title: TypeError: 'DataFrame' object is not callable python function
body: <p>I have two functions, one which creates a dataframe from a csv and another which manipulates that dataframe. There is no problem the first time I pass the raw data through the <code>lsc_age(import_data())</code> functions. However, I get the above-referenced error <code>(TypeError: 'DataFrame' object is not callable)</code> upon second+ attempts. Any ideas for how to solve the problem?</p>
  
  <pre><code>def import_data(csv,date1,date2):
      global data
      data = pd.read_csv(csv,header=1)
      data = data.iloc[:,[0,1,4,6,7,8,9,11]]
      data = data.dropna(how='all')
      data = data.rename(columns={"National: For Dates 9//1//"+date1+" - 8//31//"+date2:'event','Unnamed: 1':'time','Unnamed: 4':'points',\
                'Unnamed: 6':'name','Unnamed: 7':'age','Unnamed: 8':'lsc','Unnamed: 9':'club','Unnamed: 11':'date'})
      data = data.reset_index().drop('index',axis=1)
      data = data[data.time!='Time']
      data = data[data.points!='Power ']
      data = data[data['event']!="National: For Dates 9//1//"+date1+" - 8//31//"+date2]
      data = data[data['event']!='USA Swimming, Inc.']
      data = data.reset_index().drop('index',axis=1)
      for i in range(len(data)):
          if len(str(data['event'][i])) &lt;= 3:
              data['event'][i] = data['event'][i-1]
          else:
              data['event'][i] = data['event'][i]
      data = data.dropna()
      age = []
      event = []
      gender = []
      for row in data.event:
          gender.append(row.split(' ')[0])
          if row[:9]=='Female 10':
              n = 4
              groups = row.split(' ')
              age.append(' '.join(groups[1:n]))
              event.append(' '.join(groups[n:]))
          elif row[:7]=='Male 10':
              n = 4
              groups = row.split(' ')
              age.append(' '.join(groups[1:n]))
              event.append(' '.join(groups[n:]))
          else:
              n = 2
              groups = row.split(' ')
              event.append(' '.join(groups[n:]))
              groups = row.split(' ')
              age.append(groups[1])
      data['age_group'] = age
      data['event_simp'] = event
      data['gender'] = gender
      data['year'] = date2
      return data
  
  def lsc_age(data_two):
      global lsc, lsc_age, top, all_performers
      lsc = pd.DataFrame(data_two['event'].groupby(data_two['lsc']).count()).reset_index().sort_values(by='event',ascending=False)
      lsc_age = data_two.groupby(['year','age_group','lsc'])['event'].count().reset_index().sort_values(by=['age_group','event'],ascending=False)
      top = pd.concat([lsc_age[lsc_age.age_group=='10 &amp; under'].head(),lsc_age[lsc_age.age_group=='11-12'].head(),\
                   lsc_age[lsc_age.age_group=='13-14'].head(),lsc_age[lsc_age.age_group=='15-16'].head(),\
                   lsc_age[lsc_age.age_group=='17-18'].head()],ignore_index=True)
      all_performers = pd.concat([lsc_age[lsc_age.age_group=='10 &amp; under'],lsc_age[lsc_age.age_group=='11-12'],\
                              lsc_age[lsc_age.age_group=='13-14'],lsc_age[lsc_age.age_group=='15-16'],\
                              lsc_age[lsc_age.age_group=='17-18']],ignore_index=True)
      all_performers = all_performers.rename(columns={'event':'no. top 100'})
      all_performers['age_year_lsc'] = all_performers.age_group+' '+all_performers.year.astype(str)+' '+all_performers.lsc
      return all_performers
  
  years = [i for i in range(2008,2018)]
  for i in range(len(years)-1):
      lsc_age(import_data(str(years[i+1])+"national100.csv",\
      str(years[i]),str(years[i+1])))
  </code></pre>