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Cleaning Data for Effective Data Science

By David Mertz
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About this book
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses.
Publication date:
March 2021



The Vicissitudes of Error


Anomaly Detection

The map is not the territory and data is not the world observed. Data is messy, inconsistent, and unreliable. The world is messier, less consistent, and less reliable.

–cf. Alfred Korzybski

When we think about anomaly detection, there are two distinct, and mostly independent, concepts that go by the name. The topic of this chapter is perhaps the less exciting of the two. Security and cryptography researchers, importantly, look for anomalies that can represent fraud, forgery, and system intrusion attempts. By the intention of perpetrators, these outliers in the normal patterns of data are subtle and hard to detect, and a conflict exists between those wishing to falsify data and those wishing to detect that falsification.

The concept of interest to us in this book is more quotidian. We wish to detect those cases where data goes bad in the ordinary course of its collection, collation, transmission, and transcription. Perhaps an instrument gives a bad reading some or all of the time. Perhaps some values are systematically altered in the course of reencoding to a different data format. Perhaps the wrong units of measure were used for a subset of the data. And so on. By accident, these broader checks may occasionally identify changes that reflect actual malice, but more often they will simply detect errors, and perhaps bias (but less often, since bias still is usually toward plausible values).

Anomaly detection has an especially close connection to Chapter 5, Data Quality, and often to the topic of Chapter 6, Value Imputation. The loose contrast between this chapter and the next one on data quality is that anomalies are individual data values that can be diagnosed as probably wrong, whereas data quality more broadly looks at patterns of the dataset as a whole that can present or identify problems.

When anomalies are detected it sometimes makes sense to impute more likely values rather than to discard those observations altogether. In terms of the structure of this book, the lessons of this chapter will allow you to identify and mark anomalies as “missing” while Chapter 6, Value Imputation, will pick up with filling in those better-imputed values (imputation is simply replacing mising data points with values that are likely, or at least plausible).

These connected chapters—4, 5, and 6—form a broader unit, and roughly describe a pipeline or series of steps. That is, given your inevitably flawed data you might first look for anomalies and mark them missing. Next you might look for more systematic attributes of your dataset, and remediate them in various ways. Finally, you might impute (or drop) data that was either missing to start with or marked so because of properties this chapter will help you detect. The step past the final step of this sequence is the actual modeling or analysis you perform, and is the subject of many excellent books, but not of this one.clean code

clean code

My mention of these steps is a good opportunity to repeat an admonishment that has occurred elsewhere herein. The steps of your data processing pipeline should be coded and documented carefully and reproducibly. It is often easy and tempting to make changes to datasets in an exploratory way—as this book does—but in the process lose a good record of exactly what steps were taken. The exploration is an integral part of data science, but reproducibility should not be lost in that process. Good practice is to retain your original dataset—in whatever data format it originally presents itself—and generate the final version via scripts (maintained in version control) rather than within notebooks or interactive shells. Care must always be taken to allow someone else to repeatably move from the raw original dataset to the version that is fed into a machine learning model or other analytic tool. Keeping an audit trail of what tool or function produced what change is hygienic practice.


Before we get to the sections of this chapter, let us run our standard setup code:

from src.setup import *
%load_ext rpy2.ipython

Missing Data

Gregory: Is there any other point to which you would wish to draw my attention?

Holmes: To the curious incident of the dog in the night-time.

Gregory: The dog did nothing in the night-time.

Holmes: That was the curious incident.

–Arthur Conan Doyle


  • Sentinels versus explicit absence
  • Semantics of NULL, NaN, and N/A
  • Nullable columns in SQL
  • Absence in hierarchies
  • Pitfalls of sentinels

Some data formats explicitly support missing data while other formats use a special value, known as a sentinel value, of one sort or another to indicate missingness. Non-tabular formats may indicate missing data simply by not including any value in a position where it might otherwise occur. However, sentinel values are sometimes ambiguous, unfortunately.

In particular, within many data formats, and within most data frame libraries, missing numeric values are represented by the special IEEE-754 floating-point value NaN (Not-a-Number). The problem here is that NaN, by design and intention, can arise as the result of some attempts at computation that are not obviously unreasonable. While such an unrepresentable value is indeed unavailable, this is potentially semantically different from data that was simply never collected in the first place. As a small digression, let us look at coaxing a NaN to arise in an “ordinary” computation (albeit a contrived one).

for n in range(7, 10):
    exp1 = 2**n
    a = (22/7) ** exp1 
    b = π ** exp1
    # Compute answer in two "equivalent" ways
    res1 = (a * a) / (b * b)
    res2 = (a / b) * (a / b)
    print(f"n={n}:\n  "
          f"method1: {res1:.3f}\n  "
          f"method2: {res2:.3f}")
  method1: 1.109
  method2: 1.109
  method1: 1.229
  method2: 1.229
  method1: nan
  method2: 1.510

Parallel to the pitfall of missing floats being represented as NaNs, missing strings are almost always represented as strings. Generally, one or more reserved values such as “N/A” or the empty string are used when a string value is missing. However, those sentinels do not clearly distinguish between “not applicable” and “not available,” which are subtly different.

As a toy example, we might have collected names of people, including “middle name.” Having a sentinel value for “middle name” would not distinguish between survey subjects who have no middle name and those who merely had not provided it. Reaching just slightly for a data science purpose: perhaps we wish to find the correlation between certain middle names and demographic characteristics. In the United States, for example, the middle name “Santiago” would be strongly associated with Hispanic family origin; a survey subject who provided no middle name might nonetheless have that middle name. In principle, a string field could contain different sentinels for, e.g. “No middle name” and “No response,” but datasets are very rarely careful in those distinctions.


In SQL databases, an explicit NULL is available for all column types. Whether a particular column is “nullable” is determined by the database administrator (or whoever had that functional role, however much or little qualified). This allows a distinction in principle between an explicit NaN for a numeric field and a NULL for missing values.

Unfortunately, many or most actual database tables fail to utilize these available distinctions (i.e. the specific configured and populated tables). In practice, you are likely to see many combinations of empty strings, NaNs, actual NULLs, or other sentinels, even within SQL databases. This is not because any widely used RDBMS fails to support these different values and types; it is rather that in the history of various clients putting data into them, using various codebases, non-optimal choices were made.

To run the code in the next cells, you need to obtain access to an RDBMS. The PostgreSQL server running on my local system, in particular, has a database called dirty, and that in turn contains a table called missing. If you use a different RDBMS, your driver will have a different name, and your engine will use a different scheme in its connection URL. The particular user, password, host, and port will also vary. Database servers also often use authentication methods other than a password to grant access. However, the Python DB-API (database API) is quite consistent, and you will work with the connection object and engine in identical ways when you access other RDBMSs. For illustrative purposes, we show our PostgreSQL configuration function connect_local(), which is contained in setup.py.

# PostgreSQL configuration
def connect_local():
    user = 'cleaning'
    pwd = 'data'
    host = 'localhost'
    port = '5432'  
    db = 'dirty'
    con = psycopg2.connect(database=db, host=host, user=user, password=pwd)
    engine = create_engine(f'postgresql://{user}:{pwd}@{host}:{port}/{db}')
    return con, engine

With the connection established, we can examine some of our data in Python.

con, engine = connect_local()
cur = con.cursor()
# Look at table named "missing"
cur.execute("SELECT * FROM missing")
for n, (a, b) in enumerate(cur):
    print(f"{n+1} | {str(a):>4s} | {b}")
1 |  nan | Not number
2 | 1.23 | A number  
3 | None | A null    
4 | 3.45 | Santiago  
5 | 6.78 |           
6 | 9.01 | None

As Python objects, an SQL NULL is represented as the singleton None, which is a reasonable choice. Let us review this friendly data representation.

  • Row 1 contains a NaN (not computable) and a string describing the row
  • Row 2 contains a regular float value and a string describing it
  • Row 3 contains an SQL NULL (not available) and a string
  • Row 4 contains a regular float value and a regular string
  • Row 5 contains a regular float value and an empty string (“not applicable”)
  • Row 6 contains a regular float value and a NULL (“not available”)

In terms of actually supporting the distinction between a true NULL and a sentinel value like NaN, libraries are of mixed quality. Pandas has made some strides with version 1.0 by introducing the special singleton pd.NA to be used as a “missing” indicator across data types, instead of np.nan, None, and pd.NaT (Not a Time). However, as of this writing, the singleton is not utilized in any of the standard data readers, and getting the value into data requires special efforts. I hope this will have improved by the time you read this.

R’s Tidyverse does better because R itself has an NA special value. Slightly confusingly, R also contains an even more special pseudo-value NULL, which is used to indicate that something is undefined (as opposed to simply missing). R’s NULL can result from some expressions and function calls, but it cannot be an element in arrays or data frames.

# Notice NULL is simply ignored in the construction
tibble(val = c(NULL, NA, NaN, 0), 
       str = c("this", "that", NA))
# A tibble: 3 x 2
     val  str  
   <dbl>  <chr>
1     NA  this 
2    NaN  that 
3      0  NA   

What SQL calls NULL, R calls NA; NaN remains a separate value indicating “not computable.” NaN This allows R to interface correctly and unambiguously with SQL, or with the occasional other formats which also explicitly mark “missing” in a non-sentinel manner.


The IEEE-754 standard, in fact, reserves a large number of bit patterns as NaNs: 16 million of them for 32-bit floats, and vastly more for 64-bit floats. Moreover, these many NaNs are divided into a generous number each for signaling versus quiet NaNs. In concept, when the standard was developed, the choice of which of the millions of NaNs available (the “payload”) could be used to record information about exactly what kind of operation led to the NaN occurring. That said, no software used in data science—and nearly no software used in array and numeric computation—actually utilizes the distinction among the many NaNs. In practical terms, NaN is equivalent to a singleton, like R’s NA, Python’s None, or JavaScript’s null.

This R code assumes the same PostgreSQL database is available as that used in the Python example. As with the Python code, a different RDBMS will require a different driver name, and user, password, host, and port will vary in your configuration:

drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "dirty",
                 host = "localhost", port = 5432,
                 user = "cleaning", password = "data")
sql <- "SELECT * FROM missing"
data <- tibble(dbGetQuery(con, sql))
# A tibble: 6 x 2
         a   b           
     <dbl>   <chr>       
1   NaN      "Not number"
2     1.23   "A number  "
3    NA      "A null    "
4     3.45   "Santiago  "
5     6.78   "          "
6     9.01    NA         

In contrast, Pandas 1.0 produces the less correct data frame. The engine object was configured and discussed above with the connect_local() function:

pd.read_sql("SELECT * FROM missing", engine)
        A            b
0     NaN   Not number
1    1.23     A number
2     NaN       A null
3    3.45     Santiago
4    6.78             
5    9.01         None

Hierarchical Formats

In formats like JSON that nest data flexibly, there is an obvious way of representing missing data: by not representing it at all. If you perform hierarchical processing, you will need to check for the presence or absence of a given dictionary key at a given level. The JSON specification itself does not address NaN values, which means that some systems producing data may choose to use the JavaScript null value in its place, producing the ambiguity we have discussed above. However, many specific libraries extend the definition to recognize NaN (and sometimes inf, which is also a floating-point number) as a value. To illustrate:

json.loads('[NaN, null, Infinity]')  # null becomes Python None
[nan, None, inf]

Let us represent the same data of the SQL table illustrated above in a (relatively) compact way. Notice, however, that since in the Python json library NaN is a recognized value, we could explicitly represent all missing keys and match them with null as needed. Obviously, we data scientists do not usually generate the data we need to consume; so the format we get is the one we need to process.

We can read this particular data into a Pandas DataFrame easily, subject to the sentinel limitation. Since a data frame imposes a tabular format, the missing row/column positions must be filled with some value, in this case with a NaN as sentinel. Of course, as discussed in Chapter 2, Hierarchical Formats, nested data may simply not be amenable to being represented in a tabular way.

json_data = '''
{"a": {"1": NaN, "2": 1.23, "4": 3.45, "5": 6.78, "6": 9.01},
 "b": {"1": "Not number", "2": "A number", "3": "A null",
       "4": "Santiago", "5": ""}
        A            b
1     NaN   Not number
2    1.23     A number
3     NaN       A null
4    3.45     Santiago
5    6.78             
6    9.01          NaN

Let us also process this JSON data in a more hierarchical and procedural way for illustration, classifying special/missing values as we encounter them. For the example, we assume that the top level is a dictionary of dictionaries, but obviously we could walk other structures as well if needed:

data = json.loads(json_data)
rows = {row for dct in data.values() 
            for row in dct.keys()}
for row in sorted(rows):
    for col in data.keys():
        val = data[col].get(row)
        if val is None:
            print(f"Row {row}, Col {col}: Missing")
        elif isinstance(val, float) and math.isnan(val):
            print(f"Row {row}, Col {col}: Not a Number")
        elif not val:
            print(f"Row {row}, Col {col}: Empty value {repr(val)}")
Row 1, Col a: Not a Number
Row 3, Col a: Missing
Row 5, Col b: Empty value ''
Row 6, Col b: Missing


In textual data formats, mainly delimited and fixed-width files, missing data is indicated either by absence or by a sentinel. Both delimited and fixed-width formats are able to omit a certain field in a row—albeit, in fixed-width, this does not distinguish among an empty string, a string of spaces, and a missing value. Two commas next to each other in CSV should be unambiguous for “no value.”

Ideally, this absence should be used to indicate missingness, and potentially allow some other sentinel to indicate “Not Applicable,” “Not Calculable,” “No Middle Name,” or other specific markers for known values that fall outside the domain of a variable. In practice, however, the “best practice” I recommend here is often not what is used in the datasets you will actually need to work with.

The use of sentinels is not limited to text formats. Often in SQL, for example, TEXT or CHAR columns that could, in principle, be made nullable and use NULL to indicate missing values instead use sentinels (and not always single sentinels; in practice they often acquire multiple markers over multiple generations of software changes). Sometimes formats such as JSON that can hold text values likewise use sentinels rather than omitting keys. Even in formats like HDF5 that enforce data typing, sometimes sentinel numeric values are used to indicate missing values rather than relying on NaN as a special marker (which has its own problems, discussed above).

In Pandas, in particular, as of version 1.0, the following sentinel values are recognized by default as meaning “missing” when reading delimited or fixed-width files: ' ', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan', '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan', and 'null'. Some of these must arise in domains or from tools I am personally unfamiliar with, but many I have seen. However, I have also encountered numerous sentinels not in that list. You will need to consider sentinels for your specific dataset, and such defaults are only some first guesses the tool provides. Other tools will have different defaults.

Libraries for working with datasets, often as data frames, will have mechanisms to specify the particular values to treat as sentinels for missing data. Let us look at an example that is closely based on real-world data obtained from the United States National Oceanic and Atmospheric Administration (NOAA). This data was, in fact, provided as CSV files; a more descriptive filename is used here, and many of the columns are omitted. But only one data value is changed in the example. In other words, this is a dataset I actually had to work with outside of writing this book, and the issues discussed were not ones I knew about in advance of doing that.

The dataset we read below concerns weather measurements at a particular weather station. The station at Sorstokken, Norway, is chosen here more-or-less at random from thousands available. Other stations employ the same encoding, which is nowhere obviously documented. Unfortunately, undocumented or underdocumented field constraints are the rule in published data, not the exception. The column names are somewhat abbreviated, but not too hard to guess the meaning of: temperature (°F), maximum wind gust speed (mph), etc:

sorstokken = pd.read_csv('data/sorstokken-no.csv.gz')
        STATION         DATE    TEMP   VISIB   GUST   DEWP
0    1001499999   2019-01-01    39.7     6.2   52.1   30.4
1    1001499999   2019-01-02    36.4     6.2  999.9   29.8
2    1001499999   2019-01-03    36.5     3.3  999.9   35.6
3    1001499999      UNKNOWN    45.6     2.2   22.0   44.8
...         ...          ...     ...     ...    ...    ...
295  1001499999   2019-12-17    40.5     6.2  999.9   39.2
296  1001499999   2019-12-18    38.8     6.2  999.9   38.2
297  1001499999   2019-12-19    45.5     6.1  999.9   42.7
298  1001499999   2019-12-20    51.8     6.2   35.0   41.2
299 rows × 6 columns

We notice a few things in the view of a selection of the table. The DATE value UNKNOWN is included (by my construction). Also, some GUST values are 999.9 (in the original data). The use of several 9 digits as a sentinel is a common convention. The number of 9s used varies, however, as does the position of a decimal point if any is used. Another common convention is using a -1 as a sentinel for numeric values that semantically must be positive for legitimate values. For example, the -1 convention might sensibly be used for wind gust speed, but it could not be for degrees Fahrenheit or Celsius, which can perfectly well have the value -1 for ordinary Earth surface temperatures. On the other hand, if we were using the same units to measure the temperatures inside an iron forge (the melting point of iron is 2,800°F/1,538°C), -1 would be safely outside the possible operating range.

Looking at the minimum and maximum values of a given variable is often a clue about the sentinels used. For numbers—and also for dates—a value that is unreasonably large or unreasonably small is generally used for a sentinel. This can go wrong where legitimate measurements later exceed their initially anticipated range:

pd.DataFrame([sorstokken.min(), sorstokken.max()])
        STATION         DATE    TEMP    VISIB    GUST    DEWP
0    1001499999   2019-01-01    27.2      1.2    17.1    16.5
1    1001499999      UNKNOWN    88.1    999.9   999.9    63.5

Here we see that TEMP and DEWP seem always to fall within a “reasonable” range. DATE alerts us to a problem value this way; it might also do so, but possibly more subtly, if the sentinel had been for example 1900-01-01, which is an actual date but one from before NOAA measurements were taken. Likewise, VISIB and GUST have unreasonably high and special-looking values. For string values, sentinels are quite likely to occur right in the middle of valid values. “No Middle Name” is alphabetically between “Naomi” and “Nykko.” Let us look more closely at these variables with sentinels.

Outliers and standard deviation (σ) are discussed more in a later section:

print("Normal max:")
for col in ['VISIB', 'GUST']:
        s = sorstokken[col]
        print(col, s[s < 999.9].max(), 
              "...standard deviation w/ & w/o sentinel:",
              f"{s.std():.1f} / {s[s < 999.9].std():.1f}")
Normal max:
VISIB 6.8 ...standard deviation w/ & w/o sentinel: 254.4 / 0.7
GUST 62.2 ...standard deviation w/ & w/o sentinel: 452.4 / 8.1

I believe VISIB is measured in miles, and seeing a thousand miles is unreasonable. GUST wind speed is in mph, and likewise 999.9 is not something that will occur on Earth. However, one should worry when sentinels are within three orders of magnitude of actual values, as here. For power law distributed values, even that rule of thumb about orders of magnitude is of little help.

