Python code too slow ?? Access to Access database with pyodbc

I need to get some data from a huge access database (two, actually, depending on which input I have). I have a function that calls that bank by chunks (if I try a fetchall gives memory problem). This function is sometimes called by my main function until I can get all the data I want. I'll post the 2 codes here ... I need to be able to speed up the execution time ... depending on the input it runs in 8 minutes! It's too much.

def chama_chunk():
    global data
    i=0
    names = [x[0] for x in cursor.description]  #pega os nomes das colunas

    while True: 
        rows = cursor.fetchmany(200) #pega 200 linhas
        if len(rows) == 0: #parar se n tiver nd
            break 
        else: 
            if (i == 0): #se for a 1a linha, cria o dataframe com 200 linhas e colunas conforme em names
                data = pd.DataFrame(np.array(rows),columns=names) 


            else:
                data_append = [{names[i]:x[i] for i in range(len(names))} for x in rows] 
                data_append = pd.DataFrame ( data_append, columns = names) 
                data = pd.concat(objs = [data,data_append], axis = 0) 

            i = i+1 
        if i == 200:
            break

The main function is this (the columns being deleted are not required)

def faztudo(lista,ponto1,ponto2):
    global ativo
    global reativo
    lista1=lista
    chama_chunk()
    del data['CampoB']
    del data['CampoC']
    del data['Dia']
    del data ['Unidade']
    del data['Ano']
    del data['Consumo']
    del data['Ponto_maxima']
    del data['Demanda_maxima']
    del data['Fator_carga']
    gc.enable()
    ativo=pd.DataFrame(columns=data.columns)
    reativo=pd.DataFrame(columns=data.columns)
    while (data.Mes == mes).any():
        if periodo == "DOM":
            #print("entrou dom")
            data.query('Mes==@mes',inplace=True)
            data.query('Codigo in @lista1',inplace=True)
            data.query('Dia_da_semana=="DM"',inplace=True)
            ativo=ativo.append(data.query('Grandeza==1'))
            reativo=reativo.append(data.query('Grandeza==2'))


        elif periodo == "SAB":
            #print("entrou sab")
            data.query('Mes==@mes',inplace=True)
            data.query('Codigo in @lista1',inplace=True)
            data.query('Dia_da_semana=="SB"',inplace=True)
            ativo=ativo.append(data.query('Grandeza==1'))
            reativo=reativo.append(data.query('Grandeza==2'))

        else:
            #print("entrou resto")
            data.query('Mes==@mes',inplace=True)
            data.query('Codigo in @lista1',inplace=True)
            data.query('Dia_da_semana==@diasuteis',inplace=True)
            ativo=ativo.append(data.query('Grandeza==1'))
            reativo=reativo.append(data.query('Grandeza==2'))


        chama_chunk()
        del data['CampoB']
        del data['CampoC']
        del data['Dia']
        del data ['Unidade']
        del data['Ano']
        del data['Consumo']
        del data['Ponto_maxima']
        del data['Demanda_maxima']
        del data['Fator_carga']

and my query is done by the function start:

def inicia (dbpath,mes):
    #dbpath="W:\CuCa-2018-ZFA.mdb"
    driver='{Microsoft Access Driver (*.mdb)}'
    con = pyodbc.connect("DRIVER={}; DBQ={}".format(driver,dbpath))
    query="SELECT * from Curvas_de_carga"
    global cursor
    cursor = con.cursor()
    cursor.execute(query)

Some other pieces of code have not been mentioned, but I think the performance problem is pretty much in those 3 functions.

I'm new to database access: / And the option to change databases does not exist here, because the database is not mine, I'm accessing a file from someone else.