FinalProject_group086.ipynb

{
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   "source": [
    "# COGS 108 - Final Project "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "The type of music a person listens to is affected by numerous factors -- such as preference, location, language, etc. -- but how is it affected by a person’s changing mood throughout the seasons? Research suggests the existence of the phenomenon of Seasonal Affective Disorder (SAD), where people experience low mood during cold, dark seasons and high mood during warm, bright seasons. This claim, combined with other research that suggests that people explicitly listen to music that matches their mood, implores a discussion around how the changing seasons can affect the type of music a person listens to.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Names\n",
    "- Peiwen Cheng\n",
    "- Yunshu Gao\n",
    "- Golden Hong\n",
    "- Yoshiichiro Russell\n",
    "- Anthony Tran\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Research Question\n",
    "How do the seasons of the year affect the type of music the general public listens to (in terms of danceability, energy, loudness, and valence) and how do the traits of these songs vary based on these seasons?\n"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "## Background and Prior Work"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Music is one of the few things in the world that connects all humans, thanks to its ability to dynamically articulate emotion. In conjunction with the fact that emotions are ever-changing, a question is raised: what is the relation between our emotions and the music we listen to? According to the NCBI, the mood of a person can actually be reflected in the type of music they listen to, such that “participants in a sad mood were inclined to listen to sad (and slow) music [and] those in a happy mood preferred to listen to happy (and fast) music.” Thus, it seems that people tend to choose “valence-consistent music” in order to match the mood they’re in.\n",
    "\n",
    "As a followup to this basis, it would also be appropriate to ask when emotions tend to change. In the past forty years, the term “Seasonal Affective Disorder” (SAD) has gained popularity and is generally understood as “winter depression.” As the cold seasons approach, “the mornings start later and the evenings begin earlier” and result in less total sunlight each day, possibly leading to symptoms of “low mood, lethargy, and social withdrawal,” as noted by MedicalNewsToday. Thus, it would seem fair to work under the assumption that people generally experience lower moods in cold months and higher moods during warm months.\n",
    "\n",
    "Since emotions are reflected in music choice, and people’s emotions tend to be low-spirited during fall/winter and high-spirited during spring/summer, this project will attempt to identify a relationship between the music people choose, depending on their mood at a given time.\n",
    "\n",
    "\n",
    "References (include links):\n",
    "- 1) Seasonal Affective Disorder: https://www.medicalnewstoday.com/articles/10306.php\n",
    "- 2) Relationship between Music and Mood: https://www.ncbi.nlm.nih.gov/pubmed/30003367\n",
    "- 3) Infographic on Spotify Age Demographics: https://www.adweek.com/brand-marketing/infographic-what-marketers-need-know-about-millennials-music-habits-170869/"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Hypothesis\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "We believe that danceability, energy, loudness, and valence are likely to be higher during warmer, brighter seasons (spring/summer), and lower during colder, darker seasons (fall/winter). Warm seasons include more outdoor activities, resulting in people being generally happier and needing music to complement the mood. In contrast, the colder months (fall/winter) cause people to spend more time dormant indoors and experience low mood.\n",
    "\n",
    "However, it can be conceded that wintery months involve the holiday season and may result in a relative increase of loudness and danceability, whereas valence and energy may stay the same. \n"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "# Dataset(s)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "For our question, we think the most reliable data would come from a popular music streaming services. Spotify provides trending songs on a daily basis and also offers APIs for developers to retrieve audio features of a song, such as a song’s danceability. We assume that, if we use Spotify as our main data source, we can reach a comprehensive conclusion considering the wide user base of Spotify. Spotify data will comprise our entire dataset. \n",
    "\n",
