5 Data Cleaning and EDA

Code

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)

Learning Outcomes

Recognize common file formats
Categorize data by its variable type
Build awareness of issues with data faithfulness and develop targeted solutions

In the past few lectures, we’ve learned that pandas is a toolkit to restructure, modify, and explore a dataset. What we haven’t yet touched on is how to make these data transformation decisions. When we receive a new set of data from the “real world,” how do we know what processing we should do to convert this data into a usable form?

Data cleaning, also called data wrangling, is the process of transforming raw data to facilitate subsequent analysis. It is often used to address issues like:

Unclear structure or formatting
Missing or corrupted values
Unit conversions
…and so on

Exploratory Data Analysis (EDA) is the process of understanding a new dataset. It is an open-ended, informal analysis that involves familiarizing ourselves with the variables present in the data, discovering potential hypotheses, and identifying possible issues with the data. This last point can often motivate further data cleaning to address any problems with the dataset’s format; because of this, EDA and data cleaning are often thought of as an “infinite loop,” with each process driving the other.

In this lecture, we will consider the key properties of data to consider when performing data cleaning and EDA. In doing so, we’ll develop a “checklist” of sorts for you to consider when approaching a new dataset. Throughout this process, we’ll build a deeper understanding of this early (but very important!) stage of the data science lifecycle.

5.1 Structure

We often prefer rectangular data for data analysis. Rectangular structures are easy to manipulate and analyze. A key element of data cleaning is about transforming data to be more rectangular.

There are two kinds of rectangular data: tables and matrices. Tables have named columns with different data types and are manipulated using data transformation languages. Matrices contain numeric data of the same type and are manipulated using linear algebra.

5.1.1 File Formats

There are many file types for storing structured data: TSV, JSON, XML, ASCII, SAS, etc. We’ll only cover CSV, TSV, and JSON in lecture, but you’ll likely encounter other formats as you work with different datasets. Reading documentation is your best bet for understanding how to process the multitude of different file types.

5.1.1.1 CSV

CSVs, which stand for Comma-Separated Values, are a common tabular data format. In the past two pandas lectures, we briefly touched on the idea of file format: the way data is encoded in a file for storage. Specifically, our elections and babynames datasets were stored and loaded as CSVs:

pd.read_csv("data/elections.csv").head(5)

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.21
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.79
2	1828	Andrew Jackson	Democratic	642806	win	56.20
3	1828	John Quincy Adams	National Republican	500897	loss	43.80
4	1832	Andrew Jackson	Democratic	702735	win	54.57

To better understand the properties of a CSV, let’s take a look at the first few rows of the raw data file to see what it looks like before being loaded into a DataFrame. We’ll use the repr() function to return the raw string with its special characters:

with open("data/elections.csv", "r") as table:
    i = 0
    for row in table:
        print(repr(row))
        i += 1
        if i > 3:
            break

'Year,Candidate,Party,Popular vote,Result,%\n'
'1824,Andrew Jackson,Democratic-Republican,151271,loss,57.21012204\n'
'1824,John Quincy Adams,Democratic-Republican,113142,win,42.78987796\n'
'1828,Andrew Jackson,Democratic,642806,win,56.20392707\n'

Each row, or record, in the data is delimited by a newline \n. Each column, or field, in the data is delimited by a comma , (hence, comma-separated!).

5.1.1.2 TSV

Another common file type is TSV (Tab-Separated Values). In a TSV, records are still delimited by a newline \n, while fields are delimited by \t tab character.

Let’s check out the first few rows of the raw TSV file. Again, we’ll use the repr() function so that print shows the special characters.

with open("data/elections.txt", "r") as table:
    i = 0
    for row in table:
        print(repr(row))
        i += 1
        if i > 3:
            break

'\ufeffYear\tCandidate\tParty\tPopular vote\tResult\t%\n'
'1824\tAndrew Jackson\tDemocratic-Republican\t151271\tloss\t57.21012204\n'
'1824\tJohn Quincy Adams\tDemocratic-Republican\t113142\twin\t42.78987796\n'
'1828\tAndrew Jackson\tDemocratic\t642806\twin\t56.20392707\n'

TSVs can be loaded into pandas using pd.read_csv. We’ll need to specify the delimiter with parametersep='\t' (documentation).

pd.read_csv("data/elections.txt", sep='\t').head(3)

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.21
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.79
2	1828	Andrew Jackson	Democratic	642806	win	56.20

An issue with CSVs and TSVs comes up whenever there are commas or tabs within the records. How does pandas differentiate between a comma delimiter vs. a comma within the field itself, for example 8,900? To remedy this, check out the quotechar parameter.

5.1.1.3 JSON

JSON (JavaScript Object Notation) files behave similarly to Python dictionaries. A raw JSON is shown below.

with open("data/elections.json", "r") as table:
    i = 0
    for row in table:
        print(row)
        i += 1
        if i > 8:
            break

[

 {

   "Year": 1824,

   "Candidate": "Andrew Jackson",

   "Party": "Democratic-Republican",

   "Popular vote": 151271,

   "Result": "loss",

   "%": 57.21012204

 },

JSON files can be loaded into pandas using pd.read_json.

pd.read_json('data/elections.json').head(3)

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.21
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.79
2	1828	Andrew Jackson	Democratic	642806	win	56.20

5.1.1.3.1 EDA with JSON: Berkeley COVID-19 Data

The City of Berkeley Open Data website has a dataset with COVID-19 Confirmed Cases among Berkeley residents by date. Let’s download the file and save it as a JSON (note the source URL file type is also a JSON). In the interest of reproducible data science, we will download the data programatically. We have defined some helper functions in the ds100_utils.py file that we can reuse these helper functions in many different notebooks.

from ds100_utils import fetch_and_cache

covid_file = fetch_and_cache(
    "https://data.cityofberkeley.info/api/views/xn6j-b766/rows.json?accessType=DOWNLOAD",
    "confirmed-cases.json",
    force=False)
covid_file          # a file path wrapper object

Using cached version that was downloaded (UTC): Mon Mar 18 21:13:08 2024

PosixPath('data/confirmed-cases.json')

5.1.1.3.1.1 File Size

Let’s start our analysis by getting a rough estimate of the size of the dataset to inform the tools we use to view the data. For relatively small datasets, we can use a text editor or spreadsheet. For larger datasets, more programmatic exploration or distributed computing tools may be more fitting. Here we will use Python tools to probe the file.

