#import liraries 
import pandas as pd
import seaborn as sns
import numpy as np 
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings("ignore")

from matplotlib import patches
from scipy.spatial import ConvexHull

DATA_DIR = "/content/sample_data/SeoulBikeData.csv"
data = pd.read_csv(DATA_DIR, encoding= 'unicode_escape')
data.head()

print("---------------------------------------------------------------------------------")
print(f"There are {data.shape[0]} observations and {data.shape[1]} features ")
print("---------------------------------------------------------------------------------")

data.info()

# Prepare Data 
# Create as many colors as there are unique midwest['category']
categories = np.unique(data['Seasons'])
colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]

# Draw Plot for Each Category
plt.figure(figsize=(10, 8), dpi= 80, facecolor='w', edgecolor='k')

for i, category in enumerate(categories):
    plt.scatter('Temperature(°C)', 'Rented Bike Count', 
                data= data.loc[data["Seasons"]==category, :], 
                s=20, c=colors[i], label=str(category))

# Decorations
plt.gca().set(xlim=(np.min(data['Temperature(°C)']), np.max(data['Temperature(°C)'])), ylim=(np.min(data["Rented Bike Count"]), np.max(data["Rented Bike Count"])),
              xlabel='Temperature(°C) ', ylabel='Rented Bike Count')

plt.xticks(fontsize=12); plt.yticks(fontsize=12)
plt.title("Scatterplot of Temperature(°C) vs Rented Bike Count", fontsize=15)
plt.legend(fontsize=12,loc="best")    
plt.show()    

# As many colors as there are unique midwest['category']
categories = np.unique(data['Seasons'])
colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]

# Step 2: Draw Scatterplot with unique color for each category
fig = plt.figure(figsize=(10, 8), dpi= 80, facecolor='w', edgecolor='k')    

for i, category in enumerate(categories):
    plt.scatter('Temperature(°C)', 'Rented Bike Count', data=data.loc[data.Seasons==category, :], s=20, c=colors[i], label=str(category), edgecolors='black', linewidths=.5)

# Step 3: Encircling
# https://stackoverflow.com/questions/44575681/how-do-i-encircle-different-data-sets-in-scatter-plot
def encircle(x,y, ax=None, **kw):
    if not ax: ax=plt.gca()
    p = np.c_[x,y]
    hull = ConvexHull(p)
    poly = plt.Polygon(p[hull.vertices,:], **kw)
    ax.add_patch(poly)

# Select data to be encircled
data_encircle = data.loc[data.Seasons=='Summer', :]                         

# Draw polygon surrounding vertices    
encircle(data_encircle["Temperature(°C)"], data_encircle['Rented Bike Count'], ec="k", fc="gold", alpha=0.1)
encircle(data_encircle["Temperature(°C)"], data_encircle['Rented Bike Count'], ec="firebrick", fc="none", linewidth=1.5)

# Step 4: Decorations
plt.gca().set(xlim=(np.min(data['Temperature(°C)']), np.max(data['Temperature(°C)'])), ylim=(np.min(data["Rented Bike Count"]), np.max(data["Rented Bike Count"])),
              xlabel='Temperature(°C) ', ylabel='Rented Bike Count')

plt.xticks(fontsize=12); 
plt.yticks(fontsize=12)
plt.title("Bubble Plot with Encircling", fontsize=15)
plt.legend(fontsize=12)    
plt.show()    

# Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.pairplot(data[["Rented Bike Count","Temperature(°C)","Seasons","Wind speed (m/s)"]], kind="scatter", hue="Seasons", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))
plt.show()

# Prepare Data
data_df = pd.read_csv(DATA_DIR, encoding= 'unicode_escape', parse_dates=['Date']).head(100)
x = np.arange(data_df.shape[0])
y_returns = (data_df['Rented Bike Count'].diff().fillna(0)/data_df['Rented Bike Count'].shift(1)).fillna(0) * 100

# Plot
plt.figure(figsize=(10,8), dpi= 80)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] >= 0, facecolor='green', interpolate=True, alpha=0.7)
plt.fill_between(x[1:], y_returns[1:], 0, where=y_returns[1:] <= 0, facecolor='red', interpolate=True, alpha=0.7)

