clevertable 3.1.0

Low effort conversion of tabular data into numerical values.

CleverTable

Consistent, intelligent transformation of text-based tabular data into numerical data.
Minimal configuration required.

Installation:

pip install clevertable

Example:

from clevertable import *

profile = ConversionProfile({
    # optionally specify converters for specific columns:
    "Country": OneHot(),
    "Diagnosis": Binary(positive="cancer", negative="benign"),
    "Hospitalized": None,  # ignore column
}, pre_processing=None)

df = profile.fit_transform("datasets/survey.xlsx")  # transformed pandas.DataFrame

Why this Library?

• CleverTable makes it really easy to convert text-based tabular data (optionally mixed with numbers) into numerical data, e.g. a medical survey into a Pandas DataFrame or a NumPy array.
• If something is obvious, you should not need to specify it. CleverTable will try to make choices for you if you don't make them.
• You stay in control: All choices made by CleverTable can be modified and overridden.

This is how CleverTable works: (see below for a full tutorial)

1. You create a new profile = ConversionProfile(). Here, you can optionally specify certain converters.
2. You call profile.fit(data) on a sample data set, which creates a fixed conversion profile.
   • CleverTable chooses the best converter for each column if you don't specify it.
   • The converter (chosen by you or by CleverTable) adapts its internal state to fit the data.
3. You call profile.transform(data) on the actual data set (which may be the same as for fit()), which converts the data according to the fixed profile.

Here are some examples on what you can do with CleverTable:

• Chain multiple converters to achieve complex conversions:

  profile["Column 7"] = [
      Split(),
      ForEach(Strip()),
      Flatten(),
      Infer()  # Infer() -> CleverTable will choose what to put here
  ]
  

• Use the Infer() converter where you want CleverTable to figure out the best solution (see above).
• Concise shorthand writings with Python syntax:

  profile["Column 1"] = [  # Python lists create pipelines
    str.lower,             # functions /
    lambda s: s.strip(),   # lambda expressions are allowed
  ]
  profile["Column 2"] = {"Hello": 1, "Bye": 2}
  profile["Column 3"] = Float(), 1  # tries conversion to float, defaults to 1 on error
  

• Incremental configuration: If a column already has a correct converter, you can further process the column by adding another converter. This implicitly creates a pipeline.

  profile["Column 5"] += OneHot()
  

• After fit(), you can access the inferred state of the converters.

  my_weather_conv = profile["Weather"]            # e.g. OneHot()
  my_weather_categories = my_weather_conv.values  # e.g. ["sunny", "cloudy", "rainy"]
  

• Send multiple columns into one converter:

  profile["Column 1", "Column 2"] = max
  

• Send nested columns into one converter:

  profile[("Column A", "Column B"), "Column C"] = [Parallel(max, floor), min]  # min(max(A, B), floor(C))
  

Tutorial

Suppose you want to convert the following table of survey results in a 2D numpy array of numbers:

Country	Age	Diagnosis	Hospitalized	Education level	Symptoms
China	32	benign	no	University	cough, fever
France	45	cancer	yes	PhD	fever
Italy	19	benign	yes	High School	cough
Germany	56	cancer	yes	High School	fever and cough
Nigeria	23	benign	no	University	cough
India	34	benign	yes	University	cough, fever
...	...	...	...	...	...

For example, you might want to convert the Country column into a column of integers, with every integer representing a different country.
However:

• You don't really care which number represents which country.
• But you want to make sure that the same country always gets the same number, even if you add more data to the table later.
• You also want to know which integer was chosen for which country.

That's what CleverTable is for:

• First, you call fit() on a sample data set, which creates a fixed conversion profile.
• Then, you call transform() on the actual data set, and it converts the data according to the fixed profile.

Moreover, CleverTable does many things automatically:

• It chooses the best converter if you don't specify it.
• And then, the converter also adapts its internal state to fit the data.

