Lecture 5 (Part 1 Tuberculosis) – Data 100, Spring 2024

Data 100, Fall 2024

Acknowledgments Page

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns
#%matplotlib inline
plt.rcParams['figure.figsize'] = (12, 9)

sns.set()
sns.set_context('talk')
np.set_printoptions(threshold=20, precision=2, suppress=True)
pd.set_option('display.max_rows', 30)
pd.set_option('display.max_columns', None)
pd.set_option('display.precision', 2)
# This option stops scientific notation for pandas
pd.set_option('display.float_format', '{:.2f}'.format)

# Silence some spurious seaborn warnings
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)

Tuberculosis in the United States

What can we say about the presence of Tuberculosis in the United States?

Let's look at the data included in the original CDC article published in 2021.

You could download the data directly from the web using Pandas:

tbls = pd.read_html("https://www.cdc.gov/mmwr/volumes/71/wr/mm7112a1.htm?s_cid=mm7112a1_w#T1_down")
df = tbls[0] # First table on the website
df

	U.S. jurisdiction	No. of TB cases*			TB incidence†
	U.S. jurisdiction	2019	2020	2021	2019	2020	2021
0	Total	8900	7173	7860	2.71	2.16	2.37
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28
...	...	...	...	...	...	...	...
47	Virginia	191	169	161	2.23	1.96	1.86
48	Washington	221	163	199	2.90	2.11	2.57
49	West Virginia	9	13	7	0.50	0.73	0.39
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

52 rows × 7 columns

However, it is good practice to make a snapshot of the data for your analysis so we will work from a local copy.

---

CSV and Nice Field Names

Someone, already downloaded table 1 and saved it as a CSV file located in data/cdc_tuberculosis.csv.

We can then explore the CSV (which is a text file, and does not contain binary-encoded data) in many ways:

1. Using a the jupyter lab explorer tool look at the data
2. Opening the CSV directly in DataHub (read-only), Excel, Google Sheets, etc.
3. The Python file object
4. pandas, using pd.read_csv()

---

Play with the data in the Jupyter Lab Explorer

1, 2. Let's start with the first two so we really solidify the idea of a CSV as rectangular data (i.e., tabular data) stored as comma-separated values.

---

Play with the data in python

3. Next, let's try using the Python file object. Let's check out the first four lines:

with open("data/cdc_tuberculosis.csv", "r") as f:
    for i, row in enumerate(f):
        print(row)
        if i >= 3: break

,No. of TB cases,,,TB incidence,,

U.S. jurisdiction,2019,2020,2021,2019,2020,2021

Total,"8,900","7,173","7,860",2.71,2.16,2.37

Alabama,87,72,92,1.77,1.43,1.83

Whoa, why are there blank lines interspaced between the lines of the CSV?

You may recall that all line breaks in text files are encoded as the special newline character \n. Python's print() prints each string (including the newline), and an additional newline on top of that.

If you're curious, we can use the repr() function to return the raw string with all special characters:

with open("data/cdc_tuberculosis.csv", "r") as f:
    for i, row in enumerate(f):
        print(repr(row)) # print raw strings
        if i >= 3: break

',No. of TB cases,,,TB incidence,,\n'
'U.S. jurisdiction,2019,2020,2021,2019,2020,2021\n'
'Total,"8,900","7,173","7,860",2.71,2.16,2.37\n'
'Alabama,87,72,92,1.77,1.43,1.83\n'

A brief tangent on reading files (optional)

Here is a shorter way to read the first few lines. It has a problem ...

with open("data/cdc_tuberculosis.csv", "r") as f:
    for row in f.readlines()[:4]:
        print(repr(row)) # print raw strings    

',No. of TB cases,,,TB incidence,,\n'
'U.S. jurisdiction,2019,2020,2021,2019,2020,2021\n'
'Total,"8,900","7,173","7,860",2.71,2.16,2.37\n'
'Alabama,87,72,92,1.77,1.43,1.83\n'

