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Solution: Visualizing Datasets

Solution: Visualizing Datasets

Let's solve the challenge to check your understanding of visualizing datasets.

We'll cover the following...

Here’s the solution to the problem that creates a bar chart and scatterplot after creating the desired subsets and dropping the NULL values.

Solution

C++
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
## Required datasets
poverty = pd.read_csv('../data/poverty.csv', low_memory=False)
## Code from here
gini = 'GINI index (World Bank estimate)'
year = 2016
## first plot
df =\
poverty[poverty['year']==year].sort_values(gini).dropna(subset=[gini])
fig = go.Figure()
fig =px.bar (df,
     x='Country Name',
     y=gini,
     title=' - '.join([gini, str(year)]))
fig.write_image("/usercode/output/abc.png", width=2000, height=500)
fig.show()
## Second plot
perc_pov_cols = poverty.filter(regex='Poverty gap').columns
perc_pov_55 = perc_pov_cols[2]
country = 'United States'
mode= "markers"
df =poverty[poverty['Country Name']==country][['year', perc_pov_55]].dropna()
fig = go.Figure()
fig.add_scatter(x=df['year'],
                y=df[perc_pov_55],
                text=df[perc_pov_55],
                mode=mode)
fig.layout.title = str(perc_pov_55) +' in the ' + country + ' by Year '         
fig.show()

Explanation

  • Lines 8–9: We create a gini variable and set it to 'GINI index (World Bank estimate)', and we create a year variable and set it to 2016.

  • Lines 11–12: We create subset df from the poverty DataFrame by sorting based on the gini variable and dropping the NULL values after sort. ...