SEABORN plots - confidence interval, regression and correlation in Python

# Count and Hist plot
plt.figure(figsize = (12,8))
sns.set(font_scale = 2, palette = "viridis")
sns.countplot(data = titanic, x = "sex", hue = "pclass")
plt.show()


sns.histplot(data = penguins, x = "body_mass_g", kde = True)


sns.histplot(data = penguins, x = "body_mass_g", kde = True, hue = "species")

#############################################################################################################################
# Categorical plots
plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.stripplot(data = titanic, x = "sex", y = "age", jitter = True, hue = "pclass", dodge = True)
plt.show()


plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.swarmplot(data = titanic, x = "sex", y = "age", hue = "pclass", dodge = True)
plt.show() # the plot is the same as previous plot, but we can see the distribution


plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.violinplot(data = titanic, x = "sex", y = "age", hue = "pclass", dodge = True)
sns.swarmplot(data = titanic, x = "sex", y = "age", hue = "pclass", dodge = True, color = "black")
plt.show() # we can see the distribution


plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.violinplot(data = titanic, x = "pclass", y = "age", hue = "sex", dodge = True, split = True )
plt.show()


plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.barplot(data = titanic, x = "pclass", y = "age", hue = "sex", dodge = True)
plt.show() # with confidence intervals


plt.figure(figsize=(12,8))
sns.set(font_scale=1.5)
sns.pointplot(data = titanic, x = "pclass", y = "age", hue = "sex", dodge = True)
plt.show() # only CONFIDENCE INTERVALS for each groups

##########################################################################################################################
# Joint / Regression plot
sns.set(font_scale=1.5)
sns.regplot(data = df, x = 'total_spend', y = 'sales')

sns.jointplot(data = titanic, x = "age", y = "fare", height = 8, kind = "kde")
plt.show()


sns.set(font_scale=1.5)
sns.jointplot(data = titanic, x = "age", y = "fare", height = 8, kind = "scatter")
plt.show()


sns.set(font_scale=1.5)
sns.jointplot(data = titanic, x = "age", y = "fare", height = 8, kind = "hex")
plt.show()


sns.set(font_scale=1.5)
sns.jointplot(data = titanic, x = "age", y = "fare", height = 8, kind = "reg")
plt.show()


sns.set_style('whitegrid')
sns.lmplot(x ='total_bill', y ='tip', data = dataset,
           hue ='sex', markers = ['o', 'v']) # LINEAR REGRESSION PLOT


sns.set_style('whitegrid')
sns.lmplot(x ='total_bill', y ='tip', data = dataset, hue ='sex',
           markers =['o', 'v'], scatter_kws ={'s':100},
           palette ='plasma')


sns.set(font_scale=1.5)
sns.lmplot(data = titanic, x = "age", y = "fare", aspect= 1, height=8, col = "sex")
plt.show() # we are creating two subplots and actually two columns for male and female


sns.set(font_scale=1.5)
sns.lmplot(data = titanic, x = "age", y = "fare", aspect= 1, height=8, row = "sex")
plt.show() # we are creating two subplots and actually two rows for male and female


sns.lmplot(x ='total_bill', y ='tip', data = dataset,
           col ='sex', row ='time', hue ='smoker')


sns.lmplot(x ='total_bill', y ='tip', data = dataset, col ='sex',
           row ='time', hue ='smoker', aspect = 0.6,
           size = 4, palette ='coolwarm')


sns.set(font_scale=1.5)
sns.lmplot(data = titanic, x = "age", y = "survived", aspect= 1, height=8, col = "sex", logistic= True)
plt.show() # plot for LOGISTIC REGRESSION


###########################################################################################################################
# Heatmaps and Matrixplots
pd.crosstab(titanic.sex, titanic.pclass)
plt.figure(figsize=(12,8))
sns.set(font_scale=1.4)
sns.heatmap(pd.crosstab(titanic.sex, titanic.pclass), annot= True, fmt = "d", cmap = "Reds", vmax = 150)
plt.show()


pd.crosstab(titanic.sex, titanic.pclass, values= titanic.survived, aggfunc= "mean")
plt.figure(figsize=(12,8))
sns.set(font_scale=1.4)
sns.heatmap(pd.crosstab(titanic.sex, titanic.pclass, values= titanic.survived, aggfunc= "mean"), annot= True, cmap = "Reds")
plt.show()


titanic.corr()
plt.figure(figsize=(12,8))
sns.set(font_scale=1.4)
sns.heatmap(titanic.corr(), annot= True, cmap = "Reds")
plt.show()

#########################################################################################################################
# Seaborn pairplot
plt.figure(figsize=(12,8))
sns.set(font_scale=1.4)
sns.pairplot(cars, kind = 'scatter')
plt.show()


sns.set(font_scale=1.5)
sns.pairplot(data = cars[["gpm", "horsepower", "origin"]], kind = "scatter", hue = "origin", aspect = 2, height = 3)
plt.show()