The main drawback here is that we read the entire file when we only want the first few lines. That can be wasteful. The Python zip built-in function (docs here) is a useful thing to know about. This code may look a little odd at first, but it does the same as the first example above but much more concisely, and once you get used to thinking about zip, it becomes a very natural tool to express various iteration strategies:

with open("data/cdc_tuberculosis.tsv", "r") as f:
    for _, row in zip(range(4), f):
        print(repr(row)) # print raw strings

'\tNo. of TB cases\t\t\tTB incidence\t\t\n'
'U.S. jurisdiction\t2019\t2020\t2021\t2019\t2020\t2021\n'
'Total\t"8,900"\t"7,173"\t"7,860"\t2.71\t2.16\t2.37\n'
'Alabama\t87\t72\t92\t1.77\t1.43\t1.83\n'

As data gets bigger it will be important to read only the parts you need into the notebook.

---

4. Finally, let's see the tried-and-true Data 100 approach: pandas.

tb_df = pd.read_csv("data/cdc_tuberculosis.csv",)
tb_df

	Unnamed: 0	No. of TB cases	Unnamed: 2	Unnamed: 3	TB incidence	Unnamed: 5	Unnamed: 6
0	U.S. jurisdiction	2019	2020	2021	2019.00	2020.00	2021.00
1	Total	8,900	7,173	7,860	2.71	2.16	2.37
2	Alabama	87	72	92	1.77	1.43	1.83
3	Alaska	58	58	58	7.91	7.92	7.92
4	Arizona	183	136	129	2.51	1.89	1.77
...	...	...	...	...	...	...	...
48	Virginia	191	169	161	2.23	1.96	1.86
49	Washington	221	163	199	2.90	2.11	2.57
50	West Virginia	9	13	7	0.50	0.73	0.39
51	Wisconsin	51	35	66	0.88	0.59	1.12
52	Wyoming	1	0	3	0.17	0.00	0.52

53 rows × 7 columns

Wait, what's up with the "Unnamed" column names? And the first row, for that matter?

Congratulations -- you're ready to wrangle your data. Because of how things are stored, we'll need to clean the data a bit to name our columns better.

A reasonable first step is to identify the row with the right header. The pd.read_csv() function (documentation) has the convenient header parameter.

You could also try:

1. Shift+Tab while your cursor is in the function call parenthesis.
2. Cmd+i or Ctrl+i to get contextual help.

tb_df = pd.read_csv("data/cdc_tuberculosis.csv", header=1) # row index
tb_df

	U.S. jurisdiction	2019	2020	2021	2019.1	2020.1	2021.1
0	Total	8,900	7,173	7,860	2.71	2.16	2.37
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28
...	...	...	...	...	...	...	...
47	Virginia	191	169	161	2.23	1.96	1.86
48	Washington	221	163	199	2.90	2.11	2.57
49	West Virginia	9	13	7	0.50	0.73	0.39
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

52 rows × 7 columns

Wait...but now we can't differentiate betwen the "Number of TB cases" and "TB incidence" year columns. pandas has tried to make our lives easier by automatically adding ".1" to the latter columns, but this doesn't help us as humans understand the data.

We can do this manually with df.rename() (documentation):

rename_dict = {'2019': 'TB cases 2019',
               '2020': 'TB cases 2020',
               '2021': 'TB cases 2021',
               '2019.1': 'TB incidence 2019',
               '2020.1': 'TB incidence 2020',
               '2021.1': 'TB incidence 2021'}
tb_df = tb_df.rename(columns=rename_dict)
tb_df

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
0	Total	8,900	7,173	7,860	2.71	2.16	2.37
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28
...	...	...	...	...	...	...	...
47	Virginia	191	169	161	2.23	1.96	1.86
48	Washington	221	163	199	2.90	2.11	2.57
49	West Virginia	9	13	7	0.50	0.73	0.39
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

52 rows × 7 columns

Return to slides!

---

Record Granularity

You might already be wondering: What's up with that first record?

Row 0 is what we call a rollup record, or summary record. It's often useful when displaying tables to humans. The granularity of record 0 (Totals) vs the rest of the records (States) is different.

tb_df.head()

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
0	Total	8,900	7,173	7,860	2.71	2.16	2.37
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28

Okay, EDA step two. How was the rollup record aggregated?