Since there seem to be text files, let’s investigate the number of lines, which often corresponds to the number of records

import os

print(covid_file, "is", os.path.getsize(covid_file) / 1e6, "MB")

with open(covid_file, "r") as f:
    print(covid_file, "is", sum(1 for l in f), "lines.")

data/confirmed-cases.json is 0.116367 MB
data/confirmed-cases.json is 1110 lines.

5.1.1.3.1.2 Unix Commands

As part of the EDA workflow, Unix commands can come in very handy. In fact, there’s an entire book called “Data Science at the Command Line” that explores this idea in depth! In Jupyter/IPython, you can prefix lines with ! to execute arbitrary Unix commands, and within those lines, you can refer to Python variables and expressions with the syntax {expr}.

Here, we use the ls command to list files, using the -lh flags, which request “long format with information in human-readable form.” We also use the wc command for “word count,” but with the -l flag, which asks for line counts instead of words.

These two give us the same information as the code above, albeit in a slightly different form:

!ls -lh {covid_file}
!wc -l {covid_file}

-rw-r--r--  1 Ishani  staff   114K Mar 18 21:13 data/confirmed-cases.json

    1109 data/confirmed-cases.json

5.1.1.3.1.3 File Contents

Let’s explore the data format using Python.

with open(covid_file, "r") as f:
    for i, row in enumerate(f):
        print(repr(row)) # print raw strings
        if i >= 4: break

'{\n'
'  "meta" : {\n'
'    "view" : {\n'
'      "id" : "xn6j-b766",\n'
'      "name" : "COVID-19 Confirmed Cases",\n'

We can use the head Unix command (which is where pandas’ head method comes from!) to see the first few lines of the file:

!head -5 {covid_file}

{
  "meta" : {
    "view" : {
      "id" : "xn6j-b766",
      "name" : "COVID-19 Confirmed Cases",

In order to load the JSON file into pandas, Let’s first do some EDA with Oython’s json package to understand the particular structure of this JSON file so that we can decide what (if anything) to load into pandas. Python has relatively good support for JSON data since it closely matches the internal python object model. In the following cell we import the entire JSON datafile into a python dictionary using the json package.

import json

with open(covid_file, "rb") as f:
    covid_json = json.load(f)

The covid_json variable is now a dictionary encoding the data in the file:

type(covid_json)

dict

We can examine what keys are in the top level JSON object by listing out the keys.

covid_json.keys()

dict_keys(['meta', 'data'])

Observation: The JSON dictionary contains a meta key which likely refers to metadata (data about the data). Metadata is often maintained with the data and can be a good source of additional information.

We can investigate the metadata further by examining the keys associated with the metadata.

covid_json['meta'].keys()

dict_keys(['view'])

The meta key contains another dictionary called view. This likely refers to metadata about a particular “view” of some underlying database. We will learn more about views when we study SQL later in the class.

covid_json['meta']['view'].keys()

dict_keys(['id', 'name', 'assetType', 'attribution', 'averageRating', 'category', 'createdAt', 'description', 'displayType', 'downloadCount', 'hideFromCatalog', 'hideFromDataJson', 'newBackend', 'numberOfComments', 'oid', 'provenance', 'publicationAppendEnabled', 'publicationDate', 'publicationGroup', 'publicationStage', 'rowsUpdatedAt', 'rowsUpdatedBy', 'tableId', 'totalTimesRated', 'viewCount', 'viewLastModified', 'viewType', 'approvals', 'columns', 'grants', 'metadata', 'owner', 'query', 'rights', 'tableAuthor', 'tags', 'flags'])

Notice that this a nested/recursive data structure. As we dig deeper we reveal more and more keys and the corresponding data:

meta
|-> data
    | ... (haven't explored yet)
|-> view
    | -> id
    | -> name
    | -> attribution 
    ...
    | -> description
    ...
    | -> columns
    ...

There is a key called description in the view sub dictionary. This likely contains a description of the data:

print(covid_json['meta']['view']['description'])

Counts of confirmed COVID-19 cases among Berkeley residents by date.

5.1.1.3.1.4 Examining the Data Field for Records

We can look at a few entries in the data field. This is what we’ll load into pandas.

for i in range(3):
    print(f"{i:03} | {covid_json['data'][i]}")

000 | ['row-kzbg.v7my-c3y2', '00000000-0000-0000-0405-CB14DE51DAA7', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-28T00:00:00', '1', '1']
001 | ['row-jkyx_9u4r-h2yw', '00000000-0000-0000-F806-86D0DBE0E17F', 0, 1643733903, None, 1643733903, None, '{ }', '2020-02-29T00:00:00', '0', '1']
002 | ['row-qifg_4aug-y3ym', '00000000-0000-0000-2DCE-4D1872F9B216', 0, 1643733903, None, 1643733903, None, '{ }', '2020-03-01T00:00:00', '0', '1']

Observations: * These look like equal-length records, so maybe data is a table! * But what do each of values in the record mean? Where can we find column headers?

For that, we’ll need the columns key in the metadata dictionary. This returns a list:

type(covid_json['meta']['view']['columns'])

list

5.1.1.3.1.5 Summary of exploring the JSON file

The above metadata tells us a lot about the columns in the data including column names, potential data anomalies, and a basic statistic.
Because of its non-tabular structure, JSON makes it easier (than CSV) to create self-documenting data, meaning that information about the data is stored in the same file as the data.
Self-documenting data can be helpful since it maintains its own description and these descriptions are more likely to be updated as data changes.

5.1.1.3.1.6 Loading COVID Data into `pandas`

Finally, let’s load the data (not the metadata) into a pandas DataFrame. In the following block of code we:

Translate the JSON records into a DataFrame:
- fields: covid_json['meta']['view']['columns']
- records: covid_json['data']
Remove columns that have no metadata description. This would be a bad idea in general, but here we remove these columns since the above analysis suggests they are unlikely to contain useful information.
Examine the tail of the table.

