Cleaning_Messy_Data.ipynb

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   "source": [
    "## Cleaning a \"Messy\" Data Set \n",
    "## By Anna M. Imhoff and Randy Leon\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8G14FX2g2VO"
   },
   "source": [
    "## **Introduction**"
   ]
  },
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    "id": "FCZKJ1CAhXID"
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   "source": [
    "PREFACE\n",
    "\n",
    "We wanted to take the time to provide this write-up to show our basis and decision making in how we decided to “clean up” the variables for this dataset. (found on the UCI Machine Learning Repository website: https://archive.ics.uci.edu/dataset/109/wine)\n",
    "\n",
    "We figured the more domain knowledge we could obtain, the better we could make educated decisions as to what to do about the erroneous/missing data.\n",
    "\n",
    "DATA DICTIONARY\n",
    "\n",
    "In this section, we want to go into more detail regarding each variable of the dataset--including what we believe are accurate units of measurement as well as typical ranges for our subject matter of wine.\n",
    "\n",
    "Each row of the dataset, to our knowledge, represents a single type of wine. Sources from the web are listed as subsequent bullet points to information found out about the variable.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**INDEX**\n",
    "\n",
    "A unique identifier.\n",
    "\n",
    "\n",
    "**TARGET**\n",
    "\n",
    "This variable simply indicates the number of cases of wine sold. Cases of wine are always sold in amounts of 9 liters which are divided up into 12 bottles. This seems to be an international standard as per our research.\n",
    "https://winefolly.com/tips/case-of-wine-guide/\n",
    "https://www.thekitchn.com/buy-wine-case-tips-265821\n",
    "https://home.binwise.com/blog/case-of-wine\n",
    "\n",
    "\n",
    "**AcidIndex**\n",
    "\n",
    "This variable measures the total acidity of the wine.  To be more specific, “Titratable acidity, sometimes referred to as fixed or erroneously total acidity, is a measurement of the total concentration of titratable acids and free hydrogen ions present in your wine. Total acidity and titratable acidity are not the same thing and the terms should not be used interchangeably.” https://winemakersacademy.com/understanding-wine-acidity/\n",
    "\n",
    "\n",
    "**Alcohol**\n",
    "\n",
    "\n",
    "This variable measures the total alcohol content of the wine as a percentage of volume. (%) As per masterclass.com’s definition of it, “The range of ABV for unfortified wine is about 5.5% to 16%, with an average of 11.6%. Fortified wines range from 15.5% to 25% ABV, with an average of 18%.” \n",
    "I think it is safe to say that wines anywhere outside of 5.5%-25% alcohol could probably be considered anomalies/outliers.\n",
    "“https://www.masterclass.com/articles/learn-about-alcohol-content-in-wine-highest-to-lowest-abv-wines#:~:text=ABV%20is%20the%20global%20standard,with%20an%20average%20of%2018%25.”\n",
    "\n",
    "\n",
    "**Chlorides**\n",
    "\n",
    "\n",
    "This measures the total chloride content of the wine as (mg/L) More specifically, “The concentration of chloride ions is determined by subtracting the titration findings of the moles of silver ions that reacted with the thiocyanate from the total moles of silver nitrate added to the solution. This method is used when the pH of the solution, after the sample has been prepared, is acidic.” https://www.canterbury.ac.nz/media/documents/science-outreach/chloride_volhard.pdf\n",
    "\n",
    "\n",
    "**CitricAcid**\n",
    "\n",
    "\n",
    "After a hypothetical titration, we presume this variable to be a percentage of volume.\n",
    "A drop of pure lemon juice is about 5% citric acid, so we can assume that no wine contains a higher citric acid percentage than lemon juice. It is for this basic reasoning that we set the absolute upper limit for this variable at 5%.\n",
    "https://home.binwise.com/blog/is-wine-acidic\n",
    "\n",
    "\n",
    "**Density**\n",
    "\n",
    "\n",
    "This is a ratio representing the density of the wine compared to the density of water at a given temperature. “In other words, if your hydrometer reads 1.1, that means your liquid was 1.1 times as dense as water at that temperature. Note that specific gravity is a unitless measurement. These measurements should be around water since wine is close to water (1.000).”\n",
    "https://www.thetipsygrape.com/articles/using-a-hydrometer-in-winemaking.html#:~:text=A%20hydrometer%20is%20an%20instrument,called%20specific%20gravity%20(SG).\n",
    "\n",
    "\n",
    "**FixedAcidity**\n",
    "\n",
    "\n",
    "From our research, we also found this to be known as titratable acidity.\n",
    " “Expressed in milliquivalents per liter (mEq/L)”\n",
    "https://www.oiv.int/public/medias/3732/oiv-ma-as313-02.pdf\n",
    "https://winemakersacademy.com/understanding-wine-acidity/\n",
    "\n",
    "\n",
    "**FreeSulfurDioxide**\n",
    "\n",
    "\n",
    "Bonded sulfur dioxide content of the wine. \n",
    "Typically measured in (mg/L)\n",
    "“Free sulphur dioxide is defined as the sulphur dioxide present in the must or wine in the following forms: H2SO3, HSO3-, whose equilibrium as a function of pH and temperature is: H2SO3 ⇆ H+ + HSO3- \n",
    "https://eprints.ucm.es/id/eprint/29446/8/PIMCD%20N%C2%BA%20243.%20ANEXO%202.%20E-BOOK-%20WINE%20AND%20ITS%20ANALYSIS.pdf (Departamento de Nutrición y Bromatología II Facultad de FarmaciaUniversidad Complutense de Madrid)\n",
    "\n",
    "\n",
    "**LabelAppeal**\n",
    "\n",
    "\n",
    "LabelAppeal is simply the subjective marketing score that indicates the appeal of the design of the label on the bottle.\n",
    "\n",
    "\n",
    "**ResidualSugar**\n",
    "\n",
    "\n",
    "Residual sugar is measured as a concentration is expressed in grams per liter (g/L) or as a percentage of weight to volume. \n",
    "“For example, a wine with 0.2% residual sugar contains two grams of sugar in a liter of wine, or approximately 1/4 ounce in a gallon.” “Dry wines are typically in the 0.2–0.3 percent range, off-dry wines in the 1.0–5.0 percent range, and sweet dessert wines in the 5.0–15 percent range.” - https://winemakermag.com/technique/501-measuring-residual-sugar-techniques \n",
    "For this data set, residual sugar is expressed in (g/L).  According to https://winefolly.com/deep-dive/sugar-in-wine-chart/, bone dry is less than 1 g/L, dry is 1 - 10 g/L, off-dry is 10 - 35 g/L, sweet is 35 - 120 g/L, and very sweet wine is 120 - 220 g/L.\n",
    "\n",
    "\n",
    "**STARS**\n",
    "\n",
    "\n",
    "This is the wine rating as determined by experts in the field. They base the wine on a score of 1 to 4, with 4 being excellent and 1 being poor. This variable is also subjective and has no scientific basis.\n",
    "\n",
    "\n",
    "**Sulphates**\n",
    "\n",
    "\n",
    "Sulfate content of the wine. (mg/L)\n",
    " “Wine ranges from about 5 mg/L (5 parts per million) to about 200 mg/L. The maximum legal limit in the United States is 350 mg/l. A well made dry red wine typically has about 50 mg/l sulfites.”\n",
    "https://winefolly.com/deep-dive/sulfites-in-wine/\n",
    "\n",
    "\n",
    "**TotalSulfurDioxide**\n",
    "\n",
    "\n",
    "Measures the amount of total sulfur dioxide present in the wine by adding “free and bound quantities of SO2…: free SO2 + bound SO2 = total SO2”\n",
    "“Total sulphur dioxide is defined as the total of all the various forms of sulphur dioxide present in wine, either in the free state or combined with their constituents. “\n",
    "Typically measured in (mg/L)\n",
    "https://www.awri.com.au/wp-content/uploads/2018/12/s2037.pdf(The Australian Wine Research Institute)\n",
    "https://eprints.ucm.es/id/eprint/29446/8/PIMCD%20N%C2%BA%20243.%20ANEXO%202.%20E-BOOK-%20WINE%20AND%20ITS%20ANALYSIS.pdf (Departamento de Nutrición y Bromatología II Facultad de FarmaciaUniversidad Complutense de Madrid)\n",
    "\n",
    "\n",
    "**VolatileAcidity**\n",
    "\n",
    "\n",
    "According to the OIV, (International Organisation of Vine and Wine) volatile acidity “---is derived from the acids of the acetic series present in wine in the free state and combined as salts content of the wine.” and is “expressed in milliequivalents per liter to one decimal place\n",
    "“Volatile acids are different from titratable acids in that they cannot be measured through a titration but must be quantified using a steam distillation process. “In this a sample of wine is exposed to steam which in turn encourages the volatile acids to leave the wine.” \n",
    "“Expressed in milliquivalents per liter (mEq/L)”\n",
    "https://www.oiv.int/public/medias/3732/oiv-ma-as313-02.pdf\n",
    "https://winemakersacademy.com/understanding-wine-acidity/\n",
    "\n",
    "\n",
    "**pH**\n",
    "\n",
    "\n",
    "pH of the wine.\n",
    "“pH (pondus hydrogenii hydrogen ́s weight) is the co-logarithm of hydrogen ion activity (concentration) pH = - log 10 aH+” \n",
    "“Low pH wines will taste tart and crisp, while higher pH wines are more susceptible to bacterial growth. Most wine pH's fall around 3 or 4; about 3.0 to 3.4 is desirable for white wines, while about 3.3 to 3.6 is best for reds.”\n",
    "https://daily.sevenfifty.com/how-winemakers-analyze-ph-and-its-impact-on-wine/\n",
    "https://www.winespectator.com/articles/what-do-ph-and-ta-numbers-mean-to-a-wine-5035\n",
    "https://eprints.ucm.es/id/eprint/29446/8/PIMCD%20N%C2%BA%20243.%20ANEXO%202.%20E-BOOK-%20WINE%20AND%20ITS%20ANALYSIS.pdf (Department of Nutrition and Bromatology II Faculty of Pharmacy Complutense University of Madrid)"
   ]
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   "source": [
    "Presented to us was a dataset featuring wine information. \n",
    "\n",
    "Upon further investigation, we noticed lots of missing and erroneous values. Our plan is to find the best methods according to best practices to fill in or replace the missing/erroneous data while simulataneously keeping data integrity as best as possible.\n",
    "\n",
    "We plan to:\n",
    "\n",
    "-conduct thorough domain research and find out what the proper ranges are for each column to get an idea as to what data is wrong for the respective measure.\n",
    "\n",
    "-conduct exploratory data analysis to further determine any outliers/incorrect values. \n",
    "\n",
    "-replace outliers with realistic numbers for the possible range using what we believe the best practice is (for example, for citric acid, no wine has a valye more than 5%, so any amounts over 5% would be replaced and imputated with realistic values)\n",
    "\n",
    "-any outright missing values will be replaced.\n",
    "\n",
    "-re-conduct EDA to show how we were accurately able to maintain data integrity after cleaning a messy data set."
