcUVGRCAgIEAaDQfj6+oqAgADHhWZubm4iMDDQsb3o6Ghx/vx5VeanFLn7ZrfbRXx8vDCbzSIqKkrExcWJlpYWtaanGCWOt+vp9SJZuft25coVsWjRImGxWERUVJRYtmyZ6OzsVGt6ilDiWBseHhapqanCbDYLs9ksysrKVJmbkpT6HV2/fr3Iz893+XxchcsvEBERka7wtBQRERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDRGp4vHHH0dAQAAuXbo07b5t27bB3d1dfysVE5FLMNwQkSo++OADXL58Ga+++qrT90+cOIE33ngDubm5uvxANiJSHj/nhohUU1paihdffBGtra2wWq0AgKSkJPT19aGrqwuenp6K7n9kZAReXl6K7oOIXI+v3BCRajZu3Air1Yrs7GyMj4/jo48+QnNzM3bv3u0INna7HVu2bEFwcDA8PDxgMplQXl7utJ3u7m6sW7cORqMRXl5euPfee1FQUICxsTHHGLvdDkmSsH37dmzevBl333037rzzTgBAf38/1qxZA39/f3h6esJoNOpypWSi/wquCk5EqpkzZw4qKioQGxuLvLw87N27F1lZWUhISABwbXG/tLQ0tLW1oaCgAGazGS0tLcjJycHcuXPx1FNPAQAGBwcRERGBjIwM3H777fjxxx9RWFiIs2fPYvfu3U77LCkpQVxcHCorKzE6Ogrg2hpFv/76K3bu3In58+fj9OnTaGpqcmkviEg+PC1FRKrbunUrCgsLMX/+fPz000+OxQEbGxuRkpKCpqYmpwX+nn/+eTQ2NqKvr2/atoQQuHr1Kqqrq5GTk4PffvsNnp6esNvt8PLygsViQVdXFyRJcow3GAwoLS1Fdna2S+ZLRMriaSkiUt2WLVsAXDtNdf2qx01NTfDz88MjjzyCiYkJx9fy5ctx6tQp2Gw2AMAff/yBvLw8hIWFwdPTE+7u7njmmWcwNjY2LQCtXLnSEWwAQJIkLFq0CEVFRSgrK3NaDZ6IZieGGyJSncFgcPpzyvDwMM6fPw93d3enr/T0dADXrpUBgJdffhlvv/02MjMz0dDQgI6ODpSUlAC4dq3N9fz9/aftv76+HsuWLcNrr70Gk8mEsLAw7NmzR/Z5EpFr8JobItIsX19f+Pv7o6Gh4Yb3R0ZGAgA+/vhj5OTkOF4BAoBvv/32hj9z/as2UxYsWICqqioIIdDZ2ekISpGRkY53cRHR7MFXbohIs5KSkmCz2eDh4YH7779/2pe3tzeEELDb7dPeNl5TU3PL+5MkCbGxsY5Xfbq7u2WZBxG5Fl+5ISLNSklJwcqVK/Hoo48iNzcX0dHRGBkZwc8//4yOjg7U1dVBkiQkJiZi165dCA8Ph7+/P6qqqnD27Nl/tI9z585h1apVePLJJxEREYHJyUlUVlbC09MTS5cuVXiGRKQEhhsi0ixJklBfX48333wTZWVl6O3txW233YbIyEhkZGQ4xpWVlWHjxo146aWXYDAYsHbtWrz11ltIS0v72334+PjAbDbj/fffx8DAAAwGA6Kjo3Ho0CGYTCYlp0dECuFbwYmIiEhXeM0NERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6QrDDREREekKww0RERHpCsMNERER6cr/APBZXO232dDyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar plot for the depression rates over the years \n",
    "depression_rate_list=[]\n",
    "\n",
    "#Years which will be our x axis\n",
    "years=[2012,2013,2014,2015,2016,2017]\n",
    "\n",
    "#Getting the depression rates to list\n",
    "depression_rate_list=df1['Percent'].tolist()\n",
    "\n",
    "#Increasing our figure size\n",
    "figure(num=None, figsize=(8, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "#Adding labels\n",
    "plt.ylabel('Depression Rates',fontsize=14) \n",
    "plt.xlabel('Years',fontsize=14)\n",
    "plt.title('Depression Rates over 2013-2018',fontsize=18)\n",
    "\n",
    "#Plots of male and female depression rates in a stacked bar plot\n",
    "p2=plt.bar(years ,depression_rate_list,align='center', alpha = 0.25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "s3oTuNOoEGCS"
   },
   "source": [
    "From the histogram above, we can find the depression rate in California is \n",
    "* slightly increase from 2012 to 2014\n",
    "* goes down a little from 2014 to 2015\n",
    "* goes up extremely from 2016 to 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TFpRHdSUpujp"
   },
   "source": [
    "## Part Two: Trend of Change in Music in 2013-2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "svc67WWsVmD_"
   },
   "source": [
    "We first sort each characteristic of music in every year seperately, and then try to plot them in one graph to compare them. In this way, we can know more about our music data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iF34adH4TQwk"
   },
   "source": [
    "### The Valence of Top 100 Songs in Every Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "colab_type": "code",
    "id": "7crLeA7qSWoZ",
    "outputId": "2733d036-f260-47ab-9dd8-ff6f7446efe1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We try to plot the valence of the top 100 songs in every year \n",
    "\n",
    "\n",
    "# --------------- set up x axis ------------#\n",
    "\n",
    "# x-axis is the index of song in every year(we sort it in order by the valence ) \n",
    "x = []\n",
    "for i in range(1,101):\n",
    "  x.append(i)\n",
    "\n",
    "# ------------- zoom in the plot ----------# \n",
    "\n",
    "figure(num=None , figsize = (12,12))\n",
    "\n",
    "  \n",
    "# ------------- doing 2013 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2013\n",
    "a1 = df_2013['Valence'].tolist()\n",
    "a1.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, a1, label = \"2013\") \n",
    "\n",
    "\n",
    "# ------------- doing 2014 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2014\n",
    "b = df_2014['Valence'].tolist()\n",
    "b.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, b , label = \"2014\") \n",
    "\n",
    "# ------------- doing 2015 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2015 \n",
    "c = df_2015['Valence'].tolist()\n",
    "c.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, c , label = \"2015\") \n",
    "\n",
    "# ------------- doing 2016 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2016 \n",
    "d = df_2016['Valence'].tolist()\n",
    "d.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, d , label = \"2016\") \n",
    "\n",
    "# ------------- doing 2017 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2017 \n",
    "e = df_2017['Valence'].tolist()\n",
    "e.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, e , label = \"2017\") \n",
    "\n",
    "# ------------- doing 2018 ----------------#\n",
    "\n",
    "# y-axis is the valence of the song in 2018 \n",
    "f = df_2018['Valence'].tolist()\n",
    "f.sort()\n",
    "plt.plot(x, f , label = \"2018\") \n",
    "\n",
    "\n",
    "\n",
    "# --------- show the plot ------------#\n",
    "\n",
    "plt.xlabel('Index of Song') \n",
    "plt.ylabel('Valence') \n",
    "plt.title('Change of Valence of popular song in the past years ') \n",
    "plt.legend() \n",
    "plt.show() \n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nDWDlbOKTeX8"
   },
   "source": [
    "### The Tempo of Top 100 Songs in Every Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "colab_type": "code",
    "id": "u7KjSwv7TfI1",
    "outputId": "d929c657-6277-49da-93a3-f9f5f0450111"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We try to plot the tempo of the top 100 songs in every year \n",
    "\n",
    "\n",
    "# --------------- set up x axis ------------#\n",
    "\n",
    "# x-axis is the index of song in every year(we sort it in order by the valence ) \n",
    "x = []\n",
    "for i in range(1,101):\n",
    "  x.append(i)\n",
    "\n",
    "# ------------- zoom in the plot ----------# \n",
    "\n",
    "figure(num=None , figsize = (12,12))\n",
    "\n",
    "  \n",
    "# ------------- doing 2013 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2013\n",
    "a1 = df_2013['Tempo'].tolist()\n",
    "a1.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, a1, label = \"2013\") \n",
    "\n",
    "\n",
    "# ------------- doing 2014 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2014\n",
    "b = df_2014['Tempo'].tolist()\n",
    "b.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, b , label = \"2014\") \n",
    "\n",
    "# ------------- doing 2015 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2015 \n",
    "c = df_2015['Tempo'].tolist()\n",
