$```$

$[]$ import requests

import pandas as pd

import statsmodels.api as sm

import matplotlib.pyplot as plt

$```$

PART $1$

Create a function, wbDF that takes a world bank indicator, a county, and a time frame and returns a dataFrame with the retrieved data.

To make this useful for later, make sure:

There is a column with the year

There is a column with the country $($this will be constant for now, but will be useful when combinding many such dataFrames$)$

$-$ The column with the data is named specific to indicator

Use the following to test your function

$```$

df$1=$ wbDF$($indicator$="$SL$.$TLF$.$CACT.FE$.$ZS$",$ country$=$"USA", start$\_$year$=2000,$ end$\_$year$=2022)$

$[]$ ## Here are a few WB indicators, and thier descriptions

indicator$\_$dict $=\{$

$\text{'}$SL$.$TLF$.$CACT.FE$.$ZS$\text{'}$: $"$F$-$LF$",$ #Female Labor Force Participation Rate

$\text{'}$NY$.$GDP$.$MKTP$.$CD$\text{'}$: $"$GDP$",$ #GDP$,$ duh

$\text{'}$SP$.$DYN$.$CBRT$.$IN$\text{'}$: 'BirthRate', #birth rate

$\}$

$```$

$[]$ Start coding or generate with AI$.$

Now create a function buildCountryData that takes a single country and date range and a list of indicators, returning a single dataFrame with columns for each indicator. Use the wbDF as a building block $($i$.$e$.,$ just merge the dataframes you created above.