Index timestamp shiny for python

def prepare_dataframe(df):
    df.rename(columns={
        'Bomba Calor - Temperatura de Aire (°C)': 'temp_aire',
        'Bomba Calor - Temperatura Entrada (°C)': 'temp_entrada',
        'Bomba Calor - Temperatura Salida (°C)' : 'temp_salida',
        'Bomba Calor - Estado Caldera 2 (estado)':'estado_caldera2',
        'Bomba Calor - Estado Caldera 1 (estado)': 'estado_caldera1',
        'Bomba Calor - Estado Bomba de Calor (estado)': 'estado_bomba_calor'
    }, inplace=True, errors='ignore')

    if 'timestamp' in df.columns:
        df['timestamp'] = pd.to_datetime(df['timestamp'], format="%d-%m-%y %H:%M").astype("datetime64[s]")
        # Se establece el 'timestamp' como índice para poder filtrar
        df.set_index('timestamp', inplace=True)

        df.sort_index(inplace=True)
    elif 'Fecha' in df.columns and 'Hora' in df.columns:
        
        df['timestamp'] = pd.to_datetime(df['Fecha'] + ' ' + df['Hora'], format="%d-%m-%y %H:%M").astype("datetime64[s]")
     
        df.set_index('timestamp', inplace=True)

        df.sort_index(inplace=True)

        final_columns = [
        'temp_aire', 'temp_entrada', 'temp_salida', 
        'estado_caldera2', 'estado_caldera1', 'estado_bomba_calor'
    ]
    existing_columns = [col for col in final_columns if col in df.columns]
    
    return df[existing_columns]

def load_data(source="csv", file_path="caldera_comepa.csv"):
    if source == "csv":
        # Lógica de carga del CSV
        df_crudo = pd.read_csv(file_path)
        #proximamente
        #elif fuente == "elastic":

        df_clean = prepare_dataframe(df_crudo)
        return df_clean

Hi guys, i would like to know if what i'm doing is a good practice, because i'm renaming the columns because it's name is very long, also i'm establishing the timestamp as an index for filter the data easily, you can check the example of csv below, as you can see, it's a wide format csv file, but i would like to know if i can change that into a long format, because of that way i can render a graphic using the plotly library. Finally after working with a csv i would like to migrate that into a database using elasticsearch, so i want to make a function that read a csv or elasticsearch

Fecha,Hora,Bomba Calor - Temperatura de Aire (°C),Bomba Calor - Temperatura Entrada (°C),Bomba Calor - Temperatura Salida (°C),Bomba Calor - Estado Caldera 2 (estado),Bomba Calor - Estado Caldera 1 (estado),Bomba Calor - Estado Bomba de Calor (estado)
04-10-25,00:01,22.2,63.4,63.4,0.0,0.0,0.0
04-10-25,00:11,21.9,61.8,61.7,0.0,0.0,0.0
04-10-25,00:21,21.7,60.3,60.3,0.0,0.0,0.0