In Pandas and other tools, we can instruct the tool to look for specific sentinels, and substitute specific values. Of course, we could do so after data is read into a data structure using regular data frame filtering and manipulation techniques. If we can do so at read time, so much the better. Here we look for sentinels on a column-specific basis:

sorstokken = pd.read_csv('data/sorstokken-no.csv.gz', 
                         na_values={'DATE': 'UNKNOWN', 
                                    'VISIB': '999.9',
                                    'GUST': '999.9'},
        STATION         DATE    TEMP    VISIB    GUST    DEWP
0    1001499999   2019-01-01    39.7      6.2    52.1    30.4
1    1001499999   2019-01-02    36.4      6.2     NaN    29.8
2    1001499999   2019-01-03    36.5      3.3     NaN    35.6
3    1001499999          NaT    45.6      2.2    22.0    44.8
4    1001499999   2019-01-06    42.5      1.9     NaN    42.5

The topics in this section are largely driven by data formats themselves. Let us turn to anomalies caused more often by collection processes.

Miscoded Data

“When I use a word,” Humpty Dumpty said, in rather a scornful tone, “it means just what I choose it to mean—neither more nor less.”

–Lewis Carroll


  • Categorical and ordinal constraints
  • Encoded values and metadata definitions
  • Rare categories

When I discuss miscoded data in this section, I am primarily addressing categorical data, also called “factors” in R (and sometimes elsewhere). Ordinal data might be included too inasmuch as it has known bounds. For example, if a ranking scale is specified as ranging from 1 to 10, any values outside of that numeric range—or if genuinely ordinal, any values that are not integral—must be miscoded in some manner.

Quantitative data can obviously be miscoded as well, in some sense. A data entry intending a value of 55 might be carelessly entered as 555. But equally, a value intended as 55 might be mis-entered as 54, which is less likely to be caught as obviously wrong. In any event, the examination of quantitative features for errors is addressed in the later sections of this chapter. Numbers, especially real numbers (or complex numbers, integers, fractions, etc.), do not present as immediately wrong, but only in their distribution or domain constraints.

For an ordinal value, verifying its type and range should assure the validity of the coding, in most cases (ordinals with non-contiguous integers as valid values do occur sometimes, but less common). In the Dermatology Data Set available from the UCI Machine Learning Repository, most fields are coded as 0, 1, 2, or 3. One field is only 0 or 1; the age and target (the skin condition) are continuous and factor variables, respectively.

In this example, nothing is miscoded; note that verifying that is not the same as knowing all values are correct:

from src.dermatology import *
    [derm.min(), derm.max(), derm.dtypes])
     .rename(columns={0:'min', 1:'max', 2:'dtype'})
                                    Min                  max      dtype
erythema                              0                    3      int64
scaling                               0                    3      int64
definite borders                      0                    3      int64
itching                               0                    3      int64
...                                 ...                  ...        ...
inflammatory                          0                    3      int64
monoluclear infiltrate
band-like infiltrate                  0                    3      int64
Age                                   0                   75    float64
TARGET                cronic dermatitis  seboreic dermatitis     object
35 rows × 3 columns

Minimum, maximum, and verifying the use of the integer data type is sufficient to assure ordinals are not miscoded. Categorical variables are sometimes encoded in an ordinal fashion, but often consist of words naming their values. For example, the below dataset is very similar to the one used in an exercise of Chapter 6, Value Imputation. However, in this version, some errors exist that we will look at in the next several sections. This data contains the (hypothetical) height, weight, hair length, and favorite color of 25,000 survey subjects:

humans = pd.read_csv('data/humans-err.csv')
# random_state for deterministic sample
humans.sample(5, random_state=1)
           Height        Weight   Hair_Length    Favorite
21492  176.958650     72.604585          14.0         red
9488   169.000221     79.559843           0.0        blue
16933  171.104306     71.125528           5.5         red
12604  174.481084     79.496237           8.1        blue
8222   171.275578     77.094118          14.6       green

As one would expect semantically, Favorite is a categorical value, with a small number of legitimate values. Generally, the way to examine such a feature for miscoding starts with examining the unique values it takes. Obviously, if documentation exists as to the expected values that can help us. However, keep in mind a software developers’ motto that “documentation” is a synonym for “lies.” It may not accurately reflect the data itself:

array(['red', 'green', 'blue', 'Red', ' red', 'grееn', 'blüe',
       'chartreuse'], dtype=object)

At an initial look at unique values, we already see several likely problems. For example, ' red' with a space at the beginning is a common kind of data entry error, and we can most likely assume it was intended simply as 'red'. On the other hand, 'Red' capitalized versus in lowercase is not necessarily self-evident as to which is correct. The string 'blüe' looks like another misspelling of the English word. Something strange is happening with 'green' still; we will return to that.

To get a sense of the intention of the data, we can check whether some variations are rare with others common. This is often a strong hint:

red           9576
blue          7961
green         7458
Red              1
chartreuse       1
 red             1
grееn            1
blüe             1
Name: Favorite, dtype: int64

These counts tell us a lot. The color 'chartreuse' is a perfectly good color name, albeit a less commonly used word. It could be a legitimate value, but most likely its rarity indicates some sort of improper entry, given that only three colors (modulo some spelling issues we are working on) seem to be otherwise available. Most likely, we will want to mark this value as missing for later processing. But only most likely; there may be domain knowledge that indicates that despite its rarity, it is a value we wish to consider. If documentation exists describing it, that lends weight to the option of simply keeping it.

The rare occurrence of ' red' with a leading space and 'Red' capitalized give us strong support for the assumption that they are simply miscoded versions of 'red'. However, if we were roughly evenly split on capitalized and lowercase versions, or even if neither was rare, the correct action would be less clear. Nonetheless, in many cases, canonicalization or normalization to one particular case (case folding) would be good practice, and data frame tools make this easy to vectorize on large datasets. However, sometimes capitalization represents intended differences, for example in otherwise identical last names that have distinct capitalization among different families. Likewise, in many scientific fields, short names or formulae can be case-sensitive and should not be case-folded. Having a sense of the content domain remains important.

We are left with the curious case of the two greens. They look identical; likewise, for example, a trailing space in the above categorical values would not be visible on screen. Manually looking closer at those values is needed here:

for color in sorted(humans.Favorite.unique()):
    print(f"{color:>10s}", [ord(c) for c in color])
       red [32, 114, 101, 100]
       Red [82, 101, 100]
      blue [98, 108, 117, 101]
      blüe [98, 108, 252, 101]
chartreuse [99, 104, 97, 114, 116, 114, 101, 117, 115, 101]
     green [103, 114, 101, 101, 110]
     grееn [103, 114, 1077, 1077, 110]
       red [114, 101, 100]

What we find here from the Unicode code points is that one of our greens in fact has two Cyrillic “ye” characters rather than Roman “e” characters. This substitution of near-identical glyphs is often—as in this instance of a sneaky book author—a result of malice or deception. However, in the large world of human languages, it genuinely can occur that a particular string of characters innocently resembles some other string that it is not. Other than perhaps making it more difficult to type some strings at the particular keyboard with which you are familiar, this visual similarity is not per se a data integrity issue. However, here, with the one mixed-language version also being rare, clearly it is something to correct to the regular English word in Roman letters.

Once we have made decisions about the remediations desired—in a manner sensitive to domain knowledge—we can translate troublesome values. For example:

humans.loc[humans.Favorite.isin(['Red', ' red']), 'Favorite'] = 'red'
humans.loc[humans.Favorite == 'chartreuse', 'Favorite'] = None
humans.loc[humans.Favorite == 'blüe', 'Favorite'] = 'blue'
humans.loc[humans.Favorite == 'grееn', 'Favorite'] = 'green'
red      9578
blue     7962
green    7459
Name: Favorite, dtype: int64

Let us turn to areas where domain knowledge can inform anomaly detection.

Fixed Bounds

“Cricket is an art. Like all arts it has a technical foundation. To enjoy it does not require technical knowledge, but analysis that is not technically based is mere impressionism.”

–C.L.R. James, Beyond A Boundary


  • Domain versus measurement limits
  • Imputation and clipping
  • Improbability versus impossibility
  • Exploring hypotheses for data errors

Based on our domain knowledge of the problem and dataset at hand, we may know of fixed bounds for particular variables. For example, we might know that the tallest human who has lived was Robert Pershing Wadlow at 271cm, and that the shortest adult was Chandra Bahadur Dangi at 55cm. Values outside this range are probably unreasonable to allow in our dataset. In fact, we may perhaps wish to assume much stricter bounds; as an example, let us choose between 92cm and 213cm (which will include the vast majority of all adult humans). Let us check whether our humans dataset conforms with these bounds:

((humans.Height < 92) | (humans.Height > 213)).any()

For height, then, our domain-specific fixed bounds are not exceeded in the dataset. What about the variable Hair_Length? From the actual physical meaning of the measurement, hair cannot be negative length.

However, let us stipulate as well that the measuring tape used for our observations was 120cm long (i.e. hypothetical domain knowledge), and that, therefore, a length more than that cannot be completely legitimate (such a length is rare, but not impossible among humans). First, let us look at the hair lengths that exceed the measuring instrument:

humans.query('Hair_Length > 120')
           Height     Weight  Hair_Length   Favorite
1984   165.634695  62.979993        127.0        red
8929   175.186061  73.899992        120.6       blue
14673  174.948037  77.644434        130.1       blue
14735  176.385525  68.735397        121.7      green
16672  173.172298  71.814699        121.4        red
17093  169.771111  77.958278        133.2       blue

There are just a few samples with a hair length longer than a possible measurement. However, all of these numbers are only modestly longer than the measuring instrument or scale. Without more information on the collection procedure, it is not possible to be confident of the source of the error. Perhaps some subjects made their own estimates of their very long hair length rather than using the instrument. Perhaps one data collection site actually had a longer measuring tape that was not documented in our metadata or data description. Or perhaps there is a transcription error, such as adding a decimal point; e.g. maybe the 124.1cm hair was 24.1cm in reality. Or perhaps the unit was confused, and millimeters were actually measured rather than centimeters (as is standard in hair clippers and other barbering equipment).

In any case, this problem affects only 6 of the 25,000 observations. Dropping those rows would not lose us a large amount of data, so that is a possibility. Imputing values would perhaps be reasonable (for example, stipulating that these 6 subjects had average hair length). Value imputation is the subject of Chapter 6, and options are discussed there in more detail; at this stage, the first pass might be marking those values as missing.

However, for these out-of-range values that cluster relatively close to legitimate values, clipping the values to the documented maximum might also be a reasonable approach. The operation “clip” is also sometimes called “clamp,” “crop,” or “trim” depending on the library you are working with. The general idea is simply that a value outside of a certain bound is treated as if it is that bound itself. We can version our data as we modify it:

humans2 = humans.copy()  # Retain prior versions of dataset
humans2['Hair_Length'] = humans2.Hair_Length.clip(upper=120)
humans2[humans2.Hair_Length > 119]
           Height     Weight  Hair_Length   Favorite
1984   165.634695  62.979993        120.0        red
4146   173.930107  72.701456        119.6        red
8929   175.186061  73.899992        120.0       blue
9259   179.215974  82.538890        119.4      green
14673  174.948037  77.644434        120.0       blue
14735  176.385525  68.735397        120.0      green
16672  173.172298  71.814699        120.0        red
17093  169.771111  77.958278        120.0       blue

A slightly lower threshold for a filter shows that 119.6 was left unchanged, but the values over 120.0 were all set to 120 exactly.

The too-big values were not difficult to massage. Let us look at the physical lower bound of zero next. A value of exactly zero is perfectly reasonable. Many people shave their heads or are otherwise bald. This is invented data, pulled from a distribution that feels vaguely reasonable to this author, so do not put too much weight in the exact distributions of lengths. Just note that zero length is a relatively common occurrence in actual humans:

humans2[humans2.Hair_Length == 0]
           Height      Weight   Hair_Length   Favorite
6      177.297182   81.153493           0.0       blue
217    171.893967   68.553526           0.0       blue
240    161.862237   76.914599           0.0       blue
354    172.972247   73.175032           0.0        red
...           ...         ...           ...        ...
24834  170.991301   67.652660           0.0      green
24892  177.002643   77.286141           0.0      green
24919  169.012286   74.593809           0.0       blue
24967  169.061308   65.985481           0.0      green
517 rows × 4 columns

However, what about the impossible negative lengths? We can easily create a filter to look at those also:

neg_hair = humans2[humans2.Hair_Length < 0]
           Height       Weight   Hair_Length   Favorite
493    167.703398    72.567763          -1.0       blue
528    167.355393    60.276190         -20.7      green
562    172.416114    60.867457         -68.1      green
569    177.644146    74.027147          -5.9      green
...           ...          ...           ...        ...
24055  172.831608    74.096660         -13.3        red
24063  172.687488    69.466838         -14.2      green
24386  176.668430    62.984811          -1.0      green
24944  172.300925    72.067862         -24.4        red
118 rows × 4 columns

There are a moderate number of these obviously miscoded rows. As elsewhere, simply dropping the problem rows is often a reasonable approach. However, a quick glance at the tabular data, as well as some slight forensics, suggests that quite likely a negative sign snuck into many reasonable values. It is at least plausible that these quantities are right, but simply with an inverted sign. Let us look at some statistics of the problem values. Just for fun, we will look at very similar summaries using both R and Pandas:

%%R -i neg_hair
   Min.   1st Qu.   Median     Mean   3rd Qu.   Max. 
 -95.70    -38.08   -20.65   -24.35    -5.60   -0.70 
count    118.000000
mean     -24.348305
std       22.484691
min      -95.700000
25%      -38.075000
50%      -20.650000
75%       -5.600000
max       -0.700000
Name: Hair_Length, dtype: float64

The general statistics do not contradict this sign-inversion hypothesis. However, before we draw a conclusion, let us continue to look at these bad values more closely for this exercise. There might be additional patterns:

plt.hist(neg_hair.Hair_Length, bins=30)
plt.title("Distribution of invalid negative hair length");

Figure 4.1: Histogram showing distribution of negative hair length values

This distribution of negative values roughly matches the distribution of positive ones. There are a larger number of people with short hair of varying short lengths, and a tail of fewer people at longer lengths. However, at a glance, the region close to zero seems to be a bit too much of a peak. For the one hundred or so rows of data in the example, you could eyeball them all manually, but for larger datasets, or larger bounds-violation sets, honing in on nuances programmatically is more general:

-1.0     19
-41.6     2
-6.8      2
-30.1     2
-3.3      1
-51.4     1
-25.1     1
-4.8      1
Name: Hair_Length, Length: 93, dtype: int64

Indeed there is a pattern here. There are 19 values of exactly -1, and only one or two occurrences of each other invalid negative value. It seems very likely that something different is happening between the -1 error and the other negative value errors. Perhaps -1 was used as a sentinel, for example. Of course, it is also possible that -1 could result from the stipulated sign-inversion error; we cannot entirely separate those two possibilities.

The working hypothesis I would probably use to handle this problem in the dataset (if not simply dropping everything questionable outright) would be to mark the -1 values as missing but invert the sign of other negative values:

humans3 = humans2.copy()     # Versioned changes to data
# The "sentinel" negative value means missing
humans3.loc[humans3.Hair_Length == -1, 'Hair_Length'] = None
# All other values simply become non-negative
humans3['Hair_Length'] = humans3.Hair_Length.abs()
plt.hist(humans3.Hair_Length, bins=30)
plt.title("Distribution of corrected hair lengths");

Figure 4.2: Histogram showing corrected hair lengths

We have performed a typical cleaning of bounded values. Let us turn to values without sharp bounds, but with general distribution statistics.


If Congress had meant to so limit the Act, it surely would have used words to that effect.

–Tennessee Valley Auth. v. Hill, 437 U.S. 153 (1978)


  • Z-score and unexpected values
  • Interquartile range
  • Standard deviation and frequency of occurrence

In continuous data, values that fall within normative ranges might still be strongly uncharacteristic within those bounded expectations. In the simplest case, this occurs when a value is very different from other values of the same variable. The standard way to characterize the expectedness of a value is a measure called a z-score. This value is simply the distance of each point from the mean of the variable, divided by the standard deviation of the variable.

Where is the sample mean, and is the standard deviation.

This measure is most precise for data that follows a normal distribution, but generally it is useful for any data that is unimodal (having one peak), somewhat symmetric, and scale-dependent. In more ordinary language, we just want to look for the histogram of a data variable having one peak, and tapering off at roughly the same rate on both sides. Completely normal distribution is unusual in real-world data.

A slightly different way of identifying outliers is often used as well. Box and whisker plots (usually simply called boxplots) will often include outliers as separate visual elements. While it is possible to use a z-score in such a visualization, more often these plots utilize interquartile range (IQR) and a fixed multiplier to define outliers. The different techniques will produce similar, but not identical, answers.


We can see that height and weight in our dataset follow a generally normal-like distribution by visualizing them. We have seen just above that hair length, after correction, is strictly single tailed. However, the one-sided drop-off from a mode at 0 is close enough to one tail of a normal distribution that the z-score is still reasonable to consider.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
ax1.hist(humans3.Height, bins=50)
ax2.hist(humans3.Weight, bins=50)
ax1.set_title("Distribution of Height")
ax2.set_title("Distribution of Weight");

Figure 4.3: Histograms showing distributions of height and weight

If we wish to be more precise in quantifying the normality of variables, we can use statistical tests such and Anderson-Darling, Shapiro-Wilk, or Skewness-Kurtosis All. Each of these techniques tries to reject the hypothesis that a distribution is normal. For different p-values (probabilities), different test statistics determine a threshold for this rejection (although for large samples, even small deviations from normality will reject the hypothesis, but do not matter from the point of view of the z-score being useful). In Anderson-Darling, if the test statistic is not much more than 1.0 the curve is definitely normal enough to measure outliers with a z-score. The inverse does not hold, however; many non-normal curves are still reasonable to use the z-score with. Essentially, we just need to avoid this measure for power law or exponential distributions, and for curves that are strongly multi-modal. Let us perform Anderson-Darling tests on our height, weight, and hair length variables:

from scipy.stats import anderson
for var in ('Height', 'Weight', 'Hair_Length'):
    data = humans3[var][humans3[var].notnull()]
    stat = anderson(data, 'norm').statistic
    print(f"Anderson-Darling statistic for {var:<12s}: {stat:6.2f}")
Anderson-Darling statistic for Height      :   0.24
Anderson-Darling statistic for Weight      :   0.54
Anderson-Darling statistic for Hair_Length : 578.19

Having recognized that hair length is not normal, but that it shows a one-sided decay along a linear scale nonetheless, we can add z-scores for all of our quantitative variables to the working data frame. As before, as good practice of keeping versions of our modifications, we copy the data to a new data frame before the next transformations.

We ignore the delta degrees of freedom parameter in our calculation of standard deviation because it is trivial with 25,000 samples (if we had only 10 or 20 samples, it could matter). The degrees of freedom concerns the anticipated variance within a total population based on a sample; but these only vary significantly when samples are tens of observations, not tens of thousands:

humans4 = humans3.copy()
for var in ('Height', 'Weight', 'Hair_Length'):
    zscore = (humans4[var] - humans4[var].mean()) / humans4[var].std()
    humans4[f"zscore_{var}"] = zscore
humans4.sample(5, random_state=1)
            Height      Weight   Hair_Length   Favorite   zscore_Height
21492   176.958650   72.604585          14.0        red        0.880831
9488    169.000221   79.559843           0.0       blue       -0.766210
16933   171.104306   71.125528           5.5        red       -0.330758
12604   174.481084   79.496237           8.1       blue        0.368085
8222    171.275578   77.094118          14.6      green       -0.295312
        zscore_Weight   zscore_Hair_Length
21492       -0.042032            -0.568786
9488         0.997585            -1.225152
16933       -0.263109            -0.967294
12604        0.988078            -0.845397
8222         0.629028            -0.540656

The choice of a z-score threshold is very domain- and problem-dependent. A rule of thumb is often to use a z-score of an absolute value more than 3 as a cut-off to define outliers. But what is expected very much depends on the size of a dataset.