    "c.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, c , label = \"2015\") \n",
    "\n",
    "# ------------- doing 2016 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2016 \n",
    "d = df_2016['Tempo'].tolist()\n",
    "d.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, d , label = \"2016\") \n",
    "\n",
    "# ------------- doing 2017 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2017 \n",
    "e = df_2017['Tempo'].tolist()\n",
    "e.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, e , label = \"2017\") \n",
    "\n",
    "# ------------- doing 2018 ----------------#\n",
    "\n",
    "# y-axis is the Tempo of the song in 2018 \n",
    "f = df_2018['Tempo'].tolist()\n",
    "f.sort()\n",
    "plt.plot(x, f , label = \"2018\") \n",
    "\n",
    "\n",
    "\n",
    "# --------- show the plot ------------#\n",
    "\n",
    "plt.xlabel('Index of Song') \n",
    "plt.ylabel('Tempo') \n",
    "plt.title('Change of Tempo of popular song in the past years ') \n",
    "plt.legend() \n",
    "plt.show() \n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HAclrILVT3pf"
   },
   "source": [
    "### The Danceability of Top 100 Songs in Every Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "colab_type": "code",
    "id": "MbKGh8LDT3fr",
    "outputId": "be6269b6-54e1-4dff-8fa3-b979b90870d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We try to plot the Danceability of the top 100 songs in every year \n",
    "\n",
    "\n",
    "# --------------- set up x axis ------------#\n",
    "\n",
    "# x-axis is the index of song in every year(we sort it in order by the valence ) \n",
    "x = []\n",
    "for i in range(1,101):\n",
    "  x.append(i)\n",
    "\n",
    "# ------------- zoom in the plot ----------# \n",
    "\n",
    "figure(num=None , figsize = (12,12))\n",
    "\n",
    "  \n",
    "# ------------- doing 2013 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2013\n",
    "a1 = df_2013['Danceability'].tolist()\n",
    "a1.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, a1, label = \"2013\") \n",
    "\n",
    "\n",
    "# ------------- doing 2014 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2014\n",
    "b = df_2014['Danceability'].tolist()\n",
    "b.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, b , label = \"2014\") \n",
    "\n",
    "# ------------- doing 2015 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2015 \n",
    "c = df_2015['Danceability'].tolist()\n",
    "c.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, c , label = \"2015\") \n",
    "\n",
    "# ------------- doing 2016 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2016 \n",
    "d = df_2016['Danceability'].tolist()\n",
    "d.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, d , label = \"2016\") \n",
    "\n",
    "# ------------- doing 2017 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2017 \n",
    "e = df_2017['Danceability'].tolist()\n",
    "e.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, e , label = \"2017\") \n",
    "\n",
    "# ------------- doing 2018 ----------------#\n",
    "\n",
    "# y-axis is the Danceability of the song in 2018 \n",
    "f = df_2018['Danceability'].tolist()\n",
    "f.sort()\n",
    "plt.plot(x, f , label = \"2018\") \n",
    "\n",
    "\n",
    "\n",
    "# --------- show the plot ------------#\n",
    "\n",
    "plt.xlabel('Index of Song') \n",
    "plt.ylabel('Danceability') \n",
    "plt.title('Change of Danceability of popular song in the past years ') \n",
    "plt.legend() \n",
    "plt.show() \n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cApRfQlcUKqo"
   },
   "source": [
    "### The Energy of Top 100 Songs in Every Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "colab_type": "code",
    "id": "WpXjEO2kUK4t",
    "outputId": "a284dc80-6a6d-46e5-b9a4-5eb466f8c50a"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We try to plot the Energy of the top 100 songs in every year \n",
    "\n",
    "\n",
    "# --------------- set up x axis ------------#\n",
    "\n",
    "# x-axis is the index of song in every year(we sort it in order by the valence ) \n",
    "x = []\n",
    "for i in range(1,101):\n",
    "  x.append(i)\n",
    "\n",
    "# ------------- zoom in the plot ----------# \n",
    "\n",
    "figure(num=None , figsize = (12,12))\n",
    "\n",
    "  \n",
    "# ------------- doing 2013 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2013\n",
    "a1 = df_2013['Energy'].tolist()\n",
    "a1.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, a1, label = \"2013\") \n",
    "\n",
    "\n",
    "# ------------- doing 2014 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2014\n",
    "b = df_2014['Energy'].tolist()\n",
    "b.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, b , label = \"2014\") \n",
    "\n",
    "# ------------- doing 2015 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2015 \n",
    "c = df_2015['Energy'].tolist()\n",
    "c.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, c , label = \"2015\") \n",
    "\n",
    "# ------------- doing 2016 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2016 \n",
    "d = df_2016['Energy'].tolist()\n",
    "d.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, d , label = \"2016\") \n",
    "\n",
    "# ------------- doing 2017 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2017 \n",
    "e = df_2017['Energy'].tolist()\n",
    "e.sort()\n",
    "\n",
    "# start plotting \n",
    "plt.plot(x, e , label = \"2017\") \n",
    "\n",
    "# ------------- doing 2018 ----------------#\n",
    "\n",
    "# y-axis is the Energy of the song in 2018 \n",
    "f = df_2018['Energy'].tolist()\n",
    "f.sort()\n",
    "plt.plot(x, f , label = \"2018\") \n",
    "\n",
    "\n",
    "\n",
    "# --------- show the plot ------------#\n",
    "\n",
    "plt.xlabel('Index of Song') \n",
    "plt.ylabel('Energy') \n",
    "plt.title('Change of Energy of popular song in the past years ') \n",
    "plt.legend() \n",
    "plt.show() \n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m6DtpC6jWM5S"
   },
   "source": [
    "### General Conclusion\n",
    "\n",
    "From the four plot above, we can concludes that, from a general looking\n",
    "\n",
    "* Valence is greatest in 2013 and smallest in 2015\n",
    "* The plot of Tempo is somewhat dense and complex\n",
    "* Danceability plot is really clear to see the difference between years: 2018 and 2017 is the greatest two year, while 2013 is the smallest\n",
    "* Energy is kind of clear as well, the 2013, 2014, 2015 is the greatest three year, but the 2016, 2017, 2018 is relatively small\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJwVEsF3UsF4"
   },
   "source": [
    "## Part Three: Finding the Relation between Songs and Depression Rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o3hxaNjPm1GQ"
   },
   "source": [
    "Now we integrate the data of music and depression rate together to form a new dataframe(**df4**). This dataframe include the following information \n",
    "\n",
    "* the depression_rate in each year\n",
