python script to get world bank data indicators that might correlate with gdp into a single dataframe

gdp_metrics.py

import pandas as pd
import plotly.express as px
import wbdata as wb
import json
from typing import Dict, List
from pathlib import Path

# import metrics from JSON
metricsPath = Path("./metrics-gdp.json")
metrics = json.load(metricsPath.open())

colNames = {
    'indicator.id' : 'indicator_id',
    'indicator.value' : 'indicator',
    'country.id' : 'country_code',
    'country.value' : 'country',
    'countryiso3code' : 'country_isocode',
}

colDrops = [
    'unit',
    'obs_status',
    'decimal'
]

def fetchMetric(metricDef: Dict[str,str]) -> pd.DataFrame :
    id = metricDef['id']
    try:
        data = pd.json_normalize(
            wb.get_data(id)
            )\
            .rename(
                columns=colNames
            )\
            .drop(
                columns=colDrops
            )
    except RuntimeError as err:
        print(metricDef)
        raise err
    return data

def colFormat(title: str) -> str:
    charMap = [ 
        ('/', 'per'),
        (' of ', ' '),
        ('-', '_'),
        (' ', '_'),
        ('us$','usd'),
        ('$','usd'),
        ('(', ''),
        (')' , ''),
        (',', ''),
        ('%', 'percent')
    ]
    title = title.lower()
    
    for (char, repl) in charMap:
        title = title.replace(char,repl)
    return title

def splitDateInterval(df: pd.DataFrame) -> pd.DataFrame :
    intervals = df[df.date.str.contains('-')].copy()
    if not len(intervals):
        return df
    minYear = intervals.date.apply(lambda itv : int(itv.split('-')[0].strip()))
    maxYear = intervals.date.apply(lambda itv : int(itv.split('-')[1].strip()))
    midYear = ((minYear + maxYear) / 2).astype(int)
    mindf = intervals.copy()
    mindf.date = minYear
    maxdf = intervals.copy()
    maxdf.date = maxYear
    middf = intervals.copy()
    middf.date = midYear
    return pd.concat(
        [
            df[~df.date.str.contains('-')].copy(),
            mindf,
            maxdf,
            middf
        ],
        axis=0
        )
def preformat(df: pd.DataFrame, minYear: int | None = None) -> pd.DataFrame :
    df = df.drop(columns=['country', 'country_code', 'indicator_id'])
    name = colFormat(df.indicator.unique().tolist().pop())
    df = df.rename(columns={'value' : name }).drop(columns=['indicator'])
    df = splitDateInterval(df)
    if minYear:
        df = df.loc[df.date.astype(int) > minYear, :].copy()
    return df

def fetchMetrics(metricList: List[Dict[str,str]], minYear: int | None = None) -> pd.DataFrame :
    metricDfs = map(fetchMetric, metricList)
    left = next(metricDfs)
    remaining = metricDfs
    for right in remaining:
        left = pd.merge(left=left, right=preformat(right, minYear=minYear), how='inner', on=['date', 'country_isocode'])
    return left

if __name__ == '__main__':
    df = fetchMetrics(metrics, minYear=2010)