Converting Unordered List Of Tuples To Pandas Dataframe

I am using the library usaddress to parse addresses from a set of files I have. I would like my final output to be a data frame where column names represent parts of the address (e

Solution 1:

Not sure if there is a DataFrame constructor that can handle info exactly as you have it now. (Maybe from_records or from_items?--still don't think this structure would be directly compatible.)

Here's a bit of manipulation to get what you're looking for:

cols = [j for _, j in info[0]]

# Could use nested list comprehension here, but this is probably#     more readable.
info2 = []
for row in info:
    info2.append([i for i, _ in row])

pd.DataFrame(info2, columns=cols)

  AddressNumber    StreetName StreetNamePostType StreetNamePostDirectional   PlaceName StateName ZipCode
0           123  Pennsylvania                Ave                   NW       Washington        DC   20008
1           652          Polk                 St                  San       Francisco,        CA   94102

Solution 2:

Thank you for your responses! I ended up doing a completely different workaround as follows:

I checked the documentation to see all possible parse_tags from usaddress, created a DataFrame with all possible tags as columns, and one other column with the extracted addresses. Then I proceeded to parse and extract information from the columns using regex. Code below!

parse_tags = ['Recipient','AddressNumber','AddressNumberPrefix','AddressNumberSuffix',
'StreetName','StreetNamePreDirectional','StreetNamePreModifier','StreetNamePreType',
'StreetNamePostDirectional','StreetNamePostModifier','StreetNamePostType','CornerOf',
'IntersectionSeparator','LandmarkName','USPSBoxGroupID','USPSBoxGroupType','USPSBoxID',
'USPSBoxType','BuildingName','OccupancyType','OccupancyIdentifier','SubaddressIdentifier',
'SubaddressType','PlaceName','StateName','ZipCode']

addr = ['123 Pennsylvania Ave NW Washington DC 20008', 
        '652 Polk St San Francisco, CA 94102', 
        '3711 Travis St #800 Houston, TX 77002']

df = pd.DataFrame({'Addresses': addr})
pd.concat([df, pd.DataFrame(columns = parse_tags)])

Then I created a new column that made a string out of the usaddress parse list and called it "Info"

df['Info'] = df['Addresses'].apply(lambda x: str(usaddress.parse(x)))

Now here's the major workaround. I looped through each column name and looked for it in the corresponding "Info" cell and applied regular expressions to extract information where they existed!

for colname in parse_tags:
    df[colname] = df['Info'].apply(lambda x: re.findall("\('(\S+)', '{}'\)".format(colname), x)[0] if re.search(
    colname, x) else "")

This is probably not the most efficient way, but it worked for my purposes. Thanks everyone for providing suggestions!