    "* the mean of the music's valence / danceability / enegry / tempo in each year (calculated above)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eu1i5bIfn0XJ"
   },
   "source": [
    "We first try to calculate the \"list of mean value\" that used to form the new dataframe, it include the mean value of the following data \n",
    "\n",
    "  *   valence\n",
    "  *   danceability\n",
    "  *   energy\n",
    "  *  tempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "i8S-0t_pn_ib",
    "outputId": "be1d4cd6-8c59-4011-dc9d-2044cdf18b3b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.48210100000000006, 0.47144399999999997, 0.41156599999999999, 0.43679099999999998, 0.46046400000000004]\n",
      "[0.61293999999999993, 0.63692999999999989, 0.63743000000000005, 0.64446999999999988, 0.67929000000000017]\n",
      "[0.66362920299999995, 0.63178000000000001, 0.58759520099999996, 0.58316999999999997, 0.57788070100000011]\n",
      "[116.91646999999999, 126.24982, 119.59464, 119.58673999999999, 114.50700999999999]\n"
     ]
    }
   ],
   "source": [
    "#Getting the mean of valence in each year\n",
    "valence_list = [ df_2013['Valence'].mean(),df_2014['Valence'].mean(),df_2015['Valence'].mean(),df_2016['Valence'].mean(),df_2017['Valence'].mean()]\n",
    "print(valence_list)\n",
    "\n",
    "\n",
    "#Getting the mean of danceability in each years\n",
    "dance_list=[ df_2013['Danceability'].mean(),df_2014['Danceability'].mean(),df_2015['Danceability'].mean(),df_2016['Danceability'].mean(),df_2017['Danceability'].mean()]\n",
    "print(dance_list)\n",
    "\n",
    "\n",
    "#Getting the mean of energy in each years\n",
    "energy_list=[ df_2013['Energy'].mean(),df_2014['Energy'].mean(),df_2015['Energy'].mean(),df_2016['Energy'].mean(),df_2017['Energy'].mean()]\n",
    "print(energy_list)\n",
    "\n",
    "\n",
    "#Getting the mean of tempo in each years\n",
    "tempo_list=[ df_2013['Tempo'].mean(),df_2014['Tempo'].mean(),df_2015['Tempo'].mean(),df_2016['Tempo'].mean(),df_2017['Tempo'].mean()]\n",
    "print(tempo_list)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-1yGMIBXoT0A"
   },
   "source": [
    "Then we try to get the list of depression rate in each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "woegma7H5l7E",
    "outputId": "e2f3824f-91b9-4019-9a37-6bcaec6f2d60",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012</td>\n",
       "      <td>1920</td>\n",
       "      <td>11.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>1689</td>\n",
       "      <td>13.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2014</td>\n",
       "      <td>1309</td>\n",
       "      <td>13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2015</td>\n",
       "      <td>1848</td>\n",
       "      <td>12.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2016</td>\n",
       "      <td>1645</td>\n",
       "      <td>13.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Frequency  Percent\n",
       "0   2012       1920    11.74\n",
       "23  2013       1689    13.08\n",
       "46  2014       1309    13.30\n",
       "69  2015       1848    12.92\n",
       "92  2016       1645    13.77"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rSelA3KBofOM"
   },
   "outputs": [],
   "source": [
    "#make a list standing for the index of year \n",
    "year = ['2013','2014','2015','2016','2017']\n",
    "\n",
    "#making a list of the total percentages over the years\n",
    "depression_rate=df1['Percent'].tolist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VXt0f5W7XMaW"
   },
   "source": [
    "Now, we begin to create the new dataframe df4 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Y0x2tM4-no_P",
    "outputId": "7056918f-a35e-4766-d969-4aea4707d46d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Years</th>\n",
       "      <th>Depression_Rate</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.08</td>\n",
       "      <td>0.482101</td>\n",
       "      <td>0.61294</td>\n",
       "      <td>0.663629</td>\n",
       "      <td>116.91647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>13.30</td>\n",
       "      <td>0.471444</td>\n",
       "      <td>0.63693</td>\n",
       "      <td>0.631780</td>\n",
       "      <td>126.24982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12.92</td>\n",
       "      <td>0.411566</td>\n",
       "      <td>0.63743</td>\n",
       "      <td>0.587595</td>\n",
       "      <td>119.59464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.77</td>\n",
       "      <td>0.436791</td>\n",
       "      <td>0.64447</td>\n",
       "      <td>0.583170</td>\n",
       "      <td>119.58674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>19.04</td>\n",
       "      <td>0.460464</td>\n",
       "      <td>0.67929</td>\n",
       "      <td>0.577881</td>\n",
       "      <td>114.50701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Years  Depression_Rate   Valence  Danceability    Energy      Tempo\n",
       "0  2013            13.08  0.482101       0.61294  0.663629  116.91647\n",
       "1  2014            13.30  0.471444       0.63693  0.631780  126.24982\n",
       "2  2015            12.92  0.411566       0.63743  0.587595  119.59464\n",
       "3  2016            13.77  0.436791       0.64447  0.583170  119.58674\n",
       "4  2017            19.04  0.460464       0.67929  0.577881  114.50701"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Getting rid of the year 2012\n",
    "modified_depression_rate=depression_rate[1:]\n",
    "\n",
    "#creating a dataset with mean valence and depression rates\n",
    "df4=pd.DataFrame({'Years':year,'Depression_Rate':modified_depression_rate, 'Valence': valence_list,'Danceability':dance_list , 'Energy':energy_list , 'Tempo':tempo_list})\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QP5BsUzBrBcp"
   },
   "source": [
    "## Part Four: Ploting the Relationship We Find"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x6A2SIBIXhKd"
   },
   "source": [
    "After getting all data in every year in part one, we try to plot the change of depression rate and valence / energy / tempo / danceability as line plots here. In this way, we can find whether there are some relationships between them.\n",
    "\n",
    "After finding which characteric is most relevant to the depression rate, we can look through it more deeply to analyze the relationship between them.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kPmryquLm_aq"
   },
   "source": [
    "However, we find our Valence, Energy, and Danceability is too small comparing to the Tempo and depression rate. \n",
    "\n",
    "In order to see the relationship between these variable, we change our Valence, Energy and Danceability column to a percentage values so we can get better graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "7of-z2sxjL6T",
    "outputId": "fc997061-386f-434a-edff-acb4c72c929c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Years</th>\n",
       "      <th>Depression_Rate</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.08</td>\n",
