def match(mar_cps_path='asec2014_pubuse_tax_fix_5x8.dat',
puf_path='puf2009.csv'):
# Add arguments for specifying path to CPS file in CSV format
# this will allow the program to skip the process of creating the CPS from
# a .DAT file.
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--cps', help='path to CPS file in CSV format')
parser.add_argument('-d', '--dat', help='path to CPS file in DAT format')
parser.add_argument('-p', '--puf', help='path to PUF file in CSV format')
args = parser.parse_args()
# Create CPS file either from a CPS or through create_cps method
if args.cps is not None:
mar_cps = pd.read_csv(args.cps)
else:
if args.dat is not None:
mar_cps_path = args.dat
mar_cps = cpsmar.create_cps(mar_cps_path)
# If you already have the CPS in CSV format, comment out the line above and
# uncomment the line bellow to skip creation from the DAT file and use CSV
# do this initially in an effort to fix warning:
# "A value is trying to be set on a copy of a slice from a DataFrame"
if args.puf is not None:
puf_path = args.puf
puf = pd.read_csv(puf_path)
puf = puf[puf['recid'] != 999999]
print('CPS Created')
rets = Returns(mar_cps)
cps = rets.computation()
print('CPS Tax Units Created')
filers, nonfilers = adjfilst(cps)
print('Adjustment Complete')
soi = create_soi(puf.copy())
print('PUF Created')
soi_final, cps_final, counts = phaseone(filers, soi)
print('Start Phase Two')
match = phasetwo(
soi_final.loc[:, ['cellid', 'soiseq', 'wt', 'factor', 'yhat']],
cps_final.loc[:, ['cellid', 'cpsseq', 'wt', 'factor', 'yhat']])
print('Creating final file')
cpsrets = add_cps(filers, match, puf)
cps_matched = add_nonfiler(cpsrets, nonfilers)
# Rename variables for use in PUF data prep
renames = {
'icps1': 'age_head',
'icps2': 'age_spouse',
'wasp': 'wage_head',
'wass': 'wage_spouse'
}
cps_matched = cps_matched.rename(columns=renames)
return cps_matched
def match():
# If there is a .CSV version of the CPS, simply read that in. Otherwise
# convert the .DAT file to a .CSV
cps_csv_path = 'cpsmar2016.csv'
if os.path.isfile(cps_csv_path):
print('Reading CPS Data from .CSV')
mar_cps = pd.read_csv(cps_csv_path)
else:
cps_dat_path = 'asec2016_pubuse_v3.dat'
if os.path.isfile(cps_dat_path):
print('Converting .DAT to .CSV')
mar_cps = cpsmar.create_cps(cps_dat_path)
else:
m = ('You must have either the .DAT or .CSV version of the 2016' +
' CPS in your directory')
raise FileNotFoundError(m)
print('Reading PUF Data')
puf_path = 'puf2011.csv'
puf = pd.read_csv(puf_path)
# Change PUF columns to lowercase
puf.columns = map(str.lower, puf.columns)
# Remove aggregated variables from the PUF
puf = puf[(puf['recid'] != 999996) & (puf['recid'] != 999997) &
(puf['recid'] != 999998) & (puf['recid'] != 999999)]
print('Creating CPS Tax Units')
rets = Returns(mar_cps)
cps = rets.computation()
print('CPS Tax Units Created')
filers, nonfilers = adjfilst(cps)
print('Adjustment Complete')
soi = create_soi(puf.copy())
print('Start Phase One')
filers = filers.fillna(0)
soi = soi.fillna(0)
soi_final, cps_final, counts = phaseone(filers, soi)
print('Start Phase Two')
match = phasetwo(
soi_final.loc[:, ['cellid', 'soiseq', 'wt', 'factor', 'yhat']],
cps_final.loc[:, ['cellid', 'cpsseq', 'wt', 'factor', 'yhat']])
print('Creating final file')
cpsrets = add_cps(filers, match, puf)
cps_matched = add_nonfiler(cpsrets, nonfilers)
# add age range variable
cps_matched['agerange'] = 0
# Rename variables for use in PUF data prep
renames = {
'icps1': 'age_head',
'icps2': 'age_spouse',
'wasp': 'wage_head',
'wass': 'wage_spouse'
}
cps_matched = cps_matched.rename(columns=renames)
return cps_matched