In statistics, we sometimes recall the 68–95–99.7 rule, which lists the percentage of observations that fall within one, two, or three standard deviations in a normal distribution.

At any distance from the mean, some observations would be expected if they are numerous enough, but the number diminishes rapidly with more standard deviations’ distance.

Let us look at that common z-score threshold of 3. Remember that we are working with 25,000 samples here, so generally we expect to find roughly 75 of them outside of 3 standard deviations, under the 68–95–99.7 rule discussed above. Let us look at the table for height, but just check the number of rows outside this bound for the other variables:

humans4[humans4.zscore_Height.abs() > 3]
            Height      Weight   Hair_Length   Favorite   zscore_Height
138     187.708718   86.829633          19.3      green        3.105616
174     187.537446   79.893761          37.5       blue        3.070170
412     157.522316   62.564977           6.8       blue       -3.141625
1162    188.592435   86.155948          53.1        red        3.288506
...            ...         ...           ...        ...             ...
22945   157.293031   44.744929          18.4        red       -3.189077
23039   187.845548   88.554510           6.9       blue        3.133934
24244   158.153049   59.725932          13.8      green       -3.011091
24801   189.310696   85.406727           2.3      green        3.437154
          zscore_Weight    zscore_Hair_Length
138            2.084216             -0.320304
174            1.047496              0.532971
412           -1.542673             -0.906345
1162           1.983518              1.264351
...                 ...                   ...
22945         -4.206272             -0.362499
23039          2.342037             -0.901657
24244         -1.967031             -0.578162
24801          1.871531             -1.117320
51 rows × 7 columns
print("Outlier weight:", (humans4.zscore_Weight.abs() > 3).sum())
print("Outlier hair length:", (humans4.zscore_Hair_Length.abs() > 3).sum())
Outlier weight: 67
Outlier hair length: 285

We have already noted that hair length is single-tailed, so we might expect approximately twice as many outliers. The actual number is somewhat more than twice that many, but that is not itself an extreme divergence of values. Height and weight actually have modestly lower kurtosis than we would expect from the normal distribution (the tails thin out slightly faster).

In any case, a z-score of 3 is probably too small to be useful for our sample size. 4 sigma is probably more relevant for our purpose of distinguishing merely unusual from probably wrong observations, and maybe 4.5 for the one-tailed hair length.

A table of the frequency of once-a-day observations falling outside of a given standard deviation (σ) provides a helpful intuition. A shorthand trick to remember the effect of sigma is the 68–95–99.7 rule mentioned earlier; that is, the percentage of things falling within one, two, and three standard deviations:


Proportion of observations

Frequency for daily event

± 1σ

1 in 3

Twice a week

± 2σ

1 in 22

Every three weeks

± 3σ

1 in 370


± 4σ

1 in 15,787

Every 43 years (twice in a lifetime)

± 5σ

1 in 1,744,278

Every 5,000 years (once in recorded history)

± 6σ

1 in 506,797,346

Every 1.4 million years (twice in history of humankind)

± 7σ

1 in 390,682,215,445

Every 1 billion years (four times in history of Earth)

Let us see the outliers given the broader z-score bounds:

cond = (
    (humans4.zscore_Height.abs() > 4) |
    (humans4.zscore_Weight.abs() > 4) |
    (humans4.zscore_Hair_Length.abs() > 4.5))
            Height      Weight   Hair_Length   Favorite   zscore_Height
13971   153.107034   63.155154           4.4      green       -4.055392
14106   157.244415   45.062151          70.7        red       -3.199138
22945   157.293031   44.744929          18.4        red       -3.189077
          zscore_Weight     zscore_Hair_Length
13971         -1.454458              -1.018865
14106         -4.158856               2.089496
22945         -4.206272              -0.362499

Using modest domain knowledge of human physical characteristics, even though they are outside the “norm,” persons of 153cm or 45kg are small, but not outside of bounds we would expect. The small number of 4 sigma outliers are both short and light according to the data, which we would expect to be correlated to a relatively high degree, lending plausibility to the measurements.

Moreover, the height bounds we discussed in the above section on fixed bounds were considerably wider than this 4 sigma (or even 5 sigma) detects. Therefore, while we could discard or mark missing values in these outliers rows, the analysis does not seem to motivate doing so.

Interquartile Range

Using the IQR rather than the z-score makes less of an assumption of normality of a distribution. However, this technique will also fail to produce meaningful answers for power law or exponential data distributions. If you can identify a distribution as one that ranges over many orders of magnitude like those, looking at the quartiles of either an Nth root or a logarithm of the raw data might still produce reasonable results. The same transformation, in fact, can be equally relevant if you use z-score analysis.

The idea of the IQR is simply to look at the quartile cut-offs in a variable and measure the numeric distance between the first and third quartile, i.e. between the 25% and 75% percentiles. Exactly half the data is in that range, but we often also expect that most data will be within some distance beyond those cut-offs, defined as a multiplier of the range between cut-offs. Most commonly, a multiplier of 1.5 is chosen; this is merely a convention that is often useful but lacks any deeper meaning.

I include in this text a brief function to visualize boxplots that show the IQR defined outliers. Normally, this functionality is only included in the source code repository for the book, but here I think it is worthwhile for readers to see the configuration that goes into these few lines in Matplotlib (other visualization libraries have similar capabilities; often higher-level abstractions with more visual pizzazz, in fact):

# Function defined but not run in this cell
def show_boxplots(df, cols, whis=1.5):
    # Create as many horizontal plots as we have columns
    fig, axes = plt.subplots(len(cols), 1, figsize=(10, 2*len(cols)))
    # For each one, plot the non-null data inside it
    for n, col in enumerate(cols):
        data = df[col][df[col].notnull()]
        axes[n].set_title(f'{col} Distribution')
        # Extend whiskers to specified IQR multiplier
        axes[n].boxplot(data, whis=whis, vert=False, sym='x')
    # Fix spacing of subplots at the end

While the default multiplier (the “whisker” width) is 1.5, we have already seen that the human data is large enough that values have to be relatively extreme to appear as genuinely unlikely to be genuine. We choose, therefore, a whisker width of 2.5 instead:

show_boxplots(humans4, ["Height", "Weight", "Hair_Length"], 2.5)

Figure 4.4: Boxplots showing height, weight, and hair length distribution

The central boxes represent the IQR, from 25% to 75% percentile. The whiskers extend to multiplier times IQR above/below the box. An x marks outliers past the whiskers.

Only one outlier appears at this threshold for height, at the short end. Likewise, only two appear for weight, both at the light end. This was the same pattern we found with the z-score. Rather more “outlier” long hair lengths occur, but we already had used a larger z-score to filter that more restrictively. We could similarly use a larger whisker width to filter more hair lengths out, if we wished.

While the visualization is handy, we want to find the actual data rows that are marked with x’s in the plots. Let us code that. We find the quartiles, compute the IQR, then display the inlier ranges:

quartiles = (
    humans4[['Height', 'Weight']]
    .quantile(q=[0.25, 0.50, 0.75, 1.0]))
            Height       Weight
0.25    169.428884    68.428823
0.50    172.709078    72.930616
0.75    175.953541    77.367039
1.00    190.888112    98.032504
IQR = quartiles.loc[0.75] - quartiles.loc[0.25]
Height    6.524657
Weight    8.938216
dtype: float64
for col, length in IQR.iteritems():
    high = quartiles.loc[0.75, col] + 2.5*IQR[col]
    low = quartiles.loc[0.25, col] - 2.5*IQR[col]
    print(f"Inliers for {col}: [{low:.3f}, {high:.3f}]")
Inliers for Height: [153.117, 192.265]
Inliers for Weight: [46.083, 99.713]

Actually, filtering using the inlier range in this case gives us the same answer as the z-score approach. Of necessity, the very shortest person is the shortest regardless of which outlier detection technique we use. But selecting a domain-motivated IQR multiplier may identify more or fewer outliers than using a domain-motivated z-score, depending on actual data distributions:

cond = (
    (humans4.Height > 192.265) |
    (humans4.Height < 153.117) |
    (humans4.Weight > 99.713)  |
    (humans4.Weight < 46.083))
           Height     Weight  Hair_Length  Favorite    zscore_Height
13971  153.107034  63.155154          4.4     green        -4.055392
14106  157.244415  45.062151         70.7       red        -3.199138
22945  157.293031  44.744929         18.4       red        -3.189077
       zscore_Weight  zscore_Hair_Length
13971      -1.454458           -1.018865
14106      -4.158856            2.089496
22945      -4.206272           -0.362499

Univariate outliers can be important to detect, but sometimes it is a combination of features that becomes anomalous.

Multivariate Outliers

If you are not part of the solution, you are part of the precipitate.



  • Variance in deterministic synthetic features
  • Expectations of relative rarity

Sometimes univariate features can fall within relatively moderate z-score boundaries, and yet combinations of those features are unlikely or unreasonable. Perhaps an actual machine learning model might predict that combinations of features are likely to be wrong. In this section we only look at simpler combinations of features to identify problematic samples.

In Chapter 7, Feature Engineering we discuss polynomial features. That technique multiplies together the values of two or more variables pertaining to the same observation and treats the result as a new feature. For example, perhaps neither height nor weight in our working example are outside a reasonable bound, and yet the multiplicative product of them is. While this is definitely possible, we generally expect these features to be positively correlated to start with, so multiplication would probably only produce something new slightly outside the bounds already detected by univariate outlier detection.

However, let us consider a derived feature that is well-motivated by the specific domain. Body Mass Index (BMI) is a measure often used to measure healthy weights for people, and is defined as:

That is, weight and height are in an inverse relationship in this derived quantity rather than multiplicatively combined. Perhaps this multivariate derived feature shows some problem outliers. Let us construct another data frame version that discards previous calculated columns, but adds BMI and its z-score:

humans5 = humans4[['Height', 'Weight']].copy()
# Convert weight from cm to m
humans5['BMI'] = humans5.Weight / (humans5.Height/100)**2
humans5["zscore_BMI"] = (
    (humans5.BMI - humans5.BMI.mean()) / 
             Height         Weight           BMI        zscore_BMI
0        167.089607      64.806216     23.212279         -0.620410
1        181.648633      78.281527     23.724388         -0.359761
2        176.272800      87.767722     28.246473          1.941852
3        173.270164      81.635672     27.191452          1.404877
...             ...            ...           ...               ...
24996    163.952580      68.936137     25.645456          0.618008
24997    164.334317      67.830516     25.117048          0.349063
24998    171.524117      75.861686     25.785295          0.689182
24999    174.949129      71.620899     23.400018         -0.524856
25000 rows × 4 columns

Looking for outliers in the derived feature, we see strong signals. As was discussed, at a z-score of 4 and a dataset of 25,000 records, we expect to see slightly more than one record appearing as an outlier by natural random distribution. Indeed, the two z-scores we see below that are only slightly more than 4 in absolute value occurred in the dataset before it was engineered to highlight the lesson of this section:

humans5[humans5.zscore_BMI.abs() > 4]
            Height       Weight          BMI     zscore_BMI
21388   165.912597    90.579409    32.905672       4.313253
23456   187.110000    52.920000    15.115616      -4.741383
23457   158.330000    92.780000    37.010755       6.402625
24610   169.082822    47.250297    16.527439      -4.022805

As well as one example of a moderate outlier for high BMI and one for low BMI, we also have two more extreme values on each side. In this case, these were constructed for the section, but similar multivariate outliers will occur in the wild. The -4.74 z-score is not an extreme we would expect in 25,000 samples, but is perhaps not completely implausible. However, the +6.4 z-score is astronomically unlikely to occur without a data error (or a construction by a book author). Since BMI is a derived feature that combines height and weight—and moreover since each of those is within reasonable bounds on its own—the correct approach is almost surely simply to discard these problem rows. Nothing in the data themselves guides us toward knowing whether weight or height is the problem value, and no remediation is sensible.

Fortunately for this particular dataset, only 2 (or maybe 4) samples display the problem under discussion. We have plentiful data here, and no real harm is done by discarding those rows. Obviously, the particular decisions made about z-score thresholds and disposition of particular data rows that are illustrated in this section and the last several are only examples. You will need to decide within your problem and domain what the most relevant levels and tests are, and what remediations to perform.


The two exercises in this chapter ask you to look for anomalies first in quantitative data, then in categorical data.

A Famous Experiment

The Michelson–Morley experiment was an attempt in the late 19th century to detect the existence of the luminiferous aether, a widely assumed medium that would carry light waves. This was the most famous “failed experiment” in the history of physics in that it did not detect what it was looking for—something we now know not to exist at all.

The general idea was to measure the speed of light under different orientations of the equipment relative to the direction of movement of the Earth, since relative movement of the ether medium would add or subtract from the speed of the wave. Yes, it does not work that way under the theory of relativity, but it was a reasonable guess 150 years ago.

Apart from the physics questions, the dataset derived by the Michelson–Morley experiment is widely available, including as a sample built into R. The same data is available at:


Figuring out the format, which is not complex, is a good first step of this exercise (and typical of real data science work).

The specific numbers in this data are measurements of the speed of light in km/s with a zero point of 299,000. So, for example, the mean measurement in experiment 1 was 299,909 km/s. Let us look at the data in the R bundle:

%%R -o morley
morley %>%
    group_by('Expt') %>%
    summarize(Mean = mean(Speed), Count = max(Run))
'summarise()' ungrouping output (override with '.groups' argument)
# A tibble: 5 x 3
   Expt  Mean Count
  <int> <dbl> <int>
1     1  909     20
2     2  856     20
3     3  845     20
4     4  820.    20
5     5  832.    20

In the summary, we just look at the number of runs of each experimental setup, and the mean across that setup. The raw data has 20 measurements within each setup.

Using whatever programming language and tools you prefer, identify the outliers first within each setup (defined by an Expt number) and then within the data collection as a whole. The hope in the original experiment was that each setup would show a significant difference in central tendency, and indeed their means are somewhat different.

This book and chapter does not explore confidence levels and null hypotheses in any detail, but create a visualization that aids you in gaining visual insight into how much apparent difference exists between the several setups.

If you discard the outliers within each setup, are the differences between setups increased or decreased? Answer with either a visualization or by looking at statistics on the reduced groups.

Misspelled Words

For this exercise we return to the 25,000 human measurements we have used to illustrate a number of concepts. However, in this variation of the dataset, each row has a person’s first name (pulled from the US Social Security Agency list of common first names over the last century; apologies that the names lean Anglocentric because of the past history of US population and immigration trends).

The dataset for this exercise can be found at:


Unfortunately, our hypothetical data collectors for this dataset are simply terrible typists, and they make typos when entering names with alarming frequency. There are some number of intended names in this dataset, but quite a few simple miscodings of those names as well. The problem is: how do we tell a real name from a typo?

There are a number of ways to measure the similarity of strings and that provide a clue as to likely typos. One general class of approach is in terms of edit distance between strings. The R package stringdist, for example, provides Damerau–Levenshtein, Hamming, Levenshtein, and optimal string alignment as measures of edit distance. Less edit-specific fuzzy matching techniques utilize a “bag of n-grams” approach, and include q-gram, cosine distance, and Jaccard distance. Some heuristic metrics like Jaro and Jaro-Winkler are also included in stringdist along with the other measures mentioned. Soundex, soundex variants, and metaphone look for similarity of the sounds of words as pronounced, but are therefore specific to languages and even regional dialects.

In a reversal of the more common pattern of Python versus R libraries, Python is the one that scatters string similarity measures over numerous libraries, each including just a few measures. However, python-Levenshtein is a very nice package including most of these measures. If you want cosine similarity, you may have to use sklearn.metrics.pairwise or another module. For phonetic comparisons, fonetika and soundex both support multiple languages (but different languages for each; English is in common for almost all packages).

On my personal system, I have a command-line utility called similarity that I use to measure how close strings are to each other. This particular few-line script measures Levenshtein distance, but also normalizes it to the length of the longer string. A short name will have a small numeric measure of distance, even between dissimilar strings, while long strings that are close overall can have a larger measure before normalization (depending on what measure is chosen, but for most of them). A few examples show this:

String 1

String 2

Levenshtein distance

Similarity ratio









the quick brown fox jumped

thee quikc brown fax jumbed



For this exercise, your goal is to identify every genuine name and correct all the misspelled ones to the correct canonical spelling. Keep in mind that sometimes multiple legitimate names are actually close to each other in terms of similarity measures. However, it is probably reasonable to assume that rare spellings are typos, at least if they are also relatively similar to common spellings. You may use whatever programming language, library, and metric you feel is the most useful for the task.

Reading in the data, we see it is similar to the human measures we have seen before:

names = pd.read_csv('data/humans-names.csv')
       Name        Height     Weight
0     James    167.089607  64.806216
1     David    181.648633  78.281527
2   Barbara    176.272800  87.767722
3      John    173.270164  81.635672
4   Michael    172.181037  82.760794

It is easy to see that some “names” occur very frequently and others only rarely. Look at the middling values as well when working on this exercise:

Elizabeth    1581
Barbara      1568
Jessica      1547
Jennifer     1534
ichael          1
Wlliam          1
Richrad         1
Mray            1
Name: Name, Length: 249, dtype: int64


When you have eliminated the impossible, whatever remains, however improbable, must be the truth.

–Arthur Conan Doyle

Topics covered in this chapter: Missing Data; Sentinels; Miscoded Data; Fixed Bounds; Outliers.

The anomalies that we have discussed in this chapter fall into a few relatively distinct categories. For the first kind, there are the special values that explicitly mark missing data, although those markers are sometimes subject to pitfalls. However, an explicit indication of missingness is probably the most straightforward kind of anomaly. A second kind of anomaly is categorical values that are miscoded; some finite number of values are proper (although not always clearly documented), and anything that isn’t one of those few values is an anomaly.

The third kind of anomaly is in continuous—or at least ranged—data values that fall outside of the bounds of our expectations. These are also called outliers, although exactly how much a value has to lie outside typical values to be a problem is very domain- and problem-dependent. Expectations may take the form of a priori assumptions that arise from domain knowledge of the measurement. They may also arise from the distribution of data within a variable overall, and the deviation of one particular value from others measured as that variable. At times, our expectations about bounds can even be multivariate, and some numeric combination of multiple variables produces a value outside of expectation bounds.

For all of these kinds of anomalies, there are essentially two actions we might take. We may decide to discard an observation or sample altogether if it has one of these problems. Or alternately, we may simply more explicitly mark one feature within an observation as missing based on its value not being reliable. When we modify values to the “missing” special value, keeping track of our changes and data versions is extremely important practice. What we choose to do with those values marked as explicitly missing is a downstream decision that is discussed at more length in later chapters.