       "      <td>48.2101</td>\n",
       "      <td>61.294</td>\n",
       "      <td>66.36292</td>\n",
       "      <td>116.91647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>13.30</td>\n",
       "      <td>47.1444</td>\n",
       "      <td>63.693</td>\n",
       "      <td>63.17800</td>\n",
       "      <td>126.24982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12.92</td>\n",
       "      <td>41.1566</td>\n",
       "      <td>63.743</td>\n",
       "      <td>58.75952</td>\n",
       "      <td>119.59464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.77</td>\n",
       "      <td>43.6791</td>\n",
       "      <td>64.447</td>\n",
       "      <td>58.31700</td>\n",
       "      <td>119.58674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>19.04</td>\n",
       "      <td>46.0464</td>\n",
       "      <td>67.929</td>\n",
       "      <td>57.78807</td>\n",
       "      <td>114.50701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Years  Depression_Rate  Valence  Danceability    Energy      Tempo\n",
       "0  2013            13.08  48.2101        61.294  66.36292  116.91647\n",
       "1  2014            13.30  47.1444        63.693  63.17800  126.24982\n",
       "2  2015            12.92  41.1566        63.743  58.75952  119.59464\n",
       "3  2016            13.77  43.6791        64.447  58.31700  119.58674\n",
       "4  2017            19.04  46.0464        67.929  57.78807  114.50701"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#changing values like valence and danceability to percentage values for better graph\n",
    "def standardize_values(val):\n",
    "  out=val\n",
    "  out=out*100.00\n",
    "  return out\n",
    "\n",
    "df4['Valence'] = df4['Valence'].apply(standardize_values)\n",
    "df4['Danceability'] = df4['Danceability'].apply(standardize_values)\n",
    "df4['Energy'] = df4['Energy'].apply(standardize_values)\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K_WoGFrwNdfS"
   },
   "source": [
    "Now, we begin to plot the relationship between our variables.\n",
    "\n",
    "### Relation between Valence and Depression Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "UZMxNe6gFB80",
    "outputId": "c0025174-f234-4c6d-f145-ade898467d0c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8cabad5e10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot for the average Valence of year\n",
    "\n",
    "#years which will be our x-axis\n",
    "x = ['2013','2014','2015','2016','2017']\n",
    "\n",
    "#increasing figure size\n",
    "figure(num=None, figsize=(9, 9))\n",
    "\n",
    "#adding labels\n",
    "plt.xlabel('Years',fontsize=14);\n",
    "plt.ylabel('Valence',fontsize=14);\n",
    "\n",
    "plt.title('Average Valence of each year',fontsize=18);\n",
    "\n",
    "#plotting\n",
    "plt.plot(x,df4['Valence'])\n",
    "plt.plot(df4['Years'], df4['Depression_Rate'], '--k', linewidth=4, label='Depression Rates');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "63aXk8YPNwLG"
   },
   "source": [
    "### Relation between Energy and Depression Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "9p5zi8OSuQ-U",
    "outputId": "2d3be4fd-8413-4d83-fcf0-bac16206ed1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8ca9a0c860>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot for the average energy of year\n",
    "\n",
    "#years which will be our x-axis\n",
    "x = ['2013','2014','2015','2016','2017']\n",
    "\n",
    "#increasing figure size\n",
    "figure(num=None, figsize=(9, 9))\n",
    "\n",
    "#adding labels\n",
    "plt.xlabel('Years',fontsize=14);\n",
    "plt.ylabel('Energy',fontsize=14);\n",
    "plt.title('Average Energy of each year',fontsize=18);\n",
    "\n",
    "#plotting\n",
    "plt.plot(x,df4['Energy'])\n",
    "plt.plot(df4['Years'], df4['Depression_Rate'], '--k', linewidth=4, label='Depression Rates');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DXWhaJFENz-E"
   },
   "source": [
    "### Relation between Tempo and Depression Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "0ayxlUL-u0R_",
    "outputId": "45ef1889-3e5f-47da-a333-e05906ceb836"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8ca996e358>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot for the average tempo of year\n",
    "\n",
    "#years which will be our x-axis\n",
    "x = ['2013','2014','2015','2016','2017']\n",
    "\n",
    "#increasing figure size\n",
    "figure(num=None, figsize=(9, 9))\n",
    "\n",
    "#adding labels\n",
    "plt.xlabel('Years',fontsize=14);\n",
    "plt.ylabel('Tempo',fontsize=14);\n",
    "plt.title('Average Tempo of each year',fontsize=18);\n",
    "\n",
    "#plotting\n",
    "plt.plot(x,df4['Tempo'])\n",
    "plt.plot(df4['Years'], df4['Depression_Rate'], '--k', linewidth=4, label='Depression Rates');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2L_NOgANN2Bm"
   },
   "source": [
    "### Relation between Danceability and Depression Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "gYBObKvDu7Xm",
    "outputId": "cd6cbfb9-d588-479b-fa4e-c781512860cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8ca99788d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot for the average danceability of year\n",
    "\n",
    "#years which will be our x-axis\n",
    "x = ['2013','2014','2015','2016','2017']\n",
    "\n",
    "#increasing figure size\n",
    "figure(num=None, figsize=(9, 9))\n",
    "\n",
    "#adding labels\n",
    "plt.xlabel('Years',fontsize=14);\n",
    "plt.ylabel('Danceability',fontsize=14);\n",
    "plt.title('Average Danceability of each year',fontsize=18);\n",
    "\n",
    "#plotting\n",
    "plt.plot(x,df4['Danceability'])\n",
    "plt.plot(df4['Years'], df4['Depression_Rate'], '--k', linewidth=4, label='Depression Rates');\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mRlHqv9wN49q"
   },
   "source": [
    "### General Conclusion for Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qVniySk7v0LQ"
   },
   "source": [
    "As the graph shows, we can make a general conclusion: the average depression rate are \n",
    "*  relatively proportion to the **danceability** \n",
    "*  relatively inversely proportion to the **energy** \n",
    "\n",
    "For the relationship between depression rate and tempo / valence, it is a little complex. \n",
    "\n",
    "In the following data analysis part, we will deal with the relationship in depth "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uENfwt5KzSEz"
   },
   "source": [
    "# Data: Linear Regression and Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dJ2Qd8X2Rdz2"
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-4YxnVDP19bB"
   },
   "source": [
    "In this section we use the corr function of data to see the correalation of depression rates with danceability and valence at a glance. Then we use patsy's OLS function to do linear regression on our data. We later plot these results for better analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "nvOzdh7f3OdU",
    "outputId": "9e6a9f57-0204-4467-d000-c74ccc08915a"
   },
