The purpose of this project is to learn to create analyses and visualizations that are useful in real life. #You are required to choose one dataset from four provided datasets. The datasets will be available for download on Apr 15. Complete all requirements outlined below for the dataset you have chosen. #Requirements #Your analysis should include:
1. At least two statistical summary (mean, sum, count, median etc).
2. At least two analyses of patterns, relationship etc. found in the data.
3. At least two visualizations - at least one for statistical summary, at least one for analysis. 4. A written summary of no less than 200 words describing the analysis and the results.
*In here is only showing the graphs and not the actually project: If you want to see the project here is the link:
#1. At least two statistical summary (mean, sum, count, median etc).df.head()
For the purpose of this blog we will just have a few example here click Data Analytics to see more:
df.plot.scatter(x='Freedom to make life choices', y= 'Generosity')
import seaborn as snssns.set_style('white')sns.set_style ('ticks')sns.regplot(x='Ladder score', y='Social support', color='tab:pink', data=df)
import pandas as pdimport matplotlib.pyplot as pltdf=pd.read_csv('world_happiness_report_2020.csv')# Filtering df by healthy life less 70df = df[df['Healthy life expectancy'] >75]# Grouping by countriesdf = df.groupby('Country name').count()# Filtering Healthy less than than 2df1 = df[df['Healthy life expectancy'] >=2]df.reset_index(level=0, inplace=True)plt.pie(df['Healthy life expectancy'], labels=df['Country name'])plt.show()
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