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Solve the following python programming problem given the following code:
# As lots of waterpoints are missing a value for amount_tsh. For that field the missing
# data will be replaced by the mean data to drop less data for the model fit
imp = preprocessing.Imputer(missing_values=0, strategy='mean')
imp.fit(data['amount_tsh'].values.reshape(-1, 1))
data['water_amount'] = imp.transform(data['amount_tsh'].values.reshape(-1, 1)).ravel()

imp = preprocessing.Imputer(missing_values=0, strategy='median')
imp.fit(data['construction_year'].values.reshape(-1, 1))
data['construction_year'] = imp.transform(data['construction_year'].values.reshape(-1, 1)).ravel()

imp = preprocessing.Imputer(missing_values=0, strategy='mean')
imp.fit(data['gps_height'].values.reshape(-1, 1))
data['height'] = imp.transform(data['gps_height'].values.reshape(-1, 1)).ravel()

# Recode missing data as NaN
for field in ('longitude', 'latitude'):
    data[field] = data[field].map(lambda x: x if x else pd.np.nan)

def group_installer(data):
    def gather_installer(x):
        installer_map = {
            'organisation' : ('bank', 'msf', 'wwf', 'unicef', 'unisef', 'oxfam', 'oxfarm', 'club', 'care', 'without', 'faim', 'rain', 'red', 'angels', 'fundat', 'foundation'),
            'church' : ('church', 'churc', 'rcchurch', 'roman', 'missionsry', 'lutheran', 'islamic', 'islam', 'vision'),
            'private' : ('consulting', 'engineer', 'private', 'ltd', 'co.ltd', 'contractor', 'enterp', 'enterpr', 'company', 'contract'),
            'community' : ('village', 'community', 'communit', 'district', 'council', 'commu', 'villigers', 'villagers'),
            'government' : ('government', 'gov', 'govt', 'gover', 'gove', 'governme', 'ministry'),
            'other' : ('0', 'nan', 'known', 'other', 'unknown'), # Group 'unknown' data with 'other' as finally this means the same for interpretation
            'danida' : ('danida', 'danid'),
            'foreign government' : ('netherlands', 'germany', 'european')
        }

        for substr in x.split():
            for subsubstr in substr.split('/'):
                for key...
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Measures
The functional status of the water points are categorized in three groups: functional, functional needs repair and non functional. 
The potential predictors will be:

The amount of water available; missing data are coded as 0, they will be replaced by the mean value to suppress minimum amount of data.
The organization having funded the well

From the various actors, the following categories will be created :
'organisation' : ('bank', 'msf', 'wwf', 'unicef', 'unisef', 'oxfam', 'oxfarm', 'rotary club', 'lion's club', 'care', 'without', 'action contre la faim', 'rain', 'red cross', 'blue angels', 'fundat', 'foundation'),
'church' : ('church', 'churc', 'rcchurch', 'roman', 'missionsry', 'lutheran', 'islamic', 'islam', 'vision'),
'private' : ('consulting', 'engineer', 'private', 'ltd', 'co.ltd', 'contractor', 'enterp', 'enterpr', 'company', 'contract'),
'community' : ('village', 'community', 'communit', 'district', 'council', 'commu', 'villigers', 'villagers'),
'government' : ('government', 'gov', 'govt', 'gover', 'gove', 'governme', 'ministry'),
'other' : ('0', 'nan', 'known', 'other', 'unknown'),
'danida' : ('danida', 'danid'),
'foreign government' : ('netherlands', 'germany', 'european')

Then the 9 most funders will be kept and the others will be gathered in the other category.

The installer of the well; the grouping technique applied on the funders will be applied on the installer categories.
The GPS coordinates (height, longitude and latitude); missing data are coded as 0, they will be coded as NaN except for the height for which the missing data will be replaced by the mean values to keep a maximum of records for the analysis.
The geographic water basin
The geographic region
The population around the well; missing data are coded as 0, they will be coded as NaN.
Organization of public meeting at the water point; dichotomous variable (True/False)
The operator of the water point
The management of the water point
Does the waterpoint receive a construction permit?
Year the waterpoint was constructed;...