   ]
  },
  {
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     "base_uri": "https://localhost:8080/"
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   "source": [
    "# import dependencies needed\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "from matplotlib import rcParams\n",
    "from sklearn.impute import KNNImputer\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_5pvFGWlvl1i"
   },
   "source": [
    "## **Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
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     "base_uri": "https://localhost:8080/",
     "height": 400
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    "id": "Ll9CXKlqcYnX",
    "outputId": "bacec446-506b-4c32-85aa-af1532d29c4c"
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       "      <td>6.2</td>\n",
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       "   Unnamed: 0  INDEX  TARGET  FixedAcidity  VolatileAcidity  CitricAcid  \\\n",
       "0           0      1       3           3.2            1.160        0.98   \n",
       "1           1      2       3           4.5            0.160        0.81   \n",
       "2           2      4       5           7.1            2.640        0.88   \n",
       "3           3      5       3           5.7            0.385        0.04   \n",
       "4           4      6       4           8.0            0.330        1.26   \n",
       "5           5      7       0          11.3            0.320        0.59   \n",
       "6           6      8       0           7.7            0.290        0.40   \n",
       "7           7     11       4           6.5            1.220        0.34   \n",
       "8           8     12       3          14.8            0.270        1.05   \n",
       "9           9     13       6           5.5            0.220        0.39   \n",
       "\n",
       "   ResidualSugar  Chlorides  FreeSulfurDioxide  TotalSulfurDioxide  Density  \\\n",
       "0          54.20      0.567                NaN               268.0  0.99280   \n",
       "1          26.10      0.425               15.0               327.0  1.02792   \n",
       "2          14.80      0.037              214.0               142.0  0.99518   \n",
       "3          18.80      0.425               22.0               115.0  0.99640   \n",
       "4           9.40        NaN              167.0               108.0  0.99457   \n",
       "5           2.20      0.556               37.0                15.0  0.99940   \n",
       "6          21.50      0.060              287.0               156.0  0.99572   \n",
       "7           1.40      0.040              523.0               551.0  1.03236   \n",
       "8          11.25      0.007              213.0                 NaN  0.99620   \n",
       "9           1.80      0.277               62.0               180.0  0.94724   \n",
       "\n",
       "     pH  Sulphates  Alcohol  LabelAppeal  AcidIndex  STARS  \n",
       "0  3.33       0.59      9.9            0          8    2.0  \n",
       "1  3.38       0.70      NaN           -1          7    3.0  \n",
       "2  3.12       0.48     22.0           -1          8    3.0  \n",
       "3  2.24       1.83      6.2           -1          6    1.0  \n",
       "4  3.12       1.77     13.7            0          9    2.0  \n",
       "5  3.20       1.29     15.4            0         11    NaN  \n",
       "6  3.49       1.21     10.3            0          8    NaN  \n",
       "7  3.20        NaN     11.6            1          7    3.0  \n",
       "8  4.93       0.26     15.0            0          6    NaN  \n",
       "9  3.09       0.75     12.6            0          8    4.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data From Github\n",
    "df1 = pd.read_csv('https://raw.githubusercontent.com/randleon/Cleaning-a-Messy-Data-Set-w-Python/main/Cleaning_Messy_Data.csv')\n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "d5M06jmv-3np",
    "outputId": "c95e59f4-2001-49f9-b92f-1d34fbefc037"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Unnamed: 0</th>\n",
       "      <th>INDEX</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>FixedAcidity</th>\n",
       "      <th>VolatileAcidity</th>\n",
       "      <th>CitricAcid</th>\n",
       "      <th>ResidualSugar</th>\n",
       "      <th>Chlorides</th>\n",
       "      <th>FreeSulfurDioxide</th>\n",
       "      <th>TotalSulfurDioxide</th>\n",
       "      <th>Density</th>\n",
       "      <th>pH</th>\n",
       "      <th>Sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>LabelAppeal</th>\n",
       "      <th>AcidIndex</th>\n",
       "      <th>STARS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12179.000000</td>\n",
       "      <td>12157.000000</td>\n",
       "      <td>12148.000000</td>\n",
       "      <td>12113.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12400.000000</td>\n",
       "      <td>11585.000000</td>\n",
       "      <td>12142.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>9436.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6397.000000</td>\n",
       "      <td>8069.980305</td>\n",
       "      <td>3.029074</td>\n",
       "      <td>8.063251</td>\n",
       "      <td>0.641086</td>\n",
       "      <td>0.686315</td>\n",
       "      <td>23.367809</td>\n",
       "      <td>0.222559</td>\n",
       "      <td>106.679042</td>\n",
       "      <td>204.319120</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>3.207628</td>\n",
       "      <td>0.846668</td>\n",
       "      <td>10.523778</td>\n",
       "      <td>-0.009066</td>\n",
       "      <td>7.772724</td>\n",
       "      <td>2.041755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3693.742682</td>\n",
       "      <td>4656.905107</td>\n",
       "      <td>1.926368</td>\n",
       "      <td>4.996119</td>\n",
       "      <td>0.555614</td>\n",
       "      <td>0.606005</td>\n",
       "      <td>24.945667</td>\n",
       "      <td>0.234288</td>\n",
       "      <td>108.102694</td>\n",
       "      <td>163.116881</td>\n",
       "      <td>0.026538</td>\n",
       "      <td>0.679687</td>\n",
       "      <td>0.655612</td>\n",
       "      <td>3.629154</td>\n",
       "      <td>0.891089</td>\n",
       "      <td>1.323926</td>\n",
       "      <td>0.902540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888090</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3198.500000</td>\n",
       "      <td>4037.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.046000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.987720</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6397.000000</td>\n",
       "      <td>8110.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>0.098000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>0.994490</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9595.500000</td>\n",
       "      <td>12106.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>0.368000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>1.000515</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12794.000000</td>\n",
       "      <td>16129.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>3.680000</td>\n",
       "      <td>3.860000</td>\n",
       "      <td>141.150000</td>\n",
       "      <td>1.351000</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>1057.000000</td>\n",
       "      <td>1.099240</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         INDEX        TARGET  FixedAcidity  \\\n",
       "count  12795.000000  12795.000000  12795.000000  12795.000000   \n",
       "mean    6397.000000   8069.980305      3.029074      8.063251   \n",
       "std     3693.742682   4656.905107      1.926368      4.996119   \n",
       "min        0.000000      1.000000      0.000000      0.000000   \n",
       "25%     3198.500000   4037.500000      2.000000      5.600000   \n",
       "50%     6397.000000   8110.000000      3.000000      7.000000   \n",
       "75%     9595.500000  12106.500000      4.000000      9.800000   \n",
       "max    12794.000000  16129.000000      8.000000     34.400000   \n",
       "\n",
       "       VolatileAcidity    CitricAcid  ResidualSugar     Chlorides  \\\n",
       "count     12795.000000  12795.000000   12179.000000  12157.000000   \n",
       "mean          0.641086      0.686315      23.367809      0.222559   \n",
       "std           0.555614      0.606005      24.945667      0.234288   \n",
       "min           0.000000      0.000000       0.000000      0.000000   \n",
       "25%           0.250000      0.280000       3.600000      0.046000   \n",
       "50%           0.410000      0.440000      12.900000      0.098000   \n",
       "75%           0.910000      0.970000      38.700000      0.368000   \n",
       "max           3.680000      3.860000     141.150000      1.351000   \n",
       "\n",
       "       FreeSulfurDioxide  TotalSulfurDioxide       Density            pH  \\\n",
       "count       12148.000000        12113.000000  12795.000000  12400.000000   \n",
       "mean          106.679042          204.319120      0.994203      3.207628   \n",
       "std           108.102694          163.116881      0.026538      0.679687   \n",
       "min             0.000000            0.000000      0.888090      0.480000   \n",
       "25%            28.000000          100.000000      0.987720      2.960000   \n",
       "50%            56.000000          154.000000      0.994490      3.200000   \n",
       "75%           172.000000          262.000000      1.000515      3.470000   \n",
       "max           623.000000         1057.000000      1.099240      6.130000   \n",
       "\n",
       "          Sulphates       Alcohol   LabelAppeal     AcidIndex        STARS  \n",
       "count  11585.000000  12142.000000  12795.000000  12795.000000  9436.000000  \n",
       "mean       0.846668     10.523778     -0.009066      7.772724     2.041755  \n",
       "std        0.655612      3.629154      0.891089      1.323926     0.902540  \n",
       "min        0.000000      0.000000     -2.000000      4.000000     1.000000  \n",
       "25%        0.430000      9.000000     -1.000000      7.000000     1.000000  \n",
       "50%        0.590000     10.400000      0.000000      8.000000     2.000000  \n",
       "75%        1.100000     12.400000      1.000000      8.000000     3.000000  \n",
       "max        4.240000     26.500000      2.000000     17.000000     4.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the description\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1QZbRJ6aGe7a",