Let's check if Total TB cases is the sum of all state TB cases. We can drop it and try to sum up all the remaining rows.

tb_df.drop(0)

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28
5	California	2,111	1,706	1,750	5.35	4.32	4.46
...	...	...	...	...	...	...	...
47	Virginia	191	169	161	2.23	1.96	1.86
48	Washington	221	163	199	2.90	2.11	2.57
49	West Virginia	9	13	7	0.50	0.73	0.39
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

51 rows × 7 columns

tb_df.drop(0).sum()

U.S. jurisdiction    AlabamaAlaskaArizonaArkansasCaliforniaColorado...
TB cases 2019        8758183642,11166671824558302997326108523766881...
TB cases 2020        7258136591,70652541719412221928216923937679917...
TB cases 2021        9258129691,75058544319499228106425512749435786...
TB incidence 2019                                               107.23
TB incidence 2020                                                90.93
TB incidence 2021                                               100.57
dtype: object

Whoa, what's going on? Check out the column types:

tb_df.dtypes

U.S. jurisdiction     object
TB cases 2019         object
TB cases 2020         object
TB cases 2021         object
TB incidence 2019    float64
TB incidence 2020    float64
TB incidence 2021    float64
dtype: object

Looks like those commas are causing all TB cases to be read as the object datatype, or storage type (close to the Python string datatype), so pandas is concatenating strings instead of adding integers.

Fortunately read_csv also has a thousands parameter (for what it's worth, I didn't know this beforehand--I googled this):

# improve readability: chaining method calls with outer parentheses/line breaks
tb_df = (
    pd.read_csv("data/cdc_tuberculosis.csv", header=1, thousands=',')
    .rename(columns=rename_dict)
)
tb_df

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
0	Total	8900	7173	7860	2.71	2.16	2.37
1	Alabama	87	72	92	1.77	1.43	1.83
2	Alaska	58	58	58	7.91	7.92	7.92
3	Arizona	183	136	129	2.51	1.89	1.77
4	Arkansas	64	59	69	2.12	1.96	2.28
...	...	...	...	...	...	...	...
47	Virginia	191	169	161	2.23	1.96	1.86
48	Washington	221	163	199	2.90	2.11	2.57
49	West Virginia	9	13	7	0.50	0.73	0.39
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

52 rows × 7 columns

tb_df.drop(0).sum()

U.S. jurisdiction    AlabamaAlaskaArizonaArkansasCaliforniaColorado...
TB cases 2019                                                     8900
TB cases 2020                                                     7173
TB cases 2021                                                     7860
TB incidence 2019                                               107.23
TB incidence 2020                                                90.93
TB incidence 2021                                               100.57
dtype: object

tb_df.head(1)

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
0	Total	8900	7173	7860	2.71	2.16	2.37

The Total TB cases look right. Phew!

(We'll leave it to your own EDA to figure out how the TB incidence "Totals" were aggregated...you may want to check out the bonus section first, though.)

In order to compute incidence we are going to need more data: population information!!

Return to the lecture!

---

Gather Census Data

U.S. Census population estimates source (2019), source (2020-2021).

Running the below cells cleans the data. We encourage you to closely explore the CSV and study these lines after lecture...

There are a few new methods here:

• df.convert_dtypes() (documentation) conveniently converts all float dtypes into ints and is out of scope for the class.
• df.drop_na() (documentation) will be explained in more detail next time.

census_2010s_df = pd.read_csv("data/nst-est2019-01.csv", header=3, thousands=",")
census_2010s_df