In the next chapter, we move from looking for problems with particular data points and on to looking for problems with the overall “shape” of a dataset.


Data Quality

All data is dirty, some data is useful.

–cf. George Box

Welcome to the mid-point of the book. In something like the loose way in which a rock “concept album” tells an overarching story through its individual songs, this book is meant, to a certain degree, to follow the process a data scientist goes through from acquiring raw data to feeding suitable data into a machine learning model or data analysis. Up until this point, we have looked at how one goes about getting data into a program or analysis system (e.g. a notebook), and we touched on identifying data that has clearly “gone bad” at the level of individual data points in Chapter 4, Anomaly Detection. In the chapters after this one, we will look at remediation of that messy and marked data that earlier chapters delivered in stages.

Now, however, is the time to look for ways in which your data may have problems, not in its individual details, but in its overall “shape” and character. In some cases, these problems will pertain to the general collection techniques used, and in particular to systematic bias that might be introduced during collection. In other cases, problems are not the fault of data collectors, but simply of units and scales, and correction can be quite mechanical and routine. At this point, we gradually ease into active interventions that do not simply detect dirt as we have done hitherto, but also go about cleaning it. One such cleanup might involve handling the inherent biases that cyclicities in data often create (often over time periods, but not exclusively).

In the last section of this chapter, we look at the idea of performing validation that is domain-specific and utilizes rules that are practical, beyond being simply numeric. Of course, every domain might have its own such rules, and an example in this chapter is meant to inspire thought, not provide a blueprint for your specific tasks. In fact, it can hardly be said often enough that everything within this book is meant to provide inspiration for ways of thinking about data science problems, and never merely recipes to copy directly to the task you have in front of you.


Before we get to the sections of this chapter, let us run our standard setup code.

from src.setup import *
%load_ext rpy2.ipython

Missing Data

Absence of evidence is not evidence of absence.

–Martin Rees


  • Aspects of missing data
  • Distribution of records in parameter space
  • Bias in missing data

The story of missing data forms a trilogy in this book. The prior chapter, Chapter 4, Anomaly Detection, led with a section on missing data. In that case, our concern was to identify “missingness,” which can be marked in various ways by various datasets in various data formats. The next chapter, Chapter 6, Value Imputation, is primarily about what we might do to fill missing values with reasonable guesses.

This chapter falls between the previous and the next one. We have already taken mechanical or statistical tests to identify some data as missing (or as unreliable enough that it is better to pretend it is missing). But we have not yet decided whether to keep or drop the observations to which those missing data points belong. For this section, we need to assess the significance of that missing data to our overall dataset.

When we have a record with missing data, we essentially have two choices about its disposition. On the one hand, we can discard that particular record. On the other hand, we can impute some value for the missing value, as will be discussed in Chapter 6. Actually, in some sense there is a third option as well: we may decide that because of the amount or distribution of missing data in our dataset, the data is simply not usable for the purpose at hand. While, as data scientists, we never want to declare a task hopeless, as responsible researchers we need to consider the possibility that particular data simply cannot support any conclusions. Missing data is not the only thing that could lead us to this conclusion, but it is certainly one common fatal deficit.

If we wish to discard records—but also to a large extent if we wish to impute values—we need to think about whether what remains will be a fair representation of the parameter space of the data. Sample bias can exist not only in the overall composition of a dataset, but also more subtly in the distribution of missing values. Keep in mind that “missing” here might result from the processing in Chapter 4, in which some values may have been marked missing because we determined they were unreliable, even if they were not per se absent in the raw data.

For example, I created a hypothetical dataset of persons with names, ages, genders, and favorite colors and flowers. The ages, genders, and names are modeled on the actual distribution of popular names over time reported by the United States Social Security Administration. I assigned favorite colors and flowers to the people for this illustration.

df = pd.read_parquet('data/usa_names.parq')
     Age  Gender       Name  Favorite_Color   Favorite_Flower
0     48       F       Lisa          Yellow             Daisy
1     62       F      Karen           Green              Rose
2     26       M    Michael          Purple              None
3     73       F   Patricia             Red            Orchid
...  ...     ...        ...             ...               ...
6338  11       M      Jacob             Red              Lily
6339  20       M      Jacob           Green              Rose
6340  72       M     Robert            Blue              Lily
6341  64       F      Debra          Purple              Rose
6342 rows × 5 columns

In general, this is an ordinary-looking dataset, with a moderately large collection of records. We can notice in the data frame summary that at least some data is missing. This is worth investigating more carefully.

with show_more_rows():
                Age   Gender      Name  Favorite_Color  Favorite_Flower
count   6342.000000     6342      6342            5599             5574
unique          NaN        2        69               6                5
top             NaN        F   Michael          Yellow           Orchid
freq            NaN     3190       535             965             1356
mean      42.458846      NaN       NaN             NaN              NaN
std       27.312662      NaN       NaN             NaN              NaN
min        2.000000      NaN       NaN             NaN              NaN
25%       19.000000      NaN       NaN             NaN              NaN
50%       39.000000      NaN       NaN             NaN              NaN
75%       63.000000      NaN       NaN             NaN              NaN
max      101.000000      NaN       NaN             NaN              NaN

Using Pandas’ .describe() method or similar summaries by other tools allows us to see that Age, Gender, and Name have values for all 6,342 records. However, Favorite_Color and Favorite_Flower are missing for approximately 750 records each. In itself, missing data in 10-15% of the rows is quite likely not to be a huge problem. This statement assumes that missingness is not itself biased. Even if we need to discard those records altogether, that is a relatively small fraction of a relatively large dataset. Likewise, imputing values would probably not introduce too much bias, and other features could be utilized within those records. In the below section and in Chapter 6, Value Imputation, in relation to undersampling and oversampling, we discuss the dangers of exclusion resulting in class imbalance.

While uniformly randomly missing data can be worked around relatively easily, data that is missing in a biased way can present a more significant problem. To figure out which category we are in with this dataset, let us compare those missing flower preferences to the ages of the people. Looking at every individual age up to 101 years old is hard to visualize; for this purpose, we will group people into 10-year age groups. The graph below uses a statistical graphing library called Seaborn, which is built on top of Matplotlib.

df['Age Group'] = df.Age//10 * 10
fig, ax = plt.subplots(figsize=(12, 4.5))
sns.countplot(x="Age Group", hue="Favorite_Flower", 
              ax=ax, palette='gray', data=df)
ax.set_title("Distribution of flower preference by age");

Figure 5.1: Distribution of flower preference by age

A few patterns jump out in this visualization. It appears that older people tend to have a strong preference for orchids, and young people a moderate preference for roses. This is perhaps a property of the data meriting analysis. More significantly for this section, there are few data points for favorite flower at all in the 20-30 age group. One might imagine several explanations, but the true answer would depend on problem and domain knowledge. For example, perhaps the data corresponding to these ages was not collected during a certain time period. Or perhaps people in that age group reported a different favorite flower but its name was lost in some prior inaccurate data validation/cleaning step.

If we look at the records with missing color preference, we see a similar pattern in relation to age. The drop in frequency of available values occurs instead in the 30-40 age group though.

fig, ax = plt.subplots(figsize=(12, 4.5))
sns.countplot(x="Age Group", hue="Favorite_Color", 
              ax=ax, palette='gray', data=df)
ax.set_title("Distribution of color preference by age");

Figure 5.2: Distribution of color preference by age

If we were to drop all records with missing data, we would wind up with nearly no representation of people in the entire 20-40 age range. This biased unavailability of data would be likely to weaken the analysis generally. The number of records would remain fairly large, but the parameter space, as mentioned, would have an empty (or at least much less densely occupied) region. Obviously, these statements depend both on the purpose of our data analysis and our assumptions about the underlying domain. If age is not an important aspect of the problem in general, our approach may not matter much. But if we think age is a significant independent variable, dropping this data would probably not be a workable approach.

This section, like many others, shows the kinds of exploration one should typically perform of a dataset. It does not provide one simple answer for the best remediation of bias in missing data. That decision will be greatly dependent upon the purpose for which the data is being used and also on background domain knowledge that may clarify the reasons for the data being missing. Remediation is inevitably a per-problem decision.

Let us turn to ways that bias might occur in relation to other features rather than simply globally in a dataset.

Biasing Trends

It is not the slumber of reason that engenders monsters, but vigilant and insomniac rationality.

–Gilles Deleuze


  • Collection bias versus trends in underlying domain
  • Perspective as source of bias
  • Artifact of collection methods
  • Visualization to identify bias
  • Variance by group
  • Externally identifying base rates
  • Benford’s law

At times, you may be able to detect sample bias within your data, and will need to make a domain area judgment about the significance of that bias. There are at least two kinds of sample bias that you should be on the lookout for. On the one hand, the distribution of observations may not match the distribution in the underlying domain. Quite likely, you will need to consult other data sources—or simply use your own domain area knowledge—to detect such a skew in the samples. On the other hand, the data themselves may reveal a bias by trends that exist between multiple variables. In this latter case, it is important to think about whether the detected “trend” could be a phenomenon you have detected in the data, or is a collection or curation artifact.

Understanding Bias

Bias is an important term in both statistics and human sciences, with a meaning that is strongly related, but that assumes a different valence across fields. In the most neutral statistical sense, bias is simply the fact, more commonly true than not, that a dataset does not accurately represent its underlying population of possible observations. This bare statement hides more nuance than is evident, even outside of observations about humans and politically laden matters. More often than not, neither we data scientists, who analyze data, nor the people or instruments that collected the raw data in the first place can provide an unambiguous delineation of exactly what belongs to the underlying population. In fact, the population is often somewhat circularly defined in terms of data collection techniques.

An old joke observes someone looking for their lost keys at night in the area under a street light. Asked why they do not also look elsewhere, they answer that it is because the visibility is better where they are looking. This is a children’s joke, not told particularly engagingly, but it also lays the pattern for most data collection of most datasets.

Observers make observations of what they can see (metaphorically, most are probably voltages in an instrument, or bits on a wire, rather than actual human eyes), and not what they cannot. Survivorship bias is a term for the cognitive error of assuming those observations we have available are representative of the underlying population.

It is easy not to be conscious of bias that exists in data, and probably that much easier when it indeed does concern human or social subjects and human observers bring in psychological and social biases. But it is humans, in the end, even if aided by instruments we set up, who make observations of everything else too. For example, the history of ethology (the study of animal behavior) is largely a history of scientists seeing the behaviors in animals that exist—or that they believe should exist—in the humans around them, that they impose by metaphor and blindness. If you make a survey of books in your local library to determine the range of human literature or music, you will discover the predominance of writers and musicians who use your local language and play your local musical style. Even in areas that seem most obviously not about humans, our vantage point may create a perspectival bias. For example, if we catalog the types of stars that exist in the universe, and the prevalence of different types, we are always observing those within our cosmological horizon, which not only expresses an interaction of space and time, but also may not uniformly describe the entire universe. Cosmologists know this, of course, but they know it as an inherent bias to their observations.

In most of this section, we will look at a version of the synthetic United States name/age data to detect both of these patterns. As in the last section, this data approximately accurately represents the frequency of different names across different age groups, based on Social Security Administration data. We can see that within the actual domain, the popularity of various names authentically changed over time. As in the last section, it is useful to aggregate people into coarser age groups for visualization.

Throughout this book I have attempted to avoid social bias in the datasets I select or create as examples. For the imagined people in the rows of the name tables, I added features like favorite color or flower, rather than more obviously ethnically or culturally marked features like eye color, favorite food, or musical preference. Even those invented features I use are not entirely independent of culture though, and perhaps my position in the social world leads me to choose different factor values than would someone located elsewhere.

Moreover, by choosing the top 5 most popular names in the United States each year, I impose a kind of majority bias: all are roughly Anglo names, and none, for example, are characteristically African-American, Latino, Chinese, or Polish, though such are all common outside of that top-5-by-year collation methodology.

names = pd.read_parquet('data/usa_names_states.parq')
names['Age Group'] = names.Age//10 * 10
     Age   Birth_Month         Name  Gender            Home  Age Group
0     17          June      Matthew       M          Hawaii         10
1      5     September         Emma       F   West Virginia          0
2      4       January         Liam       M          Alaska          0
3     96         March      William       M        Arkansas         90
...  ...           ...          ...     ...             ...        ...
6338  29        August      Jessica       F   Massachusetts         20
6339  51         April      Michael       M         Wyoming         50
6340  29           May  Christopher       M  North Carolina         20
6341  62      November        James       M           Texas         60
6342 rows × 6 columns

The fields Birth_Month and Home are added to this dataset, and let us stipulate that we suspect they may indicate some bias in the observations. Before we look at that, let us take a look at a more-or-less expected trend. Note that this dataset was artificially constructed only based on the most popular male and female names for each birth year. A particular name may not be in this top 5 (per gender) for a particular year, or even a particular decade, but nonetheless, a certain number of people in the United States were probably given that name (and would be likely to show up in non-synthetic data).

fig, ax = plt.subplots(figsize=(12, 4.5))
somenames = ['Michael', 'James', 'Mary', 'Ashley']
popular = names[names.Name.isin(somenames)]
sns.countplot(x="Age Group", hue="Name", 
              ax=ax, palette='gray', data=popular)
ax.set_title("Distribution of name frequency by age");

Figure 5.3: Distribution of name frequency by age

We can see trends in this data. Mary is a popular name among the older people in the dataset, but no longer shows up in the most popular names for younger people. Ashley is very popular among 20-40-year-olds, but we do not see it present outside that age group. James seems to have been used over most age ranges, although it fell out of the top-5 spot among 10-40-year-olds, resurging among children under 10. Michael, similarly, seems especially represented from 10-60 years of age.

The top-5 threshold used in the generation of the data has definitely created a few artifacts in the visualization, but a general pattern of some names becoming popular and others waning is exactly a phenomenon we would expect with a bare minimum of domain knowledge. Moreover, if we know only a little bit more about popular baby names in the United States, the specific distribution of names will seem plausible; both for the 4 shown and for the remaining 65 names that you can investigate within the dataset if you download it.

Detecting Bias

Let us apply a similar analysis to birth month as we did to name frequency. A minimum of domain knowledge will tell you that while there are small annual cyclicities in birth month, there should not be a general trend over ages. Even if some world-historical event had dramatically affected births in one particular month of one particular year, this should create little overall trend when we aggregate over decades of age.

fig, ax = plt.subplots(figsize=(12, 4.5))
months = ['January', 'February', 'March', 'April']
popular = names[names.Birth_Month.isin(months)]
sns.countplot(x="Age Group", hue="Birth_Month", 
              ax=ax, palette='gray', data=popular)
ax.set_title("Distribution of birth month frequency by age");

Figure 5.4: Distribution of birth month frequency by age

Contrary to our hope of excluding a biasing trend, we have discovered that—for unknown reasons—January births are dramatically underrepresented among the youngest people and dramatically overrepresented among the oldest people. This is overlain on an age trend of there being more young people, in general, but the pattern nonetheless appears strong. We have not looked at months beyond April, but of course we could in a similar fashion.

A certain amount of random fluctuation occurs in the dataset simply because of sampling issues. The fact that April is a somewhat more common birth month for 50-something people than for 40-something people in the dataset is quite likely meaningless since there are relatively few data points (on the order of 50) once we have cross-cut by both age and birth month. Distinguishing genuine data bias from randomness can require additional analysis (albeit, by construction, the January pattern jumps out strongly even in this simple visualization).

There are numerous ways we might analyze it, but looking for notable differences in the spread of one variable in relation to another can be a good hint. For example, we think we see an oddness in the pattern of January birth months, but is there a general irregularity in the distribution per age? We could attempt to analyze this using exact age, but that probably makes the distinction too fine-grained to have good subsample sizes. The decade of age is an appropriate resolution for this test. As always, think about your subject matter in making such judgments.

Since the number of people decreases with age, we need to find statistics that are not overly influenced by the raw numbers. In particular, we can count the number of records we have for each age group and birth month and see if those counts are notably divergent. Variance or standard deviation (of counts) will increase as the size of the age group increases. However, we can normalize that simply by dividing by the raw count within the age group of all months.

A little bit of Pandas magic gets us this. We want to group the data by age group, look at the birth month, and count the number of records that fall within each AgeBirth_Month. We wish to look at this in a tabular way rather than with a hierarchical index. This operation arranges months in order of their occurrence in the data, but ordering by chronology is more friendly.

by_month = (names
    .groupby('Age Group')
by_month = by_month[month_names]
Birth_Month  January  February  March  April  May  June  July  August
  Age Group
          0       20        67     59     76   66    77    71      65
         10       37        72     71     78   70    73    82      81
         20       52        60     76     72   65    65    71      66
         30       54        56     66     64   73    58    87      82
        ...      ...       ...    ...    ...  ...   ...   ...     ...
         70       57        43     39     33   39    36    45      34
         80       57        39     28     21   31    37    23      28
         90       55        17     31     24   21    23    30      29
        100       10         7      4      2    6     2     4       6
Birth_Month  September  October  November  December
  Age Group
          0         67       67        56        63
         10         83       79        70        79
         20         68       75        76        71
         30         66       65        57        58
        ...        ...      ...       ...       ...
         70         38       30        37        37
         80         27       31        34        37
         90         33       25        28        20
        100          5        5         7         7
11 rows × 12 columns

That data grid remains a bit too much to immediately draw a conclusion about, so as described, let us look at the normalized variance across age groups.

with show_more_rows():
    print(by_month.var(axis=1) / by_month.sum(axis=1))
Age Group
0          0.289808
10         0.172563
20         0.061524
30         0.138908
40         0.077120
50         0.059772
60         0.169321
70         0.104118
80         0.227215
90         0.284632
100        0.079604
dtype: float64

The over-100-years-old group shows a low normalized variance, but it is a small subset. Among the other age groups, the middle ages show a notably lower normalized variance across months than do the older or younger people. This difference is quite striking for those under 10 and those over 80 years old. We can reasonably conclude at this point that some kind of sample bias occurred in the collection of the birth month; specifically, there is a different bias in effect based on the age group of persons sampled. Whether or not this bias matters for the purpose at hand, the fact should be documented clearly in any work products of your analyses or models. In principle, some sampling technique that will be discussed in Chapter 6, Value Imputation, might be relevant to adjust for this.

Comparison to Baselines

The setup of this synthetic dataset is a giveaway, of course. As well as introducing birth month, I also added Home in the sense of state or territory of residence and/or birth. While there is no documented metadata that definitively clarifies the meaning of the column, let us take it as the state of current residence. If we had chosen to interpret it as birthplace, we might need to find historical data on populations at the times people of various ages were born; clearly that is possible, but the current assumption simplifies our task.

Let us take a look at the current population of the various US states. This will provide an external baseline relative to which we can look for sample bias in the dataset under consideration.

states = pd.read_fwf('data/state-population.fwf')
                State   Population_2019   Population_2010   House_Seats
  0        California          39512223          37254523          53.0
  1             Texas          28995881          25145561          36.0
  2           Florida          21477737          18801310          27.0
  3          New York          19453561          19378102          27.0
...               ...               ...               ...           ...
 52              Guam            165718            159358           0.5
 53   U.S. Virgin Isl            104914            106405           0.5
 54    American Samoa             55641             55519           0.5
 55    N. Mariana Isl             55194             53883           0.5
 56 rows × 4 columns

As most readers will know, the range of population sizes across different US states and territories is quite large. In this particular dataset, representation of states in the House of Representatives is given as a whole number, but in order to indicate the special status of some entities that have non-voting representation, the special value of 0.5 is used (this is not germane to this section, just as a note).