Let's see how that works:

from clevertable import *

table = "datasets/survey.xlsx"  # filename or pandas.DataFrame

profile = ConversionProfile()
profile.fit(table)  # chooses best converters and creates a fixed conversion profile

print(profile) will show the inferred conversion profile:

{
    "Country": Enumerate('china', 'france', 'germany', ...),  # lots of countries
    "Age": Float(),
    "Diagnosis": Binary(),
    "Hospitalized": Binary(),
    "Education level": OneHot('high school', 'phd', 'university'),
    "Symptoms": ListAndOr(),
}

We can access the individual converters and their properties by indexing the profile with the column name:

country_converter = profile["Country"]  # Enumerate('china', 'france', 'germany', ...)

# see which integer corresponds to which country:
countries_list = country_converter.values  # ('china', 'france', 'germany', ...)

You can now use this profile to convert data:

# transform the whole table:
df = profile.transform(table)  # pandas.DataFrame
arr = df.to_numpy()  # 2D numpy array

# transform a single data point:
data_point = {"Country": "Germany"}
transformed = profile.transform_single(data_point)  # {'Country': 2}

The nice thing is that you can now use the fixed profile to find out after conversion where the numerical values originated from:

# find out which country corresponds to the number 2:
country_id = 2
country = profile["Country"].values[country_id]  # 'germany'

You may have noticed that all the strings appear in lowercase. That is because the ConversionProfile pre-processes all strings to lowercase by default. You can disable this behavior by passing pre_processing=None to the constructor or setting this property after construction:

profile.pre_processing = None  # disable pre-processing
profile.pre_processing = str.lower  # default behavior
profile.pre_processing = lambda s: s.strip().lower()

It's okay to provide a pre-processing function that doesn't work for some entries (e.g. str.lower will fail for non-string entries), because CleverTable will catch errors and ignore them during pre-processing.

You may also have noticed that the Education level column was converted to OneHot(), even though it contains arbitrary words, just like the Country column. That's because CleverTable detected that there are too many different values in the Country column for a OneHot() converter, so it chose the Enumerate() converter.

But you can always override this behavior by explicitly setting the conversion method before calling fit():

from clevertable import *

table = "datasets/survey.xlsx"

profile = ConversionProfile()

# explicitly specify some converters:
profile["Country"] = OneHot()
profile["Diagnosis"] = Binary(positive="cancer", negative="benign")

profile.fit(table)

In this example, we also made sure that the "Diagnosis" column is choosing the correct positive and negative values.

You can also achieve the same by passing a dictionary to the constructor:

from clevertable import *

table = "datasets/survey.xlsx"

profile = ConversionProfile({
    "Country": OneHot(),
    "Diagnosis": Binary(positive="cancer", negative="benign"),
}).fit(table)  # fit() returns self

Two final notes:

• You can ignore columns by setting their converter to None (which is shorthand for the Ignore() converter).
• You can use fit_transform() to perform fit() and transform() with the same data in one call.

This leaves us with this very concise code:

from clevertable import *

df = ConversionProfile({
    "Country": OneHot(),
    "Diagnosis": Binary(positive="cancer", negative="benign"),
    "Hospitalized": None,
}, pre_processing=None).fit_transform("datasets/survey.xlsx")

Which produces the following transformed table:

Country=China	Country=France	...	Country=Zimbabwe	Age	Diagnosis	Education level=High School	Education level=PhD	Education level=University	Symptoms=cough	Symptoms=fever
1	0	...	0	32	0	0	0	1	1	1
0	1	...	0	45	1	0	1	0	0	1
0	0	...	0	19	0	1	0	0	1	0
0	0	...	0	56	1	1	0	0	1	1
0	0	...	0	23	0	0	0	1	1	0
0	0	...	0	34	0	0	0	1	1	1

CLI

pip install clevertable also makes the command clevertable available in the command line. It can convert files with tabular data. Execute clevertable --help to see what arguments can be passed to the tool:

usage: clevertable [-h] [-i IGNORE [IGNORE ...]] src out

Consistent and intelligent conversion of tabular data into numerical values.

positional arguments:
  src                   Path to input file.
  out                   Path to output file.

optional arguments:
  -h, --help            show this help message and exit
  -i IGNORE [IGNORE ...], --ignore IGNORE [IGNORE ...]
                        Column names to ignore.

