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dvp-u6-who-data's Introduction

Hi there 👋

I'm a Software Developer & Tutor currently residing in the midwest.

I enjoy coding, critiquing UI designs, absorbing all of the information about tech that I can & and teaching all I know along the way.

Tech Stack:

React.js, TypeScript, JavaScript, HTML, CSS, Node, GraphQL APIs, PostgreSQL, AWS, C#

What I'm up to:

🌱 I’m currently digging into ... Node.js, Apollo Express Server, MongoDB, React Query and Jest!

📖 I'm currently reading... Modern Software Engineering - Dave Farley Engineering Management for the Rest of Us - Sarah Drasner

👩🏽‍🏫 I work as a web development tutor & mentor new software developers trying to enter the field, and I'm really enjoying it.

dvp-u6-who-data's People

Contributors

dvp-u6-who-data's Issues

Nice customization!

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Lines 126 to 128 in 448eda0

    
           ax = sns.barplot(x="Country", y="GDP", data=df, palette="rocket") 
        
           plt.xlabel("Country", fontsize=14) 
        
           plt.ylabel("GDP", fontsize=14)

I like your choice of color palette for the bar graph and adjusting the font size for the axes and title labels.

Great additional research, but may be good to move legend

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Lines 279 to 286 in 448eda0

    
           #just playing around a bit here 
        
           f3, ax3 = plt.subplots(figsize=(10,10)) 
        
           sns.set_style=("whitegrid") 
        
           ax3 = sns.stripplot(x="Year", y="LEABY",hue="Country", data=df, palette="rocket") 
        
           plt.xticks(rotation=45) 
        
           plt.title("Life Expectancy by Year and Country", fontsize=16) 
        
           plt.savefig("LEABYByYearAndCountry_Stripplot.png") 
        
           plt.show()

I appreciate the other visualizations you are putting together for this dataset. However, your legend is blocking some of the data points - you can move the legend with a line of code like:
plt.legend(loc=4).

You can read more on legend customization in the documentation here.

Nice responses

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Lines 301 to 321 in 448eda0

    
           # First impressions: 
        
           # 1. What countries' bars change the most? 
        
           #      Zimbabwe's life expectancy bars change the most, and is the only country that has a decrease and then a steep increase in 
        
           #      the average life expectancy years. China's GDP looks like it has the most dramatic change out of all of the countries 
        
           # 2. What years have biggest change?  
        
           # 3. Least change in GDP over time:   
        
           # 4. Countries compare to each other: 
        
           #        Its strange that Chile seems to have the current highest life expectency and yet has the second lowest GDP between all  
        
           #        of the countries. 
        
           # 5. What do you think about the relationship between GDP and life expectancy?  
        
           #        The years seem to all have a stable increase between them for all countries except Zimbabwe.  There are a few instances in 
        
           #        China and Mexico where the life expectancy drops and then climbs back up the next year.  Zimbabwe reached it's lowespt point  
        
           #        for life expectancy in 2003, and from the chart that just shows Zimbabwe's data, it looks like 2004 was the lowest point for 
        
           #        GDP, which would show a correlation between a shorter life expectancy and a lower GDP within the country. 
        
           # 6. The data seems to be showing that the smaller, poorer countries are more significantly impacted by a lower average life expectancy. 
        
           #    Why is this?  
        
           #       Because the best way to eliminate poverty is through economic growth.  
        
           #       Poverty being one of the principle drivers of short lifespans, you would naturally expect to see life expectancy go up  
        
           #       as GDP per capita increases.

Your impressions are on the right track - good use of Markdown to respond to these questions

Unanswered Question

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Line 66 in 448eda0

# In[ ]:

You left this question blank. You could have answered it with inline comments, changing the cell to Markdown, or running code that answer the question:

list(df.Country.unique())
['Chile', 'China', 'Germany', 'Mexico', 'United States of America', 'Zimbabwe']

Great extra graphs

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Lines 215 to 243 in 448eda0

    
           f2, ax2 = plt.subplots(figsize=(10,10)) 
        