   "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>Depression_Rate</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Depression_Rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160953</td>\n",
       "      <td>0.892770</td>\n",
       "      <td>-0.497382</td>\n",
       "      <td>-0.598985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valence</th>\n",
       "      <td>0.160953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.188250</td>\n",
       "      <td>0.726061</td>\n",
       "      <td>0.012794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danceability</th>\n",
       "      <td>0.892770</td>\n",
       "      <td>-0.188250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.790710</td>\n",
       "      <td>-0.346263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Energy</th>\n",
       "      <td>-0.497382</td>\n",
       "      <td>0.726061</td>\n",
       "      <td>-0.790710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.249074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tempo</th>\n",
       "      <td>-0.598985</td>\n",
       "      <td>0.012794</td>\n",
       "      <td>-0.346263</td>\n",
       "      <td>0.249074</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Depression_Rate   Valence  Danceability    Energy     Tempo\n",
       "Depression_Rate         1.000000  0.160953      0.892770 -0.497382 -0.598985\n",
       "Valence                 0.160953  1.000000     -0.188250  0.726061  0.012794\n",
       "Danceability            0.892770 -0.188250      1.000000 -0.790710 -0.346263\n",
       "Energy                 -0.497382  0.726061     -0.790710  1.000000  0.249074\n",
       "Tempo                  -0.598985  0.012794     -0.346263  0.249074  1.000000"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iykbnOam34JG"
   },
   "source": [
    "We notice that depression rates have a very low corelation with valence but a greater coorelation with danceability. Energy and Tempo have negative correlation with depression rates so we will go ahead with our regression with these two characterstics. Since, we are looking for a negative correlation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6AXz0vSpXgaQ"
   },
   "outputs": [],
   "source": [
    "#To install statsmodel manually from git\n",
    "#UNCOMMENT ONLY WHILE RUNNING IN COLLABORATORY\n",
    "#!pip install --upgrade Cython\n",
    "#!pip install --upgrade git+https://github.com/statsmodels/statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "T-P1vOwBWwSH",
    "outputId": "ae357a03-bc54-4d4a-9942-7f9770b6129c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        Depression_Rate   R-squared:                       0.247\n",
      "Model:                            OLS   Adj. R-squared:                 -0.003\n",
      "Method:                 Least Squares   F-statistic:                    0.9861\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.394\n",
      "Time:                        18:40:51   Log-Likelihood:                -10.606\n",
      "No. Observations:                   5   AIC:                             25.21\n",
      "Df Residuals:                       3   BIC:                             24.43\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     35.4801     21.238      1.671      0.193     -32.108     103.068\n",
      "Energy        -0.3459      0.348     -0.993      0.394      -1.454       0.763\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.511\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.551\n",
      "Skew:                           0.729   Prob(JB):                        0.759\n",
      "Kurtosis:                       2.280   Cond. No.                     1.11e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.11e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/stats/stattools.py:72: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "#Running the model of Depression Rates and Energy\n",
    "outcome, predictors = patsy.dmatrices('Depression_Rate~ Energy', df4)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 701
    },
    "colab_type": "code",
    "id": "17prILifYVE4",
    "outputId": "2a54ab8e-14eb-4de0-9f33-03c7c1e9f681"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the model fit line\n",
    "x=df4['Energy']\n",
    "y=df4['Depression_Rate']\n",
    "figure(num=None , figsize = (8,8))\n",
    "\n",
    "plt.scatter(x,y, s=100)\n",
    "plt.plot(figsize=(14,10))\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs = np.arange(df4['Energy'].min(), df4['Energy'].max())\n",
    "ys = res.params[1] * xs + res.params[0]\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model');\n",
    "plt.xlabel('Energy')\n",
    "plt.ylabel('Depression Rate')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_xHs0qJdnWqc"
   },
   "source": [
    "This simple scatter plot with our model line drawn shows us a more accurate representation of our correlation between Energy and Depression Rate. From our summary we can see our R-value is negative (as expected) but very small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5ZygDD6SmKGa",
    "outputId": "60f3a5f8-5749-4ac4-bf1f-80a04d69793a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        Depression_Rate   R-squared:                       0.359\n",
      "Model:                            OLS   Adj. R-squared:                  0.145\n",
      "Method:                 Least Squares   F-statistic:                     1.679\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):              0.286\n",
      "Time:                        18:41:13   Log-Likelihood:                -10.206\n",
      "No. Observations:                   5   AIC:                             24.41\n",
      "Df Residuals:                       3   BIC:                             23.63\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     56.7875     32.717      1.736      0.181     -47.333     160.908\n",
      "Tempo         -0.3549      0.274     -1.296      0.286      -1.227       0.517\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   1.886\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.489\n",
      "Skew:                           0.389   Prob(JB):                        0.783\n",
      "Kurtosis:                       1.679   Cond. No.                     3.63e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.63e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/statsmodels/stats/stattools.py:72: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "# run model for Depression rate and Tempo\n",
    "outcome, predictors = patsy.dmatrices('Depression_Rate~ Tempo', df4)\n",
    "mod1 = sm.OLS(outcome, predictors)\n",
    "res1 = mod1.fit()\n",
    "print(res1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i7nLuQTImKGe",
    "outputId": "b4e7b0b3-8927-4b61-c7d5-1d844d1d7889"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding correlation with Tempo and depression rate\n",
    "## Plot the model fit line\n",
    "x1=df4['Tempo']\n",
    "y1=df4['Depression_Rate']\n",
    "figure(num=None , figsize = (8,8))\n",
    "\n",
    "plt.scatter(x1,y1, s=100)\n",
