https://archive.ics.uci.edu/ml/datasets/Wine+Quality
Citation Request: This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib
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Title: Wine Quality
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Sources Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
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Past Usage:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
In the above reference, two datasets were created, using red and white wine samples. The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity analysis procedure).
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Relevant Information:
The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.
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Number of Instances: red wine - 1599; white wine - 4898.
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Number of Attributes: 11 + output attribute
Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection.
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Attribute information:
For more information, read [Cortez et al., 2009].
Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (score between 0 and 10)
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Missing Attribute Values: None
% lsReadme.md winequality-red.csv
Wine Quality Analysis.ipynb winequality-white.csv
Wine+Quality+Analysis.html winequality.names.txt
# import libs
%matplotlib inline
import pandas as pd
import seaborn as sns; sns.set(style="whitegrid", palette="muted")
import numpy as np
import matplotlib.pyplot as pltwhite_wine_df = pd.read_csv('winequality-white.csv', sep=";")
red_wine_df = pd.read_csv('winequality-red.csv', sep=";")ww = white_wine_df.loc[:]
ww["color"] = "white"
rw = red_wine_df.loc[:]
rw["color"] = "red"
wine_df = pd.concat([ww, rw], ignore_index=True)white_wine_df.head()| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.0 | 0.27 | 0.36 | 20.7 | 0.045 | 45.0 | 170.0 | 1.0010 | 3.00 | 0.45 | 8.8 | 6 | white |
| 1 | 6.3 | 0.30 | 0.34 | 1.6 | 0.049 | 14.0 | 132.0 | 0.9940 | 3.30 | 0.49 | 9.5 | 6 | white |
| 2 | 8.1 | 0.28 | 0.40 | 6.9 | 0.050 | 30.0 | 97.0 | 0.9951 | 3.26 | 0.44 | 10.1 | 6 | white |
| 3 | 7.2 | 0.23 | 0.32 | 8.5 | 0.058 | 47.0 | 186.0 | 0.9956 | 3.19 | 0.40 | 9.9 | 6 | white |
| 4 | 7.2 | 0.23 | 0.32 | 8.5 | 0.058 | 47.0 | 186.0 | 0.9956 | 3.19 | 0.40 | 9.9 | 6 | white |
red_wine_df.head()| fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | quality | color | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 | red |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 | red |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 | red |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 | red |
assert white_wine_df.columns.all() == red_wine_df.columns.all()
",".join(list(white_wine_df.columns))'fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,color'
assert white_wine_df.notnull().all().all()
white_wine_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4898 entries, 0 to 4897
Data columns (total 13 columns):
fixed acidity 4898 non-null float64
volatile acidity 4898 non-null float64
citric acid 4898 non-null float64
residual sugar 4898 non-null float64
chlorides 4898 non-null float64
free sulfur dioxide 4898 non-null float64
total sulfur dioxide 4898 non-null float64
density 4898 non-null float64
pH 4898 non-null float64
sulphates 4898 non-null float64
alcohol 4898 non-null float64
quality 4898 non-null int64
color 4898 non-null object
dtypes: float64(11), int64(1), object(1)
memory usage: 497.5+ KB
no null values in white wine dataframe found
assert red_wine_df.notnull().all().all()
red_wine_df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1599 entries, 0 to 1598
Data columns (total 13 columns):
fixed acidity 1599 non-null float64
volatile acidity 1599 non-null float64
citric acid 1599 non-null float64
residual sugar 1599 non-null float64
chlorides 1599 non-null float64
free sulfur dioxide 1599 non-null float64
total sulfur dioxide 1599 non-null float64
density 1599 non-null float64
pH 1599 non-null float64
sulphates 1599 non-null float64
alcohol 1599 non-null float64
quality 1599 non-null int64
color 1599 non-null object
dtypes: float64(11), int64(1), object(1)
memory usage: 162.5+ KB
no null values in red wine dataframe found
# this can be crucial :)
white_wine_df["color"] = white_wine_df["color"].astype("category")
red_wine_df["color"] = red_wine_df["color"].astype("category")
wine_df["color"] = wine_df["color"].astype("category")white_wine_df.mean()fixed acidity 6.854788