Let us take a look at the distribution of home states of persons in the dataset. The step of sorting the index is used to assure that states are listed in alphabetical order, rather than by count or something else.

    .plot(kind='bar', figsize=(12, 3), 
          title="Distribution of sample by home state")

Figure 5.5: Distribution of sample by home state

There is clearly variation in the number of samples drawn from residents of each state. However, the largest state represented, California, has only about 3x the number of samples as the smallest. In comparison, a similar view of the underlying populations emphasizes the different distribution.

    [['State', 'Population_2019']]
    .plot(kind='bar', figsize=(12, 3),
          title="2019 Population of U.S. states and territories")

Figure 5.6: 2019 population of United States states and territories

While California provides the most samples for this dataset, Californians are simultaneously the most underrepresented relative to the baseline population of the states. As a general pattern, smaller states tend to be overrepresented generally. We can, and probably should, think of this as selection bias based on the size of the various states. As before, unless we have accurate documentation or metadata that describes the collection and curation procedures, we cannot be sure of the cause of the imbalance. But a strong trend exists in this inverse relationship of population to relative sample frequency.

A note here is that sometimes sampling approaches deliberately introduce similar imbalances. If the actual samples were precisely balanced, with some fixed N collected per state, this would fairly clearly point to such a deliberate categorical sampling as opposed to a sampling based on an underlying rate. The pattern we actually have is less obvious than that. We might form a hypothesis that the sampling rate is based on some other underlying feature not directly present in this data.

For example, perhaps a fixed number of observations were made in each county of each state, and larger states tend to have more counties (this is not the actual underlying derivation, but thinking in this manner should be in your mind). Understanding data integrity issues resembles either a scientific process of experimentation and hypothesis, or perhaps even more so a murder mystery. Developing a reasonable theory of why the data is dirty is always a good first step in remediating it (or even in ignoring the issue as not pertinent to the actual problem at hand).

Benford’s Law

There is a curious fact about the distribution of digits in many observed numbers called Benford’s Law. For a large range of real-world datasets, we see leading 1 digits far more often than leading 2s, which in turn occur far more commonly than leading 3s, and so on. If you see this pattern, it probably does not reflect harmful bias; in fact, for many kinds of observations, if you fail to see it, that might itself reflect bias (or even fraud).

If a distribution precisely follows Benford’s law, it will specifically have digits distributed as:

However, this distribution is often only approximate for real-world data.

When data is distributed according to a power law or a scaling factor, it becomes relatively intuitive to understand what leading digits will be distributed in a “biased” way. However, much observational data that is not obviously scaling in nature still follows Benford’s law (at least approximately). Let us pick an example to check; I scraped and cleaned up formatting for the populations and areas of the most populous US cities.

cities = pd.read_fwf('data/us-cities.fwf')
             NAME    POP2019    AREA_KM2
0   New York City    8336817       780.9
1     Los Angeles    3979576      1213.9
2         Chicago    2693976       588.7
3         Houston    2320268      1651.1
...           ...        ...         ...
313     Vacaville     100670        75.1
314       Clinton     100471        72.8
315          Bend     100421        85.7
316    Woodbridge     100145        60.3
317 rows × 3 columns

Let us first count the leading digits of populations.

pop_digits =  cities.POP2019.astype(str).str[0].value_counts()
with show_more_rows():
1    206
2     53
3     20
4     10
6      9
5      8
8      5
7      3
9      3
Name: POP2019, dtype: int64

Now we ask the same question of area in square kilometers.

area_digits =  cities.AREA_KM2.astype(str).str[0].value_counts()
with show_more_rows():
1    118
2     47
3     31
4     23
9     21
8     21
7     20
6     20
5     16
Name: AREA_KM2, dtype: int64

Neither collection of data exactly matches the Benford’s law ideal distribution, but both show the general pattern of favoring leading digits in roughly ascending order.

Let us turn to evaluating the importance of the uneven distribution of categorical variables.

Class Imbalance

It seems to be correct to begin with the real and the concrete, with the real precondition, thus to begin [...] with the population. However, on closer examination this proves false. The population is an abstraction if I leave out, for example, the classes of which it is composed.

–Karl Marx


  • Predicting rare events
  • Imbalance in features versus in targets
  • Domain versus data integrity imbalance
  • Forensic analysis of sources of imbalance
  • Stipulating the direction of causality

The data you receive will have imbalanced classes, if it has categorical data at all. The several distinct values that a categorical variable may have are also sometimes called factor levels (“factor” is synonymous with “feature” or “variable,” as discussed in the Preface and Glossary). Moreover, as will be discussed in Chapter 6, Value Imputation in the section on Sampling, dividing a continuous variable into increments can often usefully form synthetic categories also. In principle, any variable might have a categorical aspect, depending on the purpose at hand. When these factor levels occur with notably different frequency, it may show selection bias or some other kind of bias; however, it very often simply represents the inherent nature of the data, and is an essential part of the observation.

A problem arises because many types of machine learning models have difficulty predicting rare events. Discussion of concretely rebalancing classes is deferred until the Chapter 6 discussion of undersampling and oversampling, but here we at least want to reflect on identifying class imbalance. Moreover, while many machine learning techniques are highly sensitive to class imbalance, others are more or less indifferent to it. Documentation of the characteristics of particular models, and their contrast with others, is outside the scope of this particular book.

In particular, though, the main difference between when class imbalance poses a difficulty versus when it is central to the predictive value of the data is precisely the difference between a target and the features. Or equivalently, the difference between a dependent variable and independent variables. When we think of a rare event that might cause difficulty for a model, we usually mean a rare target value, and only occasionally are we concerned about a rare feature. When we wish to use sampling to rebalance classes, it is almost always in relation to target class values.

We will work with a simple example. Two weeks of Apache server logs from my web server are provided as sample data. Such a log file has a number of features encoded in it, but one particular value in each request is the HTTP status code returned. If we imagine trying to model the behavior of my web server, quite likely we would wish to treat this status code as a target that might be predicted by the other (independent) variables. Of course, the log file itself does not impose any such purpose; it simply contains data on numerous features of each request (including response).

The status codes returned from the actual requests to my web server are extremely unbalanced, which is generally a good thing. I want most requests to result in 200 OK responses (or at least some 2xx code). When they do not, there is either a problem with the URLs that users have utilized or there is a problem with the web server itself. Perhaps the URLs were published in incorrect form, such as in links from other web pages; or perhaps deliberately wrong requests were used in attempts to hack my server. I never really want a status code outside of 2xx, but inevitably some arise. Let us look at their distribution:

zcat data/gnosis/*.log.gz | 
    cut -d' ' -f9 | 
    sort | 
    uniq -c
  10280 200
      2 206
    398 301
   1680 304
    181 403
    901 404
      9 500

The 200 status dominates here. The next highest occurrence is 304 Not Modified, which is actually fine as well. It simply indicates that a cached copy on a client remains current. Those 4xx and 5xx (and perhaps 301) status codes are generally undesirable events, and I may want to model the patterns that cause them. Let us remind ourselves what is inside an Apache access.log file (the name varies by installation, as can the exact fields).

zcat data/gnosis/20200330.log.gz | head -1 | fmt -w50 - - [30/Mar/2020:00:00:00 -0400]
"GET /TPiP/024.code HTTP/1.1" 200 75

There is a variety of data in this line, but notably it is easy to think of pretty much all of it as categorical. The IP address is a dotted quad, and the first (and often second) quad tends to be correlated with the organization or region where the address originates. Allocation of IPv4 addresses is more complex than we can detail here, but it may be that requests originating from a particular /8 or /16 origin tend to get non-200 responses. Likewise, the date—while unfortunately not encoded in ISO 8601 format—can be thought of as categorical fields for month, hour, minute, and so on.

Let us show a bit of Pandas code to read and massage these records into a data frame. The particular manipulations done are not the main purpose of this section, but gaining familiarity with some of these methods is worthwhile.

One thing to notice, however, is that I have decided that I am not really concerned with the pattern where, for example, my web server became erratic for a day. That has not occurred in this particular data, but if it had I would assume that was a one-off occurrence not subject to analysis. The separate cyclical elements of hour and minute might detect recurrent issues (which are discussed more in later sections of this chapter). Perhaps, for example, my web server gives many 404 responses around 3 a.m., and that would be a pattern/problem worth identifying.

def apache_log_to_df(fname):
    # Read one log file.  Treat is as a space separated file
    # There is no explicit header, so we assign columns
    cols = ['ip_address', 'ident', 'userid', 'timestamp', 
            'tz', 'request', 'status', 'size']
    df = pd.read_csv(fname, sep=' ', header=None, names=cols)
    # The first pass gets something workable, but refine it
    # Datetime has superfluous '[', but fmt matches that
    fmt = "[%d/%b/%Y:%H:%M:%S"
    df['timestamp'] = pd.to_datetime(df.timestamp, format=fmt)
    # Convert timezone to an integer
    # Not general, I know these logs use integral timezone
    # E.g. India Standard Time (GMT+5:30) would break this
    df['tz'] = df.tz.str[:3].astype(int)
    # Break up the quoted request into sub-components
    df[['method', 'resource', 'protocol']] = (
                df.request.str.split(' ', expand=True))
    # Break the IP address into each quad
    df[['quad1', 'quad2', 'quad3', 'quad4']] = (
                df.ip_address.str.split('.', expand=True))
    # Pandas lets us pull components from datetime
    df['hour'] = df.timestamp.dt.hour
    df['minute'] = df.timestamp.dt.minute
    # Split resource into the path/directory vs. actual page
    df[['path', 'page']] = (
                df.resource.str.rsplit('/', n=1, expand=True))
    # Only care about some fields for current purposes
    cols = ['hour', 'minute', 
            'quad1', 'quad2', 'quad3', 'quad4', 
            'method', 'path', 'page', 'status']
    return df[cols]

This function allows us to read all of the daily log files into a single Pandas DataFrame simply by mapping over the collection of file names and concatenating data frames. Everything except perhaps page in the resulting data frame is reasonable to think of as a categorical variable.

reqs = pd.concat(map(apache_log_to_df, 
# Each file has index from 0, so dups occur in raw version
reqs = reqs.reset_index().drop('index', axis=1)
# The /16 subnetwork is too random for this purpose
reqs.drop(['quad3', 'quad4'], axis=1, inplace=True)
       hour  minute  quad1  quad2  method                           path
    0     0       0    162    158     GET    /download/pywikipedia/cache
    1     0       3    172     68     GET                          /TPiP
    2     0       7    162    158     GET   download/pywikipedia/archive
    3     0       7    162    158     GET                     /juvenilia
  ...   ...     ...    ...    ...     ...                            ...
13447    23      52    162    158     GET          /download/gnosis/util
13448    23      52    172     69     GET                               
13449    23      52    162    158     GET               /publish/resumes
13450    23      56    162    158     GET    /download/pywikipedia/cache
                                 page   status
    0   DuMont%20Television%20Network      200
    1                        053.code      200
    2                        ?C=N;O=A      200
    3  History%20of%20Mathematics.pdf      200
  ...                             ...      ...
13447                     hashcash.py      200
13448                     favicon.ico      304
13449                                     200
13450          Joan%20of%20Lancaster      200
13451 rows × 8 columns

Within my web server, I have relatively few directories where content lives, but relatively many different concrete pages within many of those directories. In fact, the path /download/pywikipedia/cache is actually a robot that performs some formatting cleanup of Wikipedia pages that I had forgotten that I left running 15+ years ago. Given that it may be pointed to any Wikipedia page, there is effectively an infinite space of possible pages my server will reply to. There are also a small number of long path components because URL parameters are sometimes passed in to a few resources. Let us visualize the distribution of the other features in this dataset, with an eye to the places where class imbalance occurs.

fig, axes = plt.subplots(3, 2, figsize=(12, 9))
# Which factors should we analyze for class balance?
factors = ['hour', 'minute', 'quad1', 'quad2', 'method', 'status']
# Loop through the axis subplots and the factors
for col, ax in zip(factors, axes.flatten()):
    # Minute is categorical but too many so quantize
    if col == 'minute':
        data = (reqs[col] // 5 * 5).value_counts()
        data = reqs[col].value_counts()
    data.plot(kind='bar', ax=ax)
    ax.set_title(f"{col} distibution")
# Matplotlib trick to improve spacing of subplots

Figure 5.7: Distributions of different features

In these plots, we see some highly imbalanced classes and some mostly balanced ones. The hours show a minor imbalance, but with a fairly strong pattern of more requests around 21:00–24:00 in Atlantic Daylight Time. Why my hosted server is in that timezone is unclear to me, but this is around 6 p.m. US Pacific Time, so perhaps users in California and British Columbia tend to read my pages after work. The distribution of 5-minute increments within an hour is generally uniform, although the slight elevation of a few increments could possibly be more than random fluctuation.

The imbalance in the initial quads of IP address seems striking, and might initially suggest an important bias or error. However, after probing only slightly deeper, we can determine using online “whois” databases that (at the time of writing) both and are assigned to the CDN (content delivery network) that I use to proxy traffic. So the imbalance in these features has simply provided a clue that almost all requests are proxied through a known entity. In particular, it means that we are unlikely to be able to use these features usefully in any kind of predictive model. At most, we might perform feature engineering—as will be discussed in Chapter 7, Feature Engineering—to create a derived feature such as is_proxied.

The class imbalances that remain are in the HTTP method and in the status code returned. In neither case is it at all surprising that GET and 200 dominate the respective features. This is what I expect, and even hope for, in the behavior of my web server and website. So nothing there suggests bias in the data collection; since all requests were logged, this is not a sample but rather a complete domain.

As a side note, the population is specifically delineated, and cannot necessarily be used to describe anything beyond those lines. These are all requests made to port 80 or port 443 for the web domain gnosis.cx between March 29, 2020, and April 11, 2020; we can draw no conclusions about other web domains or other dates without further analysis or reasoning about how typical this data is of the web as a whole.

As data scientists, we are not necessarily constrained by temporal causality. For example, it is clear that in a literal and sequential way, the requesting IP address, possibly the userid, maybe the time of the request, and definitely the URL of the request, both method and path, will cause a certain status code and number of bytes to be returned. In many cases (probably all of them on my simple, static website), the size is simply that of the underlying HTML page. But in concept, a server might do something different depending on the date and time, or the requester’s address. In any case, certain facts about the request exist a few milliseconds before the server decides on the appropriate status code and response size and logs all of that.

However, for an analysis, we might want to make predictions that exactly reverse causality. Perhaps we would like to treat the size of the response as an independent variable in our effort to predict the time of day. For example, it could be that large files are always requested around 7 p.m. rather than at other times. Our model might try to predict a cause from its effect—and that is perfectly legitimate in data science, as long as we are aware of it. In fact, we may only look for correlations, entirely ignoring for a particular task the potential hidden cause of multiple features. Data science is something different from other sciences; the endeavors are, hopefully, complementary.


In this section, we focused merely on recognizing, and to a limited extent analyzing, class imbalance. What it means for the actual task to which we wish to put this data is another matter. A significant distinction to keep in mind is that between independent and dependent variables. Generally, imbalance in a dependent variable will skew classification models in a more important way than imbalance in an independent variable. So, for example, if we wish to predict the likely status code that will be produced by a request based on other features of the request, we would be likely to use sampling techniques that will be discussed in Chapter 6, Value Imputation, to balance the dataset synthetically.

On the other hand, class imbalance is not completely irrelevant in independent variables, at least not for all kinds of models. This very much depends on the kind of model. If we use something in the family of decision trees, for example, it makes little difference that HEAD requests are rare if we wish to detect the (hypothetical) fact that HEAD is strongly associated with 500 status codes. However, if we use a K-nearest neighbors family of algorithm, the actual distance in parameter space can be important. Neural networks fall somewhere in the middle in terms of sensitivity to class imbalance in independent variables. If we encode the HTTP method either as an ordinal value or using one-hot encoding, we may naïvely underweight that strong but rare feature. One-hot encoding is discussed in Chapter 7, Feature Engineering. For an independent variable, we would not generally wish to oversample a rare factor level; but we might wish to artificially overweight it.

We also should think about the numeric ranges of data, which might reflect very different underlying units.

Normalization and Scaling

Measure with a micrometer. Mark with chalk. Cut with an axe.

–Rule for precision


  • The effect of numeric ranges in variables
  • Univariate and multivariate effects
  • Numeric forms of various scalers
  • Factor and sample weighting

The idea behind normalization of data is simply bringing all the features being utilized in a dataset into a comparable numeric range. When starkly different units are used for different features—that is, for dimensions of a parameter space—some machine learning models will disproportionately utilize those features which simply have a larger numeric range. Special cases of differently scaled numeric ranges occur when one feature has outliers that have not been removed, or when one feature is normally distributed but another feature is exponentially distributed.

This book generally steers away from showing machine learning examples or code. There are many wonderful libraries that address that 20% of your work, as a data scientist, that you will do after you have done the 80% that this book teaches you. However, to emphasize the motivation for normalization, we will create a very simple machine learning model on some overly neat data that illustrates an overwhelming benefit of scaling. For this example, a small amount of code in scikit-learn is used. Notably, however, the scaler classes in scikit-learn are extremely useful even if you do not wish to use that library for modeling. It is certainly reasonable—and perhaps even best practice within Python—to use scikit-learn even if you only ever perform normalization with it.

The synthetic dataset here has two features and one target; all are continuous variables.

unscaled = make_unscaled_features()
      Feature_1     Feature_2       Target
  0    0.112999   19247.756104   11.407035
  1    0.204178   23432.270613   20.000000
  2    0.173678   19179.445753   17.336683
  3    0.161411   17579.625264   16.633166
...         ...            ...         ...
196    0.137692   20934.654450   13.316583
197    0.184393   18855.241195   18.241206
198    0.177846   19760.314890   17.839196
199    0.145229   20497.722353   14.371859
200 rows × 3 columns

At a glance, we can see that the Target values are on the order of 15, while Feature_1 is on the order of 0.1 and Feature_2 is on the order of 20,000. The invented example does not assign any specific units for these measures, but there are many quantities you might measure whose units produce numeric values in those ranges. As an initial question, we might ask whether any of the features have a univariate correlation with the target. A machine learning model will find more than just this, but it is a useful first question.

            Feature_1   Feature_2      Target
Feature_1    1.000000   -0.272963    0.992514
Feature_2   -0.272963    1.000000   -0.269406
   Target    0.992514   -0.269406    1.000000

We see that Feature_1 has a very strong positive correlation with the Target, and Feature_2 has a moderate negative correlation. So on the face of it, a model should have plenty to work with. Indeed, we can tell from the correlation matrix that linear models would do extremely well, with or without normalization; but that is the topic of a different book. This point can be made visually by plotting Target against each feature.


