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All our activities include solutions with explanations on how they work and why we chose them.
Start by cleaning the Population 2023 column by removing commas and converting the values from strings to integers. This ensures that the population data is treated numerically rather than as text.
Next, sort the DataFrame in descending order based on the Population 2023 column to identify the top 10 most populous countries.
Finally, plot the chart using the following information:
Figure size : 10 by 6
Title: Top 10 Most Populous Countries in 2023
Title Fontsize : 14
x and y label Fontsize : 12
Color : skyblue
Start by selecting the top three countries with the highest net migration from the DataFrame . Next, Plot the bar chart by placing the country names on the x-axis and their net migration values on the y-axis, using the following information:
Figure size : 10 by 6
Color : Green
Title : Top 3 Countries with the Largest Net Migration
x-axis lablel : Country
y-axis label : Net Migration
Data Prep:
Firstly, convert the World Population Share % column from a string to a float by removing the percentage sign. Next, sort the DataFrame by Population 2023 column in descending order to identify the top 5 countries by population.
Calculate "Other" Category: Top 5 Countries: Choose the top 5 countries based on their 2023 population. Compute "Other": Calculate the combined world population share for the remaining countries by subtracting the sum of the top 5 countries' shares from 100%. This will create an "Other" category to represent the rest of the world.
Plot Pie Chart: Here, each slice will represent a country with the remaining share labelled as other. Utilize the following information for this task:
Figure size : 8 by 8
colors : plt.cm.Paired.colors
autopct : '%1.1f%%' to display the population percentages.
Rotatate the chart for better visual balance using startangle : 140
Title : World Population Share of Top 5 Countries (2023)
Create a scatter plot to examine the relationship between the land area and population of countries in 2023. Use the Land Area (Km²) column for the x-axis and the Population 2023 for the y-axis. Use the following information to plot the graph:
Color : Blue
Figure size : 10 by 6
Transparency : alpha=0.7
Title: Relationship Between Population and Land Area (2023)'
Title Fontsize : 14
x and y label Fontsize : 12
Start by creating bins that categorize countries based on their median age : '0-20', '21-30', '31-40', and '41+'. Then, use these bins to create an Age Group column in the DataFrame. Next, calculate the average fertility rate for each age group by grouping the data accordingly. Finally, plot these averages using a bar chart, where each bar represents the average fertility rate for a specific age group, giving you a clear visual comparison. Use the following information for the chart:
Figure size : 10 by 6
Color : Pink
Title: Average Fertility Rate by Age Group
Title Fontsize : 14
x and y label Fontsize : 12
Generate a histogram to visualize the distribution of median ages across all countries, using the following information to plot the graph:
Figure size : 10 by 6
Edgecolor : black
Transparency : alpha=0.7
Bins : 15
Title: Distribution of Median Ages Across Countries
Create a grouped bar chart to compare the population and net population change for the top 10 countries. The chart uses blue bars for Population 2023 and red bars for Net Population Change. The x-axis should show the country names, and the y-axis represents the values. The x-axis labels are rotated for better readability.
Figure size : 12 by 8
bar_width : 0.35
The chart should include a legend.
Title: Population and Net Population Change for Top 10 Countries
Start by calculating the mean fertility rate across all countries to get a reference point. Next, create a figure and a subplot with a size of 10 by 6 inches to set up your histogram. Plot the histogram using the fertility rates from your dataset, dividing the data into 20 bins. Choose a sky-blue color for the bars with black edges, and make them slightly transparent(alpha:0.7) to enhance visibility.
Then, add a red dashed vertical line at the average fertility rate to highlight this key value on the histogram. Label this line as average_fertility_rate so it shows the exact average fertility rate.
Finally, add a title to the plot and label the x-axis as Fertility Rate and the y-axis as Frequency to make the chart clear. Display the plot to visualize how fertility rates are distributed across countries, with the average rate clearly marked.
Hey there, I'm Ade. Think of me as a data detective on a mission to uncover the secrets hidden in numbers. I love the feeling of uncovering hidden stories within data. It's like being a detective, but with spreadsheets and algorithms.I'm on a mission
