Reset index in Python Pandas DataFrame

Hi,
I have some json data that I'm converting to a pandas DataFrame. I'd like to make the current index column the 'code' column and reset the index, so the output would be:

      code      parameters                       units
1      1              Temperature               C
2      10              Specific Conductivity      NaN
3      11              Resistivity                      NaN
4      113              NaN                              ppm
5      114       NaN                              ppt
6      117       NaN                              mg/L


This is my example python code:

import pandas as pd
import json
import requests

data = {  
   'parameters':{  
      '22':'NO₃⁻',
      '23':'NH₄⁺',
      '24':'Cl⁻',
      '25':'Turbidity',
      '26':'Battery Voltage',
      '49':'Velocity',
      '28':'Flow Rate',
      '29':'Total Flow (volume)',
      'flowVelocity':'Flow Velocity',
      '50':'Chl-a Concentration',
      '51':'Chl-a Fluorescence',
      '30':'Partial Pressure O₂',
      '31':'Total Suspended Solids',
      '54':'BGA-PC Concentration',
      '32':'External Voltage',
      '10':'Specific Conductivity',
      '55':'BGA-PC Fluorescence',
      '33':'Battery Level',
      '11':'Resistivity',
      '34':'RWT Concentration',
      '12':'Salinity',
      '35':'RWT Fluorescence',
      '13':'Total Dissolved Solids',
      '58':'BGA-PE Concentration',
      '36':'Cl⁻ mV',
      '14':'Density',
      '59':'BGA-PE Fluorescence',
      'density':'Density',
      '37':'NO₃⁻ as N',
      '16':'Baro',
      '38':'NO₃⁻ mV',
      '39':'NH₄⁺ as N',
      '17':'pH',
      '18':'pH MV',
      '19':'ORP',
      'concentration':'Concentration',
      '1':'Temperature',
      '2':'Pressure',
      '3':'Depth',
      '4':'Level: Depth to Water',
      '5':'Level: Elevation',
      '9':'Actual Conductivity',
      '40':'NH₄⁺ mV',
      '41':'NH₃ as N',
      '20':'DO',
      '42':'Σ NH₃',
      '21':'% Saturation O₂'
   },
   'units':{  
      'megaliters':'ML',
      'yard':'yd',
      'acreInchPerMinute':'ac-in/min',
      'g/mL':'g/mL',
      'acreFeetPerSecond':'ac-ft/sec',
      'kilolitersPerMinute':'kL/min',
      'milliter':'mL',
      '193':'FNU',
      '194':'NTU',
      'litersPerDay':'L/d',
      'megalitersPerDay':'ML/d',
      '230':'ac-ft',
      '274':'mL/hr',
      '275':'L/min',
      '276':'L/hr',
      'millilitersPerSecond':'mL/s',
      '113':'ppm',
      '114':'ppt',
      'acreInchPerDay':'ac-in/d',
      '117':'mg/L',
      '118':'µg/L',
      'megalitersPerMinute':'ML/min',
      '97':'psu',
      'acreInchPerHour':'ac-in/hr',
      '17':'psi',
      'liter':'L',
      '19':'kPa',
      'acreFeetPerMinute':'ac-ft/min',
      'kilolitersPerSecond':'kL/sec',
      'acreInch':'ac-in',
      '162':'mV',
      '163':'V',
      '241':'%',
      '120':'g/L',
      '121':'ppb',
      '1':'C',
      '2':'F',
      '129':'g/cm³',
      'kilolitersPerDay':'kL/d',
      'megalitersPerSecond':'ML/sec',
      '209':'ft³/s',
      '20':'bar',
      '21':'mbar',
      '65':'µS/cm',
      '66':'mS/cm',
      '22':'mm Hg',
      '23':'in Hg',
      '24':'cm H₂O',
      'megalitersPerHour':'ML/hr',
      '26':'torr',
      '27':'atm',
      'kiloliters':'kL',
      '177':'% sat',
      '210':'ft³/min',
      '211':'ft³/hr',
      '212':'ft³/day',
      'kilolitersPerHour':'kL/hr',
      '257':'RFU',
      '213':'gal/s',
      '214':'gal/min',
      '215':'gal/hr',
      '216':'Mgal/day',
      '337':'ft/s',
      '338':'m/s',
      '217':'m³/s',
      '218':'m³/min',
      '219':'m³/hr',
      'millilitersPerDay':'mL/day',
      '33':'mm',
      '34':'cm',
      '35':'m',
      '37':'in',
      '38':'ft',
      'acreInchPerSecond':'ac-in/sec',
      'acreFeetPerHour':'ac-ft/hr',
      '220':'m³/day',
      '221':'L/s',
      '222':'ac-ft/d',
      '145':'pH',
      '223':'mL/min',
      '225':'ft³',
      '226':'gal',
      '227':'Mgal',
      '228':'m³',
      '81':'Ω-cm',
      'inH2o':'in H₂O'
   }
}

val = json.loads(json.dumps(data))

df = pd.DataFrame.from_dict(val, orient='columns')
df.columns = ['parameters', 'units']
print(df)

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