volatile acidity 0.278241
citric acid 0.334192
residual sugar 6.391415
chlorides 0.045772
free sulfur dioxide 35.308085
total sulfur dioxide 138.360657
density 0.994027
pH 3.188267
sulphates 0.489847
alcohol 10.514267
quality 5.877909
dtype: float64
red_wine_df.mean()fixed acidity 8.319637
volatile acidity 0.527821
citric acid 0.270976
residual sugar 2.538806
chlorides 0.087467
free sulfur dioxide 15.874922
total sulfur dioxide 46.467792
density 0.996747
pH 3.311113
sulphates 0.658149
alcohol 10.422983
quality 5.636023
dtype: float64
mean_diff = white_wine_df.mean() - red_wine_df.mean()
mean_diff_abs = mean_diff.apply(lambda x: abs(x))
mean_diff[mean_diff_abs >= 1.0]fixed acidity -1.464850
residual sugar 3.852609
free sulfur dioxide 19.433163
total sulfur dioxide 91.892865
dtype: float64
sns.countplot(data=white_wine_df, x="quality")
sns.plt.title("White Wines")<matplotlib.text.Text at 0x1112b1ac8>
white_wine_df.quality.describe()count 4898.000000
mean 5.877909
std 0.885639
min 3.000000
25% 5.000000
50% 6.000000
75% 6.000000
max 9.000000
Name: quality, dtype: float64
x = sns.countplot(data=red_wine_df, x="quality")
sns.plt.title("Red Wines")<matplotlib.text.Text at 0x11144de80>
red_wine_df.quality.describe()count 1599.000000
mean 5.636023
std 0.807569
min 3.000000
25% 5.000000
50% 6.000000
75% 6.000000
max 8.000000
Name: quality, dtype: float64
To find out, the percentual mean differences for low quality to high quality wines over the total mean are calculated, resulting in percentual changes.
x = white_wine_df.groupby(["quality"]).mean()
lower_quals = x.loc[:4].mean()
higher_quals = x.loc[7:].mean()
ww_perc_means = (higher_quals - lower_quals) / white_wine_df.mean() * 100
ww_perc_meansalcohol 14.068993
chlorides -38.372538
citric acid 7.758984
density -0.254610
fixed acidity -6.235608
free sulfur dioxide -10.177344
pH 1.934262
quality NaN
residual sugar -8.100321
sulphates 1.999155
total sulfur dioxide -18.439304
volatile acidity -27.979143
dtype: float64
Comparing low quality means to high quality ones, the following attributes differ more than 5 per cent:
ww_perc_means[abs(ww_perc_means) > 5]alcohol 14.068993
chlorides -38.372538
citric acid 7.758984
fixed acidity -6.235608
free sulfur dioxide -10.177344
residual sugar -8.100321
total sulfur dioxide -18.439304
volatile acidity -27.979143
dtype: float64
Comparing low quality means to high quality ones, the following attributes differ more than 10 per cent:
ww_perc_means[abs(ww_perc_means) > 10]alcohol 14.068993
chlorides -38.372538
free sulfur dioxide -10.177344
total sulfur dioxide -18.439304
volatile acidity -27.979143
dtype: float64
x = red_wine_df.groupby(["quality"]).mean()
lower_quals = x.loc[:4].mean()
higher_quals = x.loc[7:].mean()
rw_perc_means = (higher_quals - lower_quals) / red_wine_df.mean() * 100
rw_perc_meansalcohol 16.023546
chlorides -38.955960
citric acid 77.707371
density -0.134937
fixed acidity 7.811538
free sulfur dioxide 12.783852
pH -3.345302
quality NaN
residual sugar -0.609713
sulphates 26.028988
total sulfur dioxide 7.875629
volatile acidity -71.161438
dtype: float64
Comparing low quality means to high quality ones, the following attributes differ more than 5 per cent:
rw_perc_means[abs(rw_perc_means) > 5]alcohol 16.023546
chlorides -38.955960
citric acid 77.707371
fixed acidity 7.811538
free sulfur dioxide 12.783852
sulphates 26.028988
total sulfur dioxide 7.875629
volatile acidity -71.161438
dtype: float64
Comparing low quality means to high quality ones, the following attributes differ more than 10 per cent:
rw_perc_means[abs(rw_perc_means) > 10]alcohol 16.023546
chlorides -38.955960
citric acid 77.707371
free sulfur dioxide 12.783852
sulphates 26.028988
volatile acidity -71.161438
dtype: float64
- Alcohol
- Chlorides
- Citric Acid
- Sulphates
- Sulfur Dioxides
- Volatile Acidity
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(14.5, 4.5)
fig.dpi = 300
sns.stripplot(data=white_wine_df, x="quality", y="total sulfur dioxide", jitter=True, ax=ax1)
sns.stripplot(data=white_wine_df, x="quality", y="free sulfur dioxide", jitter=True, ax=ax2)<matplotlib.axes._subplots.AxesSubplot at 0x114f6d4e0>
high_qual_ww_tsd_mean = white_wine_df[white_wine_df["quality"] >= 7]["total sulfur dioxide"].mean()
high_qual_ww_tsd_mean = format(high_qual_ww_tsd_mean, '.1f')
print(f"The mean for higher quality white wines (quality >= 7) is {high_qual_ww_tsd_mean}")The mean for higher quality white wines (quality >= 7) is 125.2
Both plots show, that higher quality white wines tend to have less total sulfur dioxide in it.
