update from forked repo

taxdata/puf/dcpentargetamt2011.csv

@@ -0,0 +1,21 @@
+,total,u26,26u35,35u45,45u55,55u60,60u65,65u75,75plus
+total,220.5554,3.2668,25.0779,49.9194,70.3845,37.9728,23.3431,9.9572,0.6337
+u5K,1.1581,0.0411,0.0841,0.1733,0.3118,0.1667,0.2209,0.1563,0.0040
+5u10K,1.1304,0.0743,0.1418,0.1530,0.2795,0.1545,0.1692,0.1414,0.0166
+10u15K,1.7084,0.1143,0.2178,0.2361,0.3626,0.3336,0.2293,0.1764,0.0384
+15u20K,2.8057,0.2239,0.3736,0.4198,0.7278,0.3787,0.4233,0.2336,0.0249
+20u25K,4.3694,0.2873,0.6468,0.6841,1.0589,0.6781,0.6144,0.3710,0.0287
+25u30K,5.6387,0.2935,0.9197,1.0006,1.3722,1.1272,0.6375,0.2767,0.0112
+30u40K,16.1258,0.5373,2.6527,3.0594,4.3521,2.7221,1.9665,0.7954,0.0402
+40u50K,19.1377,0.4829,3.0644,4.0657,5.6255,3.0436,1.9112,0.8700,0.0743
+50u75K,48.6375,0.7072,6.5134,10.8502,14.9928,8.2860,5.1546,2.0320,0.1013
+75Ku0.1M,37.2251,0.2379,4.2673,9.0029,12.1241,6.3733,3.8528,1.3138,0.0529
+0.1u0.2M,55.5740,0.2371,4.6679,13.9483,18.9280,9.8799,5.5415,2.2351,0.1361
+0.2u0.5M,21.2800,0.0211,1.3460,5.0850,8.0092,3.7434,1.9665,1.0250,0.0838
+0.5u1.0M,4.2527,0.0022,0.1305,0.9381,1.6156,0.8203,0.4980,0.2246,0.0063
+1.0u1.5M,0.8252,0.0022,0.0276,0.1866,0.3378,0.1288,0.0812,0.0632,0.0063
+1.5u2.0M,0.2767,0.0022,0.0071,0.0511,0.1204,0.0507,0.0297,0.0176,0.0063
+2u5M,0.3171,0.0010,0.0124,0.0535,0.1321,0.0620,0.0354,0.0189,0.0017
+5Mplus,0.0930,0.0013,0.0046,0.0117,0.0340,0.0240,0.0111,0.0058,0.0006
+5u10M,0.0674,0.0010,0.0035,0.0088,0.0231,0.0194,0.0074,0.0038,0.0005
+10u30M,0.0256,0.0003,0.0011,0.0029,0.0109,0.0046,0.0037,0.0020,0.0001

taxdata/puf/dcpentargetcnt2011.csv

@@ -0,0 +1,21 @@
+,total,u26,26u35,35u45,45u55,55u60,60u65,65u75,75plus
+total,46.9781,2.7796,8.9035,11.2926,12.9201,5.8450,3.5901,1.5212,0.1261
+u5K,1.1298,0.2766,0.2415,0.1942,0.1904,0.0785,0.0679,0.0673,0.0135
+5u10K,1.3069,0.3113,0.3088,0.2089,0.2054,0.0926,0.0908,0.0747,0.0145
+10u15K,1.6370,0.3211,0.3539,0.2724,0.2990,0.1508,0.1208,0.0995,0.0195
+15u20K,2.1262,0.3323,0.4603,0.3810,0.4441,0.1947,0.1833,0.1186,0.0119
+20u25K,2.7738,0.3519,0.6175,0.5553,0.5872,0.3019,0.2284,0.1194,0.0122
+25u30K,3.1692,0.2899,0.7167,0.6741,0.7544,0.3936,0.2300,0.1063,0.0041
+30u40K,6.7748,0.3608,1.5822,1.5853,1.7121,0.8025,0.5353,0.1868,0.0098
+40u50K,6.0192,0.2355,1.3411,1.4875,1.6533,0.7287,0.4167,0.1461,0.0103
+50u75K,10.1723,0.2178,1.8331,2.6402,3.0594,1.3791,0.7824,0.2488,0.0114
+75Ku0.1M,5.0471,0.0486,0.7485,1.3961,1.6301,0.7069,0.3885,0.1237,0.0046
+0.1u0.2M,5.1803,0.0309,0.5872,1.4571,1.7622,0.7604,0.4110,0.1627,0.0087
+0.2u0.5M,1.3446,0.0024,0.1010,0.3668,0.5043,0.2062,0.1064,0.0530,0.0046
+0.5u1.0M,0.2197,0.0001,0.0085,0.0563,0.0855,0.0370,0.0213,0.0098,0.0003
+1.0u1.5M,0.0416,0.0001,0.0016,0.0105,0.0174,0.0059,0.0037,0.0025,0.0003
+1.5u2.0M,0.0141,0.0001,0.0005,0.0030,0.0062,0.0024,0.0013,0.0008,0.0003
+2u5M,0.0170,0.0001,0.0008,0.0033,0.0072,0.0030,0.0017,0.0009,0.0001
+5Mplus,0.0046,0.00006,0.0003,0.0007,0.0018,0.0008,0.0006,0.0003,0.00003
+5u10M,0.0033,0.00004,0.0002,0.0005,0.0013,0.0006,0.0004,0.0002,0.00002
+10u30M,0.0013,0.00002,0.0001,0.0002,0.0005,0.0002,0.0002,0.0001,0.00001