# Load the data from JSON and assign column titles
covid = pd.DataFrame(
    covid_json['data'],
    columns=[c['name'] for c in covid_json['meta']['view']['columns']])

covid.tail()

	sid	id	created_at	created_meta	updated_at	updated_meta	meta	Date	New Cases	Cumulative Cases
699	row-49b6_x8zv.gyum	00000000-0000-0000-A18C-9174A6D05774	1643733903	None	1643733903	None	{ }	2022-01-27T00:00:00	106	10694
700	row-gs55-p5em.y4v9	00000000-0000-0000-F41D-5724AEABB4D6	1643733903	None	1643733903	None	{ }	2022-01-28T00:00:00	223	10917
701	row-3pyj.tf95-qu67	00000000-0000-0000-BEE3-B0188D2518BD	1643733903	None	1643733903	None	{ }	2022-01-29T00:00:00	139	11056
702	row-cgnd.8syv.jvjn	00000000-0000-0000-C318-63CF75F7F740	1643733903	None	1643733903	None	{ }	2022-01-30T00:00:00	33	11089
703	row-qywv_24x6-237y	00000000-0000-0000-FE92-9789FED3AA20	1643733903	None	1643733903	None	{ }	2022-01-31T00:00:00	42	11131

5.1.2 Primary and Foreign Keys

Last time, we introduced .merge as the pandas method for joining multiple DataFrames together. In our discussion of joins, we touched on the idea of using a “key” to determine what rows should be merged from each table. Let’s take a moment to examine this idea more closely.

The primary key is the column or set of columns in a table that uniquely determine the values of the remaining columns. It can be thought of as the unique identifier for each individual row in the table. For example, a table of Data 100 students might use each student’s Cal ID as the primary key.

	Cal ID	Name	Major
0	3034619471	Oski	Data Science
1	3035619472	Ollie	Computer Science
2	3025619473	Orrie	Data Science
3	3046789372	Ollie	Economics

The foreign key is the column or set of columns in a table that reference primary keys in other tables. Knowing a dataset’s foreign keys can be useful when assigning the left_on and right_on parameters of .merge. In the table of office hour tickets below, "Cal ID" is a foreign key referencing the previous table.

	OH Request	Cal ID	Question
0	1	3034619471	HW 2 Q1
1	2	3035619472	HW 2 Q3
2	3	3025619473	Lab 3 Q4
3	4	3035619472	HW 2 Q7

5.1.3 Variable Types

Variables are columns. A variable is a measurement of a particular concept. Variables have two common properties: data type/storage type and variable type/feature type. The data type of a variable indicates how each variable value is stored in memory (integer, floating point, boolean, etc.) and affects which pandas functions are used. The variable type is a conceptualized measurement of information (and therefore indicates what values a variable can take on). Variable type is identified through expert knowledge, exploring the data itself, or consulting the data codebook. The variable type affects how one visualizes and inteprets the data. In this class, “variable types” are conceptual.

After loading data into a file, it’s a good idea to take the time to understand what pieces of information are encoded in the dataset. In particular, we want to identify what variable types are present in our data. Broadly speaking, we can categorize variables into one of two overarching types.

Quantitative variables describe some numeric quantity or amount. We can divide quantitative data further into:

Continuous quantitative variables: numeric data that can be measured on a continuous scale to arbitrary precision. Continuous variables do not have a strict set of possible values – they can be recorded to any number of decimal places. For example, weights, GPA, or CO₂ concentrations.
Discrete quantitative variables: numeric data that can only take on a finite set of possible values. For example, someone’s age or the number of siblings they have.

Qualitative variables, also known as categorical variables, describe data that isn’t measuring some quantity or amount. The sub-categories of categorical data are:

Ordinal qualitative variables: categories with ordered levels. Specifically, ordinal variables are those where the difference between levels has no consistent, quantifiable meaning. Some examples include levels of education (high school, undergrad, grad, etc.), income bracket (low, medium, high), or Yelp rating.
Nominal qualitative variables: categories with no specific order. For example, someone’s political affiliation or Cal ID number.

Note that many variables don’t sit neatly in just one of these categories. Qualitative variables could have numeric levels, and conversely, quantitative variables could be stored as strings.

5.2 Granularity, Scope, and Temporality

After understanding the structure of the dataset, the next task is to determine what exactly the data represents. We’ll do so by considering the data’s granularity, scope, and temporality.

5.2.1 Granularity

The granularity of a dataset is what a single row represents. You can also think of it as the level of detail included in the data. To determine the data’s granularity, ask: what does each row in the dataset represent? Fine-grained data contains a high level of detail, with a single row representing a small individual unit. For example, each record may represent one person. Coarse-grained data is encoded such that a single row represents a large individual unit – for example, each record may represent a group of people.

5.2.2 Scope

The scope of a dataset is the subset of the population covered by the data. If we were investigating student performance in Data Science courses, a dataset with a narrow scope might encompass all students enrolled in Data 100 whereas a dataset with an expansive scope might encompass all students in California.

5.2.3 Temporality

The temporality of a dataset describes the periodicity over which the data was collected as well as when the data was most recently collected or updated.

Time and date fields of a dataset could represent a few things:

when the “event” happened
when the data was collected, or when it was entered into the system
when the data was copied into the database

To fully understand the temporality of the data, it also may be necessary to standardize time zones or inspect recurring time-based trends in the data (do patterns recur in 24-hour periods? Over the course of a month? Seasonally?). The convention for standardizing time is the Coordinated Universal Time (UTC), an international time standard measured at 0 degrees latitude that stays consistent throughout the year (no daylight savings). We can represent Berkeley’s time zone, Pacific Standard Time (PST), as UTC-7 (with daylight savings).

5.2.3.1 Temporality with `pandas`’ `dt` accessors

Let’s briefly look at how we can use pandas’ dt accessors to work with dates/times in a dataset using the dataset you’ll see in Lab 3: the Berkeley PD Calls for Service dataset.