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(14.5, 4.5)
fig.dpi = 300
sns.stripplot(data=red_wine_df, x="quality", y="total sulfur dioxide", jitter=True, ax=ax1)
sns.stripplot(data=red_wine_df, x="quality", y="free sulfur dioxide", jitter=True, ax=ax2)<matplotlib.axes._subplots.AxesSubplot at 0x114e00cc0>
high_qual_rw_tsd_mean = red_wine_df[red_wine_df["quality"] >= 7]["total sulfur dioxide"].mean()
high_qual_rw_tsd_mean = format(high_qual_rw_tsd_mean, '.1f')
print(f"The mean for higher quality red wines (quality >= 7) is {high_qual_rw_tsd_mean}")The mean for higher quality red wines (quality >= 7) is 34.9
For the red wines, there are much lower concentrations of sulfur dioxides. Additionally, there seems to be no direct correlation between sulfur dioxide concentration and percepted quality.
Regarding high quality white wines (>= 7), those wines have a mean of sulfur dioxides of around 125. Respectively high quality Red Wines (>=7) have a mean concentration of sulfur dioxide of 35.
sns.stripplot(data=wine_df, x="quality", y="sulphates", jitter=True, hue="color", split=True)<matplotlib.axes._subplots.AxesSubplot at 0x117849240>
white_wine_df.groupby("quality")["alcohol"].describe()| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| quality | ||||||||
| 3 | 20.0 | 10.345000 | 1.224089 | 8.0 | 9.55 | 10.45 | 11.00 | 12.6 |
| 4 | 163.0 | 10.152454 | 1.003217 | 8.4 | 9.40 | 10.10 | 10.75 | 13.5 |
| 5 | 1457.0 | 9.808840 | 0.847065 | 8.0 | 9.20 | 9.50 | 10.30 | 13.6 |
| 6 | 2198.0 | 10.575372 | 1.147776 | 8.5 | 9.60 | 10.50 | 11.40 | 14.0 |
| 7 | 880.0 | 11.367936 | 1.246536 | 8.6 | 10.60 | 11.40 | 12.30 | 14.2 |
| 8 | 175.0 | 11.636000 | 1.280138 | 8.5 | 11.00 | 12.00 | 12.60 | 14.0 |
| 9 | 5.0 | 12.180000 | 1.013410 | 10.4 | 12.40 | 12.50 | 12.70 | 12.9 |
red_wine_df.groupby("quality")["alcohol"].describe()| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| quality | ||||||||
| 3 | 10.0 | 9.955000 | 0.818009 | 8.4 | 9.725 | 9.925 | 10.575 | 11.0 |
| 4 | 53.0 | 10.265094 | 0.934776 | 9.0 | 9.600 | 10.000 | 11.000 | 13.1 |
| 5 | 681.0 | 9.899706 | 0.736521 | 8.5 | 9.400 | 9.700 | 10.200 | 14.9 |
| 6 | 638.0 | 10.629519 | 1.049639 | 8.4 | 9.800 | 10.500 | 11.300 | 14.0 |
| 7 | 199.0 | 11.465913 | 0.961933 | 9.2 | 10.800 | 11.500 | 12.100 | 14.0 |
| 8 | 18.0 | 12.094444 | 1.224011 | 9.8 | 11.325 | 12.150 | 12.875 | 14.0 |
sns.barplot(data=wine_df, x="quality", y="alcohol", hue="color")<matplotlib.axes._subplots.AxesSubplot at 0x114e342e8>
sns.lmplot(data=wine_df, x="quality", y="alcohol", hue="color")<seaborn.axisgrid.FacetGrid at 0x118bd3160>
hq_wines = wine_df[wine_df.quality >= 7]
sns.lmplot(data=hq_wines, x="quality", y="alcohol", hue="color")
