chap01ex.ipynb

Kernel: Python 3

Examples and Exercises from Think Stats, 2nd Edition

http://thinkstats2.com

Copyright 2016 Allen B. Downey

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

from __future__ import print_function, division

import nsfg

Examples from Chapter 1

Read NSFG data into a Pandas DataFrame.

preg = nsfg.ReadFemPreg()
preg.head()

	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.

preg.columns

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.

preg.columns[1]

'pregordr'

Select a column and check what type it is.

pregordr = preg['pregordr']
type(pregordr)

pandas.core.series.Series

Print a column.

pregordr

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.

pregordr[0]

1

Select a slice from a column.

pregordr[2:5]

2    1
3    2
4    3
Name: pregordr, dtype: int64

Select a column using dot notation.

pregordr = preg.pregordr

Count the number of times each value occurs.

preg.outcome.value_counts().sort_index()

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

Check the values of another variable.

preg.birthwgt_lb.value_counts().sort_index()

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.

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

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

# Solution goes here

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

preg.birthord.isnull().sum()

4445

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

# Solution goes here

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:

preg.totalwgt_lb.mean()

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.

# Solution goes here

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

resp = nsfg.ReadFemResp()

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

resp.head()

	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?

# Solution goes here

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:

resp[resp.caseid==2298]

	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:

preg[preg.caseid==2298]

	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
2610	2298	1	NaN	NaN	NaN	NaN	6.0	NaN	1.0	NaN	...	0	0	0	3247.916977	5123.759559	5556.717241	2	18	NaN	6.8750
2611	2298	2	NaN	NaN	NaN	NaN	6.0	NaN	1.0	NaN	...	0	0	0	3247.916977	5123.759559	5556.717241	2	18	NaN	5.5000
2612	2298	3	NaN	NaN	NaN	NaN	6.0	NaN	1.0	NaN	...	0	0	0	3247.916977	5123.759559	5556.717241	2	18	NaN	4.1875
2613	2298	4	NaN	NaN	NaN	NaN	6.0	NaN	1.0	NaN	...	0	0	0	3247.916977	5123.759559	5556.717241	2	18	NaN	6.8750

4 rows × 244 columns

How old is the respondent with caseid 1?

# Solution goes here

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

# Solution goes here

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

# Solution goes here