taxdata/puf/finalprep.py

@@ -69,11 +69,14 @@ def finalprep(data):
     data = data.round(0).astype("int64")
 
     # - Impute pension contributions:
-    data = impute_pension_contributions(data.copy())
+    data = impute_pension_contributions(data.copy(), max_flpdyr)
 
     # - Rename 'filer' to 'data_source'
     data = data.rename(columns={"filer": "data_source"})
 
+    # - Sort columns to ensure every PUF is the same
+    data.sort_index(axis=1, inplace=True)
+
     return data
 
 

taxdata/puf/impute_pencon.py

@@ -42,19 +42,21 @@
 import sys
 import numpy as np
 import pandas as pd
+from pathlib import Path
 
 if sys.version_info[0] < 3:
     from StringIO import StringIO
 else:
     from io import StringIO
 
 
+CURPATH = Path(__file__).resolve().parent
 DUMP0 = False
 DUMP1 = False
 DUMP2 = False
 
 
-def targets():
+def targets(year):
     """
     Return a DataFrame containing number of taxpayers & spouses with earnings
     that make a pension contribution (in millions of people) and a DataFrame
@@ -68,62 +70,8 @@ def targets():
     revised data specified here.  Also, the top two wage groups (5u10M and
     10u30M) are combined into a single group (5Mplus).
     """
-    cnt = """
-        ,  total,   u26, 26u35,  35u45,  45u55, 55u60, 60u65, 65u75,75plus
-total   ,46.9781,2.7796,8.9035,11.2926,12.9201,5.8450,3.5901,1.5212,0.1261
-u5K     ,1.1298,0.2766,0.2415,0.1942,0.1904,0.0785,0.0679,0.0673,0.0135
-5u10K   ,1.3069,0.3113,0.3088,0.2089,0.2054,0.0926,0.0908,0.0747,0.0145
-10u15K  ,1.6370,0.3211,0.3539,0.2724,0.2990,0.1508,0.1208,0.0995,0.0195
-15u20K  ,2.1262,0.3323,0.4603,0.3810,0.4441,0.1947,0.1833,0.1186,0.0119
-20u25K  ,2.7738,0.3519,0.6175,0.5553,0.5872,0.3019,0.2284,0.1194,0.0122
-25u30K  ,3.1692,0.2899,0.7167,0.6741,0.7544,0.3936,0.2300,0.1063,0.0041
-30u40K  ,6.7748,0.3608,1.5822,1.5853,1.7121,0.8025,0.5353,0.1868,0.0098
-40u50K  ,6.0192,0.2355,1.3411,1.4875,1.6533,0.7287,0.4167,0.1461,0.0103
-50u75K  ,10.1723,0.2178,1.8331,2.6402,3.0594,1.3791,0.7824,0.2488,0.0114
-75Ku0.1M,5.0471,0.0486,0.7485,1.3961,1.6301,0.7069,0.3885,0.1237,0.0046
-0.1u0.2M,5.1803,0.0309,0.5872,1.4571,1.7622,0.7604,0.4110,0.1627,0.0087
-0.2u0.5M,1.3446,0.0024,0.1010,0.3668,0.5043,0.2062,0.1064,0.0530,0.0046
-0.5u1.0M,0.2197,0.0001,0.0085,0.0563,0.0855,0.0370,0.0213,0.0098,0.0003
-1.0u1.5M,0.0416,0.0001,0.0016,0.0105,0.0174,0.0059,0.0037,0.0025,0.0003
-1.5u2.0M,0.0141,0.0001,0.0005,0.0030,0.0062,0.0024,0.0013,0.0008,0.0003
-2u5M    ,0.0170,0.0001,0.0008,0.0033,0.0072,0.0030,0.0017,0.0009,0.0001
-5Mplus  ,0.0046,0.00006,0.0003,0.0007,0.0018,0.0008,0.0006,0.0003,0.00003
-"""
-    """
-5u10M   ,0.0033,0.00004,0.0002,0.0005,0.0013,0.0006,0.0004,0.0002,0.00002
-10u30M  ,0.0013,0.00002,0.0001,0.0002,0.0005,0.0002,0.0002,0.0001,0.00001
-    """
-    amt = """
-        ,   total,   u26,  26u35,  35u45,  45u55,  55u60,  60u65, 65u75,75plus
