Course notes: Exploratory Data Analysis in Python
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    Course Notes

    Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

    # Import any packages you want to use here
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

    Take Notes

    Add notes here about the concepts you've learned and code cells with code you want to keep.

    #Importing unemployment dataset
unemployment = pd.read_csv('datasets/clean_unemployment.csv')
unemployment.head()
    #Information about unemployment columns
unemployment.info()
    # summary statistics
unemployment.describe()
    unemployment.shape
    #Exploring continent column
unemployment['continent'].value_counts().sort_values(ascending=False)
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    #global unemployment rates in 2021
#Data is represented as percentage in the column
sns.histplot(data=unemployment, x='2021', binwidth=1)

    As observed from the graph, many countries had unemployment between 3% to 8% and a few had above 20%

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    unemployment.dtypes
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    not_oceania = unemployment[~unemployment['continent'].isin(['Oceania'])]
not_oceania
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    #Exploring unemployment rate in 2021
sns.boxplot(x='2021', y='continent', data=unemployment)
plt.show()