Can someone explain how does svr_rbf.predict(dates) work?

[j2ee]

I am new to Python and cannot fully understand how this Python svr_rbf.predict function work. There is this predicted_price = predict_price(dates, prices, 29) which would return the predicted price in command prompt, does that mean if there is 28 rows of data in the csv, then using this 29 would predict the next day price? How does the training for RBF work in this code? Thanks.

import csv
import numpy as np
from sklearn.svm import SVR
import matplotlib.pyplot as plt


#plt.switch_backend('QT5Agg')  



dates = []
prices = []

def get_data(filename):
    with open(filename, 'r') as csvfile:
        csvFileReader = csv.reader(csvfile)
        next(csvFileReader) # skipping column names
        for row in csvFileReader:
            #dates.append(int(row[0].split('-')[0]))
            dates.append(float(row[0]))
            prices.append(float(row[1]))
    return

def predict_price(dates, prices, x):
    dates = np.reshape(dates,(len(dates), 1)) # converting to matrix of n X 1

    #svr_lin = SVR(kernel= 'linear', C= 1e3)
    #svr_poly = SVR(kernel= 'poly', C= 1e3, degree= 2)
    svr_rbf = SVR(kernel= 'rbf', C= 1e3, gamma= 0.1) # defining the support vector regression models
    svr_rbf.fit(dates, prices) # fitting the data points in the models
    #svr_lin.fit(dates, prices)
    #svr_poly.fit(dates, prices)


    plt.scatter(dates, prices, color= 'black', label= 'Data') # plotting the initial datapoints 
    plt.plot(dates, svr_rbf.predict(dates), color= 'red', label= 'RBF model') # plotting the line made by the RBF kernel
    #plt.plot(dates,svr_lin.predict(dates), color= 'green', label= 'Linear model') # plotting the line made by linear kernel
    #plt.plot(dates,svr_poly.predict(dates), color= 'blue', label= 'Polynomial model') # plotting the line made by polynomial kernel
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.title('Support Vector Regression')
    plt.legend()
    plt.show()

    #return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0]
    return svr_rbf.predict(x)[0]


get_data('aapl.csv') # calling get_data method by passing the csv file to it
print ("Dates- ", dates)
print ("Prices- ", prices)

predicted_price = predict_price(dates, prices, 29)  

print(predicted_price)