chap01soln


Examples and Exercises from Think Stats, 2nd Edition

http://thinkstats2.com

Copyright 2016 Allen B. Downey

MIT License: https://opensource.org/licenses/MIT


In [2]:
from __future__ import print_function, division

import nsfg

Examples from Chapter 1

Read NSFG data into a Pandas DataFrame.


In [4]:
preg = nsfg.ReadFemPreg()
preg.head()


Out[4]:
caseid pregordr howpreg_n howpreg_p moscurrp nowprgdk pregend1 pregend2 nbrnaliv multbrth ... laborfor_i religion_i metro_i basewgt adj_mod_basewgt finalwgt secu_p sest cmintvw totalwgt_lb
0 1 1 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 0 3410.389399 3869.349602 6448.271112 2 9 NaN 8.8125
1 1 2 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 0 3410.389399 3869.349602 6448.271112 2 9 NaN 7.8750
2 2 1 NaN NaN NaN NaN 5.0 NaN 3.0 5.0 ... 0 0 0 7226.301740 8567.549110 12999.542264 2 12 NaN 9.1250
3 2 2 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 0 7226.301740 8567.549110 12999.542264 2 12 NaN 7.0000
4 2 3 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 0 7226.301740 8567.549110 12999.542264 2 12 NaN 6.1875

5 rows × 244 columns

Print the column names.


In [5]:
preg.columns


Out[5]:
Index(['caseid', 'pregordr', 'howpreg_n', 'howpreg_p', 'moscurrp', 'nowprgdk',
       'pregend1', 'pregend2', 'nbrnaliv', 'multbrth',
       ...
       'laborfor_i', 'religion_i', 'metro_i', 'basewgt', 'adj_mod_basewgt',
       'finalwgt', 'secu_p', 'sest', 'cmintvw', 'totalwgt_lb'],
      dtype='object', length=244)

Select a single column name.


In [6]:
preg.columns[1]


Out[6]:
'pregordr'

Select a column and check what type it is.


In [7]:
pregordr = preg['pregordr']
type(pregordr)


Out[7]:
pandas.core.series.Series

Print a column.


In [8]:
pregordr


Out[8]:
0        1
1        2
2        1
3        2
4        3
5        1
6        2
7        3
8        1
9        2
10       1
11       1
12       2
13       3
14       1
15       2
16       3
17       1
18       2
19       1
20       2
21       1
22       2
23       1
24       2
25       3
26       1
27       1
28       2
29       3
        ..
13563    2
13564    3
13565    1
13566    1
13567    1
13568    2
13569    1
13570    2
13571    3
13572    4
13573    1
13574    2
13575    1
13576    1
13577    2
13578    1
13579    2
13580    1
13581    2
13582    3
13583    1
13584    2
13585    1
13586    2
13587    3
13588    1
13589    2
13590    3
13591    4
13592    5
Name: pregordr, Length: 13593, dtype: int64

Select a single element from a column.


In [9]:
pregordr[0]


Out[9]:
1

Select a slice from a column.


In [10]:
pregordr[2:5]


Out[10]:
2    1
3    2
4    3
Name: pregordr, dtype: int64

Select a column using dot notation.


In [11]:
pregordr = preg.pregordr

Count the number of times each value occurs.


In [12]:
preg.outcome.value_counts().sort_index()


Out[12]:
1    9148
2    1862
3     120
4    1921
5     190
6     352
Name: outcome, dtype: int64

Check the values of another variable.


In [13]:
preg.birthwgt_lb.value_counts().sort_index()


Out[13]:
0.0        8
1.0       40
2.0       53
3.0       98
4.0      229
5.0      697
6.0     2223
7.0     3049
8.0     1889
9.0      623
10.0     132
11.0      26
12.0      10
13.0       3
14.0       3
15.0       1
Name: birthwgt_lb, dtype: int64

Make a dictionary that maps from each respondent's caseid to a list of indices into the pregnancy DataFrame. Use it to select the pregnancy outcomes for a single respondent.


In [14]:
caseid = 10229
preg_map = nsfg.MakePregMap(preg)
indices = preg_map[caseid]
preg.outcome[indices].values


Out[14]:
array([4, 4, 4, 4, 4, 4, 1])

Exercises

Select the birthord column, print the value counts, and compare to results published in the codebook


In [15]:
# Solution

preg.birthord.value_counts().sort_index()


Out[15]:
1.0     4413
2.0     2874
3.0     1234
4.0      421
5.0      126
6.0       50
7.0       20
8.0        7
9.0        2
10.0       1
Name: birthord, dtype: int64

We can also use isnull to count the number of nans.