    "outputId": "16903383-c475-43f6-b01f-a2c3be6846d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12795 entries, 0 to 12794\n",
      "Data columns (total 17 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   Unnamed: 0          12795 non-null  int64  \n",
      " 1   INDEX               12795 non-null  int64  \n",
      " 2   TARGET              12795 non-null  int64  \n",
      " 3   FixedAcidity        12795 non-null  float64\n",
      " 4   VolatileAcidity     12795 non-null  float64\n",
      " 5   CitricAcid          12795 non-null  float64\n",
      " 6   ResidualSugar       12179 non-null  float64\n",
      " 7   Chlorides           12157 non-null  float64\n",
      " 8   FreeSulfurDioxide   12148 non-null  float64\n",
      " 9   TotalSulfurDioxide  12113 non-null  float64\n",
      " 10  Density             12795 non-null  float64\n",
      " 11  pH                  12400 non-null  float64\n",
      " 12  Sulphates           11585 non-null  float64\n",
      " 13  Alcohol             12142 non-null  float64\n",
      " 14  LabelAppeal         12795 non-null  int64  \n",
      " 15  AcidIndex           12795 non-null  int64  \n",
      " 16  STARS               9436 non-null   float64\n",
      "dtypes: float64(12), int64(5)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "#Show the info\n",
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ec85lOSLRrKX",
    "outputId": "548714b1-6210-4543-ce38-4ad1416bd3d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12795, 17)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the shape\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jEzEwGuGX6zO",
    "outputId": "7d78975c-8779-4ef2-f107-fc8839b6f5cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'INDEX', 'TARGET', 'FixedAcidity', 'VolatileAcidity',\n",
       "       'CitricAcid', 'ResidualSugar', 'Chlorides', 'FreeSulfurDioxide',\n",
       "       'TotalSulfurDioxide', 'Density', 'pH', 'Sulphates', 'Alcohol',\n",
       "       'LabelAppeal', 'AcidIndex', 'STARS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the columns\n",
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S1o-fenn_G-l",
    "outputId": "5a9a993f-c99f-4c8f-d392-15dacd24fd55"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              int64\n",
       "INDEX                   int64\n",
       "TARGET                  int64\n",
       "FixedAcidity          float64\n",
       "VolatileAcidity       float64\n",
       "CitricAcid            float64\n",
       "ResidualSugar         float64\n",
       "Chlorides             float64\n",
       "FreeSulfurDioxide     float64\n",
       "TotalSulfurDioxide    float64\n",
       "Density               float64\n",
       "pH                    float64\n",
       "Sulphates             float64\n",
       "Alcohol               float64\n",
       "LabelAppeal             int64\n",
       "AcidIndex               int64\n",
       "STARS                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the data type\n",
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "qbUbS8ir8ApI",
    "outputId": "602320b5-c0b1-42b7-da6f-789a60c8f887"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>FixedAcidity</th>\n",
       "      <th>VolatileAcidity</th>\n",
       "      <th>CitricAcid</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.160</td>\n",
       "      <td>0.98</td>\n",
       "      <td>54.2</td>\n",
       "      <td>0.567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.99280</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.59</td>\n",
       "      <td>9.9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.81</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0.425</td>\n",
       "      <td>15.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>1.02792</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.640</td>\n",
       "      <td>0.88</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.037</td>\n",
       "      <td>214.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.99518</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.04</td>\n",
       "      <td>18.8</td>\n",
       "      <td>0.425</td>\n",
       "      <td>22.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.99640</td>\n",
       "      <td>2.24</td>\n",
       "      <td>1.83</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.330</td>\n",
       "      <td>1.26</td>\n",
       "      <td>9.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>167.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.99457</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.77</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  FixedAcidity  VolatileAcidity  CitricAcid  ResidualSugar  \\\n",
       "0       3           3.2            1.160        0.98           54.2   \n",
       "1       3           4.5            0.160        0.81           26.1   \n",
       "2       5           7.1            2.640        0.88           14.8   \n",
       "3       3           5.7            0.385        0.04           18.8   \n",
       "4       4           8.0            0.330        1.26            9.4   \n",
       "\n",
       "   Chlorides  FreeSulfurDioxide  TotalSulfurDioxide  Density    pH  Sulphates  \\\n",
       "0      0.567                NaN               268.0  0.99280  3.33       0.59   \n",
       "1      0.425               15.0               327.0  1.02792  3.38       0.70   \n",
       "2      0.037              214.0               142.0  0.99518  3.12       0.48   \n",
       "3      0.425               22.0               115.0  0.99640  2.24       1.83   \n",
       "4        NaN              167.0               108.0  0.99457  3.12       1.77   \n",
       "\n",
       "   Alcohol  LabelAppeal  AcidIndex  STARS  \n",
       "0      9.9            0          8    2.0  \n",
       "1      NaN           -1          7    3.0  \n",
       "2     22.0           -1          8    3.0  \n",
       "3      6.2           -1          6    1.0  \n",
       "4     13.7            0          9    2.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the unnamed column and the index column\n",
    "df1 = df1.drop(df1.columns[[0, 1]], axis=1)\n",
    "\n",
    "#View the data frame to ensure that the columns were removed\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-R_dEB0OQdVY"
   },
   "source": [
    "There are 15 columns with 12,795 rows which are all numeric.  Missing data accounts for 4.3% of the dataset.  Eight columns have missing data: Alcohol, Chlorides, FreeSulfurDioxide, ResidualSugar, STARS, Sulphates, TotalSulfurDioxide and pH.  LabelAppeal and TARGET have zero values which it okay because it is possible to get a score of 0 for LabelAppeal and TARGET sales.  \n",
    "\n",
    "<h5><b>Counts, Mean, Max, Min, and Missing Values</b></h5>\n",
    "1. AcidIndex has 14 distinct values with a mean of 7.77, a minimum of 4 and a maximum of 17.<br>\n",
    "2. Alcohol has 362 distinct values with a mean of 10.52, a minimium of 0 and a maximum of 26.5. There are 653 missing values which make up 5.1% of the Alcohol data.<br>\n",
    "3. Chlorides has 997 distinct values with a mean of 0.22, a minimum of 0 and a maximum of 1.35.  There are 638 missing values which make up 5.0% of the Chlorides data.<br>\n",
    "4. CitricAcid has 347 distinct values with a mean of 0.69, a minimum of 0 and a maximum of 3.86.<br>\n",
    "5. Density has 5,933 distinct values with a mean of 0.99, a minimum of 0.89 and a maximum of 1.1.<br>\n",
    "6. FixedAcidity has 313 distinct values with a mean of 8.06, a minimum of 0 and a maximum of 34.4.<br>\n",
    "7. FreeSulfurDioxide has 618 distinct values with a mean of 106.68, a minimum of 0 and a maximum of 623.  There are 647 missing values which make up 5.1% of the FreeSulfurDioxide data. <br>\n",
    "8. LabelAppeal has 5 distinct values with a mean of -0.009, a minimum of -2 and a maximum of 2.<br>\n",
    "9. ResidualSugar has 1,343 distinct values with a mean of 23.37, a minimum of 0 and a maximum of 141.15.  There are 616 missing values which make up 4.8% of the ResidualSugar data.<br>\n",
    "10. STARS has 5 distinct values with a mean of 2.04, a minimum of 1 and a maximum of 4.  There are 3,359 mssing value which make up 26.3% of the STARS data.<br>\n",
    "11. Sulphates has 383 distinct values with a mean of 0.85, a minimum of 0 and a maximum of 4.24.  There are 1,210 missing values which make up 9.5% of the Sulphates data.<br>\n",
    "12. TARGET has 9 distinct values with a mean of 3.02, a minimum of 0 and a maximum of 8.<br>\n",
    "13. TotalSulfurDioxide has 859 distinct values with a mean of 204.32, a minimum of 0 and a maximum of 1,057.  There are 682 missing values which make up 5.3% of the TotalSulfurDioxide data.<br>\n",
    "14. VolatileAcidity has 510 distinct values with a mean of 0.64, a minimum of 0 and a maximum of 3.68.<br>\n",
    "15. pH has 498 distinct values with a mean of 3.21, a minimum of 0.48 and a maximum of 6.13.  There are 395 missing values which make up 3.1% of the pH data.\n",
    "\n",
    "<h5><b>Pearson and Spearman Correlations</b><br></h5>\n",
    "\n",
    "According to the Pearson and Spearman correlations, STARS is positively correlated at 0.5 and LabelAppel is positively correlated at 0.4 with TARGET.  \n",
    "\n",
    "AcidIndex is negatively correlated to TARGET at -0.25. \n",
    "\n",
    "AcidIndex and FixedAcidity are slightly positively correlated with each other at 0.2.  We may not want to use both of these variables in a final model to predicit TARGET.<br>\n",
    "\n",
    "<h5><b>Preliminary Predictive Inferences</b><br></h5>\n",
    "The explanatory variables that appear to be relatively more “predictive” of the response variable are STARS, LabelAppel and AcidIndex."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vfr6HVhP98rp"
   },
   "source": [
    "#Histograms and Boxplots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "IGki3-D78If7",
    "outputId": "61086a18-b8b6-4143-cb65-29e5d536cbb1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://www.python-graph-gallery.com/24-histogram-with-a-boxplot-on-top-seaborn\n",
    "\n",
    "#Set a grey background\n",
    "sns.set(style=\"darkgrid\")\n",
    " \n",
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    " \n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df1['TARGET'], ax=ax_box)\n",
    "sns.histplot(data=df1, x='TARGET', ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O3-lBzA6Qod2"
   },
   "source": [
    "TARGET values are numerical and categorical. The boxplot shows that the median target value is 3.  Q1 to Q3 is 2-4 and 8 is an outlier.  The histogram has large peaks at 0, 3, and 4 with 4 being the mode. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "qBDXXTJi-rze",
