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Getting Started#

Open in Colab

whylogs provides a standard to log any kind of data.

With whylogs, we will show how to log data, generating statistical summaries called profiles. These profiles can be used in a number of ways, like:

  • Data Visualization

  • Data Validation

  • Tracking changes in your datasets

Table of Content#

In this example, we’ll explore the basics of logging data with whylogs:

  • Installing whylogs

  • Profiling data

  • Interacting with the profile

  • Writing/Reading profiles to/from disk

Installing whylogs#

whylogs is made available as a Python package. You can get the latest version from PyPI with pip install whylogs:

# Note: you may need to restart the kernel to use updated packages.
%pip install whylogs

Minimal requirements:

  • Python 3.7+ up to Python 3.10

  • Windows, Linux x86_64, and MacOS 10+

Loading a Pandas DataFrame#

Before showing how we can log data, we first need the data itself. Let’s create a simple Pandas DataFrame:

import pandas as pd
data = {
    "animal": ["cat", "hawk", "snake", "cat"],
    "legs": [4, 2, 0, 4],
    "weight": [4.3, 1.8, 1.3, 4.1],

df = pd.DataFrame(data)

Profiling with whylogs#

To obtain a profile of your data, you can simply use whylogs’ log call, and navigate through the result to a specific profile with profile():

import whylogs as why

results = why.log(df)
profile = results.profile()

Analyzing Profiles#

Once you’re done logging the data, you can generate a Profile View and inspect it in a Pandas Dataframe format:

prof_view = profile.view()
prof_df = prof_view.to_pandas()

cardinality/est cardinality/lower_1 cardinality/upper_1 counts/inf counts/n counts/nan counts/null distribution/max distribution/mean distribution/median ... frequent_items/frequent_strings type types/boolean types/fractional types/integral types/object types/string types/tensor ints/max ints/min
animal 3.0 3.0 3.00015 0 4 0 0 NaN 0.000 NaN ... [FrequentItem(value='cat', est=2, upper=2, low... SummaryType.COLUMN 0 0 0 0 4 0 NaN NaN
legs 3.0 3.0 3.00015 0 4 0 0 4.0 2.500 4.0 ... [FrequentItem(value='4', est=2, upper=2, lower... SummaryType.COLUMN 0 0 4 0 0 0 4.0 0.0
weight 4.0 4.0 4.00020 0 4 0 0 4.3 2.875 4.1 ... NaN SummaryType.COLUMN 0 4 0 0 0 0 NaN NaN

3 rows × 31 columns

This will provide you with valuable statistics on a column (feature) basis, such as:

  • Counters, such as number of samples and null values

  • Inferred types, such as integral, fractional and boolean

  • Estimated Cardinality

  • Frequent Items

  • Distribution Metrics: min,max, median, quantile values

Writing to Disk#

You can also store your profile in disk for further inspection:

why.write(profile, "profile.bin")

This will create a profile binary file in your local filesystem.

Reading from Disk#

You can read the profile back into memory with:

n_prof = why.read("profile.bin")

Note: write expects a profile as parameter, while read returns a Profile View. That means that you can use the loaded profile for visualization purposes and merging, but not for further tracking and updates.

What’s Next?#

There’s a lot you can do with the profiles you just created. Keep getting your hands dirty with the following examples!

  • Basic

  • Integrations

    • WhyLabs - Monitor your profiles continuously with the WhyLabs Observability Platform

    • Pyspark - Use whylogs with pyspark

    • Writing Profiles - See different ways and locations to output your profiles

    • Flask - See how you can create a Flask app with whylogs and WhyLabs integration

    • Feature Stores - Learn how to log features from your Feature Store with feast and whylogs

    • BigQuery - Profile data queried from a Google BigQuery table

    • MLflow - Log your whylogs profiles to an MLflow environment

Or go to the examples page for the complete list of examples!