-total   ,220.5554,3.2668,25.0779,49.9194,70.3845,37.9728,23.3431,9.9572,0.6337
-u5K     ,1.1581,0.0411,0.0841,0.1733,0.3118,0.1667,0.2209,0.1563,0.0040
-5u10K   ,1.1304,0.0743,0.1418,0.1530,0.2795,0.1545,0.1692,0.1414,0.0166
-10u15K  ,1.7084,0.1143,0.2178,0.2361,0.3626,0.3336,0.2293,0.1764,0.0384
-15u20K  ,2.8057,0.2239,0.3736,0.4198,0.7278,0.3787,0.4233,0.2336,0.0249
-20u25K  ,4.3694,0.2873,0.6468,0.6841,1.0589,0.6781,0.6144,0.3710,0.0287
-25u30K  ,5.6387,0.2935,0.9197,1.0006,1.3722,1.1272,0.6375,0.2767,0.0112
-30u40K  ,16.1258,0.5373,2.6527,3.0594,4.3521,2.7221,1.9665,0.7954,0.0402
-40u50K  ,19.1377,0.4829,3.0644,4.0657,5.6255,3.0436,1.9112,0.8700,0.0743
-50u75K  ,48.6375,0.7072,6.5134,10.8502,14.9928,8.2860,5.1546,2.0320,0.1013
-75Ku0.1M,37.2251,0.2379,4.2673,9.0029,12.1241,6.3733,3.8528,1.3138,0.0529
-0.1u0.2M,55.5740,0.2371,4.6679,13.9483,18.9280,9.8799,5.5415,2.2351,0.1361
-0.2u0.5M,21.2800,0.0211,1.3460,5.0850,8.0092,3.7434,1.9665,1.0250,0.0838
-0.5u1.0M,4.2527,0.0022,0.1305,0.9381,1.6156,0.8203,0.4980,0.2246,0.0063
-1.0u1.5M,0.8252,0.0022,0.0276,0.1866,0.3378,0.1288,0.0812,0.0632,0.0063
-1.5u2.0M,0.2767,0.0022,0.0071,0.0511,0.1204,0.0507,0.0297,0.0176,0.0063
-2u5M    ,0.3171,0.0010,0.0124,0.0535,0.1321,0.0620,0.0354,0.0189,0.0017
-5Mplus  ,0.0930,0.0013,0.0046,0.0117,0.0340,0.0240,0.0111,0.0058,0.0006
-"""
-    """
-5u10M   ,0.0674,0.0010,0.0035,0.0088,0.0231,0.0194,0.0074,0.0038,0.0005
-10u30M  ,0.0256,0.0003,0.0011,0.0029,0.0109,0.0046,0.0037,0.0020,0.0001
-    """
-    cnt_df = pd.read_csv(StringIO(cnt), index_col=0)
-    cnt_df.columns = [name.strip() for name in cnt_df.columns]
-    cnt_df.index = [name.strip() for name in cnt_df.index]
-    amt_df = pd.read_csv(StringIO(amt), index_col=0)
-    amt_df.columns = [name.strip() for name in amt_df.columns]
-    amt_df.index = [name.strip() for name in amt_df.index]
+    cnt_df = pd.read_csv(Path(CURPATH, f"dcpentargetcnt{year}.csv"), index_col=0)
+    amt_df = pd.read_csv(Path(CURPATH, f"dcpentargetamt{year}.csv"), index_col=0)
     return cnt_df, amt_df
 
 
@@ -275,14 +223,14 @@ def impute(idata, target_cnt, target_amt):
 # end of impute() function
 
 
-def impute_pension_contributions(alldata):
+def impute_pension_contributions(alldata, year):
     """
     Main function in impute_pencon.py file.
     Argument: puf.csv DataFrame just before imputation is done.
     Returns: puf.csv DataFrame with imputed pension contribution amounts.
     """
     # specify target DataFrames with total column and total row removed
-    target_cnt, target_amt = targets()
+    target_cnt, target_amt = targets(year)
     target_cnt.drop(labels="total", axis="index", inplace=True)
     target_cnt.drop(labels="total", axis="columns", inplace=True)
     target_amt.drop(labels="total", axis="index", inplace=True)