	Unnamed: 0	Census	Estimates Base	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
0	United States	308745538.00	308758105.00	309321666.00	311556874.00	313830990.00	315993715.00	318301008.00	320635163.00	322941311.00	324985539.00	326687501.00	328239523.00
1	Northeast	55317240.00	55318443.00	55380134.00	55604223.00	55775216.00	55901806.00	56006011.00	56034684.00	56042330.00	56059240.00	56046620.00	55982803.00
2	Midwest	66927001.00	66929725.00	66974416.00	67157800.00	67336743.00	67560379.00	67745167.00	67860583.00	67987540.00	68126781.00	68236628.00	68329004.00
3	South	114555744.00	114563030.00	114866680.00	116006522.00	117241208.00	118364400.00	119624037.00	120997341.00	122351760.00	123542189.00	124569433.00	125580448.00
4	West	71945553.00	71946907.00	72100436.00	72788329.00	73477823.00	74167130.00	74925793.00	75742555.00	76559681.00	77257329.00	77834820.00	78347268.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...
58	Note: The estimates are based on the 2010 Cens...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
59	Suggested Citation:	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
60	Table 1. Annual Estimates of the Resident Popu...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
61	Source: U.S. Census Bureau, Population Division	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
62	Release Date: December 2019	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

63 rows × 13 columns

Do some basic data cleaning

census_2010s_df = (
    census_2010s_df
    .rename(columns={"Unnamed: 0": "Geographic Area"})
    .drop(columns=["Census", "Estimates Base"])
    .convert_dtypes() # "smart" converting of columns to int, use at your own risk
    .dropna()  # we'll introduce this very soon
)
census_2010s_df

	Geographic Area	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
0	United States	309321666	311556874	313830990	315993715	318301008	320635163	322941311	324985539	326687501	328239523
1	Northeast	55380134	55604223	55775216	55901806	56006011	56034684	56042330	56059240	56046620	55982803
2	Midwest	66974416	67157800	67336743	67560379	67745167	67860583	67987540	68126781	68236628	68329004
3	South	114866680	116006522	117241208	118364400	119624037	120997341	122351760	123542189	124569433	125580448
4	West	72100436	72788329	73477823	74167130	74925793	75742555	76559681	77257329	77834820	78347268
...	...	...	...	...	...	...	...	...	...	...	...
52	.Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893
53	.West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147
54	.Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434
55	.Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
57	Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

57 rows × 11 columns

What is the granularity of each row in this table.

Notice there is a '.' at the beginning of all the states. We need to remove that.

census_2010s_df['Geographic Area'] = census_2010s_df['Geographic Area'].str.strip('.')
census_2010s_df

	Geographic Area	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
0	United States	309321666	311556874	313830990	315993715	318301008	320635163	322941311	324985539	326687501	328239523
1	Northeast	55380134	55604223	55775216	55901806	56006011	56034684	56042330	56059240	56046620	55982803
2	Midwest	66974416	67157800	67336743	67560379	67745167	67860583	67987540	68126781	68236628	68329004
3	South	114866680	116006522	117241208	118364400	119624037	120997341	122351760	123542189	124569433	125580448
4	West	72100436	72788329	73477823	74167130	74925793	75742555	76559681	77257329	77834820	78347268
...	...	...	...	...	...	...	...	...	...	...	...
52	Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893
53	West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147
54	Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434
55	Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
57	Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

57 rows × 11 columns

Loading the 2020s data

The 2020s data is in a separate file so we will repeate the same data cleaning process.

# census 2020s data
census_2020s_df = pd.read_csv("data/NST-EST2022-POP.csv", header=3, thousands=",")
census_2020s_df = (
    census_2020s_df
    .drop(columns=["Unnamed: 1"])
    .rename(columns={"Unnamed: 0": "Geographic Area"})
    .convert_dtypes()                 
    .dropna()                         
)
census_2020s_df['Geographic Area'] = census_2020s_df['Geographic Area'].str.strip('.')
census_2020s_df

	Geographic Area	2020	2021	2022
0	United States	331511512	332031554	333287557
1	Northeast	57448898	57259257	57040406
2	Midwest	68961043	68836505	68787595
3	South	126450613	127346029	128716192
4	West	78650958	78589763	78743364
...	...	...	...	...
52	Washington	7724031	7740745	7785786
53	West Virginia	1791420	1785526	1775156
54	Wisconsin	5896271	5880101	5892539
55	Wyoming	577605	579483	581381
57	Puerto Rico	3281557	3262693	3221789

57 rows × 4 columns

Return to Slides

---

Join Data (Merge DataFrames)

Time to merge! Here I use the DataFrame method df1.merge(right=df2, ...) on DataFrame df1 (documentation). Contrast this with the function pd.merge(left=df1, right=df2, ...) (documentation). Feel free to use either.