    "plt.plot(figsize=(14,10))\n",
    "\n",
    "# Generate and plot the model fit line\n",
    "xs = np.arange(df4['Tempo'].min(), df4['Tempo'].max())\n",
    "ys = res1.params[1] * xs + res1.params[0]\n",
    "plt.plot(xs, ys, '--k', linewidth=4, label='Model');\n",
    "plt.xlabel('Tempo')\n",
    "plt.ylabel('Depression Rate')\n",
    "plt.legend();\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U8bCfwATnhK6"
   },
   "source": [
    "As it is clear from the plot and our r values,Tempo also has an inverse correlation with depression rates. Both our correlation models show negative correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jet3Nm2ZnzVT"
   },
   "source": [
    "Now, we will do linear regression on our data. We do it twice, once for predicting depression rates from energy and another time to predict depression rates from energy. This will be our last analysis step to see which feature from our music analysis can do a better job at predicting depression rates. Thus, having a better correlation with depression rates in California "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6iE_UB0jvSTe"
   },
   "outputs": [],
   "source": [
    "#Training data using Energy and depression rates \n",
    "X = df4[['Energy']]\n",
    "y = df4[['Depression_Rate']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "JKB1IACmnpAJ",
    "outputId": "74070163-2af4-49c6-83f1-8a092070d680"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 14.08144197]\n",
      " [ 16.13319607]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Split into training and test sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.23333)\n",
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "predictions=model.predict(X_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "1EIBW28xnpAN",
    "outputId": "55bbcf1c-b58b-4304-8f5f-6fcf7e77f0d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Score of our model -55.0918717014\n",
      "Coefficient of our model -0.422084777436\n",
      "Intercept of our model 40.7479140384\n",
      "The mean squared error of our model: 3.09767361471\n"
     ]
    }
   ],
   "source": [
    "#Printing the summary of our model \n",
    "regression_score = model.score(X_test, y_test)\n",
    "intercept = model.intercept_[0]\n",
    "coef = model.coef_[0][0]\n",
    "regression_model_mse = mean_squared_error(predictions, y_test)\n",
    "print(\"Regression Score of our model\",regression_score)\n",
    "print(\"Coefficient of our model\",coef)\n",
    "print(\"Intercept of our model\",intercept)\n",
    "print(\"The mean squared error of our model:\",regression_model_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ylVvpuSUPC82"
   },
   "source": [
    "We get a negative correlation coefficient for our model like we expected "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "colab_type": "code",
    "id": "XUfah5yFrrUL",
    "outputId": "98df2b5d-449f-4958-dceb-3fbe5085be4c",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Years</th>\n",
       "      <th>Depression_Rate</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Predicted Depression Rates With Energy</th>\n",
       "      <th>Predicted Depression Rates With Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.08</td>\n",
       "      <td>48.2101</td>\n",
       "      <td>61.294</td>\n",
       "      <td>66.36292</td>\n",
       "      <td>116.91647</td>\n",
       "      <td>[[12.7371355935]]</td>\n",
       "      <td>[[13.363627194]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>13.30</td>\n",
       "      <td>47.1444</td>\n",
       "      <td>63.693</td>\n",
       "      <td>63.17800</td>\n",
       "      <td>126.24982</td>\n",
       "      <td>[[14.0814419695]]</td>\n",
       "      <td>[[13.2994958372]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12.92</td>\n",
       "      <td>41.1566</td>\n",
       "      <td>63.743</td>\n",
       "      <td>58.75952</td>\n",
       "      <td>119.59464</td>\n",
       "      <td>[[15.9464150747]]</td>\n",
       "      <td>[[13.3452249401]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.77</td>\n",
       "      <td>43.6791</td>\n",
       "      <td>64.447</td>\n",
       "      <td>58.31700</td>\n",
       "      <td>119.58674</td>\n",
       "      <td>[[16.1331960726]]</td>\n",
       "      <td>[[13.3452792227]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>19.04</td>\n",
       "      <td>46.0464</td>\n",
       "      <td>67.929</td>\n",
       "      <td>57.78807</td>\n",
       "      <td>114.50701</td>\n",
       "      <td>[[16.3564493318]]</td>\n",
       "      <td>[[13.3801830864]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Years  Depression_Rate  Valence  Danceability    Energy      Tempo  \\\n",
       "0  2013            13.08  48.2101        61.294  66.36292  116.91647   \n",
       "1  2014            13.30  47.1444        63.693  63.17800  126.24982   \n",
       "2  2015            12.92  41.1566        63.743  58.75952  119.59464   \n",
       "3  2016            13.77  43.6791        64.447  58.31700  119.58674   \n",
       "4  2017            19.04  46.0464        67.929  57.78807  114.50701   \n",
       "\n",
       "  Predicted Depression Rates With Energy Predicted Depression Rates With Tempo  \n",
       "0                      [[12.7371355935]]                      [[13.363627194]]  \n",
       "1                      [[14.0814419695]]                     [[13.2994958372]]  \n",
       "2                      [[15.9464150747]]                     [[13.3452249401]]  \n",
       "3                      [[16.1331960726]]                     [[13.3452792227]]  \n",
       "4                      [[16.3564493318]]                     [[13.3801830864]]  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying the prediction model and adding a new column to our dataset for comparison\n",
    "df4['Predicted Depression Rates With Energy']=df4['Energy'].apply(model.predict)\n",
    "df4.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WKZ7Q8OKnpAU",
    "outputId": "1ca06d9c-7daa-4153-8122-15b0a55247a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Linear Regression Model')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(sns.hls_palette(8, l=.3, s=.8))\n",
    "x1=df4['Energy']\n",
    "y1=df4['Predicted Depression Rates With Energy']\n",
    "figure(num=None , figsize = (10,10))\n",
    "\n",
    "#Plot to show the regression model\n",
    "plt.scatter(x1,y1)\n",
    "plt.plot(x1, coef* x1 + intercept,color='pink', lw=1)\n",
    "plt.xlabel('Energy',fontsize=12)\n",
    "plt.ylabel('Predicted Depression Rates',fontsize=12)\n",
    "plt.title('Linear Regression Model', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our predictive analysis model is shown through this simple line plot after doing linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T6QfsCRkmKGv"
   },
   "outputs": [],
   "source": [
    "#Training regrssion model for test with tempo\n",
    "X1=df4[['Tempo']]\n",
    "y1=df4[['Depression_Rate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SCgOg2o8mKGx",
