data.py

# data exploration / EDAs

# Imports
import pandas as pd
import os
import json

# Read data
ev = pd.read_csv(os.path.join("data", "EV Data - EV Data.csv"), index_col=0)
chargers = pd.read_csv(
    os.path.join("data", "EV Charging Stations - EV Chargers.csv"), index_col=0
)
energy_prod = pd.read_csv(os.path.join("data", "Energy Production.csv"), index_col=0)


# Understanding variables
def export_unique_values_json(data, filename):
    unique_values = {
        col: data[col].astype(int).unique().tolist()
        if data[col].dtype == "int64"
        else list(data[col].unique())
        for col in data.columns
    }

    filepath = os.path.join("data", f"{filename}.json")

    with open(filepath, "w") as f:
        json.dump(unique_values, f, indent=4)

    return print("File saved successfully!")


## Run the following only once, then lookup unique values
## export_unique_values_json(ev, "ev_unique_values")
## export_unique_values_json(chargers, "chargers_unique_values")
## export_unique_values_json(energy_prod, "energy_prod_unique_values")


# Data cleaning
## Aligning column names.
ev.rename(columns={"region": "Country"}, inplace=True)
chargers.rename(columns={"region": "Country"}, inplace=True)

## Clean date for energy_prod
energy_prod[["Year", "Month"]] = energy_prod["Time"].str.split("-", expand=True)
energy_prod["Year"] = "20" + energy_prod["Year"]
energy_prod["Date"] = pd.to_datetime(
    energy_prod["Month"] + " " + energy_prod["Year"], format="%b %Y"
)

## Join data
## TODO: Join all data under one df.