How to Contribute

Basic workflow of contribution:

• Fork the repository
• Create a new branch
• Make your changes
• Create a pull request
• Wait for the pull request to be accepted or rejected
• If accepted, you can delete your branch
• If rejected, make the requested changes and push them to your branch
• Repeat until pull request is accepted

What to contribute:

• New converters (classes that inherit from Converter)
• Improvements to converter inference (logic in Infer() converter)
• Improvements to default preprocessing
• Make more features available through the CLI
• New tests
• New documentation, tutorials, examples
• New ideas, suggestions, bug reports → create an issue or contact me directly

Documentation

There are only two classes that:

• ConversionProfile: A collection of converters.
• Converter: Transforms columns of data into columns of data.

Converters

Here's a quick overview of all converters:

Converters	Description	Shorthand	Example Usage
Basic:
Float()	Convert numbers into floats.
Enumerate()
OneHot()
Binary()	Convert to 0 and 1. Detects common "positive" and "negative" terms in strings.
List()
ListAndOr()
Map()		dict	{   "foo": 1,   "bar": -2, }
Const()	Return a constant value.	any	42 "foo"
Text Processing:
Strip()
Split()
Combining Converters:
Pipeline()	Apply multiple converters in sequence.	list	[   Split(),   ForEach(Strip()), ]
Try()	Try multiple converters and return the first one that succeeds.	tuple	(Float(), Binary())
ForEach()	Apply the same converter to all items.
Parallel()	Apply different converters to the respective items.
Special:
Id()
Ignore()	Drop the column.	None	None
Infer()
Label()
Dimensionality:
Flatten()	Flatten a tuple of tuples into a single tuple. This is often needed after ForEach() or Parallel().
Transpose()
Arbitrary Functions:
Function()	Apply a user-defined function to the data.	callable	lambda x: x**2
StrictFunction()	Apply a user-defined function to the data. Less flexible than Function().

---

Float

Converts a column of numbers into a column of numbers. If invalid values are encountered (NaN, inf, None, etc.), a warning is printed and the value is replaced with np.nan. This can be circumvented by passing a value to the default argument:

"Temperature": Float(default=37.0)

You can also specify "mean", "median", or "mode" as the default value. This will choose the default value based on the data in the specified column:

"Temperature": Float(default="mean")

Temperature	⇒	Temperature
37.5		37.5
40.0		40.0
38.5		38.5
		38.75
39.0		39.0

Results in:

"Temperature": Float(default=38.75)

---

Enumerate

This is the extension of the Binary() conversion method to columns with more than two possible values. The values are converted into integers starting at 0, resulting in a single column of integers.

The possible values can be passed to the constructor:

"Country": Enumerate("france", "germany", "italy")

Country	⇒	Country
france		0
italy		2
germany		1

Their index in the argument list is used as the numerical value. If no values are specified, the values found in the provided data are sorted in lexically ascending order.

---

OneHot

If each entry contains one of multiple possible values. The possible values can be specified via the values argument:

"Education Level": OneHot("primary", "secondary", "tertiary")

Education Level	⇒	Education Level=primary	Education Level=secondary	Education Level=tertiary
primary		1	0	0
secondary		0	1	0
tertiary		0	0	1

If no values are specified, the possible values are inferred from the data.

---

Binary

Similar to Enumerate(), but with just two possible values, and with some extra intelligence for this purpose. For example, it can detect words commonly used for positive and negative values:

• Positive: yes, true, positive, 1, female
• Negative: no, false, negative, 0, male, none

Example:

"Hospitalized": Binary()

Hospitalized	⇒	Hospitalized
no		0
yes		1
false		0
true		1
none		0

Results in:

"Hospitalized": Binary(positive={"yes", "true"},
                       negative={"no", "false", "none"})

You can explicitly specify the values of the positive class and the negative class via the constructor:

"Hospitalized": Binary(positive="yes", negative="no")

Hospitalized	⇒	Hospitalized
yes		1
no		0
no		0
yes		1

If only one argument is specified (either positive or negative), all other values present in the data are treated as instances of the other class:

"Time served": Binary(negative="none")

Time served	⇒	Time served
none		0
1 year		1
4 years		1
none		0

It's also possible to specify more than one value for the positive and negative classes. Example:

"Hospitalized": Binary(positive={"yes", "true"}, negative={"no", "false"})

Hospitalized	⇒	Hospitalized
yes		1
no		0
false		0
true		1

If no positive or negative values are specified, a set of strings commonly used to indicate positive / negative values is tested against the available data. For instance, in the example above, the specified arguments would have been inferred automatically as positive and negative.