Code

calls = pd.read_csv("data/Berkeley_PD_-_Calls_for_Service.csv")
calls.head()

	CASENO	OFFENSE	EVENTDT	EVENTTM	CVLEGEND	CVDOW	InDbDate	Block_Location	BLKADDR	City	State
0	21014296	THEFT MISD. (UNDER $950)	04/01/2021 12:00:00 AM	10:58	LARCENY	4	06/15/2021 12:00:00 AM	Berkeley, CA\n(37.869058, -122.270455)	NaN	Berkeley	CA
1	21014391	THEFT MISD. (UNDER $950)	04/01/2021 12:00:00 AM	10:38	LARCENY	4	06/15/2021 12:00:00 AM	Berkeley, CA\n(37.869058, -122.270455)	NaN	Berkeley	CA
2	21090494	THEFT MISD. (UNDER $950)	04/19/2021 12:00:00 AM	12:15	LARCENY	1	06/15/2021 12:00:00 AM	2100 BLOCK HASTE ST\nBerkeley, CA\n(37.864908,...	2100 BLOCK HASTE ST	Berkeley	CA
3	21090204	THEFT FELONY (OVER $950)	02/13/2021 12:00:00 AM	17:00	LARCENY	6	06/15/2021 12:00:00 AM	2600 BLOCK WARRING ST\nBerkeley, CA\n(37.86393...	2600 BLOCK WARRING ST	Berkeley	CA
4	21090179	BURGLARY AUTO	02/08/2021 12:00:00 AM	6:20	BURGLARY - VEHICLE	1	06/15/2021 12:00:00 AM	2700 BLOCK GARBER ST\nBerkeley, CA\n(37.86066,...	2700 BLOCK GARBER ST	Berkeley	CA

Looks like there are three columns with dates/times: EVENTDT, EVENTTM, and InDbDate.

Most likely, EVENTDT stands for the date when the event took place, EVENTTM stands for the time of day the event took place (in 24-hr format), and InDbDate is the date this call is recorded onto the database.

If we check the data type of these columns, we will see they are stored as strings. We can convert them to datetime objects using pandas to_datetime function.

calls["EVENTDT"] = pd.to_datetime(calls["EVENTDT"])
calls.head()

	CASENO	OFFENSE	EVENTDT	EVENTTM	CVLEGEND	CVDOW	InDbDate	Block_Location	BLKADDR	City	State
0	21014296	THEFT MISD. (UNDER $950)	2021-04-01	10:58	LARCENY	4	06/15/2021 12:00:00 AM	Berkeley, CA\n(37.869058, -122.270455)	NaN	Berkeley	CA
1	21014391	THEFT MISD. (UNDER $950)	2021-04-01	10:38	LARCENY	4	06/15/2021 12:00:00 AM	Berkeley, CA\n(37.869058, -122.270455)	NaN	Berkeley	CA
2	21090494	THEFT MISD. (UNDER $950)	2021-04-19	12:15	LARCENY	1	06/15/2021 12:00:00 AM	2100 BLOCK HASTE ST\nBerkeley, CA\n(37.864908,...	2100 BLOCK HASTE ST	Berkeley	CA
3	21090204	THEFT FELONY (OVER $950)	2021-02-13	17:00	LARCENY	6	06/15/2021 12:00:00 AM	2600 BLOCK WARRING ST\nBerkeley, CA\n(37.86393...	2600 BLOCK WARRING ST	Berkeley	CA
4	21090179	BURGLARY AUTO	2021-02-08	6:20	BURGLARY - VEHICLE	1	06/15/2021 12:00:00 AM	2700 BLOCK GARBER ST\nBerkeley, CA\n(37.86066,...	2700 BLOCK GARBER ST	Berkeley	CA

Now, we can use the dt accessor on this column.

We can get the month:

calls["EVENTDT"].dt.month.head()

0    4
1    4
2    4
3    2
4    2
Name: EVENTDT, dtype: int64

Which day of the week the date is on:

calls["EVENTDT"].dt.dayofweek.head()

0    3
1    3
2    0
3    5
4    0
Name: EVENTDT, dtype: int64

Check the mimimum values to see if there are any suspicious-looking, 70s dates:

calls.sort_values("EVENTDT").head()

	CASENO	OFFENSE	EVENTDT	EVENTTM	CVLEGEND	CVDOW	InDbDate	Block_Location	BLKADDR	City	State
2513	20057398	BURGLARY COMMERCIAL	2020-12-17	16:05	BURGLARY - COMMERCIAL	4	06/15/2021 12:00:00 AM	600 BLOCK GILMAN ST\nBerkeley, CA\n(37.878405,...	600 BLOCK GILMAN ST	Berkeley	CA
624	20057207	ASSAULT/BATTERY MISD.	2020-12-17	16:50	ASSAULT	4	06/15/2021 12:00:00 AM	2100 BLOCK SHATTUCK AVE\nBerkeley, CA\n(37.871...	2100 BLOCK SHATTUCK AVE	Berkeley	CA
154	20092214	THEFT FROM AUTO	2020-12-17	18:30	LARCENY - FROM VEHICLE	4	06/15/2021 12:00:00 AM	800 BLOCK SHATTUCK AVE\nBerkeley, CA\n(37.8918...	800 BLOCK SHATTUCK AVE	Berkeley	CA
659	20057324	THEFT MISD. (UNDER $950)	2020-12-17	15:44	LARCENY	4	06/15/2021 12:00:00 AM	1800 BLOCK 4TH ST\nBerkeley, CA\n(37.869888, -...	1800 BLOCK 4TH ST	Berkeley	CA
993	20057573	BURGLARY RESIDENTIAL	2020-12-17	22:15	BURGLARY - RESIDENTIAL	4	06/15/2021 12:00:00 AM	1700 BLOCK STUART ST\nBerkeley, CA\n(37.857495...	1700 BLOCK STUART ST	Berkeley	CA

Doesn’t look like it! We are good!

We can also do many things with the dt accessor like switching time zones and converting time back to UNIX/POSIX time. Check out the documentation on .dt accessor and time series/date functionality.