# Annotate
plt.annotate('Peak \n Mar 2017', xy=(55.0, 280.0), xytext=(35.0, 300),
             bbox=dict(boxstyle='square', fc='firebrick'),
             arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white')


# Decorations
xtickvals = [str(m)[:3].upper()+"-"+str(y) for y,m in zip(data_df.Date.dt.year, data_df.Date.dt.month_name())]
plt.gca().set_xticks(x[::6])
plt.gca().set_xticklabels(xtickvals[::6], rotation=90, fontdict={'horizontalalignment': 'center', 'verticalalignment': 'center_baseline'})
plt.ylim(-100,350)
plt.xlim(1,100)
plt.title("Rented Bike Count Return", fontsize=15)
plt.ylabel('Monthly Rented Bike Count Return')
plt.grid(alpha=0.5)
plt.show()

from locale import D_FMT
# Prepare Data
df = data[['Rented Bike Count', 'Seasons']].groupby('Seasons').apply(lambda x: x.mean())
df.sort_values('Rented Bike Count', inplace=True)
df.reset_index(inplace=True)

# Draw plot
import matplotlib.patches as patches

fig, ax = plt.subplots(figsize=(10,8), facecolor='white', dpi= 80)
ax.vlines(x=df.index, ymin=0, ymax=df['Rented Bike Count'], color='firebrick', alpha=0.7, linewidth=20)

# Annotate Text
for i, rbc in enumerate(df['Rented Bike Count']):
    ax.text(i, rbc+10, round(rbc, 1), horizontalalignment='center', fontsize=10)


# Title, Label, Ticks and Ylim
ax.set_title('Bar Chart for Rented Bike Count', fontdict={'size':15})
ax.set(ylabel='Rented Bike Count', ylim=(0, 1500))
plt.xticks(df.index, df.Seasons.str.upper(), rotation=60, horizontalalignment='right', fontsize=12)

# Add patches to color the X axis labels
p1 = patches.Rectangle((.57, -0.005), width=.33, height=.13, alpha=.1, facecolor='green', transform=fig.transFigure)
p2 = patches.Rectangle((.124, -0.005), width=.446, height=.13, alpha=.1, facecolor='red', transform=fig.transFigure)
fig.add_artist(p1)
fig.add_artist(p2)
plt.show()

df = data[['Rented Bike Count', 'Seasons']].groupby('Seasons').apply(lambda x: x.mean())
df.sort_values('Rented Bike Count', inplace=True)
df.reset_index(inplace=True)

# Draw plot
fig, ax = plt.subplots(figsize=(10,8), dpi= 80)
ax.vlines(x=df.index, ymin=0, ymax=df['Rented Bike Count'], color='firebrick', alpha=0.7, linewidth=2)
ax.scatter(x=df.index, y=df['Rented Bike Count'], s=80, color='firebrick', alpha=0.7)

# Title, Label, Ticks and Ylim
ax.set_title('Lollipop Chart for Rented Bike Count', fontdict={'size':15})
ax.set_ylabel('Rented Bike Count')
ax.set_xticks(df.index)
ax.set_xticklabels(df.Seasons.str.upper(), rotation=60, fontdict={'horizontalalignment': 'right', 'size':12})
ax.set_ylim(0, 1200)

# Annotate Text
for i, rbc in enumerate(df['Rented Bike Count']):
    ax.text(i, rbc+15, round(rbc, 1), horizontalalignment='center',verticalalignment='bottom', fontsize=12)
plt.show()

# Draw Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.kdeplot(data.loc[data['Seasons'] == 'Winter', "Rented Bike Count"], shade=True, color="g", label="Winter", alpha=.7)
sns.kdeplot(data.loc[data['Seasons'] == 'Spring', "Rented Bike Count"], shade=True, color="deeppink", label="Spring", alpha=.7)
sns.kdeplot(data.loc[data['Seasons'] == 'Summer', "Rented Bike Count"], shade=True, color="dodgerblue", label="Summer", alpha=.7)
sns.kdeplot(data.loc[data['Seasons'] == 'Autumn', "Rented Bike Count"], shade=True, color="orange", label="Autumn", alpha=.7)