# Show the three tables that we are going to join
display(tb_df.tail(2))
display(census_2010s_df.tail(2))
display(census_2020s_df.tail(2))

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
50	Wisconsin	51	35	66	0.88	0.59	1.12
51	Wyoming	1	0	3	0.17	0.00	0.52

	Geographic Area	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
55	Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
57	Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

	Geographic Area	2020	2021	2022
55	Wyoming	577605	579483	581381
57	Puerto Rico	3281557	3262693	3221789

# merge TB dataframe with two US census dataframes
tb_census_df = (
    tb_df
    .merge(right=census_2010s_df,
           left_on="U.S. jurisdiction", right_on="Geographic Area")
    .merge(right=census_2020s_df,
           left_on="U.S. jurisdiction", right_on="Geographic Area")
)
tb_census_df.tail()

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	Geographic Area_x	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	Geographic Area_y	2020	2021	2022
46	Virginia	191	169	161	2.23	1.96	1.86	Virginia	8023699	8101155	8185080	8252427	8310993	8361808	8410106	8463587	8501286	8535519	Virginia	8636471	8657365	8683619
47	Washington	221	163	199	2.90	2.11	2.57	Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893	Washington	7724031	7740745	7785786
48	West Virginia	9	13	7	0.50	0.73	0.39	West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147	West Virginia	1791420	1785526	1775156
49	Wisconsin	51	35	66	0.88	0.59	1.12	Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434	Wisconsin	5896271	5880101	5892539
50	Wyoming	1	0	3	0.17	0.00	0.52	Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759	Wyoming	577605	579483	581381

This is a little unwieldy. We could either drop the unneeded columns now, or just merge on smaller census DataFrames. Let's do the latter.

# try merging again, but cleaner this time
tb_census_df = (
    tb_df
    .merge(right=census_2010s_df[["Geographic Area", "2019"]],
           left_on="U.S. jurisdiction", right_on="Geographic Area")
    .drop(columns="Geographic Area")
    .merge(right=census_2020s_df[["Geographic Area", "2020", "2021"]],
           left_on="U.S. jurisdiction", right_on="Geographic Area")
    .drop(columns="Geographic Area")
)
tb_census_df.tail()

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021
46	Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365
47	Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745
48	West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526
49	Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101
50	Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483

Reproduce incidence

Let's recompute incidence to make sure we know where the original CDC numbers came from.

From the CDC report: TB incidence is computed as “Cases per 100,000 persons using mid-year population estimates from the U.S. Census Bureau.”

If we define a group as 100,000 people, then we can compute the TB incidence for a given state population as

$$\text{TB incidence} = \frac{\text{\# TB cases in population}}{\text{\# groups in population}} = \frac{\text{\# TB cases in population}}{\text{population}/100000} $$

$$= \frac{\text{\# TB cases in population}}{\text{population}} \times 100000$$

Let's try this for 2019:

tb_census_df["recompute incidence 2019"] = (
    tb_census_df["TB cases 2019"]/tb_census_df["2019"] * 100_000
)
tb_census_df

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021	recompute incidence 2019
0	Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846	1.77
1	Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182	7.93
2	Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877	2.51
3	Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122	2.12
4	California	2111	1706	1750	5.35	4.32	4.46	39512223	39501653	39142991	5.34
...	...	...	...	...	...	...	...	...	...	...	...
46	Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365	2.24
47	Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745	2.90
48	West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526	0.50
49	Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101	0.88
50	Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483	0.17

51 rows × 11 columns

Awesome!!!

Let's use a for-loop and Python format strings to compute TB incidence for all years. Python f-strings are just used for the purposes of this demo, but they're handy to know when you explore data beyond this course (Python documentation).