5.3 Faithfulness

At this stage in our data cleaning and EDA workflow, we’ve achieved quite a lot: we’ve identified how our data is structured, come to terms with what information it encodes, and gained insight as to how it was generated. Throughout this process, we should always recall the original intent of our work in Data Science – to use data to better understand and model the real world. To achieve this goal, we need to ensure that the data we use is faithful to reality; that is, that our data accurately captures the “real world.”

Data used in research or industry is often “messy” – there may be errors or inaccuracies that impact the faithfulness of the dataset. Signs that data may not be faithful include:

Unrealistic or “incorrect” values, such as negative counts, locations that don’t exist, or dates set in the future
Violations of obvious dependencies, like an age that does not match a birthday
Clear signs that data was entered by hand, which can lead to spelling errors or fields that are incorrectly shifted
Signs of data falsification, such as fake email addresses or repeated use of the same names
Duplicated records or fields containing the same information
Truncated data, e.g. Microsoft Excel would limit the number of rows to 655536 and the number of columns to 255

We often solve some of these more common issues in the following ways:

Spelling errors: apply corrections or drop records that aren’t in a dictionary
Time zone inconsistencies: convert to a common time zone (e.g. UTC)
Duplicated records or fields: identify and eliminate duplicates (using primary keys)
Unspecified or inconsistent units: infer the units and check that values are in reasonable ranges in the data

5.3.1 Missing Values

Another common issue encountered with real-world datasets is that of missing data. One strategy to resolve this is to simply drop any records with missing values from the dataset. This does, however, introduce the risk of inducing biases – it is possible that the missing or corrupt records may be systemically related to some feature of interest in the data. Another solution is to keep the data as NaN values.

A third method to address missing data is to perform imputation: infer the missing values using other data available in the dataset. There is a wide variety of imputation techniques that can be implemented; some of the most common are listed below.

Average imputation: replace missing values with the average value for that field
Hot deck imputation: replace missing values with some random value
Regression imputation: develop a model to predict missing values and replace with the predicted value from the model.
Multiple imputation: replace missing values with multiple random values

Regardless of the strategy used to deal with missing data, we should think carefully about why particular records or fields may be missing – this can help inform whether or not the absence of these values is significant or meaningful.

5.4 EDA Demo 1: Tuberculosis in the United States

Now, let’s walk through the data-cleaning and EDA workflow to see what can we learn about the presence of Tuberculosis in the United States!

We will examine the data included in the original CDC article published in 2021.

5.4.1 CSVs and Field Names

Suppose Table 1 was saved 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 text editor like emacs, vim, VSCode, etc. 2. Opening the CSV directly in DataHub (read-only), Excel, Google Sheets, etc. 3. The Python file object 4. pandas, using pd.read_csv()

To try out options 1 and 2, you can view or download the Tuberculosis from the lecture demo notebook under the data folder in the left hand menu. Notice how the CSV file is a type of rectangular data (i.e., tabular data) stored as comma-separated values.

Next, let’s try out option 3 using the Python file object. We’ll look at the first four lines:

Code

with open("data/cdc_tuberculosis.csv", "r") as f:
    i = 0
    for row in f:
        print(row)
        i += 1
        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:

Code

with open("data/cdc_tuberculosis.csv", "r") as f:
    i = 0
    for row in f:
        print(repr(row)) # print raw strings
        i += 1
        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'

Finally, let’s try option 4 and use the tried-and-true Data 100 approach: pandas.

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

	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

You may notice some strange things about this table: what’s up with the “Unnamed” column names and the first row?

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 that we can set to use the elements in row 1 as the appropriate columns:

tb_df = pd.read_csv("data/cdc_tuberculosis.csv", header=1) # row index
tb_df.head(5)

	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

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.head(5)

	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

5.4.2 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.

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. If we sum over all rows, we should get 2x the total cases in each of our TB cases by year (why do you think this is?).

Code

tb_df.sum(axis=0)

U.S. jurisdiction    TotalAlabamaAlaskaArizonaArkansasCaliforniaCol...
TB cases 2019        8,9008758183642,111666718245583029973261085237...
TB cases 2020        7,1737258136591,706525417194122219282169239376...
TB cases 2021        7,8609258129691,750585443194992281064255127494...
TB incidence 2019                                               109.94
TB incidence 2020                                                93.09
TB incidence 2021                                               102.94
dtype: object

Whoa, what’s going on with the TB cases in 2019, 2020, and 2021? Check out the column types:

Code

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

Since there are commas in the values for TB cases, the numbers are read as the object datatype, or storage type (close to the Python string datatype), so pandas is concatenating strings instead of adding integers (recall that Python can “sum”, or concatenate, strings together: "data" + "100" evaluates to "data100").

Fortunately read_csv also has a thousands parameter (documentation):

# 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.head(5)

	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

tb_df.sum()

U.S. jurisdiction    TotalAlabamaAlaskaArizonaArkansasCaliforniaCol...
TB cases 2019                                                    17800
TB cases 2020                                                    14346
TB cases 2021                                                    15720
TB incidence 2019                                               109.94
TB incidence 2020                                                93.09
TB incidence 2021                                               102.94
dtype: object

The total TB cases look right. Phew!

Let’s just look at the records with state-level granularity:

Code

state_tb_df = tb_df[1:]
state_tb_df.head(5)

	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	2111	1706	1750	5.35	4.32	4.46

5.4.3 Gather Census Data

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

Running the below cells cleans the data. 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.

Code

# 2010s census data
census_2010s_df = pd.read_csv("data/nst-est2019-01.csv", header=3, thousands=",")
census_2010s_df = (
    census_2010s_df
    .reset_index()
    .drop(columns=["index", "Census", "Estimates Base"])
    .rename(columns={"Unnamed: 0": "Geographic Area"})
    .convert_dtypes()                 # "smart" converting of columns, use at your own risk
    .dropna()                         # we'll introduce this next time
)
census_2010s_df['Geographic Area'] = census_2010s_df['Geographic Area'].str.strip('.')

