Project Description

def add_package(package_name):
    '''
    function that takes name of python library, installs it in appropriate environment.

    :param query: string variable of the package name.
    :returns: output of installation.
    '''
    import sys
    !{sys.executable} -m pip install package_name

def pprint(lst): 
       '''
    function that a lists, sorts it and prints elements in separate rows to ease comprehension.

    :param query: list.
    :returns: elements of list per row.
    '''
    lst=sorted(lst)
    print(*lst, sep='\n')

def catchstate(df, var_name: str) -> 'pd.DataFrame':
    """
    Helper function that captures intermediate Dataframes mid-chain.
    In the global namespace, make a new variable called var_name and set it to dataframe. 
    This function is useful when chaining, through the use of 'pipe', in order to filter after applying changes to dataframe. Ex: [Gun Violence](https://projects.aaas24.io/portfolio/viz/us-gun-violence.html)
        data=(df
                .assign(year=df.date.dt.year, 
                        month=df.date.dt.month
                        )
                .pipe(catchstate, var_name="df2")
                #filtering outlier using the new variable 'year' assigned
                [(df2.sub_region!='Mountain')&(df2.year!=2017)]
                .groupby(by=['year','sub_region'])
                .agg(
                    tot_victims=pd.NamedAgg(column="tot_victims", aggfunc="sum"),
                    num_incidents=pd.NamedAgg(column="incident_id", aggfunc="count")
                ).reset_index()
            )
        print(data)


    :param query: DataFrame
    :returns: DataFrame stored in variable 
    """
    globals()[var_name] = df
    return df

def replace_column_names(my_list):
    """
    function that replaces values in columns using dictionary provided below.

    :param query: List of DataFrame column names.
    :returns: List of columns names formatted .
    """
    col_dict={
        'mailing_or_mail_box_address':'mailing_address'
        'what_is_your_mailing_address?':'mailing_address'
        'please_select_your_current_school':'current_school'
    }
    return [x if x not in col_dict else col_dict[x] for x in my_list]

def clean_column_names(df, pprint='False'):
    """
    function to call on after DataFrame is created to format column names in lowercase 
    without spaces before querying.
    
    :param query: DataFrame
    :returns: DataFrame with columns formatted  
    """
    import pandas as pd 

    columns=df.columns
    new_names=[]
    char_dict={' ':'_',
            " ":'_',
            ':':'', 
            '/n':''
            }
    for c in columns:
        new_names.append(c
            .lower()
            .translate(char_dict)
            .rstrip()
            .lstrip()
            .replace(' ','_')
        )
    names_transformed=replace_column_names(new_names)
    df.columns=names_transformed
    if pprint=='True':
        pprint(new_names)
    return df

def complete_addresses(addresses, mapping):
    """
    function to call on after DataFrame is created to format column names in lowercase 
    without spaces before querying.
    
    :param query: DataFrame
    :returns: DataFrame with columns formatted  
    """
    import usaddress
    import pandas as pd
    
    df_result=pd.DataFrame(columns=list(mapping.values()))
    for i, addr in enumerate(addresses,start=0):
        dic_address={'address': addr, 'country':'United States' }
        info= usaddress.tag(addr, tag_mapping=mapping)
        #extract nested dictionary 
        info=(str(info[0])
            .split('[')
              [1][:-2]
              .replace("', ","' : ")
              .replace("('","")
              .replace("')","")
              .replace("'","")
              .replace("'","")
              .split(',')
             )
     
        for x in info:
            key=x.split(':')[0].rstrip().lstrip()
            value=x.split(':')[1].rstrip().lstrip()
            dic_address.update({key:value})

        dct = {k:[v] for k,v in dic_address.items()}
        df_result=pd.concat([pd.DataFrame(dct), df_result])
    
    return (df_result)
 

#rename the out-of-the-box variables
mapping={
'Recipient': 'recipient',
'AddressNumber': 'number',
'AddressNumberPrefix': 'num_prefix',
'AddressNumberSuffix': 'num_sufix',
'StreetName': 'street',
'StreetNamePreDirectional': 'street_pre_directional',
'StreetNamePreModifier': 'street_pre_modifier',
'StreetNamePreType': 'street_pre_type',
'StreetNamePostDirectional': 'street_pos_directional',
'StreetNamePostModifier': 'street_pos_modifier',
'StreetNamePostType': 'street_pos_type',
'CornerOf': 'corner',
'IntersectionSeparator': 'intersection',
'LandmarkName': 'landmark',
'USPSBoxGroupID': 'usps_groupbox_id',
'USPSBoxGroupType': 'usps_groupbox_type',
'USPSBoxID': 'usps_bx_id',
'USPSBoxType': 'usps_box_type',
'BuildingName': 'building_name',
'OccupancyType': 'occupancy_type',
'OccupancyIdentifier': 'occupancy_id',
'SubaddressIdentifier': 'sub_address_id',
'SubaddressType': 'sub_address_type',
'PlaceName': 'city',
'StateName': 'state',
'ZipCode': 'zip_code', 
'CountryName':'country'
}

import pandas as pd
df=pd.read_csv(Kaggle_link)
addresses=(df
           .assign(addr=df.iloc[:,0]+df.iloc[:,1])
           .iloc[:,2]
          )
addresses=addresses[:10]

df=complete_addresses(addresses, mapping)
print(df.head(3)