    "outputId": "284a7aee-8fe9-4344-d998-dcfa982d352e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"FixedAcidity\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"FixedAcidity\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_yhJ0E1SXUpG"
   },
   "source": [
    "FixedAcidity values are numerical and continuous. The boxplot shows that the median FixedAcidity value is 7.  Q1 to Q3 is 5.6 to 9.8 with outliers from 17.8 to 34.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "v6fjfX_p-sSM",
    "outputId": "6e345413-9ba2-45d0-99a3-7cd5e752a021"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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W06ZNw8GDBwEAubm5CA0NdVW5RETkgMvONLy9vbF161Y89dRTaG5uxpw5czB//nwAwPbt25GWlgaz2YzJkycjMTERAJCeno5nnnkGb775JsaOHYtXXnnFVeUSEZEDTg+NI0eOSD/PnDkT+/fvt9tm0qRJyMnJsWsPDg7Ge++959T6iIhIPr4RTkREsjE0iIhINoYGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiko2hQUREsjE0iIhINlmhkZqaate2evXqAS+GiIiUrce5p9LT01FeXo6CggJUV1dL7RaLBSUlJU4vbqhQq1UQBPv87s9U6UREztRjaCxevBgXLlxAUVGR9NlVABAEQfo8K10/wyg/HC4ogdFkltrGjhmGuXfdyOAgIkXpMTTuuOMO3HHHHbj33nsRFBTkqpqGJGNlA4qN9e4ug4ioR7KmRjcajUhJSUFdXR1E8eev4B04cMBphRERkfLICo3nn38esbGx+MUvftGv73QTEdHgICs0NBoNHn/8cWfXQkRECifrkdsJEyagqKjI2bUQEZHCyTrTKCkpwYMPPogbbrgB3t7eUjvvaRARDS2yQmPNmjUDdsAPPvgAu3btkpZLS0sRExODa9euoaCgAL6+vgCAVatWYd68eSgsLMT69evR0NCAadOmYcOGDdBonP5pcyIickDWb9/bbrttwA740EMP4aGHHgIAXLhwAcnJyVi1ahWWLl2KXbt2wWAwdNo+JSUFmzdvxtSpU5Gamors7GzExcUNWD1ERCSfrNCYMWMGVCoVRFGUnp7S6/X4/PPPr+vgL7zwAtasWQNfX1+UlZUhNTUV5eXlmDdvHlatWgWj0YimpibpRcLY2FhkZGQwNIiI3ERWaHz33XfSzy0tLfjoo49w8eLF6zrwqVOn0NTUhAULFqCkpAQzZsxAeno6AgICsGLFCuTk5GDChAnQ6/VSH71ej/Ly8us6LhER9V+fbw54eXkhNjYWsbGx+N3vftfvA+/Zs0d6jHfcuHF44403pHUJCQnIzc1FSEhIp/dCOp7pyKXT+fe7Rr0+oM99BI0ArfbnYRUENQRBLbV1Xe62TSNg1Cg/p9Xpaqxx4HhCnZ5QI+AZdSqtRlmhUVtbK/0siiLOnz+P+vr+T3nR0tKCs2fPYuvWrQCAoqIiFBcXS/NbiaIIjUaDoKAgmEwmqV9lZaXdPY/eVFWZYbOJvW/YhV4fAJPpap/6aDQCrBYrWlstUpvVaoPVapPaui5322axoqamsde5p/pTp6uxxoHjCXV6Qo2AZ9TpjhrValWPf9nu8z0NANDpdFi/fn2/iyoqKsItt9wCP7+2v0mLoogtW7ZgxowZ8PPzw/vvv49FixYhODgY3t7eKCgowN133428vDyEhob2+7hERHR9+nxPYyCUlJR0mgBx0qRJWL58OR599FFYLBaEhYUhKioKALB9+3akpaXBbDZj8uTJSExMHNBaiIhIPlmhYbPZ8M477+Dzzz+HxWLBrFmz8OSTT/b7fYmIiAhERER0aouPj0d8fLzdtpMmTUJOTk6/jkNERANL1jQiL7/8Mv72t79h6dKlePzxx/HNN99g27Ztzq6NiIgURtapwokTJ/Dhhx9Cq9UCAO677z488MADDj8DS0REg5esMw1RFKXAANoeu+24TEREQ4Os0Jg0aRK2bNmCy5cvo6SkBFu2bBnQqUWIiMgzyAqN9PR01NfXY8mSJXjooYdQU1OD5557ztm1ERGRwvQYGi0tLfj973+P06dPY+vWrTh16hTuvPNOCIIAf//+v2lNRESeqcfQyMjIgNlsxr/8y79IbZs2bUJ9fT1ef/11pxdHRETK0mNoHDt2DC+//DJ0Op3UFhgYiG3btuHw4cNOL46IiJSlx9DQarXw8fGxa/f394eXl5fTiiIiImXqMTTUajXMZrNdu9lshsVicdCDiIgGsx5DIyoqCmlpaWhsbJTaGhsbkZaWhrCwMKcXR0REytJjaCxduhQBAQGYNWsWHn74YSxevBizZs3C8OHDkZyc7KoaiYhIIXqcRkStVmPTpk148skn8Y9//ANqtRp33nlnn79pQUREg4OsuaeCg4MRHBzs7FqIiEjh+je3OUk0GkH6WRBkvWBPROSxGBrXQaMRcPSbUhgrGwAAk0N0QB+/YU5E5EkYGtfJWNmAYmPb99KDdMPcXA0RkXPxegoREcnG0CAiItkYGkREJJtb7mkkJCSguroaGk3b4Tdu3IiGhgb84Q9/QHNzMxYsWIA1a9YAAAoLC7F+/Xo0NDRg2rRp2LBhg9RvMFOrVXZPY1ksVjdVQ0TUxuW/fUVRRHFxMY4ePSr98m9qasL8+fPx3nvvYezYsVixYgWOHz+OOXPmICUlBZs3b8bUqVORmpqK7OxsxMXFubpslzOM8sPhghIYTW1zf40dMwxz77qRwUFEbuXyy1M//vgjACApKQkPPPAAdu3ahXPnzuHmm2/GuHHjoNFoEB0djfz8fFy5cgVNTU2YOnUqACA2Nhb5+fmuLtlt2p/MKjbWS4/1EhG5k8vPNOrr6zFz5kw899xzaG1tRWJiIpYtWwa9Xi9tYzAYUF5ejoqKik7ter0e5eXlfTqeTtf/Lwzq9QG9biNoBGi1bcMoCGoIglpadtQmZxuH/TQCRo3y63ed7sYaB44n1OkJNQKeUafSanR5aNx111246667pOXFixcjIyMDd999t9QmiiJUKhVsNhtUHV6Wa2/vi6oqM2w2sc916vUBMJmu9riNRiPAarGitbVtmnir1Qar1SYtO2qTs43DfhYramoa7S5PyanT3VjjwPGEOj2hRsAz6nRHjWq1qse/bLv88tRXX32F06dPS8uiKCI4OBgmk0lqM5lMMBgMCAoK6tReWVnJyRKJiNzI5aFx9epVbNu2Dc3NzTCbzdi3bx/Wrl2Lixcv4tKlS7Barfjoo48QGhqK4OBgeHt7o6CgAACQl5eH0NBQV5esCO1PU2k0Qqd/iIhcyeWXp+bOnYtvv/0WCxcuhM1mQ1xcHO666y5s3boVTz31FJqbmzFnzhzMnz8fALB9+3akpaXBbDZj8uTJSExMdHXJitD1aSqg7Ymq2F/f5saqiGioccsLD08//TSefvrpTm0zZ87E/v377badNGkScnJyXFSZsnWc56onXc9A+JguEQ2Uwf+W3BDTdeZdvt9BRAOJoTEIyT0jISLqK849RUREsjE0iIhINoYGERHJxtAgIiLZGBpERCQbQ4OIiGTjI7ceTK1um7yx48t8XT/cREQ0kBgaHswwyg95x39AafnP72RMDtEBfZwJmIhILoaGhzNWdX6RL0g3rNN6R5+NBTi1CBH1D0NjkHM00WGw3h+/vnscrFab1MYQISI5GBpDQNdpRYJ0w/j9cSLqF4bGEMX5qYioP/ioDRERycbQICIi2Xh5qg+6ftxosLwTwSesiEguhoZMXT9uBAyedyK6+5Qsb44TUVcMjT5w9BTSYMFPyRKRHG4JjR07duDjjz8GAMyZMwfr1q3Ds88+i4KCAvj6+gIAVq1ahXnz5qGwsBDr169HQ0MDpk2bhg0bNkCjYda5g7M/JctAIlI+l//2PXXqFE6ePIl9+/ZBpVJh2bJl+PTTT3H+/Hns2rULBoOh0/YpKSnYvHkzpk6ditTUVGRnZyMuLs7VZdM/yTkj6frLXw5+25zIM7j8Tq5er8czzzwDLy8vaLVahISEoKysDGVlZUhNTUV0dDQyMjJgs9lw5coVNDU1YerUqQCA2NhY5Ofnu7pk6oP2X/67Py3C7k+LcPSbUtl92wOp2Fjf6d4RESmHy880JkyYIP1cXFyMjz/+GFlZWThz5gzS09MREBCAFStWICcnBxMmTIBer5e21+v1KC8v79PxdDr/fteq1wd0WhY0ArTan4dMENQQBLXU1nW5v9v0pR8Ap9Sk9dJg1Cg/uzHpOAbdbVNR24QrlY3S9o7G0pGO+xY0gsN9O5OcGpXAE+r0hBoBz6hTaTW67ebAhQsXsGLFCqxbtw7jx4/HG2+8Ia1LSEhAbm4uQkJCoOrwdJIoip2W5aiqMsNmE/vUR6MR8Pk5o93ssVarDa2tFqnNarV1auu63N9t+tIPgFNq0gV4Y+/RC52eqOo6BnK2EW1tNdbUNErbCIIah7+6bPckWqeaLFbU1DS67PKUXh8Ak+mqS451PTyhTk+oEfCMOt1Ro1qt6vEv224JjYKCAqxevRqpqamIjIxEUVERiouLER4eDqAtHDQaDYKCgmAymaR+lZWVdvc8nKW32WOHAjlPi/W2TXfTtxurGof8+BJ5Ipff0zAajUhOTsb27dsRGRkJoC0ktmzZgrq6OrS2tuL999/HvHnzEBwcDG9vbxQUFAAA8vLyEBoa6uqS6Tq1B3D7P1W1Te4uiYj6yeVnGu+88w6am5uxdetWqW3JkiVYvnw5Hn30UVgsFoSFhSEqKgoAsH37dqSlpcFsNmPy5MlITEx0dclERPRPLg+NtLQ0pKWlOVwXHx9v1zZp0iTk5OQ4uyxSGE5tQqRMfEuOFIlTmxApE0ODFIvf/CBSnsExTSsREbkEzzTIY3R3n6MrXr4ich6GBnmMrvc5JofoUF3fzPseRC7E0CCP0vE+R5BuGH6qbhyw+x6OJlpk+BB1xtCgIclRQHT9yBbPWojsMTRoUJFz38PR3Fd3TjDYTW3iCM9GaKhjaNCg4uj9jq73PhzNfRVssJ9JtGsAOQobno3QUMPQoEHH0SSKHe99yJ0c0dGNdzlnI0SDGUODqAddb7wTDXUMDaLrwHdHaKhhaBBdB747QkMNQ4PoOjnz3REipeHcU0REJBvPNIicjN8GocGEoUHkZI7eHQnW++PXd4+D1WrrsS+DhZSGoUHkAo7eHeENdPJEDA0iN+ntBnp3l7UcTWXSm/4GD6dNoa48IjQOHDiAN998ExaLBUuXLnX4LXGiwcbR47x15laUlv8cLI7OULq2yb0U1pXcaVMcBUt/gg1gIHkCxYdGeXk5Xn31VezduxdeXl5YsmQJpk+fjltvvdXdpRE5XdezEVNdU49TpDhq63opDHA8H5ej8Ok6bYqc+bjunGCAqbaxz8frb7gB9mEjN7TkbCcnyIbSGZniQ+PUqVOYMWMGRo4cCQAIDw9Hfn4+Vq1aJau/Wq3q8zHVahXG3zACfl4//4sQbBiGYX5aBPhqu20bqG360i9gmHePdbqjpq7b9FajEuq+Qe8PH29BUTUN5FjWmlsQ4OclbePjJcDPRyO1dV1ubwu5cUSnfd9280hcuFKHOnPzP8dtGPx8tZ36eXezr96OFzTGz27fDdcs0nJ3bSP8vXDbjaNgs7WFjVqtxv8rrUGduaXbfjfoh6G4woya+qY+7dsRR8eT008uL6/+/Zrub2j19jtT8aFRUVEBvV4vLRsMBpw7d052/1Gj+jdfUNiMm/vVj2iomXSLzt0l2Ln79qBBc7wRI3ydtu/+UPzLfTabDSrVz8knimKnZSIich3Fh0ZQUBBMJpO0bDKZYDAY3FgREdHQpfjQuPfee3H69GlUV1fj2rVrOHToEEJDQ91dFhHRkKT4exqBgYFYs2YNEhMT0draisWLF+POO+90d1lEREOSShRF0d1FEBGRZ1D85SkiIlIOhgYREcnG0CAiItkYGkREJNuQD40DBw4gIiICYWFhyMrKsltfWFiI2NhYhIeHY/369bBYLG6osvc6d+zYgblz5yImJgYxMTEOt3EFs9mMqKgolJaW2q1Tylj2VKMSxnHHjh2IjIxEZGQktm3bZrdeKePYW51KGMvXXnsNERERiIyMxJ///Ge79UoZy97qVMJYSsQh7KeffhLnzp0r1tTUiA0NDWJ0dLR44cKFTttERkaK33zzjSiKovjss8+KWVlZiqxzxYoV4tdff+3y2jr6+9//LkZFRYmTJ08WS0pK7NYrYSx7q9Hd4/jFF1+IjzzyiNjc3Cy2tLSIiYmJ4qFDhzpto4RxlFOnu8fyyy+/FJcsWSK2traK165dE+fOnSv+8MMPnbZRwljKqdPdY9nRkD7T6DgZop+fnzQZYrsrV66gqakJU6dOBQDExsZ2Wq+UOgHg/PnzeOuttxAdHY2NGzeiubm5m705T3Z2NtLT0x2+sa+UseypRsD946jX6/HMM8/Ay8sLWq0WISEhKCsrk9YrZRx7qxNw/1j+67/+K959911oNBpUVVXBarXCz89PWq+UseytTsD9Y9nRkA4NR5MhlpeXd7ter9d3Wu8qvdXZ0NCA22+/HSkpKdi3bx/q6+uRmZnp8jpffPFFTJs2zeE6pYxlTzUqYRwnTJgg/RIrLi7Gxx9/jDlz5kjrlTKOvdWphLEEAK1Wi4yMDERGRmLmzJkIDAyU1illLIGe61TKWLYb0qHR22SISpkssbc6hg0bhrfffhshISHQaDRISkrC8ePHXV5nT5Qylj1R0jheuHABSUlJWLduHW655RapXWnj2F2dShrL1atX4/Tp0zAajcjOzpbalTaW3dWppLEEhnho9DYZYtf1lZWVbpkssbc6y8rKkJOTIy2LogiNRlkzxChlLHuilHEsKCjAY489ht/97ndYtGhRp3VKGsee6lTCWP7www8oLCwEAPj6+iIsLAxFRUXSeqWMZW91KmEsOxrSodHbZIjBwcHw9vZGQUEBACAvL88tkyX2VqePjw9eeukllJSUQBRFZGVlYd68eS6vsydKGcueKGEcjUYjkpOTsX37dkRGRtqtV8o49lanEsaytLQUaWlpaGlpQUtLCz777DPcfffd0nqljGVvdSphLDtxx913Jdm/f78YGRkphoWFiTt37hRFURSXLVsmnjt3ThRFUSwsLBQffPBBMTw8XFy7dq3Y3NysyDrz8/Ol9c8884zb6hRFUZw7d670ZJISx7KnGt09jps2bRKnTp0qPvDAA9I/u3fvVtw4yqnT3WMpiqKYkZEhLliwQIyKihIzMjJEUVTmv5O91amEsWzHCQuJiEi2IX15ioiI+oahQUREsjE0iIhINoYGERHJxtAgIiLZGBo0JCQkJGDnzp127X/605+wcuVKh32+/PJLREVF9brvHTt24PDhwwDaZivNzc0FAEycOBHV1dWy6jty5AgmTpyIgwcPytq+43E6qq6uxsSJEwEAn332GTZv3gwAOHbsGF577TVZ+ybqCUODhoS4uDh8+OGHdu3Z2dmIj4+/rn1/+eWX0pTav/3tb7Fw4cI+72P37t2Ijo7GX/7yF1nbyznO/fffj7S0NADA//7v/6Kurq7PdRF1pay5JoicZN68ediyZQu++uoracLCM2fOQBRFBAYGIiEhAbW1tVCpVEhKSrL7hXzx4kVs3LgRDQ0NMJlMmDRpEv7rv/4LOTk5OH/+PLZt2wZBEPDZZ59hwoQJeOKJJzr1/+CDD/DXv/4VNpsNI0eOxHPPPYeQkBAAQElJCc6cOYOjR48iIiICf//736XJABsaGrB582Z8/fXXEAQB//Zv/4Y1a9bg2WeflY5z6NAhvPrqq/D19cWUKVOkY+7duxeffPIJfvOb32DPnj2wWq0ICAjAuXPnsGDBAjz88MMAgMzMTNTW1iI1NdVJo0+DCc80aEjQaDR4+OGHO83h8/777yMuLg4rV65EQkICDhw4gLfffhuvvPIKvvnmm079s7OzsXDhQmRnZ+PQoUMoLS3FsWPHEB8fjylTpmDdunXdTu1w5swZ5ObmIisrC7m5uVi2bBlWrVolrf/rX/+K++67DzqdDhEREZ3ONjIyMtDc3IyDBw8iNzcXX3/9Nc6cOSOtr6ysRGpqKl5//XXs3bsXwcHBdsf/5S9/iSVLliAiIgJr1qxBfHy8NCGezWZDTk4OlixZ0q9xpaGHoUFDxsMPP4zDhw/DbDajtrYWJ0+exPTp09Hc3IywsDAAQGBgIMLCwnDixIlOfVNSUjB69Gi8/fbbeOGFF1BRUYHGxkZZxz127BguXbqEJUuWICYmBi+99BLq6+tRW1uLlpYW7N27VzqzWbRoET799FMYjUYAbd9SWbx4MQRBgJeXF3bt2oXp06dL+y4oKMBtt92GW2+9FQDwyCOP9FrP3LlzUVVVhe+++w4nTpzAjTfeiPHjx8v6sxDx8hQNGYGBgbj33ntx8OBBNDY2Ijw8HCqVym46bFEU7T77uXbtWlitVixYsAD33XcfjEYj5M7AY7PZEBMTg5SUFGm5oqICI0aMQF5eHurr67Fp0ybpprVKpcJ7772HdevWQaPRdKrPaDTCx8fHrt52cmY/FQQBjzzyCHJyclBRUcGzDOoTnmnQkBIfH48DBw4gNzcX8fHxGD9+PDQaDQ4dOgQAKC8vxyeffIJ77723U7+TJ08iOTkZERERAIBvv/0WVqsVQNsv4Z6+LT179mz8z//8DyoqKgC0XY5aunQpAGDPnj148skncfToURw5cgRHjhzBCy+8gA8++ACNjY2YOXMm9u3bB5vNhpaWFqxevRpnz56V9n3PPffg+++/x3fffQeg7T6GI11rfOihh3D48GH84x//UNyMyKRsDA0aUqZPn47a2lr4+/tj4sSJ0Gq1yMzMxLvvvovo6Gg8/vjjSE5OxowZMzr1W7NmDZKTkxEdHY3nn38e99xzDy5fvgwA+PWvf41XXnkF+/btc3jM2bNn4z/+4z+QlJSE6OhofPTRR9ixYweKiopQWFiIf//3f++0/cKFCzF8+HDs27cPq1atglarRUxMDBYuXIg5c+ZIl9IAYPTo0di+fTv+8z//E4sWLUJpaanDGmbMmIGTJ09i06ZNAACdTocpU6YgKioKWq223+NJQw9nuSUagqqrq7F48WJkZWVh7Nix7i6HPAjPNIiGmOzsbEREROCJJ55gYFCf8UyDiIhk45kGERHJxtAgIiLZGBpERCQbQ4OIiGRjaBARkWwMDSIiku3/A7Ll/N6AM5eSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"VolatileAcidity\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"VolatileAcidity\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "25tQYGHJXpp7"
   },
   "source": [
    "VolatileAcidity values are numerical and continuous. The boxplot shows that the median VolatileAcidity value is 0.41.  Q1 to Q3 is 0.25-0.91 with outliers from 1.72 to 3.68. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "BPtrFoJ0-snc",
    "outputId": "b11dee28-5145-40c6-f9f5-ba11b65bc6fa"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"CitricAcid\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"CitricAcid\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u7ZGYoeuXtNY"
   },
   "source": [
    "CitricAcid values are numerical and continuous. The boxplot shows that the median CitricAcid value is 0.44.  Q1 to Q3 is 0.28-0.97 with outliers from 1.88 to 3.86."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "MV4RXozO-s_d",