# with pd.option_context('display.min_rows', 30): # shows more rows
#     display(census_2010s_df)
    
census_2010s_df.head(5)

	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

Occasionally, you will want to modify code that you have imported. To reimport those modifications you can either use python’s importlib library:

from importlib import reload
reload(utils)

or use iPython magic which will intelligently import code when files change:

%load_ext autoreload
%autoreload 2

Code

# census 2020s data
census_2020s_df = pd.read_csv("data/NST-EST2022-POP.csv", header=3, thousands=",")
census_2020s_df = (
    census_2020s_df
    .reset_index()
    .drop(columns=["index", "Unnamed: 1"])
    .rename(columns={"Unnamed: 0": "Geographic Area"})
    .convert_dtypes()                 # "smart" converting of columns, use at your own risk
    .dropna()                         # we'll introduce this next time
)
census_2020s_df['Geographic Area'] = census_2020s_df['Geographic Area'].str.strip('.')

census_2020s_df.head(5)

	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

5.4.4 Joining Data (Merging `DataFrame`s)

Time to merge! Here we 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.

# 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.head(5)

	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
0	Alabama	87	72	92	1.77	1.43	1.83	Alabama	4785437	4799069	4815588	4830081	4841799	4852347	4863525	4874486	4887681	4903185	Alabama	5031362	5049846	5074296
1	Alaska	58	58	58	7.91	7.92	7.92	Alaska	713910	722128	730443	737068	736283	737498	741456	739700	735139	731545	Alaska	732923	734182	733583
2	Arizona	183	136	129	2.51	1.89	1.77	Arizona	6407172	6472643	6554978	6632764	6730413	6829676	6941072	7044008	7158024	7278717	Arizona	7179943	7264877	7359197
3	Arkansas	64	59	69	2.12	1.96	2.28	Arkansas	2921964	2940667	2952164	2959400	2967392	2978048	2989918	3001345	3009733	3017804	Arkansas	3014195	3028122	3045637
4	California	2111	1706	1750	5.35	4.32	4.46	California	37319502	37638369	37948800	38260787	38596972	38918045	39167117	39358497	39461588	39512223	California	39501653	39142991	39029342

Having all of these columns 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.head(5)

	U.S. jurisdiction	TB cases 2019	TB cases 2020	TB cases 2021	TB incidence 2019	TB incidence 2020	TB incidence 2021	2019	2020	2021
0	Alabama	87	72	92	1.77	1.43	1.83	4903185	5031362	5049846
1	Alaska	58	58	58	7.91	7.92	7.92	731545	732923	734182
2	Arizona	183	136	129	2.51	1.89	1.77	7278717	7179943	7264877
3	Arkansas	64	59	69	2.12	1.96	2.28	3017804	3014195	3028122
4	California	2111	1706	1750	5.35	4.32	4.46	39512223	39501653	39142991

5.4.5 Reproducing Data: Compute 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"]*100000
tb_census_df.head(5)

	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

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 (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}"]*100000
tb_census_df.head(5)

	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

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.

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

5.4.6 Bonus EDA: Reproducing the Reported Statistic

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, we’ll set all indexes to more meaningful values, instead of just numbers that pertain to some row at some point. This will make our cleaning slightly easier.

Code

tb_df = tb_df.set_index("U.S. jurisdiction")
tb_df.head(5)

	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

census_2010s_df = census_2010s_df.set_index("Geographic Area")
census_2010s_df.head(5)

	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

census_2020s_df = census_2020s_df.set_index("Geographic Area")
census_2020s_df.head(5)

	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

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 DataFrame have different Geographic Area fields, which was the key we merged on:

census_2010s_df.head(5)

	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

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.head(5)

	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

# same, but for 2020s rename rolled record
census_2020s_df.rename(index={'United States':'Total'}, inplace=True)
census_2020s_df.head(5)

	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

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.head(5)

	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

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.head(5)

	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

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 $\text{percent change} = \frac{B - A}{A} \times 100$.

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

2.1637257652759883

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

2.3672448914298068

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

9.405957511804143

5.5 EDA Demo 2: Mauna Loa CO₂ Data – A Lesson in Data Faithfulness

Mauna Loa Observatory has been monitoring CO₂ concentrations since 1958.

co2_file = "data/co2_mm_mlo.txt"

Let’s do some EDA!!

5.5.1 Reading this file into `Pandas`?

Let’s instead check out this .txt file. Some questions to keep in mind: Do we trust this file extension? What structure is it?

Lines 71-78 (inclusive) are shown below:

line number |                            file contents

71          |   #            decimal     average   interpolated    trend    #days
72          |   #             date                             (season corr)
73          |   1958   3    1958.208      315.71      315.71      314.62     -1
74          |   1958   4    1958.292      317.45      317.45      315.29     -1
75          |   1958   5    1958.375      317.50      317.50      314.71     -1
76          |   1958   6    1958.458      -99.99      317.10      314.85     -1
77          |   1958   7    1958.542      315.86      315.86      314.98     -1
78          |   1958   8    1958.625      314.93      314.93      315.94     -1

Notice how:

The values are separated by white space, possibly tabs.
The data line up down the rows. For example, the month appears in 7th to 8th position of each line.
The 71st and 72nd lines in the file contain column headings split over two lines.

We can use read_csv to read the data into a pandas DataFrame, and we provide several arguments to specify that the separators are white space, there is no header (we will set our own column names), and to skip the first 72 rows of the file.

co2 = pd.read_csv(
    co2_file, header = None, skiprows = 72,
    sep = r'\s+'       #delimiter for continuous whitespace (stay tuned for regex next lecture))
)
co2.head()

	0	1	2	3	4	5	6
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
3	1958	6	1958.46	-99.99	317.10	314.85	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1

Congratulations! You’ve wrangled the data!

…But our columns aren’t named. We need to do more EDA.

5.5.2 Exploring Variable Feature Types

The NOAA webpage might have some useful tidbits (in this case it doesn’t).