# Decoration
plt.title('Density Plot of Rented Bike Count by Seasons', fontsize=15)
plt.legend()
plt.show()



# Draw Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.distplot(data.loc[data['Holiday'] == 'No Holiday', "Rented Bike Count"], color="dodgerblue", label="No Holiday'", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})
sns.distplot(data.loc[data['Holiday'] == 'Holiday', "Rented Bike Count"], color="orange", label="Holiday", hist_kws={'alpha':.7}, kde_kws={'linewidth':3})
plt.ylim(0, 0.004)

# Decoration
plt.title('Density Plot of Rented Bike Count by Holiday', fontsize=15)
plt.legend()
plt.show()

# Draw Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.boxplot(x='Seasons', y='Rented Bike Count', data=data, notch=False)

# Add N Obs inside boxplot (optional)
def add_n_obs(df,group_col,y):
    medians_dict = {grp[0]:grp[1][y].median() for grp in df.groupby(group_col)}
    xticklabels = [x.get_text() for x in plt.gca().get_xticklabels()]
    n_obs = df.groupby(group_col)[y].size().values
    for (x, xticklabel), n_ob in zip(enumerate(xticklabels), n_obs):
        plt.text(x, medians_dict[xticklabel]*1.01, "#obs : "+str(n_ob), horizontalalignment='center', fontdict={'size':14}, color='white')

add_n_obs(data,group_col='Seasons',y='Rented Bike Count')    

# Decoration
plt.title('Box Plot of Rented Bike Count by Seasons', fontsize=15)
plt.ylim(-400, 3700)
plt.show()

# Draw Plot
fig, ax = plt.subplots(figsize=(12, 7), subplot_kw=dict(aspect="equal"), dpi= 80)

# Prepare Data
df = data.groupby('Seasons').size().reset_index(name='counts')

data_df = df['counts']
categories = df['Seasons']
explode = [0,0,0,0.1]

def func(pct, allvals):
    absolute = int(pct/100.*np.sum(allvals))
    return "{:.1f}% ({:d} )".format(pct, absolute)

wedges, texts, autotexts = ax.pie(data_df, 
                                  autopct=lambda pct: func(pct, data_df),
                                  textprops=dict(color="w"), 
                                  colors=plt.cm.Dark2.colors,
                                 startangle=140,
                                 explode=explode)

# Decoration
ax.legend(wedges, categories, title="Rented Bike Count by Seasons", loc="center left", bbox_to_anchor=(1, 0, 0.5, 1))
plt.setp(autotexts, size=10, weight=700)
ax.set_title("Seasons: Pie Chart")
plt.show()

from scipy.stats import sem

# Import Data
data_mean = data.groupby('Hour')["Rented Bike Count"].mean()
data_se = data.groupby('Hour')["Rented Bike Count"].apply(sem).mul(1.96)

# Plot
plt.figure(figsize=(10,8), dpi= 80)
plt.ylabel("# Rented Bike Count", fontsize=16)  
x = data_mean.index
plt.plot(x, data_mean, color="white", lw=2) 
plt.fill_between(x, data_mean - data_se, data_mean + data_se, color="#3F5D7D")  

# Decorations
# Lighten borders
plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(1)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(1)
plt.xticks(x[::2], [str(d) for d in x[::2]] , fontsize=12)
plt.title("Bike Rented by Hour of Day (95% confidence)", fontsize=15)
plt.xlabel("Hour of Day")

s, e = plt.gca().get_xlim()
plt.xlim(s, e)

# Draw Horizontal Tick lines  
for y in range(8, 20, 2):    
    plt.hlines(y, xmin=s, xmax=e, colors='black', alpha=0.5, linestyles="--", lw=0.5)

plt.show()

from pandas.plotting import parallel_coordinates

# Plot
plt.figure(figsize=(12,9), dpi= 80)
data.drop(['Date','Holiday', 'Functioning Day'],axis=1,inplace=True)
parallel_coordinates(data, 'Seasons', colormap='Dark2')

# Lighten borders
plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)

plt.title('Parallel Coordinated of Diamonds', fontsize=15)
plt.grid(alpha=0.3)
plt.xticks(fontsize=12,rotation=60,)
plt.yticks(fontsize=12)
plt.show()