           #sns.set_style("whitegrid") 
        
           df2 = df[df.Country == "Zimbabwe"] 
        
           ax2 = sns.barplot(x="Country", y="GDP", data=df2,hue="Year", palette=colors ) 
        
           plt.title("Zimbabwe's Gross Domestic Product Totals by Year", fontsize=16) 
        
           plt.ylabel("Zimbabwe's GDP in Trillions of U.S. Dollars", fontsize=14) 
        
           plt.savefig("ZimbabweTotalGDPByYear_Barplot.png") 
        
           plt.show() 
        
           f4, ax4 = plt.subplots(figsize=(10,10)) 
        
           #sns.set_style("whitegrid") 
        
           df4 = df[df.Country == "Zimbabwe"] 
        
           ax4 = sns.barplot(x="Country", y="LEABY", data=df4,hue="Year", palette=colors ) 
        
           plt.title("Zimbabwe's Life Expectancy Average by Year", fontsize=16) 
        
           plt.ylabel("Zimbabwe's Life Expectancy Average", fontsize=14) 
        
           # Put the legend out of the figure 
        
           plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.1) 
        
           plt.savefig("ZimbabweTotalLEABYByYear_Barplot.png") 
        
           plt.show() 
        
           f3, ax3 = plt.subplots(figsize=(10,10)) 
        
           #sns.set_style("whitegrid") 
        
           df3 = df[df.Country.isin(['Zimbabwe','Chile'])] 
        
           ax3 = sns.barplot(x="Country", y="GDP", data=df3,hue="Year" ,estimator=np.median, palette=colors) 
        
           plt.title("Comparison of the GDP in Chile vs Zimbabwe", fontsize=16) 
        
           plt.ylabel("Gross Domestic Product Totals in Trillions of U.S. Dollars", fontsize=14) 
        
           plt.xlabel("Country",fontsize=14) 
        
           plt.savefig("GDPinChileVSZimbabwe_Barplot.png") 
        
           plt.show()

Excellent additional graphs that give more insight into the patterns of change in Zimbabwe (and Chile). When looking at all six countries at once, the trends in these lower-performing countries can be drowned out. By reprojecting these countries to their own axes, we can appreciate the year-to-year changes.

Summary

Rubric Score

Criteria 1: DataFrames and CSV

Score Level: 4 (Exceeds Expectations)
Comment(s): Data from all_data.csv file is successfully loaded into a DataFrame.

Criteria 2: Implementation of visualizations

Score Level: 4 (Exceeds Expectations)
Comment(s): The Jupyter notebook includes plots for all project data with Seaborn, using appropriate and meaningful color palettes to display insights about the data. Good work customizing the color in the bar graphs and customizing titles. You also went above and beyond by generating additional visualizations (like stripplot) and other charts focusing on the difficult-to-observe Zimbabwe values.

Criteria 3: Responses and Blog post

Score Level: 4 (Exceeds Expectations)
Comment(s): The Jupyter notebook includes inline responses analyzing data for most prompts. The blog post successfully expands on those ideas and includes images of the correct Seaborn plots. Your blog post does an excellent introducing the question that these data is exploring (is there correlation between GDP and life expectancy?) and interpreting the key insights from each chart. Also great job pulling from outside research (like Life Expectancy in Zimbabwe) and providing bibliographic references.

Overall Score: 12/12

Your code is very clean and generates all the charts as expected. Excellent job customizing the color and labels of these charts and providing additional visualizations . Your blog post is very thorough and explores not only the data set from many different angles, but also pulls in outside research to explain the trends we see in the data. Really nice work!

Good customization of arguments

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Line 170 in 448eda0

    
           ax = sns.violinplot(data=df, x="Country", y="LEABY", palette="muted", scale="count", inner="quartile")

Excellent work bulling for information from that dataset by using the .violinplot() arguments, like scale and inner.