Using this information, we’ll rerun pd.read_csv, but this time with some custom column names.

co2 = pd.read_csv(
    co2_file, header = None, skiprows = 72,
    sep = '\s+', #regex for continuous whitespace (next lecture)
    names = ['Yr', 'Mo', 'DecDate', 'Avg', 'Int', 'Trend', 'Days']
)
co2.head()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
3	1958	6	1958.46	-99.99	317.10	314.85	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1

5.5.3 Visualizing CO₂

Scientific studies tend to have very clean data, right…? Let’s jump right in and make a time series plot of CO₂ monthly averages.

Code

sns.lineplot(x='DecDate', y='Avg', data=co2);

The code above uses the seaborn plotting library (abbreviated sns). We will cover this in the Visualization lecture, but now you don’t need to worry about how it works!

Yikes! Plotting the data uncovered a problem. The sharp vertical lines suggest that we have some missing values. What happened here?

co2.head()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
3	1958	6	1958.46	-99.99	317.10	314.85	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1

co2.tail()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
733	2019	4	2019.29	413.32	413.32	410.49	26
734	2019	5	2019.38	414.66	414.66	411.20	28
735	2019	6	2019.46	413.92	413.92	411.58	27
736	2019	7	2019.54	411.77	411.77	411.43	23
737	2019	8	2019.62	409.95	409.95	411.84	29

Some data have unusual values like -1 and -99.99.

Let’s check the description at the top of the file again.

-1 signifies a missing value for the number of days Days the equipment was in operation that month.
-99.99 denotes a missing monthly average Avg

How can we fix this? First, let’s explore other aspects of our data. Understanding our data will help us decide what to do with the missing values.

5.5.4 Sanity Checks: Reasoning about the data

First, we consider the shape of the data. How many rows should we have?

If chronological order, we should have one record per month.
Data from March 1958 to August 2019.
We should have $ 12 (2019-1957) - 2 - 4 = 738 $ records.

co2.shape

(738, 7)

Nice!! The number of rows (i.e. records) match our expectations.

Let’s now check the quality of each feature.

5.5.5 Understanding Missing Value 1: `Days`

Days is a time field, so let’s analyze other time fields to see if there is an explanation for missing values of days of operation.

Let’s start with months, Mo.

Are we missing any records? The number of months should have 62 or 61 instances (March 1957-August 2019).

co2["Mo"].value_counts().sort_index()

1     61
2     61
3     62
4     62
5     62
6     62
7     62
8     62
9     61
10    61
11    61
12    61
Name: Mo, dtype: int64

As expected Jan, Feb, Sep, Oct, Nov, and Dec have 61 occurrences and the rest 62.

Next let’s explore days Days itself, which is the number of days that the measurement equipment worked.

Code

sns.displot(co2['Days']);
plt.title("Distribution of days feature"); # suppresses unneeded plotting output

/Users/Ishani/micromamba/lib/python3.9/site-packages/seaborn/axisgrid.py:118: UserWarning:

The figure layout has changed to tight

In terms of data quality, a handful of months have averages based on measurements taken on fewer than half the days. In addition, there are nearly 200 missing values–that’s about 27% of the data!

Finally, let’s check the last time feature, year Yr.

Let’s check to see if there is any connection between missing-ness and the year of the recording.

Code

sns.scatterplot(x="Yr", y="Days", data=co2);
plt.title("Day field by Year"); # the ; suppresses output

Observations:

All of the missing data are in the early years of operation.
It appears there may have been problems with equipment in the mid to late 80s.

Potential Next Steps:

Confirm these explanations through documentation about the historical readings.
Maybe drop the earliest recordings? However, we would want to delay such action until after we have examined the time trends and assess whether there are any potential problems.

5.5.6 Understanding Missing Value 2: `Avg`

Next, let’s return to the -99.99 values in Avg to analyze the overall quality of the CO₂ measurements. We’ll plot a histogram of the average CO₂ measurements

Code

# Histograms of average CO2 measurements
sns.displot(co2['Avg']);

/Users/Ishani/micromamba/lib/python3.9/site-packages/seaborn/axisgrid.py:118: UserWarning:

The figure layout has changed to tight

The non-missing values are in the 300-400 range (a regular range of CO₂ levels).

We also see that there are only a few missing Avg values (<1% of values). Let’s examine all of them:

co2[co2["Avg"] < 0]

	Yr	Mo	DecDate	Avg	Int	Trend	Days
3	1958	6	1958.46	-99.99	317.10	314.85	-1
7	1958	10	1958.79	-99.99	312.66	315.61	-1
71	1964	2	1964.12	-99.99	320.07	319.61	-1
72	1964	3	1964.21	-99.99	320.73	319.55	-1
73	1964	4	1964.29	-99.99	321.77	319.48	-1
213	1975	12	1975.96	-99.99	330.59	331.60	0
313	1984	4	1984.29	-99.99	346.84	344.27	2

There doesn’t seem to be a pattern to these values, other than that most records also were missing Days data.

5.5.7 Drop, `NaN`, or Impute Missing `Avg` Data?

How should we address the invalid Avg data?

Drop records
Set to NaN
Impute using some strategy

Remember we want to fix the following plot:

Code

sns.lineplot(x='DecDate', y='Avg', data=co2)
plt.title("CO2 Average By Month");

Since we are plotting Avg vs DecDate, we should just focus on dealing with missing values for Avg.

Let’s consider a few options: 1. Drop those records 2. Replace -99.99 with NaN 3. Substitute it with a likely value for the average CO₂?

What do you think are the pros and cons of each possible action?

Let’s examine each of these three options.

# 1. Drop missing values
co2_drop = co2[co2['Avg'] > 0]
co2_drop.head()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1
5	1958	8	1958.62	314.93	314.93	315.94	-1

# 2. Replace NaN with -99.99
co2_NA = co2.replace(-99.99, np.NaN)
co2_NA.head()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
3	1958	6	1958.46	NaN	317.10	314.85	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1

We’ll also use a third version of the data.

First, we note that the dataset already comes with a substitute value for the -99.99.

From the file description:

The interpolated column includes average values from the preceding column (average) and interpolated values where data are missing. Interpolated values are computed in two steps…

The Int feature has values that exactly match those in Avg, except when Avg is -99.99, and then a reasonable estimate is used instead.

So, the third version of our data will use the Int feature instead of Avg.

