In [1]:
import pandas as pd
mroz = pd.read_csv("mroz.csv", names = ('inlf', 'hours', 'kidslt6','kidsge6', 'age', 'educ','wage', 'repwage', 'hushrs','husage','huseduc', 'huswage', 'faminc','mtr', 'motheduc', 'fatheduc','unem', 'city', 'exper','nwifeinc', 'lwage', 'expersq'))
In [2]:
mroz = mroz.dropna()
mroz.index=[i for i in range(0, 753)]
In [3]:
mroz
Out[3]:
inlf
hours
kidslt6
kidsge6
age
educ
wage
repwage
hushrs
husage
...
faminc
mtr
motheduc
fatheduc
unem
city
exper
nwifeinc
lwage
expersq
0
1
1610
1
0
32
12
3.3540
2.65
2708
34
...
16310
0.7215
12
7
5.0
0
14
10.910060
1.210154
196
1
1
1656
0
2
30
12
1.3889
2.65
2310
30
...
21800
0.6615
7
7
11.0
1
5
19.499980
0.3285121
25
2
1
1980
1
3
35
12
4.5455
4.04
3072
40
...
21040
0.6915
12
7
5.0
0
15
12.039910
1.514138
225
3
1
456
0
3
34
12
1.0965
3.25
1920
53
...
7300
0.7815
7
7
5.0
0
6
6.799996
0.0921233
36
4
1
1568
1
2
31
14
4.5918
3.60
2000
32
...
27300
0.6215
12
14
9.5
1
7
20.100060
1.524272
49
5
1
2032
0
0
54
12
4.7421
4.70
1040
57
...
19495
0.6915
14
7
7.5
1
33
9.859054
1.55648
1089
6
1
1440
0
2
37
16
8.3333
5.95
2670
37
...
21152
0.6915
14
7
5.0
0
11
9.152048
2.12026
121
7
1
1020
0
0
54
12
7.8431
9.98
4120
53
...
18900
0.6915
3
3
5.0
0
35
10.900040
2.059634
1225
8
1
1458
0
2
48
12
2.1262
0.00
1995
52
...
20405
0.7515
7
7
3.0
0
24
17.305000
0.7543364
576
9
1
1600
0
2
39
12
4.6875
4.15
2100
43
...
20425
0.6915
7
7
5.0
0
21
12.925000
1.544899
441
10
1
1969
0
1
33
12
4.0630
4.30
2450
34
...
32300
0.5815
12
3
5.0
0
15
24.299950
1.401922
225
11
1
1960
0
1
42
11
4.5918
4.58
2375
47
...
28700
0.6215
14
7
5.0
0
14
19.700070
1.524272
196
12
1
240
1
2
30
12
2.0833
0.00
2830
33
...
15500
0.7215
16
16
5.0
0
0
15.000010
0.7339532
0
13
1
997
0
2
43
12
2.2668
3.50
3317
46
...
16860
0.7215
10
10
7.5
1
14
14.600000
0.8183691
196
14
1
1848
0
1
43
10
3.6797
3.38
2024
45
...
31431
0.5815
7
7
7.5
1
6
24.630910
1.302831
36
15
1
1224
0
3
35
11
1.3472
0.00
1694
38
...
19180
0.7215
16
10
7.5
1
9
17.531030
0.2980284
81
16
1
1400
0
2
43
12
3.2143
4.00
2156
45
...
18600
0.6915
10
7
7.5
1
20
14.099980
1.16761
400
17
1
640
0
5
39
12
5.1750
2.25
2250
40
...
19151
0.7215
12
12
7.5
1
6
15.839000
1.643839
36
18
1
2000
0
0
45
12
2.0000
2.30
2024
51
...
18100
0.6915
7
7
5.0
1
23
14.100000
0.6931472
529
19
1
1324
0
4
35
12
7.5529
3.94
2123
40
...
20300
0.6915
12
7
5.0
0
9
10.299960
2.021932
81
20
1
2215
0
2
42
16
3.5052
3.30
4160
48
...
30419
0.6215
10
16
7.5
0
5
22.654980
1.254248
25
21
1
1680
0
0
30
12
3.5714
3.80
2000
35
...
14090
0.7215
12
10
3.0
0
11
8.090048
1.272958
121
22
1
1600
0
0
48
13
3.2500
3.26
2420
52
...
22679
0.6615
7
3
5.0
1
18
17.479000
1.178655
324
23
1
800
0
0
45
12
3.2500
2.20
1150
53
...
12160
0.7215
7
7
11.0
0
15
9.560000
1.178655
225
24
1
1955
1
1
31
12
2.1545
2.30
2024
31
...
12487
0.7515
12
7
5.0
1
4
8.274953
0.7675587
16
25
1
660
0
2
43
17
3.7879
0.00
1904
43
...
29850
0.5815
16
14
9.5
1
21
27.349990
1.331812
441
26
1
525
0
0
59
12
4.0000
3.18
2448
53
...
18100
0.6915
3
7
9.5
1
31
16.000000
1.386294
961
27
1
1904
0
3
32
12
4.7269
6.07
2000
33
...