    "outputId": "51a1bacd-03a7-4ca7-b471-7ca91a87271f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 13.38018309]\n",
      " [ 13.36362719]]\n"
     ]
    }
   ],
   "source": [
    "#Now we will do the same linear regression with tempo and see if that gives us a better model \n",
    "\n",
    "#Training regrssion model for test with tempo\n",
    "X_train,X_test,y_train,y_test=train_test_split(X1,y1,test_size=0.23333)\n",
    "model_tempo=LinearRegression()\n",
    "model_tempo.fit(X_train,y_train)\n",
    "predictions=model_tempo.predict(X_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VPctawVLmKG0",
    "outputId": "1052ebcb-3558-4e4e-fb76-ac3bb1ca658f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regression Score of our model -0.808137689768\n",
      "Coefficient of our model -0.00687120452529\n",
      "Intercept of our model 14.1669841717\n",
      "The mean squared error of our model: 16.0569859402\n"
     ]
    }
   ],
   "source": [
    "#Printing the summary for our tempo model\n",
    "regression_score = model_tempo.score(X_test, y_test)\n",
    "intercept = model_tempo.intercept_[0]\n",
    "coef = model_tempo.coef_[0][0]\n",
    "regression_model_mse = mean_squared_error(predictions, y_test)\n",
    "print(\"Regression Score of our model\",regression_score)\n",
    "print(\"Coefficient of our model\",coef)\n",
    "print(\"Intercept of our model\",intercept)\n",
    "print(\"The mean squared error of our model:\",regression_model_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YRWJCHIUPI7x"
   },
   "source": [
    "The coefficient of our model also gives negative regression score and a pretty low mean square error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4BFlbvGmmKG3",
    "outputId": "7fb9f349-3be5-4f9e-d1ec-77164c0bbe6a"
   },
   "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>Years</th>\n",
       "      <th>Depression_Rate</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Danceability</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Predicted Depression Rates With Energy</th>\n",
       "      <th>Predicted Depression Rates With Tempo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>13.08</td>\n",
       "      <td>48.2101</td>\n",
       "      <td>61.294</td>\n",
       "      <td>66.36292</td>\n",
       "      <td>116.91647</td>\n",
       "      <td>[[12.7371355935]]</td>\n",
       "      <td>[[13.363627194]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>13.30</td>\n",
       "      <td>47.1444</td>\n",
       "      <td>63.693</td>\n",
       "      <td>63.17800</td>\n",
       "      <td>126.24982</td>\n",
       "      <td>[[14.0814419695]]</td>\n",
       "      <td>[[13.2994958372]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12.92</td>\n",
       "      <td>41.1566</td>\n",
       "      <td>63.743</td>\n",
       "      <td>58.75952</td>\n",
       "      <td>119.59464</td>\n",
       "      <td>[[15.9464150747]]</td>\n",
       "      <td>[[13.3452249401]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>13.77</td>\n",
       "      <td>43.6791</td>\n",
       "      <td>64.447</td>\n",
       "      <td>58.31700</td>\n",
       "      <td>119.58674</td>\n",
       "      <td>[[16.1331960726]]</td>\n",
       "      <td>[[13.3452792227]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>19.04</td>\n",
       "      <td>46.0464</td>\n",
       "      <td>67.929</td>\n",
       "      <td>57.78807</td>\n",
       "      <td>114.50701</td>\n",
       "      <td>[[16.3564493318]]</td>\n",
       "      <td>[[13.3801830864]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Years  Depression_Rate  Valence  Danceability    Energy      Tempo  \\\n",
       "0  2013            13.08  48.2101        61.294  66.36292  116.91647   \n",
       "1  2014            13.30  47.1444        63.693  63.17800  126.24982   \n",
       "2  2015            12.92  41.1566        63.743  58.75952  119.59464   \n",
       "3  2016            13.77  43.6791        64.447  58.31700  119.58674   \n",
       "4  2017            19.04  46.0464        67.929  57.78807  114.50701   \n",
       "\n",
       "  Predicted Depression Rates With Energy Predicted Depression Rates With Tempo  \n",
       "0                      [[12.7371355935]]                      [[13.363627194]]  \n",
       "1                      [[14.0814419695]]                     [[13.2994958372]]  \n",
       "2                      [[15.9464150747]]                     [[13.3452249401]]  \n",
       "3                      [[16.1331960726]]                     [[13.3452792227]]  \n",
       "4                      [[16.3564493318]]                     [[13.3801830864]]  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying the prediction model and adding a new column to our dataset for comparison\n",
    "df4['Predicted Depression Rates With Tempo']=df4['Tempo'].apply(model_dance.predict)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xOC68wOsmKG5",
    "outputId": "12860938-fe08-4b48-af6e-0752f7dfe2e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Linear Regression Model')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette(sns.hls_palette(8, l=.3, s=.8))\n",
    "x1=df4['Tempo']\n",
    "y1=df4['Predicted Depression Rates With Tempo']\n",
    "figure(num=None , figsize = (10,10))\n",
    "\n",
    "#Plot to show the regression model\n",
    "plt.scatter(x1,y1)\n",
    "plt.plot(x1, coef* x1 + intercept,color='pink', lw=1)\n",
    "plt.xlabel('Tempo',fontsize=12)\n",
    "plt.ylabel('Predicted Depression Rates',fontsize=12)\n",
    "plt.title('Linear Regression Model', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k8bNYyGrpifK"
   },
   "source": [
    "## Data Analysis Results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U22dLxOR8qoI"
   },
   "source": [
    "To test how accurate our prediction model is, we will use it to predict depression rate for the year 2018. We already have the webscraped data of 2018 to work on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NRykBsx58ror",
    "outputId": "479b3785-4a1f-4ef2-9b3b-17f03d67ba99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predicted depression rate for 2018 from energy data is:  [[ 15.80945705]]\n",
      "The predicted depression rate for 2018 from tempo data is:  [[ 13.33309225]]\n"
     ]
    }
   ],
   "source": [
    "#Model prediction for the year 2018\n",
    "mean_energy_2018=df_2018['Energy'].mean()\n",
    "mean_tempo_2018=df_2018['Tempo'].mean()\n",
    "mean_energy_2018=standardize_values(mean_energy_2018)\n",
    "depression_2018_tempo=model_dance.predict(mean_tempo_2018)\n",
    "depression_2018_energy=model.predict(mean_energy_2018)\n",
    "print(\"The predicted depression rate for 2018 from energy data is: \",depression_2018_energy)\n",
    "print(\"The predicted depression rate for 2018 from tempo data is: \",depression_2018_tempo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iUz8WUbe8tND"
   },
   "source": [
