Korygowanie wykresu

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.dates as dates

def pc_Ind(file, N):
    data_pd = pd.read_csv(file, parse_dates=[['Date','Time']])
    format = '%m/%d/%Y %H:%M'
    data_pd = data_pd.set_index(data_pd['Date_Time'])
    data_pd['Date_Time'] = pd.to_datetime(data_pd['Date_Time'], format=format)
    data_pd = data_pd.set_index(data_pd['Date_Time'])
    data_pd = data_pd.drop(columns='Date_Time')
    ub = np.zeros(data_pd.shape[0])
    lb = np.zeros(data_pd.shape[0])
    for i in range(N - 1, data_pd.shape[0]):
        ub[i] = np.max(data_pd['Close'][i-N+1:i+1])
        lb[i] = np.min(data_pd['Close'][i-N+1:i+1])
    fig = plt.figure(figsize=(10, 5))
    fig.add_axes()
    ax = fig.add_subplot(111)
    ax.xaxis.grid(True, which='minor')
    appl = mpatches.Patch(color='blue', label='Apple data')
    upb = mpatches.Patch(color='yellow', label='PC indicator (upper)')
    lwb = mpatches.Patch(color='red', label='PC indicator (lower)')
    plt.legend(handles=[appl, upb, lwb])
    plt.plot(data_pd.index, data_pd['Close'],'b')
    plt.plot(data_pd.index, ub + 5, 'y') 
    plt.plot(data_pd.index, lb - 5, 'r')
    plt.plot(data_pd.index, np.zeros(data_pd.shape[0]),'g')
    ax.set(title='Apple', ylabel='Close price', xlabel='Datetime')
    date_formatter = dates.DateFormatter('%m/%d/%Y')
    ax.xaxis.set_major_formatter(date_formatter)
    ax.xaxis.set_major_locator(dates.DayLocator(interval=40))
    plt.setp(ax.xaxis.get_majorticklabels(), rotation=90, fontsize=7)
    fig.autofmt_xdate()
    ax.autoscale_view()
    plt.grid()
    plt.show()
    return data_pd

pc_Ind('appl.csv', 14)

#Upper Band = Highest price in the last n periods
#Lower Band = Lowest price in the last n periods

    ub = np.empty(data_pd.shape[0])
    lb = np.empty(data_pd.shape[0])
    for i in range(N - 1, data_pd.shape[0]):
        ub[i] = np.max(data_pd['Close'][i-N+1:i+1])
        lb[i] = np.min(data_pd['Close'][i-N+1:i+1])

    ub = np.full(data_pd.shape[0], np.nan)
    lb = np.full(data_pd.shape[0], np.nan)
    for i in range(N - 1, data_pd.shape[0]):
        ub[i] = np.max(data_pd['Close'][i-N+1:i+1])
        lb[i] = np.min(data_pd['Close'][i-N+1:i+1])

df = df[df['date'] > jakas_data]