# -*- coding: utf-8 -*-
"""eda.363

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1LWUvpKSaZSgOHW-h4GzL9_0NM3RRIxTx
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv("/content/World-happiness-report-updated_2024.csv", encoding='latin-1')
df.head(5)

df.describe()

df.info()

df.isnull().sum()

numeric_cols = df.select_dtypes(include=np.number).columns
df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())

df2024 = pd.read_csv("/content/World-happiness-report-2024.csv", encoding='latin-1')
df2024.head(5)

df2024.describe()

df2024.info()

df2024.isnull().sum()

numeric_cols = df2024.select_dtypes(include=np.number).columns
df2024[numeric_cols] = df2024[numeric_cols].fillna(df2024[numeric_cols].mean())

df2024['Country name'].unique()

sns.countplot(x = 'Regional indicator', data = df2024)
plt.xticks(rotation = 60)
plt.show()

list_features = ['Social support', 'Freedom to make life choices', 'Generosity', 'Perceptions of corruption']
sns.boxplot(data=df2024.loc[:,list_features],orient='h',palette = 'Set3')
plt.show()

list_features = ['Ladder score', 'Log GDP per capita']
sns.boxplot(data=df2024.loc[:,list_features],orient='h',palette = 'Set3')
plt.show()

df2024_happiest_unhappiest = df2024[(df2024.loc[:,'Ladder score']>7.4) | (df2024.loc[:,'Ladder score']<3.5)]
sns.barplot(x = 'Ladder score', y= 'Country name', data = df2024_happiest_unhappiest, palette = 'coolwarm')
plt.title('Happiest and Unhappiest Countries in 2024')
plt.show()

plt.figure(figsize=(15,8))
sns.kdeplot(x=df2024['Ladder score'], hue = df2024['Regional indicator'], fill = True, linewidth = 2)
plt.axvline(df2024['Ladder score'].mean(),c= 'black')
plt.title('Ladder Score Distribution by Regional Indicator')
plt.show()

import plotly.express as px
fig = px.choropleth(df.sort_values('year'),
                    locations='Country name',
                    color='Life Ladder',
                    locationmode = 'country names',
                    animation_frame = 'year')
fig.update_layout(title = 'Life Ladder Comparison by Countires')

df2024_generosity = df2024[(df2024.loc[:,'Generosity']>0.6)|(df2024.loc[:,'Generosity']<0.05)]
sns.barplot(x = 'Generosity', y = 'Country name', data = df2024_generosity, palette= 'coolwarm')
plt.title('Most Generous and Most Ungenerous Countries in 2024')
plt.show()

fig = px.choropleth(df.sort_values('year'),
                    locations = 'Country name',
                    color = 'Generosity',
                    locationmode = 'country names',
                    animation_frame = 'year')
fig.update_layout(title = 'Generosity Comparison by Countries')
fig.show()

sns.swarmplot(x = "Regional indicator", y = "Generosity", data = df2024, palette="Set1")
plt.xticks(rotation = 90)
plt.title("Generous Distribution by Regional Indicator in 2021")
plt.show()

non_numeric_columns = df.select_dtypes(exclude=['float64', 'int64']).columns
df_numeric = df.drop(columns=non_numeric_columns)
correlation_matrix = df_numeric.corr()
sns.heatmap(correlation_matrix, annot = True, fmt ='.2f', linewidth = .7)
plt.title('Relationship Between Features')
plt.show()

sns.clustermap(correlation_matrix, center = 0, cmap = 'vlag', dendrogram_ratio = (0.1,0.2), annot = True, linewidth = .7, figsize=(10,10))
plt.show()