    "outputId": "8bf5f048-0266-45d1-8200-dbccda1a1732"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"ResidualSugar\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"ResidualSugar\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8CF-YbvcKCmJ"
   },
   "source": [
    "ResidualSugar values are numerical and continuous. The boxplot shows that the median ResidualSugar value is 12.9 g/L.  Q1 to Q3 is 3.6 - 38.7 g/L with outliers from 0 to 1.3 g/L and 67.8 to 141.15 g/L.  Even though this is a wide range, all the numbers in this data set are acceptable values for ResidualSugar.\n",
    "\n",
    "The histogram shows that ResidualSugar ranges from 0 to about 140 g/L with the distribution being right-skewed.  The mode is 1.4 g/L and the mean is about 23 g/L.  This shape indicates that there are a number of data points or outliers that are greater than the mode.  Nearly half of the ResidualSugar values are in the 1 - 10 g/L range which is a characteristic of dry wines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "4Hw5ZLJ4-tRq",
    "outputId": "2c2a2b22-08bc-4e7c-8d61-9bf8774a3c15"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"Chlorides\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"Chlorides\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nQklzaBmX9e-"
   },
   "source": [
    "Chlorides values are numerical and continuous. The boxplot shows that the median Chlorides value is 0.098.  Q1 to Q3 is 0.046-0.368 with outliers from 0.6342 to 1.351."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "iDBb3lDP-tq7",
    "outputId": "ff7837d0-325e-4821-bbea-e59f21e5c0a1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"FreeSulfurDioxide\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"FreeSulfurDioxide\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DNuRsI5AYRNj"
   },
   "source": [
    "FreeSulfurDioxide values are numerical and continuous. The boxplot shows that the median FreeSulfurDioxide value is 56.  Q1 to Q3 is 28-172 with outliers from 303 to 623."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "BNSiXtnE-t-7",
    "outputId": "7abc78e2-b325-4092-8400-6693323c2c47"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"TotalSulfurDioxide\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"TotalSulfurDioxide\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9QFtRLKYWG1"
   },
   "source": [
    "TotalSulfurDioixide values are numerical and continuous. The boxplot shows that the median TotalSulfurDioxide value is 154.  Q1 to Q3 is 100-262 with outliers from 536 to 1,057."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "Paaa27vt-uQy",
    "outputId": "d8f5dd74-6095-47fd-cabe-038cf74e2069"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"Density\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"Density\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DQ5-QgKLYYvy"
   },
   "source": [
    "\n",
    "Density values are numerical and continuous. The boxplot shows that the median Density value is 0.99449. Q1 to Q3 is 0.98772-1.0005 with outliers from 0.88809 to 0.94879 and 1.0398 to 0.21115."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "DsQEkb0_-ulz",
    "outputId": "bf44d57e-83be-4395-bf6c-0043ef5a1b13"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"pH\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"pH\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CNJ0J4ruYb85"
   },
   "source": [
    "pH values are numerical and continuous. The boxplot shows that the median pH value is 3.2.  Q1 to Q3 is 2.96-3.47 with outliers from 0.48 to 2.06 and 4.37 to 5.65."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "-mIZIfmu-u9E",
    "outputId": "782a6e99-060b-4bca-b31b-343c5ef90378"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"Sulphates\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"Sulphates\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XccWyrS-YfCI"
   },
   "source": [
    "Sulphates values are numerical and continuous. The boxplot shows that the median Sulphates value is 0.59.  Q1 to Q3 is 0.43-1.1 with outliers from 2.15 to 4.24."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "fYvODN7O-vZw",
    "outputId": "429b9887-3d92-492f-d2ce-2f3565e9d4b8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"Alcohol\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"Alcohol\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cSW7rAYzYiYN"
   },
   "source": [
    "Alcohol values are numerical and continuous. The boxplot shows that the median Alcohol value is 10.4.  Q1 to Q3 is 9-12.4 with outliers from 0 to 4.1 and 16.7 to 26.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "e9DVFeW7-vwr",
    "outputId": "37361c39-4df7-4b34-a5cd-95f72a61351b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"LabelAppeal\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"LabelAppeal\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b7LwyQtiYmGP"
   },
   "source": [
    "LabelAppeal values are numeric and categorical. The boxplot shows that the median LabelAppeal value is 0.  Q1 to Q3 is -1 to 1 with no outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "gYJmBAAC-wV_",
    "outputId": "b1dfe205-1353-4b1c-8de6-18fd295ef94c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"AcidIndex\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"AcidIndex\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pFXB7Kb1YqmP"
   },
   "source": [
    "AcidIndex values are numerical and categorical. The boxplot shows that the median AcidIndex values is 8.  Q1 to Q3 is 7-8 with outliers from 4 to 6 and 10 to 17."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "KwyQsq3SDgvl",
    "outputId": "a175b55f-d2e2-4a4c-a669-97aa1def103a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "sns.boxplot(df1[\"STARS\"], ax=ax_box)\n",
    "sns.histplot(data=df1, x=\"STARS\", ax=ax_hist)\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aKQ6UNu9Ysco"
   },
   "source": [
    "STARS values are numerical and categorical. The boxplot shows that the median STARS value is 2.  Q1 to Q3 is 1-3 with outliers at 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: missingno in c:\\users\\arami\\anaconda3\\lib\\site-packages (0.5.2)\n",
      "Requirement already satisfied: scipy in c:\\users\\arami\\anaconda3\\lib\\site-packages (from missingno) (1.5.2)\n",
      "Requirement already satisfied: seaborn in c:\\users\\arami\\anaconda3\\lib\\site-packages (from missingno) (0.11.0)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\arami\\anaconda3\\lib\\site-packages (from missingno) (3.3.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\arami\\anaconda3\\lib\\site-packages (from missingno) (1.19.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from seaborn->missingno) (1.1.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (8.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (1.3.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2020.6.20)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (0.10.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\arami\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn->missingno) (2020.1)\n",
      "Requirement already satisfied: six in c:\\users\\arami\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib->missingno) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 557
    },
    "id": "-4F4FqXFDhRv",
    "outputId": "0819d845-36ef-477b-b554-5e03a0e0f618"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "type object got multiple values for keyword argument 'visible'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-83-3cf86475bdf2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Use missingno to see if the data set has any contiguous areas of missing data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmissingno\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmsno\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmsno\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\missingno\\missingno.py\u001b[0m in \u001b[0;36mmatrix\u001b[1;34m(df, filter, n, p, sort, figsize, width_ratios, color, fontsize, labels, label_rotation, sparkline, freq, ax)\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     85\u001b[0m         \u001b[0mha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'left'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m         \u001b[0max0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     87\u001b[0m         \u001b[0max0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel_rotation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mget_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    449\u001b[0m                 \u001b[1;34m\"parameter will become keyword-only %(removal)s.\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m                 name=name, obj_type=f\"parameter of {func.__name__}()\")\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mset_ticks\u001b[1;34m(self, ticks, minor)\u001b[0m\n\u001b[0;32m   1819\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1820\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_major_locator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmticker\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFixedLocator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1821\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1822\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1823\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_tick_boxes_siblings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxdir\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1380\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1381\u001b[0m             \u001b[1;31m# Update the new tick label properties from the old.