sns.plt.title("Quality >= 7")<matplotlib.text.Text at 0x118fe82e8>
heat_table = wine_df[["quality", "alcohol"]].copy()
heat_table["alcohol"] = heat_table.alcohol.apply(func=lambda x: round(x * 2) / 2)
heat_table = heat_table.groupby(["quality", "alcohol"])["alcohol"].count().reset_index(name='counts')
sns.heatmap(heat_table.pivot("quality", "alcohol", "counts"))<matplotlib.axes._subplots.AxesSubplot at 0x118e0eb70>
sns.barplot(data=wine_df, hue="color", x="quality", y="chlorides")<matplotlib.axes._subplots.AxesSubplot at 0x1192a8c18>
The less chlorides in a wine the higher the quality.
g = sns.PairGrid(wine_df[["alcohol", "chlorides", "quality"]], hue="quality")
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
g = g.add_legend()
sns.pairplot(white_wine_df[["volatile acidity", "citric acid", "quality"]], hue="quality")<seaborn.axisgrid.PairGrid at 0x1194682b0>
sns.pairplot(white_wine_df[["volatile acidity", "citric acid", "quality", "free sulfur dioxide", "chlorides"]], hue="quality")<seaborn.axisgrid.PairGrid at 0x11a544128>
white_wine_df[["volatile acidity", "citric acid", "quality"]].groupby("quality").describe(percentiles=[])| citric acid | volatile acidity | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | mean | std | min | 50% | max | count | mean | std | min | 50% | max | |
| quality | ||||||||||||
| 3 | 20.0 | 0.336000 | 0.081460 | 0.21 | 0.345 | 0.47 | 20.0 | 0.333250 | 0.140827 | 0.17 | 0.26 | 0.640 |
| 4 | 163.0 | 0.304233 | 0.163857 | 0.00 | 0.290 | 0.88 | 163.0 | 0.381227 | 0.173463 | 0.11 | 0.32 | 1.100 |
| 5 | 1457.0 | 0.337653 | 0.140814 | 0.00 | 0.320 | 1.00 | 1457.0 | 0.302011 | 0.100066 | 0.10 | 0.28 | 0.905 |
| 6 | 2198.0 | 0.338025 | 0.119325 | 0.00 | 0.320 | 1.66 | 2198.0 | 0.260564 | 0.088142 | 0.08 | 0.25 | 0.965 |
| 7 | 880.0 | 0.325625 | 0.079183 | 0.01 | 0.310 | 0.74 | 880.0 | 0.262767 | 0.091106 | 0.08 | 0.25 | 0.760 |
| 8 | 175.0 | 0.326514 | 0.085439 | 0.04 | 0.320 | 0.74 | 175.0 | 0.277400 | 0.108029 | 0.12 | 0.26 | 0.660 |
| 9 | 5.0 | 0.386000 | 0.082037 | 0.29 | 0.360 | 0.49 | 5.0 | 0.298000 | 0.057619 | 0.24 | 0.27 | 0.360 |
white_wine_df[["quality", "free sulfur dioxide", "chlorides"]].groupby("quality").describe(percentiles=[])| chlorides | free sulfur dioxide | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | mean | std | min | 50% | max | count | mean | std | min | 50% | max | |
| quality | ||||||||||||
| 3 | 20.0 | 0.054300 | 0.046468 | 0.022 | 0.041 | 0.244 | 20.0 | 53.325000 | 69.420776 | 5.0 | 33.5 | 289.0 |
| 4 | 163.0 | 0.050098 | 0.025888 | 0.013 | 0.046 | 0.290 | 163.0 | 23.358896 | 20.391349 | 3.0 | 18.0 | 138.5 |
| 5 | 1457.0 | 0.051546 | 0.026496 | 0.009 | 0.047 | 0.346 | 1457.0 | 36.432052 | 18.145991 | 2.0 | 35.0 | 131.0 |