In [16]:
preg.birthord.isnull().sum()


Out[16]:
4445

Select the prglngth column, print the value counts, and compare to results published in the codebook


In [17]:
# Solution

preg.prglngth.value_counts().sort_index()


Out[17]:
0       15
1        9
2       78
3      151
4      412
5      181
6      543
7      175
8      409
9      594
10     137
11     202
12     170
13     446
14      29
15      39
16      44
17     253
18      17
19      34
20      18
21      37
22     147
23      12
24      31
25      15
26     117
27       8
28      38
29      23
30     198
31      29
32     122
33      50
34      60
35     357
36     329
37     457
38     609
39    4744
40    1120
41     591
42     328
43     148
44      46
45      10
46       1
47       1
48       7
50       2
Name: prglngth, dtype: int64

To compute the mean of a column, you can invoke the mean method on a Series. For example, here is the mean birthweight in pounds:


In [18]:
preg.totalwgt_lb.mean()


Out[18]:
7.265628457623368

Create a new column named totalwgt_kg that contains birth weight in kilograms. Compute its mean. Remember that when you create a new column, you have to use dictionary syntax, not dot notation.


In [19]:
# Solution

preg['totalwgt_kg'] = preg.totalwgt_lb / 2.2
preg.totalwgt_kg.mean()


Out[19]:
3.302558389828807

nsfg.py also provides ReadFemResp, which reads the female respondents file and returns a DataFrame:


In [20]:
resp = nsfg.ReadFemResp()

DataFrame provides a method head that displays the first five rows:


In [21]:
resp.head()


Out[21]:
caseid rscrinf rdormres rostscrn rscreenhisp rscreenrace age_a age_r cmbirth agescrn ... pubassis_i basewgt adj_mod_basewgt finalwgt secu_r sest cmintvw cmlstyr screentime intvlngth
0 2298 1 5 5 1 5.0 27 27 902 27 ... 0 3247.916977 5123.759559 5556.717241 2 18 1234 1222 18:26:36 110.492667
1 5012 1 5 1 5 5.0 42 42 718 42 ... 0 2335.279149 2846.799490 4744.191350 2 18 1233 1221 16:30:59 64.294000
2 11586 1 5 1 5 5.0 43 43 708 43 ... 0 2335.279149 2846.799490 4744.191350 2 18 1234 1222 18:19:09 75.149167
3 6794 5 5 4 1 5.0 15 15 1042 15 ... 0 3783.152221 5071.464231 5923.977368 2 18 1234 1222 15:54:43 28.642833
4 616 1 5 4 1 5.0 20 20 991 20 ... 0 5341.329968 6437.335772 7229.128072 2 18 1233 1221 14:19:44 69.502667

5 rows × 3087 columns

Select the age_r column from resp and print the value counts. How old are the youngest and oldest respondents?


In [22]:
# Solution

resp.age_r.value_counts().sort_index()


Out[22]:
15    217
16    223
17    234
18    235
19    241
20    258
21    267
22    287
23    282
24    269
25    267
26    260
27    255
28    252
29    262
30    292
31    278
32    273
33    257
34    255
35    262
36    266
37    271
38    256
39    215
40    256
41    250
42    215
43    253
44    235
Name: age_r, dtype: int64

We can use the caseid to match up rows from resp and preg. For example, we can select the row from resp for caseid 2298 like this:


In [23]:
resp[resp.caseid==2298]


Out[23]:
caseid rscrinf rdormres rostscrn rscreenhisp rscreenrace age_a age_r cmbirth agescrn ... pubassis_i basewgt adj_mod_basewgt finalwgt secu_r sest cmintvw cmlstyr screentime intvlngth
0 2298 1 5 5 1 5.0 27 27 902 27 ... 0 3247.916977 5123.759559 5556.717241 2 18 1234 1222 18:26:36 110.492667

1 rows × 3087 columns

And we can get the corresponding rows from preg like this:


In [24]:
preg[preg.caseid==2298]


Out[24]:
caseid pregordr howpreg_n howpreg_p moscurrp nowprgdk pregend1 pregend2 nbrnaliv multbrth ... religion_i metro_i basewgt adj_mod_basewgt finalwgt secu_p sest cmintvw totalwgt_lb totalwgt_kg
2610 2298 1 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 3247.916977 5123.759559 5556.717241 2 18 NaN 6.8750 3.125000
2611 2298 2 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 3247.916977 5123.759559 5556.717241 2 18 NaN 5.5000 2.500000
2612 2298 3 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 3247.916977 5123.759559 5556.717241 2 18 NaN 4.1875 1.903409
2613 2298 4 NaN NaN NaN NaN 6.0 NaN 1.0 NaN ... 0 0 3247.916977 5123.759559 5556.717241 2 18 NaN 6.8750 3.125000

4 rows × 245 columns

How old is the respondent with caseid 1?


In [25]:
# Solution

resp[resp.caseid==1].age_r


Out[25]:
1069    44
Name: age_r, dtype: int64

What are the pregnancy lengths for the respondent with caseid 2298?


In [26]:
# Solution

preg[preg.caseid==2298].prglngth


Out[26]:
2610    40
2611    36
2612    30
2613    40
Name: prglngth, dtype: int64

What was the birthweight of the first baby born to the respondent with caseid 5012?


In [27]:
# Solution

preg[preg.caseid==5012].birthwgt_lb


Out[27]:
5515    6.0
Name: birthwgt_lb, dtype: float64