runfile('/Users/rahulkulkarni/.spyder-py3/untitled10.py', wdir='/Users/rahulkulkarni/.spyder-py3')
First 5 rows of the dataset:
             LGA Name Region  ...   IRSD (avg) EGM expenditure per capita
0          whittlesea      M  ...   991.986560                 668.181005
1  northern grampians      C  ...   939.210476                 860.252333
2     greater geelong      C  ...   992.193318                 473.802552
3         colac-otway      C  ...   964.493561                 509.406940
4           moorabool      C  ...  1015.812630                 236.729365

[5 rows x 28 columns]

Data types of the dataset:
LGA Name                                          object
Region                                            object
2012                                             float64
LGA                                               object
Personal income <$400/week, persons              float64
Number of families                               float64
Female-headed lone parent families               float64
Male-headed lone parent families                 float64
Holds degree or higher, persons                  float64
Did not complete year 12, persons                float64
Unemployed, persons                              float64
Aboriginal or Torres Strait Islander, persons    float64
Born in non-English speaking country, persons    float64
population                                         int64
2012 ERP age 0-4, persons                        float64
2012 ERP age 5-9, persons                        float64
2012 ERP age 10-14, persons                      float64
2012 ERP age 15-19, persons                      float64
2012 ERP age 20-24, persons                      float64
2012 ERP age 25-44, persons                      float64
2012 ERP age 45-64, persons                      float64
2012 ERP age 65-69, persons                      float64
2012 ERP age 70-74, persons                      float64
2012 ERP age 75-79, persons                      float64
2012 ERP age 80-84, persons                      float64
2012 ERP age 85+, persons                        float64
IRSD (avg)                                       float64
EGM expenditure per capita                       float64
dtype: object

Columns after excluding 'LGA Name', 'Region', and '2012':
['LGA', 'Personal income <$400/week, persons', 'Number of families', 'Female-headed lone parent families', 'Male-headed lone parent families', 'Holds degree or higher, persons', 'Did not complete year 12, persons', 'Unemployed, persons', 'Aboriginal or Torres Strait Islander, persons', 'Born in non-English speaking country, persons', 'population', '2012 ERP age 0-4, persons', '2012 ERP age 5-9, persons', '2012 ERP age 10-14, persons', '2012 ERP age 15-19, persons', '2012 ERP age 20-24, persons', '2012 ERP age 25-44, persons', '2012 ERP age 45-64, persons', '2012 ERP age 65-69, persons', '2012 ERP age 70-74, persons', '2012 ERP age 75-79, persons', '2012 ERP age 80-84, persons', '2012 ERP age 85+, persons', 'IRSD (avg)', 'EGM expenditure per capita']

Missing values in each column:
LGA                                              0
Personal income <$400/week, persons              0
Number of families                               0
Female-headed lone parent families               0
Male-headed lone parent families                 0
Holds degree or higher, persons                  0
Did not complete year 12, persons                0
Unemployed, persons                              0
Aboriginal or Torres Strait Islander, persons    0
Born in non-English speaking country, persons    0
population                                       0
2012 ERP age 0-4, persons                        0
2012 ERP age 5-9, persons                        0
2012 ERP age 10-14, persons                      0
2012 ERP age 15-19, persons                      0
2012 ERP age 20-24, persons                      0
2012 ERP age 25-44, persons                      0
2012 ERP age 45-64, persons                      0
2012 ERP age 65-69, persons                      0
2012 ERP age 70-74, persons                      0
2012 ERP age 75-79, persons                      0
2012 ERP age 80-84, persons                      0
2012 ERP age 85+, persons                        0
IRSD (avg)                                       0
EGM expenditure per capita                       0
dtype: int64
Traceback (most recent call last):

  File /Library/anaconda3/lib/python3.10/site-packages/spyder_kernels/py3compat.py:356 in compat_exec
    exec(code, globals, locals)

  File ~/.spyder-py3/untitled10.py:58
    df.fillna(df.median(), inplace=True)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/frame.py:11706 in median
    result = super().median(axis, skipna, numeric_only, **kwargs)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/generic.py:12431 in median
    return self._stat_function(

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/generic.py:12377 in _stat_function
    return self._reduce(

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/frame.py:11562 in _reduce
    res = df._mgr.reduce(blk_func)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/internals/managers.py:1500 in reduce
    nbs = blk.reduce(func)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/internals/blocks.py:404 in reduce
    result = func(self.values)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/frame.py:11481 in blk_func
    return op(values, axis=axis, skipna=skipna, **kwds)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/nanops.py:147 in f
    result = alt(values, axis=axis, skipna=skipna, **kwds)

  File /Library/anaconda3/lib/python3.10/site-packages/pandas/core/nanops.py:787 in nanmedian
    raise TypeError(f"Cannot convert {values} to numeric")

TypeError: Cannot convert [['whittlesea' 'northern grampians' 'greater geelong' 'colac-otway'
  'moorabool' 'central goldfields' 'mitchell' 'alpine' 'benalla'
  'campaspe' 'glenelg' 'wellington' 'wodonga' 'warrnambool'
  'greater bendigo' 'ballarat' 'swan hill' 'greater shepparton' 'mildura'
  'east gippsland' 'bass coast' 'latrobe' 'surf coast' 'south gippsland'
  'macedon ranges' 'horsham' 'baw baw' 'wangaratta' 'melbourne'
  'moreland' 'darebin' 'boroondara' 'whitehorse' 'manningham' 'banyule'
  'maroondah' 'knox' 'monash' 'port phillip' 'kingston'
  'greater dandenong' 'frankston' 'casey' 'cardinia' 'yarra ranges'
  'hume' 'brimbank' 'melton' 'hobsons bay' 'wyndham'
  'mornington peninsula' 'yarra' 'maribyrnong' 'stonnington' 'glen eira'
  'bayside' 'moonee valley']] to numeric