# recompute incidence for all years
for year in [2019, 2020, 2021]:
    tb_census_df[f"recompute incidence {year}"] = (
        tb_census_df[f"TB cases {year}"]/tb_census_df[f"{year}"]*100_000
    )
tb_census_df

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021	recompute incidence 2019	recompute incidence 2020	recompute incidence 2021
0	Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846	1.77	1.43	1.82
1	Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182	7.93	7.91	7.90
2	Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877	2.51	1.89	1.78
3	Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122	2.12	1.96	2.28
4	California	2111	1706	1750	5.35	4.32	4.46	39512223	39501653	39142991	5.34	4.32	4.47
...	...	...	...	...	...	...	...	...	...	...	...	...	...
46	Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365	2.24	1.96	1.86
47	Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745	2.90	2.11	2.57
48	West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526	0.50	0.73	0.39
49	Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101	0.88	0.59	1.12
50	Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483	0.17	0.00	0.52

51 rows × 13 columns

These numbers look pretty close!!! There are a few errors in the hundredths place, particularly in 2021. It may be useful to further explore reasons behind this discrepancy. We'll leave it to you!

tb_census_df.describe()

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021	recompute incidence 2019	recompute incidence 2020	recompute incidence 2021
count	51.00	51.00	51.00	51.00	51.00	51.00	51.00	51.00	51.00	51.00	51.00	51.00
mean	174.51	140.65	154.12	2.10	1.78	1.97	6436069.08	6500225.73	6510422.63	2.10	1.78	1.97
std	341.74	271.06	286.78	1.50	1.34	1.48	7360660.47	7408168.46	7394300.08	1.50	1.34	1.47
min	1.00	0.00	2.00	0.17	0.00	0.21	578759.00	577605.00	579483.00	0.17	0.00	0.21
25%	25.50	29.00	23.00	1.29	1.21	1.23	1789606.00	1820311.00	1844920.00	1.30	1.21	1.23
50%	70.00	67.00	69.00	1.80	1.52	1.70	4467673.00	4507445.00	4506589.00	1.81	1.52	1.69
75%	180.50	139.00	150.00	2.58	1.99	2.22	7446805.00	7451987.00	7502811.00	2.58	1.99	2.22
max	2111.00	1706.00	1750.00	7.91	7.92	7.92	39512223.00	39501653.00	39142991.00	7.93	7.91	7.90

Return to slides!

---

Bonus EDA

We likely won't get to this part, so a tutorial is provided for your own curiosity.

How do we reproduce that reported statistic in the original CDC report?

Reported TB incidence (cases per 100,000 persons) increased 9.4%, from 2.2 during 2020 to 2.4 during 2021 but was lower than incidence during 2019 (2.7). Increases occurred among both U.S.-born and non–U.S.-born persons.

This is TB incidence computed across the entire U.S. population! How do we reproduce this

• We need to reproduce the "Total" TB incidences in our rolled record.
• But our current tb_census_df only has 51 entries (50 states plus Washington, D.C.). There is no rolled record.
• What happened...?

Let's get exploring!

Before we keep exploring, I'm going to set all indexes to more meaningful values, instead of just numbers that pertained to some row at some point. This will make our cleaning slightly easier.

tb_df = tb_df.set_index("U.S. jurisdiction")
tb_df

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
U.S. jurisdiction
Total	8900	7173	7860	2.71	2.16	2.37
Alabama	87	72	92	1.77	1.43	1.83
Alaska	58	58	58	7.91	7.92	7.92
Arizona	183	136	129	2.51	1.89	1.77
Arkansas	64	59	69	2.12	1.96	2.28
...	...	...	...	...	...	...
Virginia	191	169	161	2.23	1.96	1.86
Washington	221	163	199	2.90	2.11	2.57
West Virginia	9	13	7	0.50	0.73	0.39
Wisconsin	51	35	66	0.88	0.59	1.12
Wyoming	1	0	3	0.17	0.00	0.52

