https://github.com/fatihilhan42/world-happiness-report-2021-data-analysis-using-python
In this project, we tried to reflect the happiness rates of countries graphically and schematically by using data sets of 2021 with data analysis data visualization methods.
https://github.com/fatihilhan42/world-happiness-report-2021-data-analysis-using-python
Last synced: 2 months ago
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In this project, we tried to reflect the happiness rates of countries graphically and schematically by using data sets of 2021 with data analysis data visualization methods.
- Host: GitHub
- URL: https://github.com/fatihilhan42/world-happiness-report-2021-data-analysis-using-python
- Owner: fatihilhan42
- Created: 2022-08-03T13:57:41.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-03T14:01:25.000Z (almost 3 years ago)
- Last Synced: 2025-01-29T06:24:55.950Z (4 months ago)
- Language: Jupyter Notebook
- Size: 657 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# World-Happiness-Report-2021-Data-Analysis-Using-Python
In this project, we tried to reflect the happiness rates of countries graphically and schematically by using data sets of 2021 with data analysis data visualization methods.
## Libraries and Utilities
```Python
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
```
### Let's take a look at our top five data
```Python
data.head()
```
### Plot between Ladder score and GDP
```Python
plt.rcParams['figure.figsize'] = (15,7)
plt.title('Plot between Ladder score and GDP')
sns.scatterplot(x = happy_df.ladder_score, y = happy_df.logged_GDP_per_capita, hue = happy_df.regional_indicator, s = 200);
plt.legend(loc = 'upper left',fontsize = '10')
plt.xlabel('Ladder score')
plt.ylabel('GDP per capita')
```
### GDP by Region
```Python
gdp_region.plot.pie(autopct = '%1.1f%%')
plt.title('GDP by Region')
plt.ylabel('')
```
### Correlation Map
```Python
cor = happy_df.corr(method = 'pearson')
f, ax = plt.subplots(figsize = (10,5))
sns.heatmap(cor, mask = np.zeros_like(cor, dtype = np.bool), cmap = 'Blues', square = True, ax = ax)
```
### Perception of Corruption in varius Regions
```Python
dplt.rcParams['figure.figsize'] = (12,8)
plt.title('Perception of Corruption in varius Regions')
plt.xlabel('Regions', fontsize = 15)
plt.ylabel('Corruption Index ', fontsize = 15)
plt.xticks(rotation = 30,ha='right')
plt.bar(corruption.index, corruption.perceptions_of_corruption)
```
### Top 10 happiest countries Life Expectancy&&Bottom 10 least happy countries Life Expectancy
```Python
fig, axes = plt.subplots(1,2, figsize= (16,6))
plt.tight_layout(pad = 2)
xlabels = top_10.country_name
axes[0].set_title('Top 10 happiest countries Life Expectancy')
axes[0].set_xticklabels(xlabels, rotation = 45, ha = 'right')
sns.barplot(x = top_10.country_name, y = top_10.healthy_life_expectancy, ax = axes[0])
axes[0].set_xlabel('Country Name')
axes[0].set_ylabel('Life expectancy')
xlabels=bottom_10.country_name
axes[1].set_title('Bottom 10 least happy countries Life Expectancy')
axes[1].set_xticklabels(xlabels,rotation = 45, ha ='right')
sns.barplot(x = bottom_10.country_name, y= bottom_10.healthy_life_expectancy, ax = axes[1])
axes[1].set_xlabel('Country Name')
axes[1].set_ylabel('Life expectancy')
```
### Freedom to make life choices
```Python
plt.rcParams['figure.figsize'] = (15,7)
sns.scatterplot(x = happy_df.freedom_to_make_life_choices, y = happy_df.ladder_score, hue = happy_df.regional_indicator, s=200)
plt.legend(loc = 'upper left', fontsize = '12')
plt.xlabel('Freedom to make life choices')
plt.ylabel('Ladder score')
```
### top five Country of Countries with Most Perception of Corruption
```Python
country = happy_df.sort_values(by='perceptions_of_corruption').head(10)
plt.rcParams['figure.figsize'] = (12,6)
plt.title('Countries with Most Perception of Corruption')
plt.xlabel('Country', fontsize = 13)
plt.ylabel('Corruption Index', fontsize = 13)
plt.xticks(rotation = 30,ha='right')
plt.bar(country.country_name, country.perceptions_of_corruption)
```
### last five country of Countries with Most Perception of Corruption
```Python
country = happy_df.sort_values(by='perceptions_of_corruption').tail(10)
plt.rcParams['figure.figsize'] = (12,6)
plt.title('Countries with Most Perception of Corruption')
plt.xlabel('Country', fontsize = 13)
plt.ylabel('Corruption Index', fontsize = 13)
plt.xticks(rotation = 30,ha='right')
plt.bar(country.country_name, country.perceptions_of_corruption)
```
### Corruption vs Hapiness
```Python
plt.rcParams['figure.figsize'] = (15,7)
sns.scatterplot(x = happy_df.ladder_score, y = happy_df.perceptions_of_corruption, hue=happy_df.regional_indicator, s=200)
plt.legend(loc='lower left', fontsize = '14')
plt.xlabel('Ladder score')
plt.ylabel('Corruption')
```
### Hope to see you in another project. Take good care of yourself.