If this approach is not successful, the lexically smallest value is chosen as the negative argument and the positive argument is left empty, causing all other values to be treated as positive:

"Fruits": Binary()

Fruits	⇒	Fruits
banana		1
apple		0
kiwi		1
apple		0

Results in:

"Fruits": Binary(negative="apple")

---

List

Converts lists of values into multiple binary columns.

"Symptoms": List()

Symptoms	⇒	Symptoms=cough	Symptoms=fever	Symptoms=headache
fever, cough, headache		1	1	1
headache, cough		1	0	1

The default delimiter is a comma.
You can specify a custom delimiter via the delimiter argument:

"Symptoms": List(delimiter=";")
"Symptoms": List(delimiter=[",", ";"])  # also accepts lists

The passed strings are interpreted as regular expressions.

ListAndOr

"Symptoms": ListAndOr()

Symptoms	⇒	Symptoms=cough	Symptoms=fever	Symptoms=headache
fever, cough and headache		1	1	1
headache or cough		1	0	1

The default delimiters are comma, "and" and "or".
The passed strings are interpreted as regular expressions.

Map

Strip

Split

Pipeline

Try

Try(converter1, converter2, ...)

Returns value of the first converter that does not raise an exception, or the original value if all converters raise an exception. Try() always only applies one converter and returns its output (if it didn't fail).

"Product": Try(Float(), Infer())  # will infer the converter for the samples that cannot be converted to floats

Product	⇒	Product
Kiwi		48
Apple		0
712356		712356
261382		261382
Banana		1
Kiwi		48
...		...

This would result in the following profile after fit():

"Product": Try(Float(), Enumerate("Apple", "Banana", ...))

ForEach

Apply the same converter to all items.

Parallel

Parallel(converter1, converter2, ...)

Apply different converters to the respective items. Usually used in a Pipeline after other converters that create outputs with multiple items (e.g. Split()). Also, you usually want to use Flatten() after this, as each individual converter will return a tuple of items, even if it only contains one item. Example:

"Latitude;Longitude": [
    Split(";"),  # must always result in two items, because Parallel() has 2 converters
    Parallel(Ignore(), Float()),  # ignore latitude, convert longitude to float -> [[], [longitude]]
    Flatten(),  # -> [longitude]
]

Latitude;Longitude	⇒	Longitude
52.520008;13.404954		13.404954
48.137154;11.576124		11.576124

Const

Id

Identity. Keeps the input unchanged.

Ignore

Drops the column.

"registration_timestamp": None

This is chosen if no appropriate conversion method could be found.

Infer

Tries to infer the conversion method from the column name. After fit(), this converter will be replaced with the inferred converter in the profile.

This is the default converter for columns where no converter is specified. This converter can however also be used anywhere else explicitly. Examples:

"col1": [
    str.upper,
    Infer()
],
"col2": Try(Float(), Infer()),  # will infer the converter for the samples that cannot be converted to floats

Label

Flatten

Transpose

Can transpose nested tuples, given that the nested tuples are of equal length.

For example, look at this elegant implementation of the List() converter:

"Symptoms": [
    Split(r"\s*,\s*"),  # split at comma
    ForEach(OneHot()),
    Transpose(),
    ForEach(max),
    Flatten()
]

Transpose() allows us to apply max to each column of the one-hot encodings across all tuple elements.

Function

Function(transform, labels=None)

Shorthand: Instead of Function(transform, None), just write transform, where transform is some callable.

Creates a custom converter from a custom transform() function (and optionally, a custom labels() function). This is a handy way to create a converter that doesn't need fit().

Unlike StrictFunction(), this class can handle functions that don't accept or return tuples, which often allows for more concise code.