52 rows × 6 columns

census_2010s_df = census_2010s_df.set_index("Geographic Area")
census_2010s_df

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Geographic Area
United States	309321666	311556874	313830990	315993715	318301008	320635163	322941311	324985539	326687501	328239523
Northeast	55380134	55604223	55775216	55901806	56006011	56034684	56042330	56059240	56046620	55982803
Midwest	66974416	67157800	67336743	67560379	67745167	67860583	67987540	68126781	68236628	68329004
South	114866680	116006522	117241208	118364400	119624037	120997341	122351760	123542189	124569433	125580448
West	72100436	72788329	73477823	74167130	74925793	75742555	76559681	77257329	77834820	78347268
...	...	...	...	...	...	...	...	...	...	...
Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893
West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147
Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434
Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

57 rows × 10 columns

census_2020s_df = census_2020s_df.set_index("Geographic Area")
census_2020s_df

	2020	2021	2022
Geographic Area
United States	331511512	332031554	333287557
Northeast	57448898	57259257	57040406
Midwest	68961043	68836505	68787595
South	126450613	127346029	128716192
West	78650958	78589763	78743364
...	...	...	...
Washington	7724031	7740745	7785786
West Virginia	1791420	1785526	1775156
Wisconsin	5896271	5880101	5892539
Wyoming	577605	579483	581381
Puerto Rico	3281557	3262693	3221789

57 rows × 3 columns

It turns out that our merge above only kept state records, even though our original tb_df had the "Total" rolled record:

tb_df.head()

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021
U.S. jurisdiction
Total	8900	7173	7860	2.71	2.16	2.37
Alabama	87	72	92	1.77	1.43	1.83
Alaska	58	58	58	7.91	7.92	7.92
Arizona	183	136	129	2.51	1.89	1.77
Arkansas	64	59	69	2.12	1.96	2.28

Recall that merge by default does an inner merge by default, meaning that it only preserves keys that are present in both DataFrames.

The rolled records in our census dataframes have different Geographic Area fields, which was the key we merged on:

census_2010s_df

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Geographic Area
United States	309321666	311556874	313830990	315993715	318301008	320635163	322941311	324985539	326687501	328239523
Northeast	55380134	55604223	55775216	55901806	56006011	56034684	56042330	56059240	56046620	55982803
Midwest	66974416	67157800	67336743	67560379	67745167	67860583	67987540	68126781	68236628	68329004
South	114866680	116006522	117241208	118364400	119624037	120997341	122351760	123542189	124569433	125580448
West	72100436	72788329	73477823	74167130	74925793	75742555	76559681	77257329	77834820	78347268
...	...	...	...	...	...	...	...	...	...	...
Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893
West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147
Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434
Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

57 rows × 10 columns

The Census DataFrame has several rolled records. The aggregate record we are looking for actually has the Geographic Area named "United States".

One straightforward way to get the right merge is to rename the value itself. Because we now have the Geographic Area index, we'll use df.rename() (documentation):

# rename rolled record for 2010s
census_2010s_df.rename(index={'United States':'Total'}, inplace=True)
census_2010s_df

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019
Geographic Area
Total	309321666	311556874	313830990	315993715	318301008	320635163	322941311	324985539	326687501	328239523
Northeast	55380134	55604223	55775216	55901806	56006011	56034684	56042330	56059240	56046620	55982803
Midwest	66974416	67157800	67336743	67560379	67745167	67860583	67987540	68126781	68236628	68329004
South	114866680	116006522	117241208	118364400	119624037	120997341	122351760	123542189	124569433	125580448
West	72100436	72788329	73477823	74167130	74925793	75742555	76559681	77257329	77834820	78347268
...	...	...	...	...	...	...	...	...	...	...
Washington	6742830	6826627	6897058	6963985	7054655	7163657	7294771	7423362	7523869	7614893
West Virginia	1854239	1856301	1856872	1853914	1849489	1842050	1831023	1817004	1804291	1792147
Wisconsin	5690475	5705288	5719960	5736754	5751525	5760940	5772628	5790186	5807406	5822434
Wyoming	564487	567299	576305	582122	582531	585613	584215	578931	577601	578759
Puerto Rico	3721525	3678732	3634488	3593077	3534874	3473232	3406672	3325286	3193354	3193694