Good color customizatoin

dvp-u6-who-data/Life Expectancy and GDP Data/life_expectancy_gdp.py

Line 203 in 448eda0

colors=sns.cubehelix_palette(16, start=.5, rot=-.75)

Great use of pulling in outside knowledge/research on cubehelix_palette to customize the color of the bar chart by year.

	ax = sns.barplot(x="Country", y="GDP", data=df, palette="rocket")
	plt.xlabel("Country", fontsize=14)
	plt.ylabel("GDP", fontsize=14)

	#just playing around a bit here
	f3, ax3 = plt.subplots(figsize=(10,10))
	sns.set_style=("whitegrid")
	ax3 = sns.stripplot(x="Year", y="LEABY",hue="Country", data=df, palette="rocket")
	plt.xticks(rotation=45)
	plt.title("Life Expectancy by Year and Country", fontsize=16)
	plt.savefig("LEABYByYearAndCountry_Stripplot.png")
	plt.show()

	# First impressions:
	# 1. What countries' bars change the most?
	# Zimbabwe's life expectancy bars change the most, and is the only country that has a decrease and then a steep increase in
	# the average life expectancy years. China's GDP looks like it has the most dramatic change out of all of the countries
	# 2. What years have biggest change?

	# 3. Least change in GDP over time:

	# 4. Countries compare to each other:
	# Its strange that Chile seems to have the current highest life expectency and yet has the second lowest GDP between all
	# of the countries.
	# 5. What do you think about the relationship between GDP and life expectancy?
	# The years seem to all have a stable increase between them for all countries except Zimbabwe. There are a few instances in
	# China and Mexico where the life expectancy drops and then climbs back up the next year. Zimbabwe reached it's lowespt point
	# for life expectancy in 2003, and from the chart that just shows Zimbabwe's data, it looks like 2004 was the lowest point for
	# GDP, which would show a correlation between a shorter life expectancy and a lower GDP within the country.
	# 6. The data seems to be showing that the smaller, poorer countries are more significantly impacted by a lower average life expectancy.
	# Why is this?
	# Because the best way to eliminate poverty is through economic growth.
	# Poverty being one of the principle drivers of short lifespans, you would naturally expect to see life expectancy go up
	# as GDP per capita increases.

	f2, ax2 = plt.subplots(figsize=(10,10))
	#sns.set_style("whitegrid")
	df2 = df[df.Country == "Zimbabwe"]
	ax2 = sns.barplot(x="Country", y="GDP", data=df2,hue="Year", palette=colors )
	plt.title("Zimbabwe's Gross Domestic Product Totals by Year", fontsize=16)
	plt.ylabel("Zimbabwe's GDP in Trillions of U.S. Dollars", fontsize=14)
	plt.savefig("ZimbabweTotalGDPByYear_Barplot.png")
	plt.show()

	f4, ax4 = plt.subplots(figsize=(10,10))
	#sns.set_style("whitegrid")
	df4 = df[df.Country == "Zimbabwe"]
	ax4 = sns.barplot(x="Country", y="LEABY", data=df4,hue="Year", palette=colors )
	plt.title("Zimbabwe's Life Expectancy Average by Year", fontsize=16)
	plt.ylabel("Zimbabwe's Life Expectancy Average", fontsize=14)
	# Put the legend out of the figure
	plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.1)
	plt.savefig("ZimbabweTotalLEABYByYear_Barplot.png")
	plt.show()

	f3, ax3 = plt.subplots(figsize=(10,10))
	#sns.set_style("whitegrid")
	df3 = df[df.Country.isin(['Zimbabwe','Chile'])]
	ax3 = sns.barplot(x="Country", y="GDP", data=df3,hue="Year" ,estimator=np.median, palette=colors)
	plt.title("Comparison of the GDP in Chile vs Zimbabwe", fontsize=16)
	plt.ylabel("Gross Domestic Product Totals in Trillions of U.S. Dollars", fontsize=14)
	plt.xlabel("Country",fontsize=14)
	plt.savefig("GDPinChileVSZimbabwe_Barplot.png")
	plt.show()

bethanyann / dvp-u6-who-data Goto Github PK

dvp-u6-who-data's Introduction

Hi there 👋

Tech Stack:

What I'm up to:

dvp-u6-who-data's People

Contributors

dvp-u6-who-data's Issues

Rubric Score

Criteria 1: DataFrames and CSV

Criteria 2: Implementation of visualizations

Criteria 3: Responses and Blog post

Overall Score: 12/12

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