This is achieved during fit() as follows:

1. If all incoming items are 1-element tuples, it sets a flag UNPACK_OUTPUT to always unpack the element before passing them to the wrapped function during transform().
2. If during fit() the wrapped function doesn't return tuples, it tries to turn that output into a tuple:
   • If the output is always a non-string iterable, it will simply set a flag CONVERT_ITERABLE to always convert the iterable output into a tuple during transform().
   • Otherwise, it sets a flag WRAP_OUTPUT to always wrap the output in a 1-element tuple during transform().

A similar logic is applied to the labels. If a custom labels function is given, the following procedure is followed during labels():

1. If the incoming labels are a 1-element tuples, the single label is unpacked before it is passed to the custom labels function.
2. If the custom labels function returns something other than a tuple, this class tries to convert it into a tuple:
   • If the output is a non-string iterable, it is converted into a tuple.
   • Otherwise, the output is wrapped in a 1-element tuple.

If no custom labels function is given, the output labels are generated based on the output cardinality inferred during fit() and according to the following logic:

• If the number of incoming labels is identical to the output cardinality, the labels will be returned unchanged.
• If there are multiple incoming labels but a single output label, the output label is formed by joining the incoming labels with , .
• If there is a single incoming label but multiple output labels, the output labels are formed by adding suffixes _0, _1, etc. to the single input label.

	1 Output Label	M Output Labels
1 Input Label	identical	suffixes _0, _1, ...
N Input Labels	join with ,	identical if M=N, else ERROR

A special case are functions returning output of varying cardinality during fit(). In this case, a single label is returned. If only one input label is given, that single label is returned. If multiple input labels are given, they are joined with _.

The following example turns a text column into two columns containing the ascii code of the first and last letter.

"Name": lambda x: (ord(x[0]), ord(x[-1]))

Name	⇒	Name_0	Name_1
Alice		97	101
Bob		98	98

(Remember that by default, all text entries are converted to lowercase before further processing.)

As you can see, the number of columns is inferred directly from the return value of the conversion function. If the function returns a tuple, the resulting column names are indexed.

You can also set the labels explicitly with a lambda function that takes the input column name as an argument and returns output column names:

"Name": Function(lambda x: (ord(x[0]), ord(x[-1])),
                 labels=lambda s: (f"ord(first letter of {s})", f"ord(last letter of {s})")),

Name	⇒	ord(first letter of Name)	ord(last letter of Name)
Alice		97	101
Bob		98	98

However, remember that you can always simply use Label() to rename the columns after the conversion, if you don't need the output column names to depend on the input column names.

"Name": [lambda x: (ord(x[0]), ord(x[-1])),
         Labels("ord(first letter)", "ord(last letter)")],

StrictFunction

StrictFunction(transform, labels=None)

Works mostly like Function(), but simpler: transform and labels must both accept and return tuples. Instead of something like this:

"Name": str.lower,

you have to write this:

"Name": StrictFunction(lambda x: (str.lower(x[0]),))  # notice the comma, which makes it a 1-element tuple

That is, you will still receive 1-element tuples as tuples to the function, even if all input elements during fit() are 1-element tuples. Also, you must now explicitly return a tuple, even if it is just a 1-element tuple, as otherwise an error will be raised.

See Function() for a convenient extension of this converter.

---

Understanding Multi-Column Converters

A converter returns two things:

• transform(): the items of the transformed data
• labels(): a label for each item

Both return values are tuples.

For top-level converters, this then creates the corresponding amount of columns. This includes the case of

• a 1-element tuple (item,), which is the case for most converters.
• an empty tuple (), in which the result is ignored. (In fact, this is exactly how Ignore() is implemented.)

This means, however, that for top-level converters, labels() and transform() must return the same number of items. That is because labels() is used to create the output column names. If transform() returns a different number of items, that will raise an error for top-level converters.

However, for nested converters, labels() and transform() can return different numbers of items. For example Split.labels() always returns only one item, because the number of items returned by Split.transform() varies from input to input. Therefore, Split() can't be used as a top-level converter and has to be used inside a Pipeline or similar devices, so that other converters can ensure that the final output is of constant size.