57 rows × 10 columns

# same, but for 2020s rename rolled record
census_2020s_df.rename(index={'United States':'Total'}, inplace=True)
census_2020s_df

	2020	2021	2022
Geographic Area
Total	331511512	332031554	333287557
Northeast	57448898	57259257	57040406
Midwest	68961043	68836505	68787595
South	126450613	127346029	128716192
West	78650958	78589763	78743364
...	...	...	...
Washington	7724031	7740745	7785786
West Virginia	1791420	1785526	1775156
Wisconsin	5896271	5880101	5892539
Wyoming	577605	579483	581381
Puerto Rico	3281557	3262693	3221789

57 rows × 3 columns

Next let's rerun our merge. Note the different chaining, because we are now merging on indexes (df.merge() documentation).

tb_census_df = (
    tb_df
    .merge(right=census_2010s_df[["2019"]],
           left_index=True, right_index=True)
    .merge(right=census_2020s_df[["2020", "2021"]],
           left_index=True, right_index=True)
)
tb_census_df

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021
Total	8900	7173	7860	2.71	2.16	2.37	328239523	331511512	332031554
Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846
Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182
Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877
Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122
...	...	...	...	...	...	...	...	...	...
Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365
Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745
West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526
Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101
Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483

52 rows × 9 columns

Finally, let's recompute our incidences:

# recompute incidence for all years
for year in [2019, 2020, 2021]:
    tb_census_df[f"recompute incidence {year}"] = tb_census_df[f"TB cases {year}"]/tb_census_df[f"{year}"]*100000
tb_census_df

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021	recompute incidence 2019	recompute incidence 2020	recompute incidence 2021
Total	8900	7173	7860	2.71	2.16	2.37	328239523	331511512	332031554	2.71	2.16	2.37
Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846	1.77	1.43	1.82
Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182	7.93	7.91	7.90
Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877	2.51	1.89	1.78
Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122	2.12	1.96	2.28
...	...	...	...	...	...	...	...	...	...	...	...	...
Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365	2.24	1.96	1.86
Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745	2.90	2.11	2.57
West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526	0.50	0.73	0.39
Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101	0.88	0.59	1.12
Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483	0.17	0.00	0.52

52 rows × 12 columns

We reproduced the total U.S. incidences correctly!

We're almost there. Let's revisit the quote:

Reported TB incidence (cases per 100,000 persons) increased 9.4%, from 2.2 during 2020 to 2.4 during 2021 but was lower than incidence during 2019 (2.7). Increases occurred among both U.S.-born and non–U.S.-born persons.

Recall that percent change from $A$ to $B$ is computed as $$\text{percent change} = \frac{B - A}{A} \times 100$$.

tb_census_df

	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021	recompute incidence 2019	recompute incidence 2020	recompute incidence 2021
Total	8900	7173	7860	2.71	2.16	2.37	328239523	331511512	332031554	2.71	2.16	2.37
Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846	1.77	1.43	1.82
Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182	7.93	7.91	7.90
Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877	2.51	1.89	1.78
Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122	2.12	1.96	2.28
...	...	...	...	...	...	...	...	...	...	...	...	...
Virginia	191	169	161	2.23	1.96	1.86	8535519	8636471	8657365	2.24	1.96	1.86
Washington	221	163	199	2.90	2.11	2.57	7614893	7724031	7740745	2.90	2.11	2.57
West Virginia	9	13	7	0.50	0.73	0.39	1792147	1791420	1785526	0.50	0.73	0.39
Wisconsin	51	35	66	0.88	0.59	1.12	5822434	5896271	5880101	0.88	0.59	1.12
Wyoming	1	0	3	0.17	0.00	0.52	578759	577605	579483	0.17	0.00	0.52

52 rows × 12 columns

incidence_2020 = tb_census_df.loc['Total', 'recompute incidence 2020']
incidence_2020

np.float64(2.1637257652759883)

incidence_2021 = tb_census_df.loc['Total', 'recompute incidence 2021']
incidence_2021

np.float64(2.3672448914298068)

difference = (incidence_2021 - incidence_2020)/incidence_2020 * 100
difference

np.float64(9.405957511804143)

We did it!!!