Figure 5.8: Feature_1 and Feature_2 as functions of Target

Feature_1 has a visually obvious correlation; Feature_2 reveals at most a very weak one to a human eye.

Applying a Machine Learning Model

As promised, let us apply a machine learning model against this data, trying to predict the target based on the features. In ML, we conventionally use the names X and y for features and target, respectively. This follows the common pattern, from high school algebra, of naming an independent variable x and a dependent variable y. Since we generally have multiple features, a capital X is used. While we cannot discuss the motivation in any depth, good practice in machine learning is to always reserve a portion of your training data for testing, so that you do not overfit your model. That is done with the function train_test_split().

from sklearn.model_selection import train_test_split
X = unscaled.drop('Target', axis=1)
y = unscaled['Target']
X_train, X_test, y_train, y_test = (
    train_test_split(X, y, random_state=1))

For this example, we use a K-neighbors regressor to try to model our data. For many kinds of problems, this is a very effective algorithm, but it is also one that looks directly at distances in parameter space, and is hence very sensitive to scaling. If we naïvely apply this model to our raw data, the R-squared score is very low (other metrics would be similarly bad).

from sklearn.neighbors import KNeighborsRegressor
knn = KNeighborsRegressor()
knn.fit(X_train, y_train).score(X_test, y_test)

A “perfect” R-squared score is 1.0. A very bad score is 0.0 (negative scores are also sometimes possible, and even worse in a sense). But for anything below 0.25 or so, we essentially reject the model.

By using, in this case, a min-max scaler, we achieve a far better metric score. The scaler we use here simply takes the minimum value of the raw feature, and shifts all values by that amount toward zero by subtraction, then divides all values by the shifted maximum value. The effect is to produce a range that is always [0, 1], for every feature. This synthetic feature does not have any physical meaning per se, as the original measure presumably did.

But by applying this scaler, all features are guaranteed to occupy the same numeric range (with the specific values distributed differently within their ranges). Let us apply this min-max scaling to our features before fitting the model again.

from sklearn.preprocessing import MinMaxScaler
X_new = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = (
    train_test_split(X_new, y, random_state=1))
knn2 = KNeighborsRegressor()
knn2.fit(X_train, y_train).score(X_test, y_test)

Notice that I did not bother to scale the target in the above code. There would be no harm in doing so for the model, but there is no benefit either since the target is not part of the parameter space of the features. Moreover, if we scaled the target, we would have to remember to unscale it correspondingly to get a meaningful number in the desired units.

Scaling Techniques

The scaling technique we used above utilized scikit-learn’s MinMaxScaler. All of the scalers in scikit-learn use the same API, and are implemented in an efficient and correct manner. There is certainly a good argument for using those within Python, even if scikit-learn is not otherwise part of your overall modeling pipeline. However, it is not difficult to do the same scaling “by hand” using lower-level vectorized operations. For example, this would be simple in NumPy; here we show an example in R, and focus only on the algorithm. One nice detail of the scikit-learn API is that it knows to normalize column-by-column. In the comparison, we only do one column.

%%R -i X,X_new
# Import the data frame/array from Python
py_raw_data <- X$Feature_1  # only feature 1
py_scaled <- X_new[,1]      # scaled column 1
# Utility function to scale as [0, 1]
normalize <- function(x) {
    floor <- min(x)  # Only find min once
    return ((x - floor) / (max(x) - floor))
# Scale the raw data
r_scaled <- normalize(py_raw_data)
# Near equality of elements from normalize() and MinMaxScaler
all.equal(py_scaled, r_scaled)
[1] TRUE

Notice that even for a straightforward operation like this, the different implementations, across libraries and languages, do not perform identical operations in an identical order. This allows some floating-point rounding differences to creep in. Comparing for strict equality of floating-point values is almost always the wrong thing to do; measurements have finite precision and operations introduce 1-ULP (unit in the last place) errors frequently. On the other hand, these slight numeric differences make no practical difference for actual models, only for equality checks.

print("A few 'equalities':")
print("Exactly equal?")
print((py_scaled == r_scaled)[1:10])
print("Mean absolute difference:")
print(mean(abs(py_scaled - r_scaled)))
[1] "A few 'equalities':"
[1] 0.1776148 1.0000000 0.7249096 0.6142706 0.8920478
[1] 0.1776148 1.0000000 0.7249096 0.6142706 0.8920478
[1] "Exactly equal?"
[1] "Mean absolute difference:"
[1] 6.130513e-17

Another very common scaling technique is called StandardScaler in scikit-learn. It sets the mean of a feature to 0 and the standard deviation to 1. This scaling is particularly relevant when a variable is (very roughly) normally distributed. The name hints that this approach is usually the default scaler to choose (although probably it was derived from “standard deviation” when the name was chosen). Let us implement it to illustrate the simple transformation. Here we display the values from Feature_2, which are around 20,000 in the raw data.

from sklearn.preprocessing import StandardScaler
X_new2 = StandardScaler().fit_transform(X)
# Second column for example (both were scaled)
plt.hist(X_new2[:, 1], bins=30)
plt.title("Value distribution after StandardScaler");

Figure 5.9: Feature_2 value distribution after the StandardScaler transformation

StandardScaler uses more numeric operations than MinMaxScaler, since it involves standard deviation, and that gives the calculation more opportunity for introducing numeric errors. The code in scikit-learn performs tricks to minimize this error better than the simple version we present, although again the magnitude is unlikely to be genuinely important. Let us manually reproduce the basic operation of StandardScaler.

%%R -i X,X_new2
# Import the data frame/array from Python
py_raw_data <- X$Feature_2  # Only feature 2
py_scaled <- X_new2[, 2]    # scaled column 2
r_scaled = (py_raw_data - mean(py_raw_data)) / 
all.equal(py_scaled, r_scaled)
[1] "Mean relative difference: 0.002503133"

In this calculation, we do not pass the all.equal() test. R characterizes the failure beyond only a boolean FALSE. We can make the comparison with a bit more laxness by setting the tolerance parameter. Let us also verify the characteristics of the scaled data.

print("Mean from R scaling:")
print("Standard deviation:")
print("Almost equal with tolerance 0.005")
all.equal(py_scaled, r_scaled, tolerance = 0.005)
[1] "Mean from R scaling:"
[1] 6.591949e-17
[1] "Standard deviation:"
[1] 1
[1] "Almost equal with tolerance 0.005"
[1] TRUE

A number of variations are available for scaling through basic multiplication and subtraction operations. For example, rather than normalize on standard deviation, we could normalize using inter-quartile range (IQR). The scikit-learn class RobustScaler does this, for example. To some degree, IQR—or generally quantile-based approaches—are more robust against outliers. However, the degree to which IQR range scaling normalizes is limited, and a stricter quantile approach can be more aggressive.

Let us replicate Feature_1 in the sample dataset we are presenting, but make just one value (out of 200) an extreme outlier. Recall that Feature_1 has values on the order of 0.1. We will introduce a single value of 100 into the variable. Arguably, this is an extreme-enough outlier that we should have removed it already, using the techniques discussed in Chapter 4, Anomaly Detection, but for whatever reason we did not.

X['Feature_3'] = X.Feature_1
X.loc[0, 'Feature_3'] = 100

When we attempt to utilize RobustScaler, the transformed data still has one data point at an extreme value. In fact, that extreme is worse than the out-of-bounds value, 100, that we selected; moreover, the outlier is even farther out than under a StandardScaler transformation. RobustScaler is really only productive under a collection including a moderate number of moderate outliers (of the sort that might have escaped anomaly detection).

from sklearn.preprocessing import RobustScaler
X_new3 = RobustScaler().fit_transform(X)
# Third column for example (all were scaled)
plt.hist(X_new3[:, 2], bins=30)
plt.title("Value distribution after RobustScaler");

Figure 5.10: Feature_1 value distribution after RobustScaler

A stronger approach we can use is to rigorously scale values so that they fall exclusively within quantiles. In essence, this scales the data within each quantile range separately, and hence imposes both a reasonable distribution overall and strict bounds on values.

from sklearn.preprocessing import QuantileTransformer
# Ten quantiles is also called "decile"
deciles = QuantileTransformer(n_quantiles=10)
X_new4 = deciles.fit_transform(X)
# Third column for example (all were scaled)
plt.hist(X_new4[:, 2], bins=30)
plt.title("Value distribution after QuantileTransformer");

Figure 5.11: Feature_1 value distribution after QuantileTransformer

Obviously, this transformed data is not completely uniform—it would have little value if there was not some variability beyond ordinal order—but it is bounded and reasonably evenly distributed across the range [0, 1]. The single outlier point remains as a minor outlier from the main distribution, but is numerically not very distant.

In principle, even though the specific transformers in scikit-learn operate in a column-wise fashion, we might wish to apply a different scaling technique to each column or feature. As long as the particular transformation generates numeric ranges among the transformed values on roughly the same scale (i.e. usually of about distance one or two between maximum and minimum value, at least for the majority of data), all machine learning techniques that utilize distance in parameter space as part of their algorithm will be satisfied. Examples of such algorithms include linear models, support vector machines, and K-nearest neighbors. As was mentioned, algorithms in the family of decision trees simply do not care about specific distance in a dimension, and neural networks can perform a kind of scaling by allowing what we can informally call a “scaling layer” that at least might act as a multiplier of each input feature (exactly what a trained network “decides” to use neurons and layers for is always somewhat opaque to our intentions or understanding).

Factor and Sample Weighting

There are times when you will wish to give a particular feature more significance than fair scaling across features allows. This is a slightly different issue than the one that is addressed by sampling in Chapter 6, Value Imputation. In that later chapter, I discuss either undersampling or oversampling to produce more witnesses of minority target classes. That is certainly a possible approach to balancing classes within a feature rather than a target, but is not usually the best approach.

If nothing else, oversampling across two distinct unbalanced classes has the potential to explode the number of synthetic samples.

In the case of unbalanced feature classes, another approach is available. We can simply overweight minority classes rather than oversample them. Many machine learning models contain an explicit hyperparameter called something like sample_weight (the scikit-learn spelling). Separately from the sample weights, however, these same model classes will also sometimes have something like class_weight as a separate hyperparameter. The distinction here is exactly the one we have been making: sample weight allows you to overweight (or underweight) specific rows of input data, while class weight allows you to over/underweight specific target class values.

To add more nuance to this matter, we are not restricted to over/underweighting only to address class imbalance. We can, in fact, apply it for any reason we like. For example, we may know that certain measurements in our dataset are more reliable than others, and wish to overweight those. Or we may know that getting predictions right for samples with a certain characteristic is more important for task-specific reasons, even while not wishing entirely to discard those samples lacking that characteristic.

Let us return to the Apache log file example to illustrate all of these concerns. Recall that the processed data looks something like this:

reqs.sample(8, random_state=72).drop('page', axis=1)
       hour    minute    quad1    quad2    method
 3347     0         4      172       69       GET
 2729     9        43      172       69       GET
 8102     4        16      172       69       GET
 9347     0        48      162      158       GET
 6323    21        30      162      158       GET
 2352     0        35      162      158       GET
12728     9         0      162      158       GET
12235    19         3      172       69       GET
                                     path    status
 3347                /publish/programming       200
 2729                               /TPiP       200
 8102                      /member/images       404
 9347                     /publish/images       304
 6323         /download/pywikipedia/cache       200
 2352    /download/gnosis/xml/pickle/test       200
12728                     /download/relax       200
12235                              /dede2       404

We noted that both method and status are highly imbalanced in pretty much the way we expect them to be in a working web server. The method data specifically has this imbalance that we saw plotted above, in Figure 5.7. The hypothetical task we have in mind is to predict status codes based on the other features of the dataset (without actually issuing an HTTP request, which might change based on the current time, for example).

GET     13294
HEAD      109
POST       48
Name: method, dtype: int64

In other words, GET requests are 122 times more common than HEAD requests, and 277 times more common than POST requests. We may be concerned that this limits our ability to make predictions on the rare class values for the method. Often our models will simply figure this out for us, but sometimes they will not. Moreover, although it is a frequently occurring path, we have decided that we need our model to be more sensitive to paths of /TPiP and so will artificially overweight that by 5x as well. Notice that in this stipulation, the overweighting has nothing whatsoever to do with the underlying distribution of the feature, but rather is a domain requirement of the underlying purpose of our modeling.

Likewise, we are especially concerned about predicting 404 status codes (i.e. enhance the recall of this label), but are not necessarily interested in the overall balance of the target. Instead, we will weight all other outcomes as 1, but weight 404s as 10, for task purposes we have determined before performing modeling. Let us do all of that in code, in this case using a random forest model from scikit-learn. Should some row match both the overweighted path and an underrepresented method, the larger multiplier for the method will take precedence.

# The row index positions for rows to overweight
tpip_rows = reqs[reqs.path == '/TPiP'].index
head_rows = reqs[reqs.method == 'HEAD'].index
post_rows = reqs[reqs.method == 'POST'].index
# Configure the weights in a copy of data frame
reqs_weighted = reqs.copy()
reqs_weighted['weight'] = 1  # Default weight of one
reqs_weighted.loc[tpip_rows, 'weight'] = 5
reqs_weighted.loc[head_rows, 'weight'] = 122
reqs_weighted.loc[post_rows, 'weight'] = 277
# Do not use column page in the model
reqs_weighted.drop('page', axis=1, inplace=True)
# View the configured weights
reqs_weighted.sample(4, random_state=72)
      hour  minute  quad1  quad2  method                  path  status
3347     0       4    172     69     GET  /publish/programming     200
2729     9      43    172     69     GET                 /TPiP     200
8102     4      16    172     69     GET        /member/images     404
9347     0      48    162    158     GET       /publish/images     304
3347       1
2729       5
8102       1
9347       1

These sample weights are stored on a per-row basis; in other words, we have 13,451 of them. For this example, most are simply weight 1, but they could all be distinct numbers, in concept. Configuring the weights we wish to use with the target is different. We could leverage the sample weight itself to choose rows with a certain target label; however, that approach is unnecessarily clunky and is not usually our preferred approach. Instead, we simply wish to create a small mapping from label to weight.

target_weight = {code:1 for code in reqs.status.unique()}
target_weight[404] = 10
{200: 1, 304: 1, 403: 1, 404: 10, 301: 1, 500: 1, 206: 1}

Here we will create, fit, train, and score a scikit-learn model. The API will vary if you use some other library, but the concepts will remain the same. It only takes a line to perform a train/test split, as is good practice in real code. As a minor API detail, we need to encode our string categorical values for this model type, so we will use OrdinalEncoder.

from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OrdinalEncoder
# Create the model object with target weights
rfc = RandomForestClassifier(class_weight=target_weight,
# Select and encode the features and target
X = reqs_weighted[['hour', 'minute', 
                   'quad1', 'quad2',
                   'method', 'path']]
# Encode strings as ordinal integers
X = OrdinalEncoder().fit_transform(X)
y = reqs_weighted['status']
weight = reqs_weighted.weight
# Perform the train/test split, including weights
X_train, X_test, y_train, y_test, weights_train, _ = (
     train_test_split(X, y, weight, random_state=1))
# Fit the model on the training data and score it
rfc.fit(X_train, y_train, sample_weight=weights_train)
rfc.score(X_test, y_test)

As with R-squared used in the regression example, 1.0 represents perfect accuracy. Accuracy cannot be less than 0.0 though.

Without more context and analysis, I cannot say whether this model does well or poorly for the intended purpose. Quite possibly some other model class and/or some better-tuned weights would serve the hypothetical purpose better. The steps in trying those are straightforward, and mostly the same as the code shown.

We turn now to a difficult but important concept. Many times we wish to remove expected trends from data to reveal the exceptions to those trends.

Cyclicity and Autocorrelation

Do I contradict myself?

Very well then I contradict myself,

(I am large, I contain multitudes.)

–Walt Whitman


  • Detrending sequential data
  • Detected cycles versus a priori domain knowledge
  • Expected versus distinctive variability
  • Multiple cyclicities
  • Autocorrelation

There are times when you expect your data to have periodic behavior within it. In such cases—especially when multiple overlapping cyclicities exist within sequential data—the deviations from the cyclical patterns can be more informative than the raw values. Most frequently we see this in association with time series data, of course. To some degree, this concern falls under the purview of Chapter 7, Feature Engineering, and indeed we return there to some of the same concerns, and even to the same dataset we discuss here.

As a first step, we would like to be able to recognize and analyze periodicities or cyclicities in our data. Some of these are intuitively obvious once we have some domain knowledge, but others lurk in the data themselves and not necessarily in our initial intuitions. For this section, I will utilize a dataset collected many years ago by my friend, and occasional co-author, Brad Huntting. For a period in the past, Brad collected temperatures in and outside his house in Colorado (USA), generally every 3 minutes. The data presented here covers a few days less than a year.

Rooms inside the house were regulated by thermostats; the outdoors naturally shows seasonal variation. Moreover, the data itself is imperfect. When we return to this data in Chapter 7, Feature Engineering, we will look at gaps, recording errors, and other problems in the data collection. For the purpose of this section, a minor degree of data cleanup and value imputation was performed in the code that loads the dataset. See also Chapter 6, Value Imputation, for additional discussion of imputation generally, with different examples.

First, let us read in the data using a Python function that loads a Pandas DataFrame. However, beyond the loading step, we will perform the analysis and visualization in R and its Tidyverse. Very similar capabilities exist in other libraries and languages, including Pandas. The underlying concepts are important here, not the specific APIs and languages used. Brad uses a web domain name of “glarp” so we use that same invented word for some variable names referring to this data about his house temperatures.

thermo = read_glarp()
start, end = thermo.timestamp.min(), thermo.timestamp.max()
print("Start:", start)
print("  End:", end)
# Fencepost counting includes ends
print(" Days:", 1 + (end.date() - start.date()).days)
Start: 2003-07-25 16:04:00
  End: 2004-07-16 15:28:00
 Days: 358

Let us look at a few rows of the dataset to have a feeling for its nature. We can see that one row exists every 3 minutes during the interval of recording. For this section, the interval is completely regular at 3 minutes, and no missing values are present. Moreover, a few obvious recording errors in the raw data are cleaned up here with imputed values.

