import seaborn as sns
penguins = sns.load_dataset("penguins")
penguins

# Axes-level
sns.histplot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack")

# Figure-level
sns.displot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack")

# Multiples figuras (Figure-level)
sns.displot(data=penguins, x="flipper_length_mm", hue="species", col="species")

# Axis level
import matplotlib.pyplot as plt
f, axs = plt.subplots(1, 2, figsize=(8, 4), gridspec_kw=dict(width_ratios=[4, 3]))
sns.scatterplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species", ax=axs[0])
sns.histplot(data=penguins, x="species", hue="species", shrink=.8, alpha=.8, legend=False, ax=axs[1])
f.tight_layout()

# Figure-level
tips = sns.load_dataset("tips")
g = sns.relplot(data=tips, x="total_bill", y="tip")

# Lineplot
flights = sns.load_dataset("flights")
flights.head()

# Axis-level
may_flights = flights.query("month == 'May'")
sns.lineplot(data=may_flights, x="year", y="passengers")

# Axis-level
flights_wide = flights.pivot("year", "month", "passengers")
flights_wide.head()
sns.lineplot(data=flights_wide)

tips = sns.load_dataset("tips")
tips.head()

# Scatterplot
sns.scatterplot(data=tips, x="total_bill", y="tip")

sns.scatterplot(data=tips, x="total_bill", y="tip", hue="time")

# Barplot
import seaborn as sns
sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.barplot(x="day", y="total_bill", data=tips)

 sns.barplot(x="day", y="total_bill", hue="sex", data=tips)

# Histograma
penguins = sns.load_dataset("penguins")
# Axis-level
sns.histplot(data=penguins, x="flipper_length_mm")



# Axis-level
sns.histplot(data=penguins, x="flipper_length_mm", hue="species")

import seaborn as sns
sns.set_theme(style="whitegrid")
tips = sns.load_dataset("tips")
# Axis-level
ax = sns.boxplot(x=tips["total_bill"])

# Axis-level
ax = sns.boxplot(x="day", y="total_bill", data=tips)

from google.colab import drive
import os
drive.mount('/content/gdrive')
%cd '/content/gdrive/MyDrive/'

import pandas as pd
df_lluvias = pd.read_csv('pune_1965_to_2002.csv')
df_lluvias.head()

df_lluvias.index = df_lluvias['Year']
df_lluvias = df_lluvias.drop('Year', axis='columns')
df_lluvias

fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(12, 5), sharex=True, sharey=True)  
ax[0].plot(df_lluvias.index, df_lluvias['Jan'], label='Precipitaciones de enero')
ax[1].plot(df_lluvias.index, df_lluvias['Feb'], label='Precipitaciones de febrero', color='C1')
ax[2].plot(df_lluvias.index, df_lluvias['Mar'], label='Precipitaciones de marzo', color='C2')
ax[0].set_title('Precipitaciones de los primeros tres meses del año') 
ax[2].set_xlabel('Año')  
ax[1].set_ylabel('Precipitación (mm.)')

ax[0].legend()  
ax[1].legend()
ax[2].legend()    

# FacetGrid
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col="time",  row="sex")
g.map(sns.scatterplot, "total_bill", "tip")

g = sns.FacetGrid(tips, col="time", hue="sex")
g.map_dataframe(sns.scatterplot, x="total_bill", y="tip")
g.add_legend()

import matplotlib as mpl
mpl.rcParams['axes.titleweight'] = 'bold' 
mpl.rcParams['axes.titlelocation'] = 'left' 
mpl.rcParams['axes.titlecolor'] = 'firebrick' 
mpl.rcParams['axes.labelcolor'] = 'blue' 
mpl.rcParams['axes.labelsize'] = '10' 
mpl.rcParams['axes.labelweight'] = 'light' 
mpl.rcParams['axes.linewidth'] = '1' 
mpl.rcParams['grid.color'] = 'black' 
mpl.rcParams['grid.linestyle'] = '-.' 
mpl.rcParams['grid.linewidth'] = '2' 

fig, ax = plt.subplots(figsize=(7, 4))  
ax.scatter(df_lluvias['Aug'], df_lluvias['Sep'], c=df_lluvias.index)
ax.set_title('(Título rojo en negrita)')
ax.set_xlabel('(Etiqueta eje horiz.)')
ax.set_ylabel('(Etiqueta eje vert.)')

#  Restaurar por defecto
mpl.rcParams.update(mpl.rcParamsDefault)

mpl.rcParams.keys()

mpl.rcParams

sns.set_style(style="darkgrid", rc={"grid.color": ".6"})
sns.set_style(rc={"grid.linestyle": ":"})
sns.set_style(rc={"axes.titleweight": "normal"})
sns.set_style(rc={"axes.titlelocation": "left"})
sns.set_style(rc={"axes.titlecolor": "blue"})
sns.set_style(rc={"axes.labelcolor": "red"})
sns.set_style(rc={"axes.labelsize": "12"})
sns.set_style(rc={"axes.labelweight": "normal"})
sns.set_style(rc={"axes.linewidth": "0.5"})
sns.set_style(rc={"grid.color": "purple"})
sns.set_style(rc={"grid.linestyle": "--"})
sns.set_style(rc={"grid.linewidth": "0.5"})
sns.set_style(rc={"font.fantasy": "Comic Sans MS"})
sns.set_style(rc={"font.serif": "Utopia"})

penguins = sns.load_dataset("penguins")
# Axis-level
sns.histplot(data=penguins, x="flipper_length_mm")

sns.reset_orig()
penguins = sns.load_dataset("penguins")
# Axis-level
sns.histplot(data=penguins, x="flipper_length_mm")

# Ejemplo aplicado
df_ejercicio = sns.load_dataset('exercise')
df_ejercicio = df_ejercicio.drop('Unnamed: 0', axis='columns')
df_ejercicio.head()

df_30_min = df_ejercicio[df_ejercicio['time'] == '30 min']
df_30_min.head()

plt.figure()
# Figure -level
ax = sns.displot(data=df_30_min, x='pulse', kind='kde', hue='kind', fill=True)
ax.set(xlabel='Frecuencia Cardíaca', ylabel='Densidad', title='Distribución  de las pulsaciones')

ax = sns.catplot(data=df_ejercicio, kind='violin', x='time', y='pulse', hue='diet', split=True)
ax.set(xlabel='Duración de ejercicio', ylabel='Frecuencia cardíaca', title='Categorización de la distribución de pulsaciones')