    "We picked the trending charts only for the United States because the weather patterns would be different in different parts of the world. For example, Australia experiences winter during the US’s summer. Thus, to focus our observations, we scraped only data only from the United States. \n",
    "\n",
    "__NOTE__: Before getting into the data, we must also note that we discovered that the dataset for Spotify is skewed towards a certain age demographic. According to Adweek Magazine and Spotify, 72% of Spotify listeners are millennials, which fall under the ages of 20 - 35 as of 2017 and 2018, the years of our data analyzation). Thus, a large majority of Spotify users fall between an age of mid-teens to mid-thirties, so the data we retrieve and results we receive will be more representative of the tendencies of the aforementioned age groups, rather than those outside of these age groups.\n",
    "\n",
    "Our data mainly contains 2 parts.\n",
    "\n",
    "The first is raw data containing 2 years of daily trending charts scraped from Spotify’s website, which totals about 700 CSVs (there are certain days that have no charts at all because Spotify doesn’t provide it, but it wouldn’t affect our analysis because we can still see the trend of key indicators). Each chart has 50 songs experiencing virality and includes their URLs. In total, we will have around 35,000 observations (songs). \n",
    "\n",
    "The second part is the musical attributes for all 35,000 songs, such as danceability. We have 7-8 such features per track and they are gathered by API calls along with the song URLs from the raw data generated in part 1. We will be analyzing this data primarily to reach our conclusions.\n",
    "\n",
    "Part 1:\n",
    "- Dataset Name: Viral 50 Songs in United States (Any date)\n",
    "- Link to the dataset: https://spotifycharts.com/regional\n",
    "- Number of observations: 50x350x2 = 35,000 (songs raw data)\n",
    "\n",
    "Part 2:\n",
    "- Dataset Name: Audio Features of tracks\n",
    "- Link to the dataset: https://developer.spotify.com/documentation/web-api/reference/tracks/get-audio-features/\n",
    "- Number of observations: 50x350x2 = 35,000 x 5 = 175,000 (features overall)\n",
    "\n",
    "(We are retrieving around 5 features per song)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "# Data Cleaning/ Pre-processing"
   ]
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   "source": [
    "STEP ONE:\n",
    "    - This is the step where we web-scraped the top 50 songs for each day from spotify's top charts page\n",
    "    - We found that there were days within our date range that didn't contain a csv of the top 50 songs so \n",
    "      we had to catch these days and skip over them.\n",
    "    - For all the days that did work, we put them into a dataframe with their date, month, and URL in the \n",
    "      corresponding columsn: 'Date', 'Month', 'URL'.\n",
    "\n",
    "STEP TWO:\n",
    "    - This step is where we used a Spotify API to extract audio-feature for each song in the top 50 songs for\n",
    "      each day within our date range of two years (2017-1-1 to 2018-12-31. The audio-feature for each song were \n",
    "      provided in a json file containing a songs: id, acousticness, danceability, duration_ms, energy,\n",
    "      instrumentalness, key, liveness, loudness, mode, speechiness, tempo, time signature, and valence.\n",
    "    - We then created an audio_features DataFrame whose columns were the different audio-feature, date, and month, \n",
    "      and the rows consisted of every day from our start date to our end date (2017-1-1 to 2018-12-31).\n",
    "        \n",
    "STEP THREE:\n",
    "    - We were having problems pulling the csv files containing the data for each day as the API requests started to \n",
    "      time out, allowing us to only grab at most 11 months of data.\n",
    "    - To compromise, we split the date into three parts and had our STEP TWO code request data for all three parts\n",
    "      without timing out. This meant we had to hardcode the range in our STEP TWO and manually put these in three\n",
    "      different csv files.\n",
    "\n",
    "STEP FOUR:\n",
    "    - Essentially we converted all three of our csv files into dataframes and combined them into one DataFrame\n",
    "    \n",
    "STEP FIVE:\n",
    "    - This step was used to organize our final DataFrame and put each data in its respective season\n",
    " \n",
    "\n",
    "                                    \n",
    "\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import patsy\n",
    "import pandas as pd\n",
    "from datetime import timedelta, date\n",
    "import time\n",
    "from io import StringIO\n",
    "from datetime import date\n",
    "from dateutil.rrule import rrule, DAILY\n",
    "import io\n",
    "import requests\n",
    "import json\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "STEP ONE:\n",
    " \n",
