CCN data analysis

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
from pandas.plotting import scatter_matrix
from scipy import stats, integrate
import seaborn as sns
from pylab import savefig
%matplotlib inline

os.chdir(r'XXX')
Filenames=[];
for files in os.listdir('.'):
    if files.startswith('CCN 100 data'):
        Filenames.append(files)

def Getfile(fnombre):
  df=pd.read_csv(fnombre,skiprows=4)
  df['Date']=pd.read_csv(fnombre,nrows=2).iloc[0,1]
  df.columns=list(pd.Series(list(df.columns)).str.strip(' '))
  df['Datetime']=pd.to_datetime(df['Date'].astype('str')+' '+df['Time'].astype('str'))
  del df['Date']
  del df['Time']
  return df
df_list=[Getfile(fname) for fname in Filenames];
big_df=pd.concat(df_list)

big_df=big_df.rename(columns={'Datetime':'Time'})
big_df['Time']=big_df['Time'].dt.strftime('%Y-%m-%d %H:%M:%S')
big_df=big_df.set_index('Time')
big_df.index=pd.to_datetime(big_df.index)
big_df=big_df.sort_index()
big_df.to_csv('CCNdata.csv',encoding='utf-8')

import time, datetime
from datetime import datetime, timedelta
dfa=big_df[['Current SS','CCN Number Conc']]
dfa['SS']=dfa['Current SS'].shift(+1)
dfa['SS_Delta']=dfa['Current SS']-dfa['SS']
dfad=dfa.copy()
dfac=dfa[dfa['SS_Delta']!=0]
for i in np.arange(len(dfac)):
    a=dfac.index[i]
    b=a+timedelta(minutes = 5)
    dfad=dfad[~((dfad.index>=a)&(dfad.index<=b))]

dfad=dfad[['Current SS','CCN Number Conc']]
dfad.reset_index().pivot_table(index='Time',columns='Current SS',aggfunc='mean')['CCN Number Conc'].resample('5min').mean().to_csv('CCN_5min.csv')
dfad.reset_index().pivot_table(index='Time',columns='Current SS',aggfunc='mean')['CCN Number Conc'].resample('1H').mean().to_csv('CCN_1H.csv')