# 3. Use interpolated column which estimates missing Avg values
co2_impute = co2.copy()
co2_impute['Avg'] = co2['Int']
co2_impute.head()

	Yr	Mo	DecDate	Avg	Int	Trend	Days
0	1958	3	1958.21	315.71	315.71	314.62	-1
1	1958	4	1958.29	317.45	317.45	315.29	-1
2	1958	5	1958.38	317.50	317.50	314.71	-1
3	1958	6	1958.46	317.10	317.10	314.85	-1
4	1958	7	1958.54	315.86	315.86	314.98	-1

What’s a reasonable estimate?

To answer this question, let’s zoom in on a short time period, say the measurements in 1958 (where we know we have two missing values).

Code

# results of plotting data in 1958

def line_and_points(data, ax, title):
    # assumes single year, hence Mo
    ax.plot('Mo', 'Avg', data=data)
    ax.scatter('Mo', 'Avg', data=data)
    ax.set_xlim(2, 13)
    ax.set_title(title)
    ax.set_xticks(np.arange(3, 13))

def data_year(data, year):
    return data[data["Yr"] == 1958]
    
# uses matplotlib subplots
# you may see more next week; focus on output for now
fig, axes = plt.subplots(ncols = 3, figsize=(12, 4), sharey=True)

year = 1958
line_and_points(data_year(co2_drop, year), axes[0], title="1. Drop Missing")
line_and_points(data_year(co2_NA, year), axes[1], title="2. Missing Set to NaN")
line_and_points(data_year(co2_impute, year), axes[2], title="3. Missing Interpolated")

fig.suptitle(f"Monthly Averages for {year}")
plt.tight_layout()

In the big picture since there are only 7 Avg values missing (<1% of 738 months), any of these approaches would work.

However there is some appeal to option C, Imputing:

Shows seasonal trends for CO₂
We are plotting all months in our data as a line plot

Let’s replot our original figure with option 3:

Code

sns.lineplot(x='DecDate', y='Avg', data=co2_impute)
plt.title("CO2 Average By Month, Imputed");

Looks pretty close to what we see on the NOAA website!

5.5.8 Presenting the Data: A Discussion on Data Granularity

From the description:

Monthly measurements are averages of average day measurements.
The NOAA GML website has datasets for daily/hourly measurements too.

The data you present depends on your research question.

How do CO₂ levels vary by season?

You might want to keep average monthly data.

Are CO₂ levels rising over the past 50+ years, consistent with global warming predictions?

You might be happier with a coarser granularity of average year data!

Code

co2_year = co2_impute.groupby('Yr').mean()
sns.lineplot(x='Yr', y='Avg', data=co2_year)
plt.title("CO2 Average By Year");

Indeed, we see a rise by nearly 100 ppm of CO₂ since Mauna Loa began recording in 1958.

5.6 Summary

We went over a lot of content this lecture; let’s summarize the most important points:

5.6.1 Dealing with Missing Values

There are a few options we can take to deal with missing data:

Drop missing records
Keep NaN missing values
Impute using an interpolated column

5.6.2 EDA and Data Wrangling

There are several ways to approach EDA and Data Wrangling:

Examine the data and metadata: what is the date, size, organization, and structure of the data?
Examine each field/attribute/dimension individually.
Examine pairs of related dimensions (e.g. breaking down grades by major).
Along the way, we can:
- Visualize or summarize the data.
- Validate assumptions about data and its collection process. Pay particular attention to when the data was collected.
- Identify and address anomalies.
- Apply data transformations and corrections (we’ll cover this in the upcoming lecture).
- Record everything you do! Developing in Jupyter Notebook promotes reproducibility of your own work!

5.1 Structure

5.1.1 File Formats

5.1.1.1 CSV

5.1.1.2 TSV

5.1.1.3 JSON

5.1.1.3.1 EDA with JSON: Berkeley COVID-19 Data

5.1.1.3.1.1 File Size

5.1.1.3.1.2 Unix Commands

5.1.1.3.1.3 File Contents

5.1.1.3.1.4 Examining the Data Field for Records

5.1.1.3.1.5 Summary of exploring the JSON file

5.1.1.3.1.6 Loading COVID Data into pandas

5.1.2 Primary and Foreign Keys

5.1.3 Variable Types

5.2 Granularity, Scope, and Temporality

5.2.1 Granularity

5.2.2 Scope

5.2.3 Temporality

5.2.3.1 Temporality with pandas’ dt accessors

5.3 Faithfulness

5.3.1 Missing Values

5.4 EDA Demo 1: Tuberculosis in the United States

5.4.1 CSVs and Field Names

5.4.2 Record Granularity

5.4.3 Gather Census Data

5.4.4 Joining Data (Merging DataFrames)

5.4.5 Reproducing Data: Compute Incidence

5.4.6 Bonus EDA: Reproducing the Reported Statistic

5.5 EDA Demo 2: Mauna Loa CO2 Data – A Lesson in Data Faithfulness

5.5.1 Reading this file into Pandas?

5.5.2 Exploring Variable Feature Types

5.5.3 Visualizing CO2

5.5.4 Sanity Checks: Reasoning about the data

5.5.5 Understanding Missing Value 1: Days

5.5.6 Understanding Missing Value 2: Avg

5.5.7 Drop, NaN, or Impute Missing Avg Data?

5.5.8 Presenting the Data: A Discussion on Data Granularity

5.6 Summary

5.6.1 Dealing with Missing Values

5.6.2 EDA and Data Wrangling

5.1.1.3.1.6 Loading COVID Data into `pandas`

5.2.3.1 Temporality with `pandas`’ `dt` accessors

5.4.4 Joining Data (Merging `DataFrame`s)

5.5 EDA Demo 2: Mauna Loa CO₂ Data – A Lesson in Data Faithfulness

5.5.1 Reading this file into `Pandas`?

5.5.3 Visualizing CO₂

5.5.5 Understanding Missing Value 1: `Days`

5.5.6 Understanding Missing Value 2: `Avg`

5.5.7 Drop, `NaN`, or Impute Missing `Avg` Data?