    "(It takes very long time to run and retrieve the data. Hard to replicate this step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# starting date\n",
    "a = date(2017, 1, 1)\n",
    "# ending date\n",
    "b = date(2018, 12, 31)\n",
    "\n",
    "# WEB-SCRAPING FROM SPOTIFY #\n",
    "head = 'https://spotifycharts.com/viral/global/daily/'\n",
    "tail = '/download' \n",
    "dfArr = []\n",
    "dfDic = {}\n",
    "for dt in rrule(DAILY, dtstart=a, until=b):       # iterating through start to end date\n",
    "    url = head + dt.strftime(\"%Y-%m-%d\") + tail\n",
    "    data = io.StringIO(requests.get(url).text)\n",
    "    timeStr = str(dt)[:10]\n",
    "    # GRAB TOP 50 TRENDING SONGS FOR EACH DAY\n",
    "    try:\n",
    "        df = pd.read_csv(data)                    \n",
    "        day = []\n",
    "        month = [] \n",
    "        for i in range(50):\n",
    "            day.append(timeStr)\n",
    "            month.append(timeStr[5:7])\n",
    "        df['Date'] = day\n",
    "        df['Month'] = month\n",
    "        df['URL'] = df['URL'].str[31:]\n",
    "        dfArr.append(df)\n",
    "        dfDic.update( {timeStr : df} )\n",
    "    # FAILED DAYS CAUGHT \n",
    "    except:\n",
    "        print(timeStr) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "STEP TWO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# EXTRACTING AUDIO-FEATURES FOR EACH SONG USING SPOTIFY API #\n",
    "audio_features = []\n",
    "days=[]\n",
    "months = []\n",
    "for d in range(586,707):\n",
    "    perDay = dfArr[d]\n",
    "    URL = perDay['URL']\n",
    "    day = perDay['Date'][0]\n",
    "    month = perDay['Month'][0]\n",
    "    for url in URL:\n",
    "        headers = {\n",
    "            'Accept': 'application/json',\n",
    "            'Content-Type': 'application/json',\n",
    "            'Authorization': 'Bearer BQDlg0S0chc-L__S19kfTzvSdFdlLXsZv5EEk9u9tafs4WqJrSDeHAluzbKxqy2ahMBjpA4EgEeqqQpUpHmwbdnjfBCm414_YUyKslD4ynI1-vemusCvvh-BWOLZ8aRPQYH7IAXrEZ287MWXTRH0HV9UnypeItShomch8osQiobyopp1ew4cfis',\n",
    "        }\n",
    "        urlstr = 'https://api.spotify.com/v1/audio-features/'+url;\n",
    "        response = requests.get(urlstr, headers=headers)\n",
    "        audio_features.append(response.json())\n",
    "        days.append(day)\n",
    "        months.append(month)\n",
    "        \n",
    "# CONVERT TO DATAFRAME #    \n",
    "audio_features = pd.DataFrame(audio_features)\n",
    "audio_features = audio_features[['id', 'acousticness', 'danceability', 'duration_ms', 'energy', 'instrumentalness', 'key', 'liveness', 'loudness', 'mode', 'speechiness', 'tempo', 'time_signature', 'valence']]\n",
    "audio_features['Date'] = days\n",
    "audio_features['Month'] = months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "STEP THREE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "audio_features.to_csv('./audio_features_3.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "STEP FOUR:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# COMBINED THREE CSV FILES OF DATA INTO ONE BIG DATAFRAME #\n",
    "df1 = pd.read_csv('./audio_features_1.csv')\n",
    "df2 = pd.read_csv('./audio_features_2.csv')\n",
    "df3 = pd.read_csv('./audio_features_3.csv')\n",
    "combined = pd.concat([df1, df2, df3], ignore_index = True)\n",
    "combined.iloc[35000]\n",
    "df1 = combined.groupby(['Date']).mean()\n",
    "df1 = df1.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "STEP FIVE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# ORGANIZED DATAFRAME INTO SEASONS #\n",
    "seasonNum = []\n",
    "season = []\n",
    "# Add Season column\n",
    "for i in range(len(df1)):\n",
    "    day = df1.iloc[i]\n",
    "    if day['Month'] in [9, 10, 11]:\n",
    "        seasonNum.append(1)\n",
    "        season.append('Fall')\n",
    "    elif day['Month'] in [12, 1, 2]:\n",
    "        seasonNum.append(0)\n",
    "        season.append('Winter')\n",
    "    elif day['Month'] in [3, 4, 5]:\n",
    "        seasonNum.append(2)\n",
    "        season.append('Spring')\n",
    "    else:\n",
    "        seasonNum.append(3)\n",
    "        season.append('Summer') \n",
    "df1['SeasonNum'] = seasonNum\n",
    "df1['Season'] = season\n",
    "\n",
    "df_2017 = df1.head(344)\n",
    "df_2017 = df_2017.sort_values(by=['SeasonNum', 'Date'])\n",
    "\n",
    "df_2018 = df1.tail(363)\n",
    "df_2018 = df_2018.sort_values(by=['SeasonNum', 'Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# Visualization and Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Include cells that describe the steps in your data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HeatMap illustrating the correlation among attributes\n",
