Global incomes: Dispersion

A quantile is a measure of dispersion created by dividing a frequency distribution of a DataFrame into even groups. You can return values at the given quantile q of a DataFrame df with the command df.quantile(q); likewise, supplying a list as q will return a value for each given quantile.

Here, you will continue your analysis of global income distribution using two measures of dispersion: the standard deviation, or square root of variance, and the interquartile range (IQR).

pandas has been imported as pd, and the income DataFrame from the previous exercise is in your workspace.

Este exercício faz parte do curso

Importing and Managing Financial Data in Python

Ver curso

Instruções do exercício

Using the appropriate functions, calculate the mean of income per capita as mean and the standard deviation as std.
Without using .quantile(), calculate and print the upper and lower bounds of an interval of one standard deviation around the mean in a list bounds:
- subtract std from mean as the first element
- add std to mean as the second element
Using .quantile() and a list of two appropriate decimal values, calculate and print the first and the third quartile of 'Income per Capita' as quantiles. Do the values match?
Calculate and print the IQR, iqr, using the simple subtraction expression you learned in the video.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Calculate mean
mean = ____

# Calculate standard deviation
std = income['Income per Capita'].std()

# Calculate and print lower and upper bounds
bounds = [____, ____]
print(bounds)

# Calculate and print first and third quartiles
quantiles = income['Income per Capita'].____([____, ____])
print(quantiles)

# Calculate and print IQR
iqr = ____ - ____
print(iqr)

Editar e executar o código