26000
0.6615
3
7
11.0
1
9
16.999980
1.55327
81
28
1
1516
1
0
31
17
7.2559
6.00
2390
30
...
26100
0.6215
12
12
5.0
0
7
15.100060
1.981815
49
29
1
346
0
0
42
12
5.8671
6.39
1920
47
...
17730
0.7215
12
12
9.5
0
7
15.699980
1.76936
49
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
723
0
0
0
2
41
12
0.0000
0.00
2165
46
...
12400
0.7715
7
7
7.5
0
18
12.400000
.
324
724
0
0
0
2
49
5
0.0000
0.00
2230
43
...
6531
0.7815
0
0
7.5
0
7
6.531000
.
49
725
0
0
0
0
59
8
0.0000
0.00
1995
55
...
22422
0.6915
3
7
11.0
1
15
22.422000
.
225
726
0
0
0
0
58
13
0.0000
0.00
2025
57
...
22200
0.6615
7
7
11.0
1
7
22.200000
.
49
727
0
0
0
3
41
12
0.0000
0.00
2450
48
...
77000
0.4415
7
14
14.0
1
8
77.000000
.
64
728
0
0
0
2
45
12
0.0000
0.00
2160
45
...
88000
0.4415
10
10
14.0
1
8
88.000000
.
64
729
0
0
1
1
30
14
0.0000
0.00
1715
32
...
26040
0.6215
12
10
11.0
0
3
26.040000
.
9
730
0
0
0
1
41
12
0.0000
0.00
3018
42
...
63500
0.4415
7
7
7.5
1
10
63.500000
.
100
731
0
0
2
0
30
12
0.0000
0.00
2216
33
...
12100
0.7515
10
10
7.5
0
9
12.100000
.
81
732
0
0
0
1
53
12
0.0000
0.00
2499
54
...
17505
0.7515
12
7
7.5
1
24
17.505000
.
576
733
0
0
0
0
31
12
0.0000
0.00
2250
37
...
18000
0.6915
10
7
7.5
0
12
18.000000
.
144
734
0
0
0
2
43
14
0.0000
0.00
2116
44
...
28069
0.6615
12
12
11.0
1
2
28.069000
.
4
735
0
0
1
1
31
12
0.0000
0.00
2016
30
...
14000
0.7515
12
14
14.0
1
6
14.000000
.
36
736
0
0
0
0
51
12
0.0000
0.00
2470
60
...
8117
0.7515
10
3
7.5
1
18
8.117000
.
324
737
0
0
0
0
43
9
0.0000
0.00
1640
45
...
11895
0.7715
7
7
5.0
1
17
11.895000
.
289
738
0
0
1
2
31
14
0.0000
0.00
2016
34
...
45250
0.4915
16
16
5.0
1
7
45.250000
.
49
739
0
0
0
0
48
11
0.0000
0.00
2185
48
...
31106
0.6915
7
10
9.5
1
6
31.106000
.
36
740
0
0
1
1
31
12
0.0000
0.00
800
33
...
4000
0.8015
12
7
9.5
1
10
4.000000
.
100
741
0
0
0
1
44
12
0.0000
0.00
3022
46
...
40500
0.5815
7
7
7.5
1
5
40.500000
.
25
742
0
0
0
1
48
11
0.0000
0.00
1512
50
...
21620
0.7215
10
7
7.5
1
7
21.620000
.
49
743
0
0
0
1
53
12
0.0000
0.00
2677
53
...
23426
0.7215
0
0
7.5
1
11
23.426000
.
121
744
0
0
0
3
42
10
0.0000
2.75
3150
44
...
26000
0.6615
3
3
11.0
1
14
26.000000
.
196
745
0
0
2
6
39
12
0.0000
0.00
1430
34
...
7840
0.9415
7
0
9.5
1
5
7.840000
.
25
746
0
0
1
2
32
10
0.0000
0.00
3307
36
...
6800
0.7915
7
3
7.5
0
2
6.800000
.
4
747
0
0
0
2
36
12
0.0000
0.00
3120
39
...
5330
0.7915
7
12
14.0
0
4
5.330000
.
16
748
0
0
0
2
40
13
0.0000
0.00
3020
43
...
28200
0.6215
10
10
9.5
1
5
28.200000
.
25
749
0
0
2
3
31
12
0.0000
0.00
2056
33
...
10000
0.7715
12
12
7.5
0
14
10.000000
.
196
750
0
0
0
0
43
12
0.0000
0.00
2383
43
...
9952
0.7515
10
3
7.5
0
4
9.952000
.
16
751
0
0
0
0
60
12
0.0000
0.00
1705
55
...
24984
0.6215
12
12
14.0
1
15
24.984000
.
225
752
0
0
0
3
39
9
0.0000
0.00
3120
48
...
28363
0.6915
7
7
11.0
1
12
28.363000
.
144
753 rows × 22 columns
In [5]:
mroz.to_csv( 'mroz.csv' )
Content source: NlGG/Econometrics
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