    "attr = df1.drop(['Month','SeasonNum','Season'], axis=1)\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(attr.corr(), cmap=\"coolwarm\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heat map above, we can see that loudness is mostly correlated with energy as they have a whopping 0.83 or correlation. In the mean time, we find that energy (or loudness) is least correlated (even negative) with acousticness, which means as loudness goes up, there will be significant drops in acousticness.\n",
    "\n",
    "This gives us some insights that, when analyzing music valence (also positively correlated with loudness or energy and negatively correlated with acousticness) with season, it is reasonable to just analyze one group of variables with season. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Danceability vs Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for danceability and saeason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2017-2018: Danceability')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ticks = ['Winter', 'Fall', 'Spring', 'Summer']\n",
    "\n",
    "winter_list1 = df1.loc[df1['Season'] == 'Winter','danceability'].tolist()\n",
    "spring_list1 = df1.loc[df1['Season'] == 'Spring','danceability'].tolist()\n",
    "summer_list1 = df1.loc[df1['Season'] == 'Summer','danceability'].tolist()\n",
    "fall_list1 = df1.loc[df1['Season'] == 'Fall','danceability'].tolist()\n",
    "data1 = [winter_list1, fall_list1, spring_list1, summer_list1]\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.boxplot(data1)\n",
    "plt.xticks([1,2,3,4], ticks)\n",
    "plt.title('2017-2018: Danceability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS Regression for danceability and season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {},
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           danceability   R-squared:                       0.673\n",
      "Model:                            OLS   Adj. R-squared:                  0.673\n",
      "Method:                 Least Squares   F-statistic:                     1206.\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):          2.72e-144\n",
      "Time:                        23:18:16   Log-Likelihood:                -262.81\n",
      "No. Observations:                 586   AIC:                             527.6\n",
      "Df Residuals:                     585   BIC:                             532.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "SeasonNum      0.2713      0.008     34.734      0.000       0.256       0.287\n",
      "==============================================================================\n",
      "Omnibus:                    15020.276   Durbin-Watson:                   0.018\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               54.452\n",
      "Skew:                           0.259   Prob(JB):                     1.50e-12\n",
      "Kurtosis:                       1.599   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "CORRELATION: 0.23010510383246466\n"
     ]
    }
   ],
   "source": [
    "danceability = df1['danceability']\n",
    "season = df1['SeasonNum']\n",
    "# outcome, predictors = patsy.dmatrices (valence, season)\n",
    "mod = sm.OLS(danceability, season)\n",
    "res = mod.fit()\n",
    "totalcor = np.corrcoef(season,danceability)[0,1]\n",
    "print(res.summary())\n",
    "print(\"\\nCORRELATION:\", totalcor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This OLS Regression Results suggests the model of predicting the danceability for certain season as y = exp ( 0.2713x).\n",
    "\n",
    "The p-value being statistically significant (less than 0.05) suggests that we have strong evidence that some correlation exists for danceability and season.\n",
    "\n",
    "From the graphs and linear regression results above, we can see that there is indeed a trend of increasing danceability through fall, winter, spring, and summer, but the corrlation is 0.16, which is not high. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Danceability of Fall and Winter Compared with Average Danceability of Spring and Summer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x132595908>,\n",
       "  <matplotlib.axis.XTick at 0x1325952b0>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average danceability of fall and winter\n",
    "danceability_avg_fall = np.average(fall_list1)\n",
    "danceability_avg_winter = np.average(winter_list1)\n",
    "danceability_avg_fw = (danceability_avg_fall+danceability_avg_winter)/2\n",
    "# Average danceability of spring and summer\n",
    "danceability_avg_spring = np.average(spring_list1)\n",
    "danceability_avg_summer = np.average(summer_list1)\n",
    "danceability_avg_ss = (danceability_avg_spring+danceability_avg_summer)/2\n",
    "\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.bar([0,1], [danceability_avg_fw, danceability_avg_ss])\n",