| 6 | 2198.0 | 0.045217 | 0.020453 | 0.015 | 0.043 | 0.255 | 2198.0 | 35.650591 | 15.735679 | 3.0 | 34.0 | 112.0 |
| 7 | 880.0 | 0.038191 | 0.010697 | 0.012 | 0.037 | 0.135 | 880.0 | 34.125568 | 13.244737 | 5.0 | 33.0 | 108.0 |
| 8 | 175.0 | 0.038314 | 0.013164 | 0.014 | 0.036 | 0.121 | 175.0 | 36.720000 | 16.203675 | 6.0 | 35.0 | 105.0 |
| 9 | 5.0 | 0.027400 | 0.007436 | 0.018 | 0.031 | 0.035 | 5.0 | 33.400000 | 13.427584 | 24.0 | 28.0 | 57.0 |
no attribute alone is strong enough to define a high quality wine, but as the figures show. For a wine to score high, having the acids and sulfur dioxide values all within in a certain range can help.
when comparing the best (8,9) vs. worst (3,4) we can see that they well overlap each other.
qual3 = white_wine_df[white_wine_df["quality"] == 3]
qual4 = white_wine_df[white_wine_df["quality"] == 4].copy()
qual4.quality = 3
qual8 = white_wine_df[white_wine_df["quality"] == 8]
qual9 = white_wine_df[white_wine_df["quality"] == 9].copy()
qual9.quality = 8
white_wine_sample = pd.concat([qual3, qual4, qual8, qual9], ignore_index=True)
white_wine_sample
sns.pairplot(white_wine_sample[["volatile acidity", "citric acid", "quality"]], hue="quality")<seaborn.axisgrid.PairGrid at 0x11cc89ef0>
sns.pairplot(white_wine_sample[["volatile acidity", "citric acid", "quality", "sulphates", "chlorides"]], hue="quality")<seaborn.axisgrid.PairGrid at 0x11d07cb00>
But if you have to pick a wine only based on specs, i would suggest white wines close to this values:
qual8[["quality", "chlorides", "alcohol", "citric acid", "sulphates"]].describe(percentiles=[])| quality | chlorides | alcohol | citric acid | sulphates | |
|---|---|---|---|---|---|
| count | 175.0 | 175.000000 | 175.000000 | 175.000000 | 175.000000 |
| mean | 8.0 | 0.038314 | 11.636000 | 0.326514 | 0.486229 |
| std | 0.0 | 0.013164 | 1.280138 | 0.085439 | 0.147073 |
| min | 8.0 | 0.014000 | 8.500000 | 0.040000 | 0.250000 |
| 50% | 8.0 | 0.036000 | 12.000000 | 0.320000 | 0.460000 |
| max | 8.0 | 0.121000 | 14.000000 | 0.740000 | 0.950000 |
and red wines close to this values:
rqual8 = red_wine_df[red_wine_df["quality"] >= 8]
rqual8[["quality", "chlorides", "alcohol", "citric acid", "sulphates"]].describe(percentiles=[])| quality | chlorides | alcohol | citric acid | sulphates | |
|---|---|---|---|---|---|
| count | 18.0 | 18.000000 | 18.000000 | 18.000000 | 18.000000 |
| mean | 8.0 | 0.068444 | 12.094444 | 0.391111 | 0.767778 |
| std | 0.0 | 0.011678 | 1.224011 | 0.199526 | 0.115379 |
| min | 8.0 | 0.044000 | 9.800000 | 0.030000 | 0.630000 |
| 50% | 8.0 | 0.070500 | 12.150000 | 0.420000 | 0.740000 |
| max | 8.0 | 0.086000 | 14.000000 | 0.720000 | 1.100000 |