    "As we can see from our predictive model, the energy model gives us a good estimate of depression rates as it shows a definite increase. This fits our hypothesis. But, the tempo model does not really show much difference in depression rates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L5hOIyztDmv5"
   },
   "source": [
    "From our analysis, the regression models show that there is indeed an inverse correlation between depression rates with energy, and tempo.\n",
    "Our predictive model helps us predict depression rates of each year from the mean tempo or energy of that year. We can also see in our model that the year with the highest depression rate is the year which has lower average tempo and energy. \n",
    "Having said that, the year 2017 also seems like an outlier in out data as it has a rate which increases a lot from the other years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xodY6oV6Zmul"
   },
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_vgD2CxsZmum"
   },
   "source": [
    "We obtained the songs datasets for each year from Spotify’s own API, which is accessible to the public so we had permission to use it; we just had to agree to their terms and conditions. For our mental health data, we got the data from healthdata.gov which was a public database so it was also accessible for public use and it was okay for us to use it.There were no privacy concerns we had to deal with in specific, but we made sure to be credible and not alter the data at all. This ensured that the data we were using was authentic and credible, and did not invade anyone's privacy. \n",
    "\n",
    "There are some potential biases from the actual databases however. For one, we are unware of how healthdata.gov obtained their data. We do not know if there were any biases involved in their data since we do not have adequate information on how they did their interviews and gathered their data, which could influence any biases in our analysis and results. Since we do not have enought information on their dataset and study, we are not completely confident in the ethical grounds behind it because mental health is a private topic and we are not sure if they were given permission by the people they interviewed for it to be made part of a dataset visible to the public. The dataset did not reveal any personal information however, and everything was kept anonymous. We also were concerned about confidnetiality issues when it comes to mental health illnesses and the fact that not everyone is open to talk about their mental health and so not many people even know that they have mental health issues, so the numbers could be not truly reflective of the community’s mental health as a whole. \n",
    "\n",
    "We created some biasedness in our data when we cleaned our data and only took the total percentage of depression from each year. The whole dataset had more criterias and we only focused on the one we needed.\n",
    "\n",
    "We can claim that we tried to follow all ethics and privacy rules but we are also aware that no data is bias free and we create that biasedness in our data when we clean it. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eZlTwBjaZmun"
   },
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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    "This project focused on analyzing the relationship between the top 100 Spotify songs and depression rates in California over the past five years. In order to assess the songs in quantifiable measures, we analyzed the change in valence, energy and dance-ability of the top songs in each year. We then compared the general trend of each of the three mechanical audio features to the change in depression rates over the past 5 years. We hypothesized that there is an negative correlation between the two datasets whereas depression rates increase, the tempo, valence, energy and dance-ability of the top 100 songs listened to on Spotify should decrease. However based on our data analysis, energy has a relatively negative correlation with depression rates and so does tempo. But valence has a postive but low correlation with depression rates, while dance-ability has a high positive correlation with depression rates over the analyzed 5 years in California. \n",
    "\n",
    "The varying correlations can be due to the fact that each audio feature plays a different role in a song and that depression abnormally affects the individual’s behavioral response to stimuli such as music. For example, study shows that depression “flattens the emotional landscape” and depressed individuals exhibit little reactivity to emotion and emotional context  ([link](http://psychology.usf.edu/faculty/data/jrottenberg/cd.pdf)) and since valence refers to the emotion/mood of a song, this can possibly help explain the low positve correlation between valence and depression rates. . However, a dance-ability’s positive correlation with depression rates can imply that songs with high dance-ability might play a role in the type of music being listened to since dancing and physical exercise releases endorphins (feel-good chemicals) that reduce stress and boost mood. Energy and tempo have a negative correlation with depression rates which agrees with our hypothesis implying that there is some type of a relationship whereas depression rates increase, the energy and tempo of the top songs listened to decreases. To see why only tempo and energy had the correlation which we were expecting we did some research on what song tempo means. From one article, we found that \"The tempo of a song generally correlates with the kind of mood a song will set in a video or any project that utilizes music to invoke a specific feeling in the audience. The higher the BPM, the faster the song, which is great for scenes that are joyful, exciting, or need to move quickly or intensely. Meanwhile, a lower BPM means the song is slower, which is fitting scenes that are sad, dramatic or romantic.\" This explains why lower tempo years saw an increase in depression rates.  \n",
    "\n",
    "Limitations to our work include limited availability of depression data in California where we were only able to find data for the past five which affected the accuracy of our data analysis since we did not have adequate data to rely on. This can tie back with the confidentiality issue revolving mental illness. Another Limitation was that the music data sets available were for Spotify which might not have been a big enough scope like billboard’s so not necessarily all of the individuals reported in the depression dataset listened to Spotify music. Due to time constraints we could only work on data from last 5 years, if we had more time we could have done the analysis on the last 15/20 years. Another factor is that we only used 100 songs from each year as scraping data from spotify is a time consuming process.\n",
    "\n",
    "Our work can hopefully encourage future research and analysis on the relationship between music and depression at a much bigger scale and possible non-drug treatments for mental health illnesses such as music and dance therapy. "
   ]
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