    "plt.xticks([0,1],['Average danceability FW', 'Average danceability SS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average of danceability in fall/winter compared with that of spring/summer shows that there's no strong correlation between danceability and season. A warmer season won't increase danceability of music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valence vs Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for valence and saeason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2017-2018: Valence')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "winter_list2 = df1.loc[df1['Season'] == 'Winter','valence'].tolist()\n",
    "spring_list2 = df1.loc[df1['Season'] == 'Spring','valence'].tolist()\n",
    "summer_list2 = df1.loc[df1['Season'] == 'Summer','valence'].tolist()\n",
    "fall_list2 = df1.loc[df1['Season'] == 'Fall','valence'].tolist()\n",
    "data2 = [winter_list2, fall_list2, spring_list2, summer_list2]\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.boxplot(data2)\n",
    "plt.xticks([1,2,3,4], ticks)\n",
    "plt.title('2017-2018: Valence')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS Regression for valence and season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                valence   R-squared:                       0.670\n",
      "Model:                            OLS   Adj. R-squared:                  0.670\n",
      "Method:                 Least Squares   F-statistic:                     1188.\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):          5.29e-143\n",
      "Time:                        23:18:17   Log-Likelihood:                -72.313\n",
      "No. Observations:                 586   AIC:                             146.6\n",
      "Df Residuals:                     585   BIC:                             151.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "SeasonNum      0.1945      0.006     34.473      0.000       0.183       0.206\n",
      "==============================================================================\n",
      "Omnibus:                      716.489   Durbin-Watson:                   0.022\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.277\n",
      "Skew:                           0.301   Prob(JB):                     3.29e-11\n",
      "Kurtosis:                       1.729   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "CORRELATION: 0.11319741635617647\n"
     ]
    }
   ],
   "source": [
    "valence = df1['valence']\n",
    "season = df1['SeasonNum']\n",
    "# outcome, predictors = patsy.dmatrices (valence, season)\n",
    "mod = sm.OLS(valence, season)\n",
    "res = mod.fit()\n",
    "totalcor = np.corrcoef(season,valence)[0,1]\n",
    "print(res.summary())\n",
    "print(\"\\nCORRELATION:\", totalcor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This OLS Regression Results suggests the model of predicting the valence for certain season as y = exp ( 0.1945x).\n",
    "\n",
    "The p-value being statistically significant (less than 0.05) suggests that we have strong evidence that some correlation exists for valence and season.\n",
    "\n",
    "From graphs above, we can see that there does not seems a strong correlation between season number change with valence values. The correlation between valence and season is 0.113, whichb is not a significant correlation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Valence of Fall and Winter Compared with Average Valence of Spring and Summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x13381db38>,\n",
       "  <matplotlib.axis.XTick at 0x1338104a8>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average valence of fall and winter\n",
    "valence_avg_fall = np.average(fall_list2)\n",
    "valence_avg_winter = np.average(winter_list2)\n",
    "valence_avg_fw = (valence_avg_fall+valence_avg_winter)/2\n",
    "# Average valence of spring and summer\n",
    "valence_avg_spring = np.average(spring_list2)\n",
    "valence_avg_summer = np.average(summer_list2)\n",
    "valence_avg_ss = (valence_avg_spring+valence_avg_summer)/2\n",
    "\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.bar([0,1], [valence_avg_fw, valence_avg_ss])\n",
    "plt.xticks([0,1],['Average valence FW', 'Average valence SS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average of valence in fall/winter compared with that of spring/summer shows that there's no strong correlation between valence and season. A warmer season won't increase valence of music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Energy vs Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for energy and saeason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2017-2018: Energy')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "winter_list3 = df1.loc[df1['Season'] == 'Winter','energy'].tolist()\n",
    "spring_list3 = df1.loc[df1['Season'] == 'Spring','energy'].tolist()\n",
