foodPlans module

foodPlans.foodPlansmain()

This function scrapes the USDA website for the most recent food plans and returns the weekly and monthly cost of each plan

These food plans are at a federal level, so it doesn’t change for different counties.

The function returns @fused_df, a polars dataframewith the following columns:

Cohorts Age Age Cohort Thrifty Monthly Low Monthly M Moderate Monthly Liberal Monthly

Representing the monthly costs for different food plans divided by age group and cohort

foodPlans.fuse_food_plans(thrifthy_plan, low_to_lib_plan)

Fuse two DataFrames from the thrifty and low-to-liberal food plans into one.

Parameters:

• thrifthy_plan (polars.DataFrame) – A DataFrame with columns ‘Age’, ‘Weekly cost’, and ‘Monthly cost’ for the thrifty plan.

• low_to_lib_plan (polars.DataFrame) – A DataFrame with columns ‘Age’, ‘Weekly cost low’, ‘Weekly cost moderate’, ‘Weekly cost liberal’, ‘Monthly cost low’, ‘Monthly cost moderate’, and ‘Monthly cost liberal’ for the low-to-liberal plan.

Returns:

A DataFrame with columns ‘Cohorts’, ‘Age’, ‘Age Cohort’, ‘Thrifty Monthly’, ‘Low Monthly’, ‘Moderate Monthly’, and ‘Liberal Monthly’.

Return type:

polars.DataFrame

foodPlans.getAgeCohortMeans(df)

Calculate the mean of each food plan for each age group.

Parameters:

df (polars.DataFrame) – A DataFrame with columns ‘Age’, ‘Age Cohort’, ‘Thrifty Monthly’, ‘Low Monthly’, ‘Moderate Monthly’, and ‘Liberal Monthly’ for the fused food plans.

Returns:

A DataFrame with columns ‘Age Cohort’, ‘Thrifty’, ‘Low’, ‘Moderate’, and ‘Liberal’ with the mean of each food plan for each age group.

Return type:

polars.DataFrame

foodPlans.getLowLiberalTable(linkToLowLiberalPlan)

This function takes a link to the most recent low-liberal plan and returns a polars DataFrame with the following columns:

Age, Weekly cost low, Weekly cost moderate, Weekly cost liberal, Monthly cost low, Monthly cost moderate, Monthly cost liberal

Columns can be referenced as dictionary keys, so for example, to print the weekly low cost of the moderate plan you do:

print(low_to_liberaldf[‘Weekly cost low’])

And you can also get the nth value of a column by using its index:

print(low_to_liberaldf[‘Weekly cost low’][0])

foodPlans.getThriftyTable(linkToThriftyPlan)

This function takes a link to the most recent thrifty plan and returns a polars DataFrame with the following columns:

Age-sex group, Weekly cost, Monthly cost

Columns can be referenced as dictionary keys, so for example, to print the weekly cost of the thrifty plan you do:

print(thrifthy_plan[‘Weekly cost’])

And you can also get the nth value of a column by using its index:

print(thrifthy_plan[‘Weekly cost’][0])

foodPlans.splitCostList1(numberList)

Split a string of numbers with dollar signs into a list of floats.

Parameters:

numberList (str) – A string of numbers with dollar signs, separated by newlines.

Returns:

A list of floats.

Return type:

list

Examples

>>> splitCostList1('$7.80\n$8.90')
<<< [7.8, 8.9]

foodPlans.splitGroupList1(groupList)

Splits a string of age group labels into a list of individual age ranges.

Parameters:

groupList (str) – A string containing age group labels separated by newlines, with an “Individual” label at the start.

Returns:

A list containing age ranges for child, male, and female groups, excluding headers.

Return type:

list

Examples

>>> splitGroupList1('Individuals\nChild Header\nChild 1\nChild 2\n...')
<<<['Child 1', 'Child 2', ...']