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1382\u001b[1;33m             \u001b[0mtick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1383\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1384\u001b[0m             \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gridOnMajor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   2011\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2012\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2013\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2014\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2015\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_label_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 417\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    418\u001b[0m         \u001b[1;31m# x in data coords, y in axes coords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m         self.tick1line.set(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    409\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m                 **kwargs)\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[0mmarkeredgecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarkeredgewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         )\n\u001b[1;32m--> 159\u001b[1;33m         self.gridline = mlines.Line2D(\n\u001b[0m\u001b[0;32m    160\u001b[0m             \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m             \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_color\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_alpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridOn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: type object got multiple values for keyword argument 'visible'"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "type object got multiple values for keyword argument 'visible'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    339\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    340\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    342\u001b[0m             \u001b[1;31m# Finally look for special method names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(fig)\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    247\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'png'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 248\u001b[1;33m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'png'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'retina'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'png2x'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m     \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    134\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'svg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2191\u001b[0m                            else suppress())\n\u001b[0;32m   2192\u001b[0m                     \u001b[1;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2193\u001b[1;33m                         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2195\u001b[0m                     bbox_inches = self.figure.get_tightbbox(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   1861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1863\u001b[1;33m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[0;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[0;32m   1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    129\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m             \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m         \u001b[1;31m# Composite any adjacent images together\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0martist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     42\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    409\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m                 **kwargs)\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36mdraw\u001b[1;34m(self, renderer, inframe)\u001b[0m\n\u001b[0;32m   2705\u001b[0m                 \u001b[0martists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2707\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_title_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2709\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxison\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0minframe\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_update_title_position\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2636\u001b[0m                 if (ax.xaxis.get_ticks_position() in ['top', 'unknown']\n\u001b[0;32m   2637\u001b[0m                         or ax.xaxis.get_label_position() == 'top'):\n\u001b[1;32m-> 2638\u001b[1;33m                     \u001b[0mbb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2639\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2640\u001b[0m                     \u001b[0mbb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_window_extent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[1;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[0;32m   1107\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1109\u001b[1;33m         \u001b[0mticks_to_draw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1021\u001b[0m         \u001b[0mmajor_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1022\u001b[0m         \u001b[0mmajor_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1023\u001b[1;33m         \u001b[0mmajor_ticks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1024\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mtick\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor_ticks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor_locs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[1;34m(self, numticks)\u001b[0m\n\u001b[0;32m   1380\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1381\u001b[0m             \u001b[1;31m# Update the new tick label properties from the old.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1382\u001b[1;33m             \u001b[0mtick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1383\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1384\u001b[0m             \u001b[0mtick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgridline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_visible\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gridOnMajor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[1;34m(self, major)\u001b[0m\n\u001b[0;32m   2011\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2012\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2013\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mXTick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2014\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2015\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_label_position\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 417\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    418\u001b[0m         \u001b[1;31m# x in data coords, y in axes coords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    419\u001b[0m         self.tick1line.set(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    409\u001b[0m                          \u001b[1;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m                 **kwargs)\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axis.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[0mmarkeredgecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarkeredgewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         )\n\u001b[1;32m--> 159\u001b[1;33m         self.gridline = mlines.Line2D(\n\u001b[0m\u001b[0;32m    160\u001b[0m             \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m             \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_color\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid_alpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridOn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: type object got multiple values for keyword argument 'visible'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use missingno to see if the data set has any contiguous areas of missing data\n",
    "import missingno as msno\n",
    "ax = msno.matrix(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZVJEgq6qDsHC"
   },
   "source": [
    "No clear pattern can be seen so the missing data is random.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 484
    },
    "id": "jYOdKPGtDvvd",
    "outputId": "b46964d9-118e-4068-ad54-040d6b5f7f56"
   },
   "outputs": [],
   "source": [
    "#Create a heat map showing correlations where data is missing\n",
    "\n",
    "ax = msno.heatmap(df1, figsize=(6, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jrsLpAywDzVR"
   },
   "source": [
    "There are no correlations where the data is missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_5D7nZG0tT2Z"
   },
   "source": [
    "##Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0Hv1HfDHw_85"
   },
   "source": [
    "We decided to impute missing values using a K Nearest Neighbors function, KNNImputer.\n",
    "\n",
    "From our research, we have found that the optimal K value usually found is the square root of N, where N is the total number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "GuUYpyDZiKCA",
    "outputId": "8cc2ce49-7c8e-4aa1-90b3-9c2489ff40b7"
   },
   "outputs": [],
   "source": [
    "#https://medium.com/@kyawsawhtoon/a-guide-to-knn-imputation-95e2dc496e\n",
    "#Normalize the data before beginning KNN to impute missing values.\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df1 = pd.DataFrame(scaler.fit_transform(df1), columns = df1.columns)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0EgMmQaxilgU"
   },
   "outputs": [],
   "source": [
    "#Define imputer.  Take the square root of 12,795 to get N which is 113.\n",
    "\n",
    "from sklearn.impute import KNNImputer\n",
    "imputer = KNNImputer(n_neighbors=113)\n",
    "df1 = pd.DataFrame(imputer.fit_transform(df1),columns = df1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UMZWLNZ6ivqy",
    "outputId": "6e5942bf-2bcb-47be-a444-0f59f615d976"
   },
   "outputs": [],
   "source": [
    "df1.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n5hqLPxJiylq",
    "outputId": "19d24e4e-4fc4-4fdb-b75b-1c311c432fc5"
   },
   "outputs": [],
   "source": [
    "df1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iFke-tjMi8C0"
   },
   "source": [
    "Our dataframe no longer has missing values but our data is still skewed. Let's look at the skewness in our dataset and fix it so our data will be better prepared for machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CO-7ccHyjE8v",
    "outputId": "02ddf3de-68d9-4fc4-b5ed-93974948db12"
   },
   "outputs": [],
   "source": [
    "#Look at the skewness of the variables in the dataframe.\n",
    "\n",
    "df1.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FV0sd_T385cF",
    "outputId": "0dd0d05e-9b9f-428e-992c-27738e6d3d0e"
   },
   "outputs": [],
   "source": [
    "#After some trial and error, we discovered that Percentile Linearization worked best for addresses the skewness of our data.\n",
    "#Source: https://towardsdatascience.com/transforming-skewed-data-73da4c2d0d16\n",
    "\n",
    "size = len(df1)-1\n",
    "df2_perc_lin = df1.rank(method='min').apply(lambda x: (x-1)/size)\n",
    "\n",
    "#View the skew\n",
    "df2_perc_lin.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "id": "lPu-aKIJ9KL4",