    "fall_list3 = df1.loc[df1['Season'] == 'Fall','energy'].tolist()\n",
    "summer_list3 = df1.loc[df1['Season'] == 'Summer','energy'].tolist()\n",
    "data3 = [winter_list3, fall_list3, spring_list3, summer_list3]\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.boxplot(data3)\n",
    "plt.xticks([1,2,3,4], ticks)\n",
    "plt.title('2017-2018: Energy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression for energy and season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 energy   R-squared:                       0.679\n",
      "Model:                            OLS   Adj. R-squared:                  0.678\n",
      "Method:                 Least Squares   F-statistic:                     1235.\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):          2.48e-146\n",
      "Time:                        23:18:19   Log-Likelihood:                -239.45\n",
      "No. Observations:                 586   AIC:                             480.9\n",
      "Df Residuals:                     585   BIC:                             485.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "SeasonNum      0.2638      0.008     35.149      0.000       0.249       0.279\n",
      "==============================================================================\n",
      "Omnibus:                     2719.399   Durbin-Watson:                   0.019\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               52.059\n",
      "Skew:                           0.271   Prob(JB):                     4.96e-12\n",
      "Kurtosis:                       1.644   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "CORRELATION: 0.30770099746842183\n"
     ]
    }
   ],
   "source": [
    "energy = df1['energy']\n",
    "season = df1['SeasonNum']\n",
    "# outcome, predictors = patsy.dmatrices (valence, season)\n",
    "mod = sm.OLS(energy, season)\n",
    "res = mod.fit()\n",
    "totalcor = np.corrcoef(season,energy)[0,1]\n",
    "print(res.summary())\n",
    "print(\"\\nCORRELATION:\", totalcor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This OLS Regression Results suggests the model of predicting the energy for certain season as y = exp ( 0.2638x).\n",
    "\n",
    "The p-value being statistically significant (less than 0.05) suggests that we have strong evidence that some correlation exists for energy and season.\n",
    "\n",
    "From the graphs and regression analysis above, we can see that there is a rather strong correlation between energy and seasion change. As can be seen from the box plot and the correlation of almost 0.31."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Energy of Fall and Winter Compared with Average Energy of Spring and Summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x10ec20710>,\n",
       "  <matplotlib.axis.XTick at 0x131fdb390>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average energy of fall and winter\n",
    "energy_avg_fall = np.average(fall_list3)\n",
    "energy_avg_winter = np.average(winter_list3)\n",
    "energy_avg_fw = (energy_avg_fall+energy_avg_winter)/2\n",
    "# Average valence of spring and summer\n",
    "energy_avg_spring = np.average(spring_list3)\n",
    "energy_avg_summer = np.average(summer_list3)\n",
    "energy_avg_ss = (energy_avg_spring+energy_avg_summer)/2\n",
    "\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.bar([0,1], [energy_avg_fw, energy_avg_ss])\n",
    "plt.xticks([0,1],['Average energy FW', 'Average energy SS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average of energy in fall/winter compared with that of spring/summer shows that there's a insignificant positive correlation between valence and season. A warmer season would increase energy of music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loudness vs Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot for loudness and saeason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2017-2018: Loudness')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "winter_list4 = df1.loc[df1['Season'] == 'Winter','loudness'].tolist()\n",
    "spring_list4 = df1.loc[df1['Season'] == 'Spring','loudness'].tolist()\n",
    "fall_list4 = df1.loc[df1['Season'] == 'Fall','loudness'].tolist()\n",
    "summer_list4 = df1.loc[df1['Season'] == 'Summer','loudness'].tolist()\n",
    "data4 = [winter_list4, fall_list4, spring_list4, summer_list4]\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.boxplot(data4)\n",
    "plt.xticks([1,2,3,4], ticks)\n",
    "plt.title('2017-2018: Loudness')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regression for loudness and season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               loudness   R-squared:                       0.624\n",
      "Model:                            OLS   Adj. R-squared:                  0.623\n",
