# Importa das bibliotecas necessárias para manipular os dataframes (dados)
import numpy as np
import pandas as pd
import seaborn as sea
from pandas import Grouper
import matplotlib.pyplot as plt
%matplotlib inline

# lê o arquivo de dados "network.csv" em um diretório padrão. Nomeie o arquivo de log para "network.csv"
# municipio = pd.read_csv('Base_MUNIC_2020.csv')
ifood = pd.read_csv('ifood.csv', sep=";")

# Tipos dos dados
ifood.dtypes

ifood = ifood.astype({'CodMun':object})

# Tipos dos dados
ifood.dtypes

# Formato dos dados
ifood.shape

#ifood.head(30)
ifood.tail()

# Inversão da forma de visualização dos dados
ifood.T

ifood.groupby(['CodMun', 'categoria', 'nome_restaurante', 'faixa_preco']).sum()

cp_ifood = ifood.copy()

cp_ifood.head()

# df_cp = cp_ifood.drop(['CodMun', 'preco_minimo_pedido', 'nome_restaurante', 'faixa_preco','url'], axis=1)

df_cp = cp_ifood.drop(['tempo_entrega','preco_minimo_pedido', 'nome_restaurante', 'faixa_preco','url'], axis=1)

df_cp.head()

df_grupos = df_cp.groupby(['categoria'],sort=True).count().head(20)
# df_grupos = df_cp.groupby(['categoria'],sort=False).count().head(20)

df_grupos

df_grupos.plot(kind='barh', figsize=(8,8))

cp_ifood['categoria'].value_counts()

### 1. Gráfico de Barras
import matplotlib.pyplot as plt
import seaborn as sns
# sns.set_theme()
# Load the example flights dataset and convert to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
# Draw a heatmap with the numeric values in each cell
f, ax = plt.subplots(figsize=(9, 6))
sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)

import seaborn as sns
import matplotlib.pyplot as plt
# sns.set_theme(style="whitegrid")
# Load the example diamonds dataset
diamonds = sns.load_dataset("diamonds")
# Draw a scatter plot while assigning point colors and sizes to different
# variables in the dataset
f, ax = plt.subplots(figsize=(6.5, 6.5))
sns.despine(f, left=True, bottom=True)
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="ch:r=-.2,d=.3_r", 
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax)

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# sns.set_theme(style="white", context="talk")
rs = np.random.RandomState(8)
# Set up the matplotlib figure
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Generate some sequential data
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1)
ax1.axhline(0, color="k", clip_on=False)
ax1.set_ylabel("Sequencial")
# Center the data to make it diverging
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Divergente")
# Randomly reorder the data to make it qualitative
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitativo")
# Finalize the plot
sns.despine(bottom=True)
plt.setp(f.axes, yticks=[])
plt.tight_layout(h_pad=2)

import seaborn as sns
import matplotlib.pyplot as plt
# sns.set_theme(style="whitegrid")
# Initialize the matplotlib figure
f, ax = plt.subplots(figsize=(6, 15))
# Load the example car crash dataset
crashes = sns.load_dataset("car_crashes").sort_values("total", ascending=False)
# Plot the total crashes
sns.set_color_codes("pastel")
sns.barplot(x="total", y="abbrev", data=crashes,
            label="Total", color="b")
# Plot the crashes where alcohol was involved
sns.set_color_codes("muted")
sns.barplot(x="alcohol", y="abbrev", data=crashes,
            label="Alcoolizados", color="b")
# Add a legend and informative axis label
ax.legend(ncol=2, loc="lower right", frameon=True)
ax.set(xlim=(0, 24), ylabel="Estados Americanos",
       xlabel="Colisões de automóveis por bilhão de milhas")
sns.despine(left=True, bottom=True)