    "outputId": "1add6f3f-46c7-407d-c021-63f5d06c1f52"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TARGET</th>\n",
       "      <th>FixedAcidity</th>\n",
       "      <th>VolatileAcidity</th>\n",
       "      <th>CitricAcid</th>\n",
       "      <th>ResidualSugar</th>\n",
       "      <th>Chlorides</th>\n",
       "      <th>FreeSulfurDioxide</th>\n",
       "      <th>TotalSulfurDioxide</th>\n",
       "      <th>Density</th>\n",
       "      <th>pH</th>\n",
       "      <th>Sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>LabelAppeal</th>\n",
       "      <th>AcidIndex</th>\n",
       "      <th>STARS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "      <td>12795.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.407500</td>\n",
       "      <td>0.495212</td>\n",
       "      <td>0.495350</td>\n",
       "      <td>0.495217</td>\n",
       "      <td>0.498341</td>\n",
       "      <td>0.497856</td>\n",
       "      <td>0.497398</td>\n",
       "      <td>0.498805</td>\n",
       "      <td>0.499628</td>\n",
       "      <td>0.496982</td>\n",
       "      <td>0.496604</td>\n",
       "      <td>0.495416</td>\n",
       "      <td>0.343748</td>\n",
       "      <td>0.363143</td>\n",
       "      <td>0.416422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.291251</td>\n",
       "      <td>0.289524</td>\n",
       "      <td>0.291368</td>\n",
       "      <td>0.291020</td>\n",
       "      <td>0.290373</td>\n",
       "      <td>0.290510</td>\n",
       "      <td>0.290463</td>\n",
       "      <td>0.289186</td>\n",
       "      <td>0.288682</td>\n",
       "      <td>0.288770</td>\n",
       "      <td>0.290471</td>\n",
       "      <td>0.289191</td>\n",
       "      <td>0.279095</td>\n",
       "      <td>0.306250</td>\n",
       "      <td>0.290062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.232765</td>\n",
       "      <td>0.247616</td>\n",
       "      <td>0.242067</td>\n",
       "      <td>0.230577</td>\n",
       "      <td>0.247616</td>\n",
       "      <td>0.236986</td>\n",
       "      <td>0.242145</td>\n",
       "      <td>0.246053</td>\n",
       "      <td>0.249961</td>\n",
       "      <td>0.246600</td>\n",
       "      <td>0.244177</td>\n",
       "      <td>0.249023</td>\n",
       "      <td>0.039393</td>\n",
       "      <td>0.099656</td>\n",
       "      <td>0.246913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.318040</td>\n",
       "      <td>0.489526</td>\n",
       "      <td>0.493982</td>\n",
       "      <td>0.494529</td>\n",
       "      <td>0.499453</td>\n",
       "      <td>0.499766</td>\n",
       "      <td>0.497264</td>\n",
       "      <td>0.496561</td>\n",
       "      <td>0.499844</td>\n",
       "      <td>0.495388</td>\n",
       "      <td>0.490621</td>\n",
       "      <td>0.492653</td>\n",
       "      <td>0.284508</td>\n",
       "      <td>0.480929</td>\n",
       "      <td>0.470689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.522120</td>\n",
       "      <td>0.746678</td>\n",
       "      <td>0.748085</td>\n",
       "      <td>0.747929</td>\n",
       "      <td>0.749805</td>\n",
       "      <td>0.749726</td>\n",
       "      <td>0.748945</td>\n",
       "      <td>0.749883</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.747460</td>\n",
       "      <td>0.749101</td>\n",
       "      <td>0.743786</td>\n",
       "      <td>0.723542</td>\n",
       "      <td>0.480929</td>\n",
       "      <td>0.749961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.998749</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999922</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.961779</td>\n",
       "      <td>0.999531</td>\n",
       "      <td>0.952243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TARGET  FixedAcidity  VolatileAcidity    CitricAcid  \\\n",
       "count  12795.000000  12795.000000     12795.000000  12795.000000   \n",
       "mean       0.407500      0.495212         0.495350      0.495217   \n",
       "std        0.291251      0.289524         0.291368      0.291020   \n",
       "min        0.000000      0.000000         0.000000      0.000000   \n",
       "25%        0.232765      0.247616         0.242067      0.230577   \n",
       "50%        0.318040      0.489526         0.493982      0.494529   \n",
       "75%        0.522120      0.746678         0.748085      0.747929   \n",
       "max        0.998749      1.000000         1.000000      1.000000   \n",
       "\n",
       "       ResidualSugar     Chlorides  FreeSulfurDioxide  TotalSulfurDioxide  \\\n",
       "count   12795.000000  12795.000000       12795.000000        12795.000000   \n",
       "mean        0.498341      0.497856           0.497398            0.498805   \n",
       "std         0.290373      0.290510           0.290463            0.289186   \n",
       "min         0.000000      0.000000           0.000000            0.000000   \n",
       "25%         0.247616      0.236986           0.242145            0.246053   \n",
       "50%         0.499453      0.499766           0.497264            0.496561   \n",
       "75%         0.749805      0.749726           0.748945            0.749883   \n",
       "max         1.000000      1.000000           1.000000            1.000000   \n",
       "\n",
       "            Density            pH     Sulphates       Alcohol   LabelAppeal  \\\n",
       "count  12795.000000  12795.000000  12795.000000  12795.000000  12795.000000   \n",
       "mean       0.499628      0.496982      0.496604      0.495416      0.343748   \n",
       "std        0.288682      0.288770      0.290471      0.289191      0.279095   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.249961      0.246600      0.244177      0.249023      0.039393   \n",
       "50%        0.499844      0.495388      0.490621      0.492653      0.284508   \n",
       "75%        0.750000      0.747460      0.749101      0.743786      0.723542   \n",
       "max        0.999922      1.000000      1.000000      1.000000      0.961779   \n",
       "\n",
       "          AcidIndex         STARS  \n",
       "count  12795.000000  12795.000000  \n",
       "mean       0.363143      0.416422  \n",
       "std        0.306250      0.290062  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.099656      0.246913  \n",
       "50%        0.480929      0.470689  \n",
       "75%        0.480929      0.749961  \n",
       "max        0.999531      0.952243  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_perc_lin.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "NJXVTbL39QHv",
    "outputId": "5e31ec04-8125-487e-b554-b1c6de774b8b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    " \n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"TARGET\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x='TARGET', ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "60W29F899XUO",
    "outputId": "c3aeb506-bfc6-40f3-a79e-dc01cf7d6427"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"FixedAcidity\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"FixedAcidity\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "Kc_qAqBA9kW1",
    "outputId": "bf0490f0-de12-4935-daa7-ccd3468fa3b1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"VolatileAcidity\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"VolatileAcidity\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "m-2wF2bF9qpb",
    "outputId": "f27da51c-d83b-48e9-8cfa-9b3485a7164c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"CitricAcid\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"CitricAcid\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "cgck5F7Q9rDm",
    "outputId": "832d1559-2f18-4e4c-95af-2b4813e79253"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"ResidualSugar\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"ResidualSugar\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "OTFNJrKu9rVl",
    "outputId": "0b52ba22-9603-4822-c0c2-c36e97778979"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"Chlorides\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"Chlorides\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "vHcIv3Z49rn6",
    "outputId": "cfdc9ccb-76ad-49d2-f588-2e017d0e71c2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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4GGlpaf2fIREROc3paw6OuvXWW7F9+3bI5XIYDAZ0d3ejtbUV4eHhCAsLAwBoNBro9XpMnDgRZrPZttKrVqvFli1bGA5ERB7iUDhYrVa88sor2L9/PywWC2bMmIGMjAzI5X13VygU2LJlC1599VXEx8ejqakJSqXStl+lUsFgMIi2K5VKGAwGpyYSHDzSqfY963ToYXB7HynHuLSdN9XVnz722ve23xvqkrKPK4/JQLZ3Vx9X/j0PpWOvVAY53cceh6rYvHkzjh49ikWLFsFqteLtt9/Gpk2bev2FuMstW7YMixcvRkZGBurq6nos3Hfxo7FWq7XX7c44c8YEq1Ww3/AySmUQurqcv77hjj5SjaFQyHu085a6+tunr/aXz9lb6pKyz5Xm3J9xvP3YOzJnT9QldR+jsc3pPn5+sj5fVDsUDgcOHMC7774LhUIBALj77ruRnJzcZzh899136OzsxM9//nMMHz4carUaer0e/v7+tjZGoxEqlQohISEwGo227c3NzVCpVI6URkREEnDo00qCINiCAfjx46yX3u7NqVOnoNPp0NnZic7OTuzZswepqak4fvw46uvr0d3djfLycsTExCA0NBQBAQGorq4GAJSWliImJqYf0yIiov5w6J1DREQEnnnmGfzqV7+CTCbDjh077C6pERsbi5qaGtx7773w9/eHWq1GYmIixo4di8zMTHR0dCA2Nhbx8fEAgMLCQuh0OphMJkRGRiI9Pb3/syMiIpc4FA55eXlYv349UlNTYbVacdddd2Ht2rV2+2VmZiIzM7PHtujoaJSVlYnaRkREoKSkxMGyiYhISn2eVurs7MTq1atx8OBBFBQUoKqqCjfffDP8/f0xcqTrnw4iIiLv1mc4bNmyBSaTCb/4xS9s2/Lz89Ha2ooXXnhB8uKIiMgz+gyHffv2YfPmzQgODrZtGz9+PDZt2oQPP/xQ8uKIiMgz+gwHhUKBwMBA0faRI0di2LBhkhVFRESe1Wc4+Pn5wWQyibabTCYujEdENIT1GQ5JSUnQ6XRob2+3bWtvb4dOp4NarZa8OCIi8ow+w2HRokUICgrCjBkz8MADD2DevHmYMWMGRo0ahSeeeMJdNRIRkZv1+T0HPz8/5OfnIyMjA0eOHIGfnx9uvvlmLm1BRDTEOfQluNDQUISGhkpdCxEReQmnfyaUiIiGPoYDERGJMByIiEiE4UBERCIMByIiEmE4EBGRCMOBiIhEGA5ERCQiaTj88Y9/RGJiIhITE7Fp0yYAQFVVFTQaDdRqNYqKimxta2trodVqERcXh5ycHC7sR0TkQZKFQ1VVFT7++GPs2rUL7733Ho4cOYLy8nJkZ2dj69atqKiowOHDh1FZWQkAyMrKQm5uLnbv3g1BEFBcXCxVaUREZIdk4aBUKrFmzRoMGzYMCoUCP/vZz1BXV4fw8HCEhYVBLpdDo9FAr9ejoaEBZrMZUVFRAACtVgu9Xi9VaUREZIdk4XDDDTfYnuzr6urw97//HTKZDEql0tZGpVLBYDCgqampx3alUgmDwSBVaUREZIdDC+/1x7Fjx7BkyRI89dRT8Pf3R11dnW2fIAiQyWSwWq2QyWSi7c4IDh7pco0KhfMPgzv6SDnGpe28qa7+9LHXvrf93lCXlH1ceUwGsr27+rjy73koHXulMsjpPvZIGg7V1dVYtmwZsrOzkZiYiEOHDsFoNNr2G41GqFQqhISE9Nje3Nzs9LLgZ86YYLUKTteoVAahq8v5i9/u6CPVGAqFvEc7b6mrv336an/5nL2lLin7XGnO/RnH24+9I3P2RF1S9zEa25zu4+cn6/NFtWSnlRobG/HEE0+gsLAQiYmJAICpU6fi+PHjqK+vR3d3N8rLyxETE4PQ0FAEBASguroaAFBaWoqYmBipSiMiIjske+fwyiuvoKOjAwUFBbZtqampKCgoQGZmJjo6OhAbG4v4+HgAQGFhIXQ6HUwmEyIjI5Geni5VaUREZIdk4aDT6aDT6XrdV1ZWJtoWERGBkpISqcohIiIn8BvSREQkwnAgIiIRhgMREYkwHIiISIThQEREIgwHIiISYTgQEZEIw4GIiEQYDkREJMJwICIiEYYDERGJMByIiEiE4UBERCIMByIiEmE4EBGRCMOBiIhEGA5ERCTCcCAiIhFJw8FkMiEpKQmnTp0CAFRVVUGj0UCtVqOoqMjWrra2FlqtFnFxccjJyYHFYpGyLCIiskOycPjqq68wf/581NXVAQDMZjOys7OxdetWVFRU4PDhw6isrAQAZGVlITc3F7t374YgCCguLpaqLCIicoBk4VBcXIy8vDyoVCoAQE1NDcLDwxEWFga5XA6NRgO9Xo+GhgaYzWZERUUBALRaLfR6vVRlERGRA+RS3fGGDRt63G5qaoJSqbTdVqlUMBgMou1KpRIGg0GqsoiIyAGShcPlrFYrZDKZ7bYgCJDJZFfc7qzg4JEu16ZQOP8wuKOPlGNc2s6b6upPH3vte9vvDXVJ2ceVx2Qg27urjyv/nofSsVcqg5zuY4/bwiEkJARGo9F222g0QqVSibY3NzfbTkU548wZE6xWwel+SmUQurqcvwDujj5SjaFQyHu085a6+tunr/aXz9lb6pKyz5Xm3J9xvP3YOzJnT9QldR+jsc3pPn5+sj5fVLvto6xTp07F8ePHUV9fj+7ubpSXlyMmJgahoaEICAhAdXU1AKC0tBQxMTHuKouIiHrhtncOAQEBKCgoQGZmJjo6OhAbG4v4+HgAQGFhIXQ6HUwmEyIjI5Genu6usoiIqBeSh8PevXtt/x8dHY2ysjJRm4iICJSUlEhdChEROYjfkCYiIhGGAxERiTAciIhIhOFAREQiDAciIhJhOBARkQjDgYiIRBgOREQkwnAgIiIRhgMREYkwHIiISIThQEREIgwHIiISYTgQEZEIw4GIiEQYDkREJMJwICIiEYYDERGJeFU4vP/++0hISIBarcbrr7/u6XKIiHyW5L8h7SiDwYCioiLs3LkTw4YNQ2pqKm677TZMnDjR06UREfkcrwmHqqoq3H777Rg9ejQAIC4uDnq9Hr/5zW8c6u/nJ3N57DFBAV7ZR6ox5Ao5LF3+XldXf/v01f7yOXtLXVL2udKc+zOOtx97R+bsibqk7uPK85+9PjJBEASn71UCf/7zn9He3o4VK1YAAN555x3U1NQgPz/fw5UREfker7nmYLVaIZP9L8kEQehxm4iI3MdrwiEkJARGo9F222g0QqVSebAiIiLf5TXhcMcdd+DgwYM4e/YsfvjhB3zwwQeIiYnxdFlERD7Jay5Ijx8/HitWrEB6ejq6urowb9483HzzzZ4ui4jIJ3nNBWkiIvIeXnNaiYiIvAfDgYiIRBgOREQkwnAgIiIRnwkHe4v61dbWQqvVIi4uDjk5ObBYLB6ocmDZm/OHH36IuXPnIjk5GY8//jhaWlo8UOXAcnTxxn379mHWrFlurEw69ub8/fffY+HChUhOTsavf/1rnzjOR44cQUpKCpKTk7FkyRK0trZ6oMqBZTKZkJSUhFOnTon2SfL8JfiA06dPCzNnzhTOnTsnXLhwQdBoNMKxY8d6tElMTBS++OILQRAE4be//a3w+uuve6DSgWNvzm1tbcKMGTOE06dPC4IgCM8995yQn5/vqXIHhCPHWRAEwWg0CvHx8cLMmTM9UOXAsjdnq9UqqNVqobKyUhAEQfj9738vbNq0yVPlDghHjvP8+fOFffv2CYIgCBs3bhT+8Ic/eKLUAfPll18KSUlJQmRkpHDy5EnRfimev3zincOli/qNGDHCtqjfRQ0NDTCbzYiKigIAaLXaHvsHI3tz7urqQl5eHsaPHw8AmDx5MhobGz1V7oCwN+eLdDqdwws6ejt7cz5y5AhGjBhh+0JpRkYGFixY4KlyB4Qjx9lqteLChQsAgB9++AGBgYGeKHXAFBcXIy8vr9dVI6R6/vKJcGhqaoJSqbTdVqlUMBgMV9yvVCp77B+M7M15zJgx+OUvfwkAMJvN2LZtG+655x631zmQ7M0ZALZv344bb7wRU6dOdXd5krA35xMnTmDcuHHIzs7Gfffdh7y8PIwYMcITpQ4YR47zmjVroNPpcOedd6KqqgqpqanuLnNAbdiwAbfcckuv+6R6/vKJcLC3qN9QXPTP0Tm1tbXhscceQ0REBO677z53ljjg7M3522+/xQcffIDHH3/cE+VJwt6cLRYLDh06hPnz52PXrl0ICwtDQUGBJ0odMPbmbDabkZOTg7/85S/4+OOPkZaWhtWrV3uiVLeQ6vnLJ8LB3qJ+l+9vbm4e9Iv+ObKQYVNTE9LS0jB58mRs2LDB3SUOOHtz1uv1MBqNSElJwWOPPWab/2Bmb85KpRLh4eGYMmUKACApKQk1NTVur3Mg2Zvzt99+i4CAANvyOw8++CAOHTrk9jrdRarnL58IB3uL+oWGhiIgIADV1dUAgNLS0kG/6J+9OXd3dyMjIwNz5sxBTk7OoH+nBNif87Jly7B7926UlpZi27ZtUKlUeOONNzxYcf/Zm/O0adNw9uxZHD16FACwd+9eREZGeqrcAWFvzuHh4Th9+jS+//57AMCePXts4TgUSfb81e9L2oNEWVmZkJiYKKjVamHbtm2CIAjCo48+KtTU1AiCIAi1tbVCSkqKEBcXJ6xcuVLo6OjwZLkDoq85f/DBB8LkyZOF5ORk23/Z2dkerrj/7B3ni06ePDkkPq0kCPbn/OWXXwopKSlCQkKC8MgjjwjNzc2eLHdA2Jvzvn37BI1GIyQlJQmLFi0STpw44clyB8zMmTNtn1aS+vmLC+8REZGIT5xWIiIi5zAciIhIhOFAREQiDAciIhJhOBARkYjX/IY00ZVMnjwZkyZNgp/f/17L3HTTTQP6xb2dO3fib3/7GywWC7q7uxEVFYU1a9YgKCioz34LFy7EggULEB8fj5deeglvv/02oqOjsXHjRofHnjVrFhQKBQIDAyEIAgRBQEJCAhYvXgy5XI49e/bg4MGD0Ol0Ts/LYDBg+fLleOutt0T71q1bhzFjxiAzM9Pp+6Whj+FAg8Jf//pXjB07VpL7rqmpwYsvvoh3330Xo0ePRnd3N55++mn87ne/w+bNmx2+n5KSEhQWFl5xDZy+FBYW2r6o1d7ejlWrVmHjxo1Yu3YtZs+ejdmzZzt9nwAwfvz4XoOByB6eVqJB7aabbsLy5csRFxeHf//73/juu+/wyCOPQKvVYu7cuSgpKbG13bt3L+6//37ce++9SE1NxRdffAHgx+UXBEGA2WwGAPj7+2P58uW4//77AQAvvPAC1q1bZ7ufy28DwJNPPgmDwYCcnBxUVFRg4cKFPVbGvPT25TVfbsSIEcjNzcXbb78Nk8mEnTt3YsmSJQCA06dPIyMjAxqNBklJSXj55ZcBAJ9++iluu+02GAwGWK1WLFy4EC+++CJOnTqFadOmAfjx9wAujrtw4ULbN4iBH99hPPHEE9BqtdBoNPjTn/7k4hGhoYLvHGhQWLRoUY/TSq+++iqCg4PR1dWFmTNn4vnnn4fFYsHcuXOxadMmREZGoq2tDQ8++CAmTpyI0aNHo6ioCNu3b8eYMWNw7NgxPPzww7alFyoqKjBr1ixMnjwZ06ZNQ0xMDGJjYx2u77nnnsOsWbNs7wDefPPNK7a9tOYrCQkJwciRI3s8gQPAqlWrMHv2bDz88MNoa2vDggULMGHCBCQmJiI1NRU6nQ5Tp07FsGHDsHTpUvz3v/+19d2yZQsCAwOh1+tx7tw53HfffZg+fToAICsrCw899BBmzZqFjo4OLF68GNdddx0SEhIcfgxoaGE40KDQ12mli6dx6urqcOLECWRnZ9v2mc1mfP311xAEAU1NTXjooYds+2QyGU6cOIGIiAhs3rwZTz31FP75z3/is88+w+rVqxEdHY3nnntOkvk4cupJJpNh+PDhttvt7e3417/+hVdffRUAEBQUBK1Wi/379yMxMRGZmZlIS0vDm2++iffff79HmALAwYMHkZ2dDZlMhrFjx9qWbG9vb8dnn32GlpYWW2C1t7fj6NGjDAcfxnCgQe/i7xN0d3cjKCgIpaWltn3Nzc0ICgpCcXGx6Mm+sbERKpUKJSUlGDNmDGbPno3k5GQkJydj6dKlmDVrFs6ePQuZTIZLV5np6upyqK6++tj7TYWGhga0t7fjuuuus516slqtuHy1G6vVavtJyLa2NhiNRshkMtTX1/cappf29/f373G/b731li2Mzp49i4CAAIfmSUMTrznQkPHTn/4UgYGBtnBobGxEUlISDh8+jOjoaHzyySf47rvvAACVlZVITk6G2WyGn58fCgsLcfr0adt9HTt2DNdccw2uvvpqjBkzBkeOHIEgCDCZTPjoo4/s1jJ27FgcPnwYAPCf//wH33zzjcPzaG1tRX5+PhYsWNDjCXrkyJGYOnWq7TeT29ra8N577+GOO+4AAOTk5CA5ORkbN27EqlWr0NbW1uN+77rrLpSUlMBqtaKlpQV79uyx3W9UVBRee+012/jz58+37SffxHcONGQMGzYMW7duxYYNG/Dyyy/DYrFg+fLltvPq69atw8qVKyEIAuRyOV566SVcddVV0Gq1+OGHH7B48WJ0dnZCJpPhJz/5CV555RX4+/sjOTkZBw4cgFqtxvjx43HrrbeKXsFfbunSpVizZg0qKytx/fXX2z2NtGrVKgQGBsLf3x/d3d1Qq9XIyMgQtSssLMS6deuwc+dOdHZ2QqPRQKvV4vXXX0djYyOef/55KBQK3HnnnVi7di1WrVpl65uZmYm8vDzMmTMHY8eOxaRJk3rcb35+PjQaDTo7O5GUlITk5GRnHn4aYrgqKxERifC0EhERiTAciIhIhOFAREQiDAciIhJhOBARkQjDgYiIRBgOREQkwnAgIiKR/w/WD3fUUo2NdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"FreeSulfurDioxide\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"FreeSulfurDioxide\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "-vOtXdyV-89O",
    "outputId": "b3501c81-a694-437b-c537-36bcd48c2af2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"TotalSulfurDioxide\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"TotalSulfurDioxide\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "EHgVA_06_E-l",
    "outputId": "324c8e47-011a-41ad-fee4-77005814009b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"Density\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"Density\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "AoMm30hZ_Mv8",
    "outputId": "73c554d3-f689-4319-f963-3c74868154e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"pH\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"pH\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "CaB1WT11_UW-",
    "outputId": "a47dcb08-4aff-48db-840d-1b551f5674a8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"Sulphates\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"Sulphates\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "VTuqVpFH_bpN",
    "outputId": "4fb42148-987a-43cf-de86-0927fb98d804"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"Alcohol\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"Alcohol\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "eK-e0kaC_8I6",
    "outputId": "5530a571-23da-4b20-9291-20454c55e8b3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"LabelAppeal\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"LabelAppeal\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "p1WluZ64AEUQ",
    "outputId": "64aa0d11-312b-4c3a-80fd-cf06f57170ee"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"AcidIndex\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"AcidIndex\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "id": "SxCZdJ9aAOGb",
    "outputId": "f6223a63-d91a-4368-e53a-4dc3fd57993e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arami\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure composed of two matplotlib.Axes objects (ax_box and ax_hist)\n",
    "f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={\"height_ratios\": (.15, .85)})\n",
    "\n",
    "#Assigning a graph to each ax\n",
    "sns.boxplot(df2_perc_lin[\"STARS\"], ax=ax_box)\n",
    "sns.histplot(data=df2_perc_lin, x=\"STARS\", ax=ax_hist)\n",
    " \n",
    "#Remove x axis name for the boxplot\n",
    "ax_box.set(xlabel='')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q9AFv-MiHosV"
   },
   "source": [
    "## **Prepped Data Review**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IVRTnQa4JPNq"
   },
   "source": [
    "We have:\n",
    "\n",
    "- imputed all missing values for each variable.\n",
    "- normalized all variables once we rid each variable of null values.\n",
    "- fixed the skewedness of several variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vSAW8v_CVq1e"
   },
   "source": [
    "## **Conclusion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SzJtlhKzXDxT"
   },
   "source": [
    "We found that:\n",
    "\n",
    "- Research is invaluable, after reading papers on wine authorities around the world, not only did we feel we gain a new understanding for wine, but it much better helped us understand each variable with context. \n",
    "\n",
    "- Afterwords, for missing values, we presumed best practice to just fill in each missing value with a likely value from that variable's range. For example, if alcohol content ranged between 5.5-25.0% percent, missing values were filled in with KNN.\n",
    "\n",
    "- We found after re-conducting our EDA, that the data appeared to have stay in relatively the same shape after making important changes to the existence of erroneous negative values. Correlation diagrams appeared very similar after our alterations.\n",
    "\n",
    "- Data now normalized is better suited for machine learning models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xdp2iBrNXCoR"
   },
   "source": [
    "This data set was strange because a lot of the values didn't make sense or were out of range of the normal acceptable (or legal) limit.  For example, Citric Acid is a component of Fixed Acidity so Citric Acid should always be less than Fixed Acidity, but that wasn't always the case.  I saw the same issue for Free Sulfur Dioxide and Total Sulfur Dioxide.  As for possible erroneous values, the maximum value for Total Sulfur Dioxide should be 350 mg/L but we saw 2,030 values over that number.  This \"over the maximum\" issue was observed for Fixed Acidity, Volatile Acidity, Citric Acid, Chlorides, Free Sulfur Dioxide, Total Sulfur Dioxide, and pH.  We really had no idea how to even begin to fix these issues so we focused on the problem at hand which was to fill in the missing values.\n",
    "\n",
    "We normalized the data before using the KNN imptuer because it is a distance-based imputation method that requires normalization. If we had not normalized our data, the different scales of the data would have led to the KNN imputer to generate biased replacements for the missing values.  We also thought normalization would be helpful since machine learning algorithms tend to perform better when variables are on a smaller scale. Therefore, it is common practice to normalize data before training machine learning models.\n",
    "\n",
    "We also adjusted the skew of the data.  Since the data was already normalized (with values from 0 - 1), we could not use Box Cox or log transformations.  If the values of a certain independent variables are skewed, depending on the model, skewness may violate model assumptions (e.g. logistic regression) or may impair the interpretation of feature importance. Source: https://opendatascience.com/transforming-skewed-data-for-machine-learning/"
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