import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns # agrega librerías que necesites

url_wine_red = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv' url_wine_white = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv'

red = pd.read_csv(url_wine_red, delimiter=';') white = pd.read_csv(url_wine_white, delimiter=';')

red.head()

white.head()

red['category']='red' white['category']='white'

total_wine=red.append(white, ignore_index=True)

total_wine.head()

total_wine.shape

total_wine.info()

total_wine.describe()

quality = total_wine['quality'] quality

sns.histplot(quality)

sns.countplot(data= total_wine, x= quality )

total_wine['quality_category'] = total_wine['quality'].apply(lambda x: "Poor" if x < 5 else ("Medium " if x >=5 and x <= 7 else "High")) total_wine.tail()

total_wine.quality_category = total_wine['quality_category'].astype('category') total_wine.info()

sns.countplot(total_wine['quality_category'])

sns.boxplot(quality)

wine_corr = total_wine.corr() wine_corr

plt.figure(figsize =(10,10)) sns.heatmap(wine_corr, annot= True, cmap='PuBu')

totoal_wine_sort = total_wine.corr()[['quality']].sort_values(by='quality', ascending = False) totoal_wine_sort

sns.pairplot(total_wine, x_vars=["density", "volatile acidity", "chlorides"], y_vars=["alcohol", "citric acid"], hue= 'quality', kind = 'scatter');

fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2) sns.barplot(data=total_wine, x= 'quality_category', y= 'density',ax = ax1) ax1.set_ylim(0.98,1.05) sns.violinplot(data=total_wine, x= 'quality_category', y= 'density', ax = ax2) ax2.set_ylim(0.98,1.05) fig.tight_layout()

fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2) sns.barplot(data=total_wine, x= 'quality_category', y= 'volatile acidity', ax = ax1) ax1.set_ylim(-0.14, 1.83) sns.violinplot(data=total_wine, x= 'quality_category', y= 'volatile acidity', ax = ax2) ax2.set_ylim(-0.14, 1.83) fig.tight_layout()

fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2) sns.barplot(data=total_wine, x= 'quality_category', y= 'chlorides', ax = ax1) ax1.set_ylim(-0.05, 0.675) sns.violinplot(data=total_wine, x= 'quality_category', y= 'chlorides', ax = ax2) ax2.set_ylim(-0.05, 0.675) fig.tight_layout()

fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2) sns.barplot(data=total_wine, x= 'quality_category', y= 'alcohol', ax = ax1) ax1.set_ylim(6.93,15.71) sns.violinplot(data=total_wine, x= 'quality_category', y= 'alcohol', ax = ax2) ax2.set_ylim(6.93,15.71) fig.tight_layout()

fig, [ax1, ax2] = plt.subplots(nrows=1, ncols=2) sns.barplot(data=total_wine, x= 'quality_category', y= 'citric acid', ax = ax1) ax1.set_ylim(-0.21, 1.80) sns.violinplot(data=total_wine, x= 'quality_category', y= 'citric acid', ax = ax2) fig.tight_layout()