text: import numpy as np
  import pandas as pd
  from scipy import stats, integrate
  import matplotlib.pyplot as plt
  from matplotlib.ticker import FuncFormatter
  from matplotlib.dates import DateFormatter, HourLocator, MinuteLocator, AutoDateLocator
  import seaborn as sns
  import csv
  import sys
  from datetime import datetime,date,timedelta
  import random
  from math import ceil
  import math
  
  sns.set_context("paper")
  sns.set(font_scale=2)
  sns.set_style("whitegrid", {
          "font.family": "serif",
          "font.serif": ["Times", "Palatino", "serif"],
          'grid.color': '.9',
          'grid.linestyle': '--',
  })
  
  busChargerIncomeDataFrame = pd.read_pickle("busChargerIncomeDataFrame0.pkl")
  busSwappingIncomeDataFrame = pd.read_pickle("busSwappingIncomeDataFrame0.pkl")
  busDCChargerIncomeDataFrame = pd.read_pickle("busDCChargerIncomeDataFrame0.pkl")
  busSwapperIncomeDataFrame = pd.read_pickle("busSwapperIncomeDataFrame0.pkl")
  
  for iteration in range(1,30):
          busChargerIncomeDataFrame = pd.concat((busChargerIncomeDataFrame,pd.read_pickle("busChargerIncomeDataFrame"+str(iteration) + ".pkl")))
          busSwappingIncomeDataFrame = pd.concat((busSwappingIncomeDataFrame, pd.read_pickle("busSwappingIncomeDataFrame"+str(iteration) + ".pkl")))
          busDCChargerIncomeDataFrame = pd.concat((busDCChargerIncomeDataFrame, pd.read_pickle("busDCChargerIncomeDataFrame"+str(iteration) + ".pkl")))
          busSwapperIncomeDataFrame = pd.concat((busSwapperIncomeDataFrame, pd.read_pickle("busSwapperIncomeDataFrame"+str(iteration) + ".pkl")))
  
  busChargerIncomeDataFrame = busChargerIncomeDataFrame.groupby(busChargerIncomeDataFrame.index).mean()
  busSwappingIncomeDataFrame = busSwappingIncomeDataFrame.groupby(busSwappingIncomeDataFrame.index).mean()
  busDCChargerIncomeDataFrame = busDCChargerIncomeDataFrame.groupby(busDCChargerIncomeDataFrame.index).mean()
  busSwapperIncomeDataFrame = busSwapperIncomeDataFrame.groupby(busSwapperIncomeDataFrame.index).mean()
  
  plt.figure()
  ax = plt.subplot(1,1,1)
  
  busSwappingIncomeDataFrame["income"] = busSwappingIncomeDataFrame["income"]*2
  busChargerIncomeDataFrame.plot(x="number",y="income",label="Charging", ax=ax,style="r-o",lw=3)
  # busSwappingIncomeDataFrame.plot(x="number",y="income",label="Swapping", ax=ax,style="b-.o",lw=3)
  
  ax.set(ylabel= "Saturation Number", xlabel='Charging Power (kW)')
  ax.legend(numpoints=1)
  
  plt.tight_layout()
  plt.savefig('charging_power_bus.pdf')
  
  print(busDCChargerIncomeDataFrame)
  print(busSwapperIncomeDataFrame)