      "Method:                 Least Squares   F-statistic:                     970.9\n",
      "Date:                Wed, 12 Jun 2019   Prob (F-statistic):          2.29e-126\n",
      "Time:                        23:18:21   Log-Likelihood:                -1640.0\n",
      "No. Observations:                 586   AIC:                             3282.\n",
      "Df Residuals:                     585   BIC:                             3286.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "SeasonNum     -2.5527      0.082    -31.159      0.000      -2.714      -2.392\n",
      "==============================================================================\n",
      "Omnibus:                     1130.123   Durbin-Watson:                   0.018\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               48.256\n",
      "Skew:                          -0.255   Prob(JB):                     3.32e-11\n",
      "Kurtosis:                       1.690   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "CORRELATION: 0.3809376573104233\n"
     ]
    }
   ],
   "source": [
    "loudness = df1['loudness']\n",
    "season = df1['SeasonNum']\n",
    "# outcome, predictors = patsy.dmatrices (valence, season)\n",
    "mod = sm.OLS(loudness, season)\n",
    "res = mod.fit()\n",
    "totalcor = np.corrcoef(season,loudness)[0,1]\n",
    "print(res.summary())\n",
    "print(\"\\nCORRELATION:\", totalcor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This OLS Regression Results suggests the model of predicting the loudness for certain season as y = exp ( -2.5537x).\n",
    "\n",
    "The p-value being statistically significant (less than 0.05) suggests that we have strong evidence that some correlation exists for loudness and season.\n",
    "\n",
    "From loudness, we are getting the strongest positive correlation ever among the 4 features we are analyzing. We can see a clear stepping trend from boxplot and a 0.366 correlation value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Loudness of Fall and Winter Compared with Average Loudness of Spring and Summer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x10ecb4cc0>,\n",
       "  <matplotlib.axis.XTick at 0x10ecb3470>],\n",
       " <a list of 2 Text xticklabel objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average loudness of fall and winter\n",
    "loudness_avg_fall = np.average(fall_list4)\n",
    "loudness_avg_winter = np.average(winter_list4)\n",
    "loudness_avg_fw = 10-(-(loudness_avg_fall+loudness_avg_winter)/2)\n",
    "# Average loudness of spring and summer\n",
    "loudness_avg_spring = np.average(spring_list4)\n",
    "loudness_avg_summer = np.average(summer_list4)\n",
    "loudness_avg_ss = 10-(-(loudness_avg_spring+energy_avg_summer)/2)\n",
    "\n",
    "plt.figure(figsize = (9,7))\n",
    "plt.bar([0,1], [loudness_avg_fw, loudness_avg_ss])\n",
    "plt.xticks([0,1],['Average loudness FW', 'Average loudness SS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average of loudness in fall/winter compared with that of spring/summer shows that there's a significant positive correlation between loudness and season. A warmer season would significantly increase loudness of music."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Moodsic does not extract or analyze data from any specific individuals. The project will only gather general, non-identifying statistics from all users of a particular group and compile that data from trusted sources, namely from Spotify. So, in regards to individual privacy and exploitations, there will be no ethical issues because no individual will have any identifying information used. We will only use general group information, which will also eliminate biases and skews. \n",
    "\n",
    "To further guarantee our commitment to conduct ethical research, we must first understand the possible impacts our research will have on the world. As previously mentioned, Moodsic’s data analysis will reveal the trending songs and their various metrics (danceability, loudness, etc.) based on the time of the year. As a result of this, potential producers, artists, and record labels could utilize this information to create more music catered to the general trend at a particular time of the year (i.e. more upbeat and danceable music during the summer). And if they do this, they are more likely to create hit songs for the time of year they are producing music for, resulting in more money being made. Of course, this isn’t necessarily a bad thing -- we could always use more good music in the world. \n",
    "\n",
    "Another pair of outcomes of our research can come twofold. The first is that large record labels and producers will continue to create hit songs quickly, leaving less room for notoriety and success for smaller artists. Conversely, smaller artists who don’t necessarily have the money to conduct their own research can use this information to create music that will have a greater chance of being a hit. \n",