%%R -i thermo
glarp <- as.tibble(thermo)
# A tibble: 171,349 x 5
   timestamp           basement   lab livingroom outside
   <dttm>                 <dbl> <dbl>      <dbl>   <dbl>
 1 2003-07-25 16:04:00     24    25.2       29.8    27.5
 2 2003-07-25 16:07:00     24    25.2       29.8    27.3
 3 2003-07-25 16:10:00     24    25.2       29.8    27.3
 4 2003-07-25 16:13:00     24.1  25.2       29.8    27.4
 5 2003-07-25 16:16:00     24.1  25.2       29.8    27.8
 6 2003-07-25 16:19:00     24.1  25.2       29.8    27.5
 7 2003-07-25 16:22:00     24.1  25.2       29.8    27.6
 8 2003-07-25 16:25:00     24.1  25.2       29.8    27.6
 9 2003-07-25 16:28:00     24.1  25.2       29.8    27.7
10 2003-07-25 16:31:00     24.1  25.2       29.8    27.6
# ... with 171,339 more rows

We can visualize this data as a first step to removing cyclicities with the goal of focusing on the ways in which individual measurements vary from expectations. These operations are also called “detrending” the data. Let us look first at outside temperatures, plotting their pattern using ggplot2.

ggplot(glarp, aes(x=timestamp, y=outside)) +
  geom_line() + clean_theme +
  ggtitle("Outside temperature over recording interval")

Figure 5.12: Outside temperature over the recording interval

As is easy to guess, there is a general pattern of northern hemisphere temperatures being warmer in July than in January, with a great deal of jitter within the global trend. Even though only 1 year of data is available, we know from very basic domain knowledge to expect similar annual cycles for other years. In contrast, as we can also anticipate, indoor temperatures both fall within a narrower range and show less of a clear pattern.

ggplot(glarp, aes(x=timestamp, y=basement)) +
  geom_line() + clean_theme +
  ggtitle("Basement temperature over recording interval")

Figure 5.13: Basement temperature over the recording interval

Overall, indoor temperatures in the basement are relatively narrowly bound between about 14°C and 23°C. Some points fall outside of this range, both some high summer temperatures indicating that the house had a heating system but no air conditioner, and some low winter temperatures in sharp spikes, perhaps reflecting periods when windows were opened. However, the outside lows reached about -20°C while these indoor lows are generally above 10°C. Something somewhat odd seems to have happened around September and October of 2003 as well; perhaps this reflects some change in the heating system during that period.

Domain Knowledge Trends

As a first task, let us think about outdoor temperatures that are presumably little affected by the house heating system. We would like to identify unexpectedly warm or unexpectedly cold measurements as inputs to our downstream model. For example, a temperature of 10°C might either be a surprisingly cold summer temperature or a surprisingly warm winter temperature, but in itself it is merely globally typical and does not carry very much information about the observation without additional context.

Given that yearly temperatures will continue to repeat from year to year, it might make sense to model this yearly pattern as a portion of a sine wave. However, in shape, it certainly resembles a parabola for this period from roughly the warmest day of 2003 until roughly the warmest day of 2004. Since we are merely detrending a year-scale pattern, not modeling the behavior, let us fit a second-order polynomial to the data, which will account for most of the variation that exists in the measurements.

# Model the data as a second order polynomial
year.model <- lm(outside ~ poly(timestamp, 2), data = glarp)
# Display the regression and the data
ggplot(glarp, aes(x=timestamp)) + clean_theme +
  geom_line(aes(y = outside), color = "gray") +
  geom_line(aes(y = predict(year.model)), 
            color = "darkred", size = 2) +
  ggtitle("Outside temperature versus polynomial fit")

Figure 5.14: Fitting a polynomial curve to the outside temperature data

We can see in the plot that our annual detrending accounts for most of the data variation, so we can simply subtract the trend from the underlying points to get, as a first pass, the degree to which a measurement is unexpected. A new tibble named outside will hold the data for this narrower focus.

outside <- glarp[, c("timestamp", "outside")] %>%
    add_column(no_seasonal = glarp$outside - predict(year.model))
# A tibble: 171,349 x 3
   timestamp           outside no_seasonal
   <dttm>                <dbl>       <dbl>
 1 2003-07-25 16:04:00    27.5        1.99
 2 2003-07-25 16:07:00    27.3        1.79
 3 2003-07-25 16:10:00    27.3        1.79
 4 2003-07-25 16:13:00    27.4        1.89
 5 2003-07-25 16:16:00    27.8        2.29
 6 2003-07-25 16:19:00    27.5        1.99
 7 2003-07-25 16:22:00    27.6        2.10
 8 2003-07-25 16:25:00    27.6        2.10
 9 2003-07-25 16:28:00    27.7        2.20
10 2003-07-25 16:31:00    27.6        2.07
# ... with 171,339 more rows

Visualizing the seasonally detrended temperatures, we see a remaining range from around -20°C to +20°C. This is somewhat less than the range of the raw temperatures, but only somewhat. Variability has decreased, but only modestly.

However, there is no obvious overall annual trend once we have performed this removal, and the synthetic value is centered at 0.

ggplot(outside, aes(x=timestamp)) +
  geom_line(aes(y = no_seasonal)) + clean_theme +
  ggtitle("Outside temperature with removed seasonal expectation")

Figure 5.15: Outside temperature with seasonal expectation subtracted

The second obvious insight we might have into outdoor temperature cycles is that it is warmer during the day than at night. Given that there are 358 days of data, a polynomial will clearly not fit, but a trigonometric model is likely to fit to a better degree. We do not calculate a Fourier analysis here, but rather simply look for an expected daily cyclicity. Since we have observations every 3 minutes during each day, we wish to convert these 3,360 intervals into 2π radians for the regression to model. The model will simply consist of fitted sine and cosine terms, which can additively construct any sine-like curve on the specified periodicity.

# Make one day add up to 2*pi radians
x <- 1:nrow(outside) * 2*pi / (24*60/3)
# Model the data as a first order trigonometric regression
day_model <- lm(no_seasonal ~ sin(x) + cos(x), 
                data = outside)
# Create a new tibble the holds the regression 
# and its removal from the annually detrended data
outside2 <- add_column(outside, 
                day_model = predict(day_model),
                no_daily = outside$no_seasonal - day_model)
lm(formula = no_seasonal ~ sin(x) + cos(x), data = outside)
(Intercept)       sin(x)       cos(x)  
  0.0002343   -0.5914551    3.6214463  
# A tibble: 171,349 x 5
   timestamp           outside no_seasonal day_model no_daily
   <dttm>                <dbl>       <dbl>     <dbl>    <dbl>
 1 2003-07-25 16:04:00    27.5        1.99      3.61    -1.62
 2 2003-07-25 16:07:00    27.3        1.79      3.60    -1.81
 3 2003-07-25 16:10:00    27.3        1.79      3.60    -1.80
 4 2003-07-25 16:13:00    27.4        1.89      3.59    -1.69
 5 2003-07-25 16:16:00    27.8        2.29      3.58    -1.28
 6 2003-07-25 16:19:00    27.5        1.99      3.56    -1.57
 7 2003-07-25 16:22:00    27.6        2.10      3.55    -1.46
 8 2003-07-25 16:25:00    27.6        2.10      3.54    -1.44
 9 2003-07-25 16:28:00    27.7        2.20      3.53    -1.33
10 2003-07-25 16:31:00    27.6        2.07      3.51    -1.44
# ... with 171,339 more rows

It is difficult to tell from just the first few rows of the data frame, but the daily detrending is typically closer to zero than the seasonal detrending alone. The regression consists mostly of a cosine factor, but is shifted a bit by a smaller negative sine factor. The intercept is very close to zero, as we would expect from the seasonal detrending. If we visualize the three lines, we can get some sense; in order to show it better, only one week in early August of 2003 is shown. Other time periods have a similar pattern; all will be centered at zero because of the detrending.

week <- outside2[5000:8360,]
p1 <- ggplot(week, aes(x = timestamp)) +
  no_xlabel + ylim(-8, +8) + 
  geom_line(aes(y = no_seasonal))
p2 <- ggplot(week, aes(x = timestamp)) +
  no_xlabel + ylim(-8, +8) + 
  geom_line(aes(y = day_model), color = "lightblue", size = 3)
p3 <- ggplot(week, aes(x = timestamp)) +
   clean_theme + ylim(-8, +8) +
  geom_line(aes(y = no_daily), color = "darkred")
grid.arrange(p1, p2, p3,
            top = "Annual de-trended; daily regression; daily de-trended")

Figure 5.16: Annual detrended data; daily regression; daily detrended

The thicker, smooth line is the daily model of temperature. In electronic versions of this book, it will appear as light blue. At the top is the more widely varying seasonally detrended data. At the bottom, the daily detrended data has mostly lower magnitudes (in red if your reading format allows it). The third subplot is simply the subtraction of the middle subplot from the top one.

Around August 7 are some oddly low values. These look sharp enough to suggest data problems, but perhaps a thunderstorm brought August temperatures that much lower during one afternoon. One thing we can note in the date range plotted is that even the daily detrended data shows a weak daily cycle, albeit with much more noise. This would indicate that other weeks of the year have less temperature fluctuation than this one; in fact, some weeks will show an anti-cyclic pattern with the detrended data being an approximate inverse of the regression line. Notably, even on this plot, it looks like August 8 was anti-cyclic, while August 5 and 6 have a remaining signal matching the sign of the regression, and the other days have a less clear correspondence. By anti-cyclic, we do not mean that, for example, a night was warmer than the days around it, but rather that there was less than the expected fluctuation, and hence detrending produces an inverted pattern.

That said, while we have not removed every possible element of more complex cyclic trends, the range of most values in the doubly detrended data is approximately 8°C, whereas it was approximately 50°C for the raw data. Our goal is not to remove the underlying variability altogether but rather to emphasize the more extreme magnitude measurements, which this has done.

Discovered Cycles

We have good a priori beliefs about what outdoor temperatures are likely to do. Summers are warmer than winters, and nights are colder than days. However, no similarly obvious assumption presents itself for indoor temperatures. We saw earlier a plot for temperatures in Brad’s basement. The data is interestingly noisy, but in particular we noticed that for about two summer months, the basement temperatures were pinned above about 21°C throughout the day and night. From this, we inferred that Brad’s house had a heating system but no cooling system, and therefore the indoor temperature approximately followed the higher outdoor ones. We wish here to analyze only the heating system and its artificially maintained temperature, rather than the seasonal trend. Let us limit the data to non-summer days (here named according to the pattern in the data rather than the official season dates).

not_summer <- filter(glarp, 
                     timestamp >= as.Date("2003-08-15"), 
                     timestamp <= as.Date("2004-06-15")) 
# Plot only the non-summer days
ggplot(not_summer, aes(x=timestamp, y=basement)) +
  geom_line() + clean_theme +
  ggtitle("Basement temperature over non-summer days")

Figure 5.17: Basement temperature over non-summer days

Within the somewhat narrowed period, nearly every day of measurements has temperatures both above and below around 18-20°C, so most likely the heating system was operating for a portion of each day in almost all of these non-summer days. The question we would like to analyze—and perhaps to detrend—is whether cyclic patterns exist in indoor temperature data, among the considerable noisiness that is clearly present in the raw data.

A technique called autocorrelation lends itself well to this analysis. Autocorrelation is a mathematical technique that identifies repeating patterns, such as the presence of a periodic signal mixed with noise or non-periodic variation. In Pandas, the Series method .autocorr() looks for this. In R, the relevant function is called acf(). Other libraries or programming languages have similar capabilities. Let us take a look at what we discover. Note that we do not wish blindly to look for autocorrelations if our domain knowledge tells us that only certain periodicities “make sense” within the subject matter.

Although our data frame contains a timeseries column already, it is easier here simply to create one out of the basement column we will work with. The actual dates corresponding to data points are irrelevant for the operation; only their spacing in time is of interest. In particular, we can impose a frequency matching the number of observations in a day to get a plot labeled intuitively by the number of days. The acf() function generates a plot automatically, and returns an object with a number of values attached that you can utilize numerically. For the purpose of this section, the graph is sufficient.

per_day <- 24*60/3
basement.temps <- ts(not_summer$basement, frequency = per_day)
auto <- acf(basement.temps, lag.max = 10*per_day)

Figure 5.18: Density distribution of similarities at different increments

As the autocorrelation name suggests, this shows the correlation of the single data series with itself at each possible offset. Trivially, the zero increment is 100% correlated with itself. Everything other than that tells us something specific about the cyclicities within this particular data. There are strong spikes at each integral number of days. We limited the analysis to 10 days forward here. These spikes let us see that the thermostat in the basement had a setting to regulate the temperature to different levels at different times of each day, but in a way that was largely the same between one day and each of the next ten after it.

The spikes in this data are sloped rather than sharp (they are, at least, continuous rather than stepped). Any given 3-minute interval tends to have a similar temperature to those nearby it, diminishing fairly quickly, but not instantaneously, as measurements occur farther away. This is what we would expect in a house with a thermostat-controlled heating system, of course. Other systems might be different; for example, if a light was on a timer to come on for exactly 3 minutes then go out, on some schedule, the measurement of light levels would be suddenly, rather than gradually, different between adjacent measurements.

The pattern in the autocorrelation provides more information than only the daily cycle, however. We see also a lower correlation at approximately half-day intervals. This is also easily understood by thinking about the domain and the technology that produced it. To save energy, Brad set his thermostat timer to come on in the mornings when he’d wake up, then go to a lower level while he was at the office, then again to go up in the early evening when he returned home. I happen to know this was an automated setting, but the same effect might, for example, have occurred if it was simply a human pattern of manually adjusting the thermostat up and down at those times (the signal would probably be less strong than with a mechanical timer, but likely present).

Rising above the daily cyclicity, there is also a somewhat higher spike in the autocorrelation at 7 days. This indicates that days of the week are correlated with the temperature setting of the thermostat. Most likely, either because of a timer setting or human habit and comfort, a different temperature was set on weekdays versus weekends, for example. This secondary pattern is less strong than the general 24-hour cyclicity, but about as strong as the half-day cyclicity; examining the autocorrelation spikes more carefully could reveal exactly what duration Brad was at his office versus coming home, typically. The offset of the secondary spikes from the 24-hour spikes is probably not at exactly 12 hours, but is at some increment less than the full 24 hours.

We will not do these operations in this section, but think about using the autocorrelation as a detrending regression, much as we did with the trigonometric regression. This would effectively have separate periodicities of 12 and 24 hours, and at 7 days. Clearly, the raw data shown has a lot of additional noise, but it would presumably be reduced by subtracting out these known patterns. Some very atypical values would stand out even more strongly among this detrended data, and potentially thereby have even stronger analytic significance.

Sometimes the data validation that we need to perform is simply highly specific to the domain in question. For that, we tend to need more custom approaches and code.

Bespoke Validation

Explanations exist; they have existed for all time; there is always a well-known solution to every human problem—neat, plausible, and wrong.

–H. L. Mencken


  • Leveraging domain knowledge beyond anomaly detection
  • Example: evaluating duplicated data
  • Validation as sanity check to further investigation

There are many times when domain knowledge informs the shape of data that is likely to be genuine versus data that is more likely to reflect some kind of recording or collation error. Even though general statistics on the data do not show anomalies, bias, imbalance, or other generic problems, we know something more about the domain or the specific problem that informs our expectations about “clean” data.

To illustrate, we might have an expectation that certain kinds of observations should occur with roughly a particular frequency compared to other observations; perhaps this would be specified further by the class values of a third categorical variable. For example, as background domain knowledge, we know that in the United States, family size is slightly less than 2 children, on average. If we had data that was meant to contain information about all the individual people in sampled households, we could use this as a guideline for the shape of the data. In fact, if we had auxiliary data on children per household by state, we might refine this reference expectation more when validating our data.

Obviously, we do not expect every household to have exactly 1.9 children in it. Given that humans come in integral units, we in fact could never have such a fractional number in any specific household at all. However, if we found that in our sampled households we averaged 0.5 children per household, or 4 children per household-with-children, we would have a strong indication that some kind of sample bias was occurring. Perhaps children are under- or overreported in the household data for individual households. Perhaps the selection of which households to sample biases the data toward those with children, or toward those without them. This scenario is largely similar to the issue addressed earlier in this chapter of comparisons to baselines. It adds only a minor wrinkle to the earlier examples in that we only identify households where we wish to validate our expectation of the number of children (i.e. under 18 years old) based on a shared address feature across several observations (that is, a household).

Collation Validation

Let us look at a completely different example that really cannot be formulated in terms of baseline expectations. In this section, we consider genomic data on ribosomal RNA (rRNA) that was downloaded from DNA Data Bank of Japan (DDBJ), specifically the 16S rRNA (Prokaryotes) in FASTA format dataset. You do not need to know anything about genomics or cellular biology for this example; we focus simply on the data formats used and an aggregation of records in this format.

Each sequence in this dataset contains a description of the organism in question and the nature of the sequence recorded. The FASTA format is widely used in genomics and is a simple textual format. Multiple entries in the line-oriented format can simply be concatenated in the same file or text. For example, a sequence entry might look like this:

>AB000001_1|Sphingomonas sp.|16S ribosomal RNA

The description published with this dataset indicates that each sequence contained is at least 300 base pairs, and the average length is 1,104 base pairs. There are 998,911 sequences contained as of this writing. Note that in DNA or RNA, every nucleobase uniquely determines which other base is paired in a double helix, so the format does not need to notate both. A variety of high-quality tools exist for working with genomic data; details of those are outside the scope of this book. However, as an example, let us use SeqKit to identify duplicated sequences. In this dataset, there are no pairs of sequences with the same name or ID, but quite a few contain the same base pairs. This is not an error, per se, since it reflects different observations. It may, however, be redundant data that is not useful for our analysis.

cd data/prokaryotes
zcat 16S.fasta.gz | 
  seqkit rmdup --by-seq --ignore-case \
               -o clean.fasta.gz \
               -d duplicated.fasta.gz \
               -D duplicated.detail.txt
[INFO] 159688 duplicated records removed

Around 15% of all the sequences are duplicates. In general, these are multiple IDs that pertain to the same organism. We can see such in a quick examination of the duplication report produced by seqkit. As an exercise, you might think about how you would write a similar duplicate detection function in a general-purpose programming language; it is not particularly difficult, but SeqKit is certainly more optimized and better tested than would be a quick implementation you might produce yourself.

cut -c-60 data/prokaryotes/duplicated.detail.txt | head
1384  JN175331_1|Lactobacillus, MN464257_1|Lactobacillus, MN4
1383  MN438326_1|Lactobacillus, MN438327_1|Lactobacillus, MN4
1330  AB100791_1|Lactococcus, AB100792_1|Lactococcus, AB10079
1004  CP014153_1|Bordetella, CP014153_2|Bordetella, CP014153_
934   MN439952_1|Lactobacillus, MN439953_1|Lactobacillus, MN43
912   CP003166_2|Staphylococcus, CP003166_3|Staphylococcus, CP
908   CP010838_1|Bordetella, CP010838_2|Bordetella, CP010838_3
793   MN434189_1|Enterococcus, MN434190_1|Enterococcus, MN4341
683   CP007266_3|Salmonella, CP007266_5|Salmonella, CP007266_6
609   MN440886_1|Leuconostoc, MN440887_1|Leuconostoc, MN440888

Horizontal transfer of rRNA between organisms is possible, but such an occurrence in the data might also represent a misclassification of an organism under examination. We can write some code to determine if such an event of multiple IDs for the same sequence are sometimes tagged as different bacteria (or perhaps archaea).

def matched_rna(dupfile):
    """Count of distinct organisms per sequence match
    Return a mapping from line number in the duplicates
    to Counters of occurrences of species names
    counts = dict()
    for line in open(dupfile):
        line = line.rstrip()
        _, match_line = line.split('\t')
        matches = match_line.split(', ')
        first_id = matches[0].split('|')[0]
        names = [match.split('|')[1] for match in matches]
        count = Counter(names)
        counts[first_id] = count
    return counts

It turns out that cataloging multiple organisms with apparently identical rRNA sequences is quite a common occurrence. But our analysis/validation may shed light on what is likely occurring with these duplicate records. Many lines in the duplication report show just one species with many observations. A significant minority show something else. Let us look at several examples.

dupfile = 'data/prokaryotes/duplicated.detail.txt'
counts = matched_rna(dupfile)

In some examples, different observations have differing levels of specificity, but are not per se different organisms.