    "\n",
    "Now that both small and big producers have access to this valuable data, there may be an unintended problem. If everyone knows what kind of music seems to be trending at a particular time of the year, and tries to make music that matches the metrics of our research, Moodsic is effectively discouraging creativity and less popular genres of music. Music may begin to lose diversity and more songs may become more similar to one another. There is nothing Moodsic can do to prevent this from happening once the data is available.\n",
    "\n",
    "However, this problem seems to be self-correcting. If there is a saturation of one type of music, people will become more inclined to seek other types of music. This will effectively change the trend of music to something else. Moodsic will then find the new trend and producers will again strive to create music similar to the new trend. It will become a self-correcting cycle, which in the grand scheme of things is how most of the world works. Something becomes popular for a while, and then it becomes so saturated that it becomes old and society just moves on to the next trend shortly after. In considering all of this, it seems like our project does not have any major ethical issues in regards to privacy. There are just consequences to consider. \n",
    "\n",
    "Thus, before we begin our project, we need to consider the potential consequences and outcomes of our research in order to better understand how impactful our work will be. Keeping this in mind though, we still need to be cautious on what information we are analyzing and how we are doing it because, again, there are still consequences to consider.used and how it will impact our society. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Our project focused primarily on analyzing the correlation between the seasons of the year and the type of music listened to, specifically how the different attributes of music changed with the seasons. We hypothesized that the warmer seasons, such as spring and summer, would experience an increase in danceability, energy, loudness, and valence. In contrast, the colder darker seasons, such as fall and winter, would experience a decrease in these respective attributes.\n",
    "\n",
    "As mentioned previously, the Spotify user base consists mostly of users in the mid-teens to mid-thirties range. So, our data and results will be more representative of the tendencies of these age groups. Regardless, based on the data conducted from our research, we found that the various traits measured for trending music throughout the year had some individual correlations, but not necessarily group correlations; certain traits had stronger correlations than others. For each of the traits, we first analyzed seasonal data from each year of 2017-2018 and then merged the two datasets for the seasonal averages of both years to yield more accurate results. \n",
    "\n",
    "In analyzing the danceability of music during 2017-2018, it is evident from our boxplot that the danceability is indeed generally higher in the warmer seasons and lower in the colder seasons. This makes sense and seems to agree with our hypothesis and rationale because we thought that people would generally want to listen to more upbeat songs during the warmer months because people are outdoors more. \n",
    "\n",
    "In analyzing loudness, it is also evident that there is a correlation between loudness and the time of the year. Through our visualization, it can be observed that the aveage loudness is higher during the spring and summer, than it is during the fall and winter. There is also less spread during the warmer seasons as well when compared to the colder seasons.\n",
    "\n",
    "In terms of valence, there seems to be less of a correlation throughout the seasons. Over the course of two years, the medians and spread are not significant enough for us to deduce any clear trend or correlation. In retrospect, we realize the correlation in valence may be hard to measure because the there are many possible factors that go into determining the “happiness” or “sadness” of a song. Low valence songs don’t necessarily go with a particular time of the year. There can be sad songs in any season, and for that reason it makes more sense than there is little to no correlation between it and the seasons when compared to something such as danceability.\n",
    "\n",
    "The same goes for our analyzation of energy throughout the seasons. There seems to be a constant average of energy throughout the seasons with only a slight increase in summer, but it’s not clear enough for us to confidently conclude that there is a correlation based on the seasons. "
   ]
  }
 ],
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