Counter({'Mannheimia': 246, 'Pasteurellaceae': 1})
Counter({'Microbacterium': 62, 'Microbacteriaceae': 17})

Mannheimia is a genus of the family Pasteurellaceae, and Microbacterium is a genus of the family Microbacteriaceae. Whether these “discrepancies” need to be remediated in cleanup is very problem-specific, however. For example, we may wish to use the more general families in order to group matching sequences together. On the other hand, the problem may demand as much specificity in identifying organisms as is available. You have to decide how to process or handle different levels of specificity in your domain ontology.

A similar issue occurs in another record, but with what appears to be an additional, straightforward data error.

Counter({'Proteobacteria': 1, 'proteobacterium': 2, 'Phyllobacteriaceae': 8})

Phyllobacteriaceae is a family in the broad phylum Proteobacteria, so either way we are dealing with rather non-specific classification. But “proteobacterium” appears to be a non-standard way of spelling the Linnaean family, both in being singular and in lacking of capitalization of the name.

Looking at another record, we might judge the classification as an observational error, but it is obviously difficult to be certain without deeper domain knowledge.

Counter({'Shigella': 11, 'Escherichia': 153})

Both Shigella and Escherichia belong to the family Enterobacteriaceae. The identical sequence is characterized as belonging to different genera here. Whether this indicates a misidentification of the underlying organism or a horizontal transfer of rRNA between these organisms is not clear from this data alone. However, in your data science tasks, this is the sort of decision you are required to make, probably in consultation with domain experts.

One more record we can look at is very strange relative to this dataset. It shows many duplicates, but that is not really the surprising aspect.

Counter({'Aster': 1,
         "'Elaeis": 1,
         "'Tilia": 1,
         "'Prunus": 2,
         "'Brassica": 3,
         'Papaya': 1,
         "'Phalaris": 1,
         "'Eucalyptus": 1,
         "'Melochia": 1,
         'Chinaberry': 1,
         "'Catharanthus": 4,
         "'Sonchus": 1,
         "'Sesamum": 1,
         'Periwinkle': 1,
         'Candidatus': 1})

In this case, we have a number of genera of flowering plants—that is, eukaryotes—mixed with a dataset that is documented to catalog rRNA in prokaryotes. There is also a spelling inconsistency in that many of the genera listed have a spurious single-quote character at the beginning of their name. Whether or not it is plausible for these different plants, mostly trees, to share rRNA is a domain knowledge question, but it seems likely that these data do not belong within our hypothetical analysis of prokaryotic rRNA at all.

The examination of duplicated sequences in this dataset of rRNA sequences points to a number of likely problems in the collection. It also hints at problems that may lurk elsewhere within the collection. For example, even where identical sequences are not named by different levels of cladistic phylogeny, these differing levels may conflate the classification of other sequences. Perhaps, for example, this calls out for normalization of the data to a common phyletic level (which is a significantly large project, but it might be required for a task). Either way, this cursory validation suggests a need to filter the dataset to address only a well-defined collection of genera or families of organisms.

Transcription Validation

We discussed above, in this section, the possibility that the collection of records (i.e. sequences) may have problems in their annotation or aggregation. Perhaps records are inconsistent with each other or in some way present conflicting information. The examples we identified point to possible avenues for removal or remediation. In this second part of the section, we want to look at possible identifiable errors in the individual records.

This hypothetical is presented simply as a data example, not per se motivated by deep knowledge of RNA sequencing techniques. This is commonly the perspective of data scientists who work with domain experts. For example, I do not know how many of the measurements in the dataset utilized RNA-Seq versus older hybridization-based microarrays.

But for this purpose, let us suppose that a relatively common error in the sequencing technique causes inaccurate repetitions of short fragments of RNA base pairs that are not present in the actual measured rRNA. On the other hand, we also do know that microsatellites and minisatellites do occur in rRNA as well (although telomeres do not), so the mere presence of repeated sequences does not prove that a data collection error occurred; it is merely suggestive.

The purpose of this example is simply to present the idea that something as custom as what we do below may be relevant to your data validation for your specific domain. What we will look for is all the places where relatively long subsequences are repeated within a particular sequence. Whether this is an error or an interesting phenomenon is a matter for domain expertise. By default in the code below we look for repeated subsequences of 45 base pairs, but provide a configuration option to change that length. If each nucleotide were simply randomly chosen, each particular pattern of length 45 would occur with probability of about 10–27, and repetitions—even with “birthday paradox” considerations—would essentially never occur. But genetic processes are not so random as that.

As a first step, let us create a short function that iterates over a FASTA file, producing a more descriptive namedtuple for each sequence contained along with its metadata. Many libraries will do something similar, perhaps faster and more robustly than the code shown does, but the FASTA format is simple enough that such a function is simple to write.

Sequence = namedtuple("FASTA", "recno ID name locus bp")
def get_sequence(fname):
    fasta = gzip.open(fname)
    pat = re.compile(r'n+')  # One or more 'n's
    sequence = []
    recno = 0
    for line in fasta:
        line = line.decode('ASCII').strip()
        if line.startswith('>'):
            # Modify base pairs to contain single '-' 
            # rather than strings of 'n's 
            bp = "".join(sequence)
            bp = re.sub(pat, '-', bp)  # Replace pat with a dash
            if recno > 0:
                yield Sequence(recno, ID, name, locus, bp)
            ID, name, locus = line[1:].split('|')
            sequence = []
            recno += 1

The get_sequence() function allows us to iterate lazily over all the sequences contained in a single gzipped file. Given that the total data is 1.1 GiB, not reading it all at once is an advantage. Beyond assuming such files are gzipped, it also makes an assumption that headers are formatted in the manner of the DDBJ rather than according to a different convention or lacking headers. As I say, other tools are more robust. Let us try reading just one record to see how the function works:

fname = 'data/prokaryotes/16S.fasta.gz'
prokaryotes = get_sequence(fname)
rec = next(prokaryotes)
print(rec.recno, rec.ID, rec.name, rec.locus)
print(fill(rec.bp, width=60))
1 AB000106_1 Sphingomonas sp. 16S ribosomal RNA

In order to check each sequence/record for the subsequence duplication we are concerned about, another short function can help us. This Python code uses a Counter again, as did the matched_rna() function earlier. It simply looks at every subsequence of a given length, many thereby overlapping, and returns only those counts that are greater than 1.

def find_dup_subseq(bp, minlen=45):
    count = Counter()
    for i in range(len(bp)-minlen):
        count[bp[i:i+minlen]] += 1
    return {seq: n for seq, n in count.items() if n > 1}

Putting it together, let us look at only the first 2,800 records to see if any have the potential problem we are addressing. Given that the full dataset contains close to 1 million sequences, many more such duplicates occur. An initial range was only chosen by trial and error to find exactly two examples. Duplicate subsequences are comparatively infrequent, but not so rare as not to occur numerous times among a million sequences.

for seq in islice(get_sequence(fname), 2800):
    dup = find_dup_subseq(seq.bp)
    if dup:
        print(seq.recno, seq.ID, seq.name)
2180 AB051695_1 Pseudomonas sp. LAB-16
{'gtcgagctagagtatggtagagggtggtggaatttcctgtgtagc': 2,
 'tcgagctagagtatggtagagggtggtggaatttcctgtgtagcg': 2}
2534 AB062283_1 Acinetobacter sp. ST-550
{'aaaggcctaccaaggcgacgatctgtagcgggtctgagaggatga': 2,
 'aaggcctaccaaggcgacgatctgtagcgggtctgagaggatgat': 2,
 'accaaggcgacgatctgtagcgggtctgagaggatgatccgccac': 2,
 'aggcctaccaaggcgacgatctgtagcgggtctgagaggatgatc': 2,
 'ccaaggcgacgatctgtagcgggtctgagaggatgatccgccaca': 2,
 'cctaccaaggcgacgatctgtagcgggtctgagaggatgatccgc': 2,
 'ctaccaaggcgacgatctgtagcgggtctgagaggatgatccgcc': 2,
 'gcctaccaaggcgacgatctgtagcgggtctgagaggatgatccg': 2,
 'ggcctaccaaggcgacgatctgtagcgggtctgagaggatgatcc': 2,
 'ggggtaaaggcctaccaaggcgacgatctgtagcgggtctgagag': 2,
 'gggtaaaggcctaccaaggcgacgatctgtagcgggtctgagagg': 2,
 'ggtaaaggcctaccaaggcgacgatctgtagcgggtctgagagga': 2,
 'ggtggggtaaaggcctaccaaggcgacgatctgtagcgggtctga': 2,
 'gtaaaggcctaccaaggcgacgatctgtagcgggtctgagaggat': 2,
 'gtggggtaaaggcctaccaaggcgacgatctgtagcgggtctgag': 2,
 'taaaggcctaccaaggcgacgatctgtagcgggtctgagaggatg': 2,
 'taccaaggcgacgatctgtagcgggtctgagaggatgatccgcca': 2,
 'tggggtaaaggcctaccaaggcgacgatctgtagcgggtctgaga': 2,
 'tggtggggtaaaggcctaccaaggcgacgatctgtagcgggtctg': 2,
 'ttggtggggtaaaggcctaccaaggcgacgatctgtagcgggtct': 2}

As before, this validation only points in the direction of asking domain- and problem-specific questions, and does not determine the correct action. Subsequence duplications may indicate errors in the sequencing process, but they might also reveal something relevant about the underlying domain, and genomic mechanisms. Collisions are far too unlikely to occur by mere chance, however.


For the exercises of this chapter, we first ask you to perform a typical multi-step data cleanup using techniques you have learned. For the second exercise, you try to characterize sample bias in the provided dataset using analytic tools this book has addressed (or others of your choosing).

Data Characterization

For this exercise, you will need to perform a fairly complete set of data cleaning steps. The focus is on techniques discussed in this chapter, but concepts discussed in other chapters will be needed as well. Some of these tasks will require skills discussed in later chapters, so skip ahead briefly, as needed, to complete the tasks.

Here we return to the “Brad’s House” temperature data, but in its raw form. The raw data consists of four files, corresponding to the four thermometers that were present. These files may be found at:





The format of these data files is a simple but custom textual format. You may want to refer back to Chapter 1, Tabular Formats, and to Chapter 3, Repurposing Data Sources, for inspiration on parsing the format. Let us look at a few rows:

zcat data/glarp/lab.gz | head -5
2003 07 26 19 28 25.200000
2003 07 26 19 31 25.200000
2003 07 26 19 34 25.300000
2003 07 26 19 37 25.300000
2003 07 26 19 40 25.400000

As you can see, the space-separated fields represent the components of a datetime, followed by a temperature reading. The format itself is consistent for all the files. However, the specific timestamps recorded in each file are not consistent. All four data files end on 2004-07-16T15:28:00, and three of them begin on 2003-07-25T16:04:00. Various and different timestamps are missing in each file. For comparison, we can recall that the full data frame we read with a utility function that performs some cleanup has 171,346 rows. In contrast, the line counts of the several data files are:

for f in data/glarp/*.gz; do 
    echo -n "$f: "
    zcat $f | wc -l 
data/glarp/basement.gz: 169516
data/glarp/lab.gz: 168965
data/glarp/livingroom.gz: 169516
data/glarp/outside.gz: 169513

All of the tasks in this exercise are agnostic to the particular programming languages and libraries you decide to use. The overall goal will be to characterize each of the 685k data points as one of several conceptual categories that we present below.

Task 1: Read all four data files into a common data frame. Moreover, we would like each record to be identified by a proper native timestamp rather than by separated components. You may wish to refer forward to Chapter 7, Feature Engineering, which discusses date/time fields.

Task 2: Fill in all missing data points with markers indicating they are explicitly missing. This will have two slightly different aspects. There are some implied timestamps that do not exist in any of the data files. Our goal is to have 3-minute increments over the entire duration of the data. In the second aspect, some timestamps are represented in some data files but not in others. You may wish to refer to the Missing Data section of this chapter and the same-named one in Chapter 4, Anomaly Detection; as well, the discussion of date/time fields in Chapter 7 is likely relevant.

Task 3: Remove all regular trends and cycles from the data. The relevant techniques may vary between the different instruments. As we noted in the discussion in this chapter, three measurement series are of indoor temperatures regulated, at least in part, by a thermostat, and one is of outdoor temperatures. Whether or not the house in question had differences in thermostats or heating systems between rooms is left for readers to try to determine based on the data (at the very least though, heat circulation in any house is always imperfect and not uniform).

Note: As a step in performing detrending, it may be useful to temporarily impute missing data, as is discussed in Chapter 6, Value Imputation.

Task 4: Characterize every data point (timestamp and location) according to these categories:

  • A “regular” data point that falls within generally expected bounds.
  • An “interesting” data point that is likely to indicate relevant deviation from trends.
  • A “data error” that reflects an improbable value relative to expectations, and is more likely to be a recording or transcription error. Consider that a given value may be improbable based on its delta from nearby values and not exclusively because of absolute magnitude. Chapter 4 is likely to be relevant here.
  • A missing data point.

Task 5: Describe any patterns you find in the distribution of characterized data points. Are there temporal trends or intervals that show most or all data characterized in a certain way? Does this vary by which of four instruments we look at?

Oversampled Polls

Polling companies often deliberately utilize oversampling (overselection) in their data collection. This is a somewhat different issue than the overweighting discussed in a topic of this chapter, or than the mechanical oversampling that will be addressed in Chapter 6, Value Imputation. Rather, the idea here is that a particular class, or a value range, is known to be uncommon in the underlying population, and hence the overall parameter space is likely to be sparsely filled for that segment of the population. Alternately, the oversampled class may be common in the population but also represents a subpopulation about which the analytic purpose needs particularly high discernment.

The use of oversampling in data collection itself is not limited to human subjects surveyed by polling companies. There are times when it similarly makes sense for entirely unrelated subject domains, for example, the uncommon particles produced in cyclotrons or the uncommon plants in a studied forest. Responsible data collectors, such as the Pew Research Center that collected the data used in this exercise, will always explicitly document their oversampling methodology and expectations about the distribution of the underlying population. You can, in fact, read all of these details about the 2010 opinion survey we utilize at:


However, to complete this exercise, we prefer you skip initially consulting that documentation. For the work here, pretend that you received this data without adequate accompanying documentation and metadata (just to be clear: Pew is meticulous here). Such is all too often the case in the real world of messy data. The raw data, with no systematic alteration to introduce bias or oversampling, is available by itself at:


Task 1: Read in the data, and make a judgment about what ages were deliberately over- or undersampled, and to what degree. We may utilize this weighting in later synthetic sampling or weighting, but for now, simply add a new column called sampling_multiplier to each observation of the dataset matching your belief.

For this purpose, treat 1x as the “neutral” term. So, for example, if you believe 40-year-old subjects were overselected by 5x, assign the multiplier 5.0. Symmetrically, if you believe 50-year-olds were systematically underselected by 2x, assign the multiplier 0.5. Keep in mind that humans in the United States in 2010 were not uniformly distributed by age.

Moreover, with a sample size of about 2,000 and 75 different possible ages, we expect some non-uniformity of subgroup sizes simply from randomness. Merely random variation from the neutral selection rate should still be coded as 1.0.

Task 2: Some of the categorical fields seem to encode related but distinct binary values. For example, this question about technology is probably not ideally coded for data science goals:

pew = pd.read_csv('data/pew-survey.csv')
['New technology makes people closer to their friends and family',
 'New technology makes people more isolated',
 '(VOL) Both equally',
 "(VOL) Don't know/Refused",
 '(VOL) Neither equally']

Since the first two descriptions may either be mutually believed or neither believed by a given surveyed person, encoding each as a separate boolean value makes sense. How to handle a refusal to answer is an additional decision for you to make in this re-encoding. Determine which categorical values should better be encoded as multiple booleans, and modify the dataset accordingly. Explain and justify your decisions about each field.

Task 3: Determine whether any other demographic fields than age were oversampled. While the names of the columns are largely cryptic, you can probably safely assume that a field with qualitative answers indicating degree of an opinion are dependent variables surveyed rather than demographic independent variables. For example:

['Very happy', 'Pretty happy', 'Not too happy', "(VOL) Don't know/Refused"]

You may need to consult outside data sources to make judgments for this task. For example, you should be able to find the rough population distribution of US timezones (in 2010) to compare to the dataset distribution.

['Eastern', 'Central', 'Mountain', 'Pacific']

Task 4: Some fields, such as q1 presented in Task 3, are clearly ordinally encoded. While it is not directly possible to assign relative ratios for (Very happy:Pretty happy) versus (Pretty happy:Not too happy), the ranking of those three values is evident, and calling them ordinal 1, 2, and 3 is reasonable and helpful. You will, of course, also have to encode refusal to answer in some fashion. Re-encode all relevant fields to take advantage of this intuitive domain knowledge you have.


Quality is never an accident. It is always the result of intelligent effort.

–John Ruskin

Topics covered in this chapter: Missing Data (revisited); Bias; Class Imbalance; Normalization; Scaling; Overweighting; Cyclicity; Bespoke Validation.

In this chapter, we focused on the problem of bias in data. Datasets rarely, if ever, completely represent a population; rather they skew and select from that population to form a certain kind of picture. Sometimes this bias is intentional and well-founded as a way of filling parameter spaces. Other times it simply reflects the distribution of quantities or classes in the underlying reality. In this case, it is both the inherent virtue of our data and a pitfall in our analysis. But at other times still, elements of the data collection, collation, transcription, or aggregation can introduce biases that are more subtle and may need to be remediated in some manner for our analyses and modeling of the data. Detecting bias is the first step toward addressing it.

Related to bias, but somewhat parallel as a concern, are cyclicities in data. Very often a particular series of data—when the data is ordered in some manner, often as a time series—has components of “signal” and “variation” that can be usefully separated. A signal is, in some sense, a kind of bias, in that it provides an expectation that at time T there is a higher probability the measurement will be close to M. Identifying the signals is often an important aspect of data analysis—they are often not a priori—but identifying the deviations from the signal also provides an additional channel of interesting information.

The prior chapter on anomaly detection provided hints about identifying data that is generically statistically unlikely within a collection of values. But very often we want to look at problems that are more domain-specific. We are often able to take advantage of expectations we have about patterns in clean data that might be violated by the data we actually have. These patterns might only be represented by custom code that algorithmically expresses these expectations but that cannot be formulated in terms of generic statistical tests.

In the next chapter, we turn to the important and subtle question of imputing data.

About the Author
  • David Mertz

    David Mertz, Ph.D. is the founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created a data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks, he has turned to training our robot overlords as well. He previously worked for 8 years with D. E. Shaw Research and was also a Director of the Python Software Foundation for 6 years. David remains co-chair of its Trademarks Committee and Scientific Python Working Group. His columns, Charming Python and XML Matters, were once the most widely read articles in the Python world.

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Cleaning Data for Effective Data Science
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