### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Installs the whylogs Python package using pip. It's recommended to restart the kernel after installation if you are in an environment like Jupyter Notebook.

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

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started_with_UDFs.ipynb

Installs the whylogs library with a minimum version of 1.5.0.

```python
%pip install 'whylogs>=1.5.0'
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Getting_Started_with_WhyLabsV1.ipynb

Installs the whylogs library with version 1.5.0 or higher. This is a prerequisite for using the WhyLabs Platform integration.

```python
# Note: you may need to restart the kernel to use updated packages.
### The following WhyLabs Platform integration example requires the latest whylogs version: 
%pip install 'whylogs>=1.5.0'
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Constraints_Suite.ipynb

Installs the whylogs library with visualization capabilities.

```bash
%pip install 'whylogs[viz]'
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Logging_Different_Data.ipynb

Installs the whylogs library using pip. This is a prerequisite for running the subsequent examples.

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

--------------------------------

### Basic whylogs Profile Operations

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Examples covering fundamental whylogs functionalities such as visualizing profiles, logging data in various formats, inspecting generated metrics, configuring schema for tracking, setting data constraints, and merging profiles.

```python
# Visualizing Profiles
# Compare profiles to detect distribution shifts, visualize histograms and bar charts and explore your data
# from whylogs.viz import ProfileVisualizer
# ... (code to load and visualize profiles)
```

```python
# Logging Data
# See the different ways you can log your data with whylogs
# from whylogs import Profile, log_pandas
# ... (code to log data)
```

```python
# Inspecting Profiles
# A deeper dive on the metrics generated by whylogs
# from whylogs import Profile
# ... (code to inspect profile metrics)
```

```python
# Schema Configuration for Tracking Metrics
# Configure tracking metrics according to data type or column features
# from whylogs import Profile, Schema
# ... (code to configure schema)
```

```python
# Data Constraints
# Set constraints to your data to ensure its quality
# from whylogs import Profile, validate
# ... (code to set and validate constraints)
```

```python
# Merging Profiles
# Merge your profiles logged across different computing instances, time periods or data segments
# from whylogs import Profile
# ... (code to merge profiles)
```

--------------------------------

### whylogs Integrations with External Tools

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Examples demonstrating how to integrate whylogs with various external tools and platforms, including WhyLabs, PySpark, MLflow, BigQuery, and feature stores.

```python
# WhyLabs Integration
# Monitor your profiles continuously with the WhyLabs Observability Platform
# from whylogs.api import Logger
# logger = Logger()
# ... (code to log and send to WhyLabs)
```

```python
# PySpark Integration
# Use whylogs with pyspark
# from whylogs.api import log_spark
# ... (code to profile PySpark DataFrames)
```

```python
# Writing Profiles
# See different ways and locations to output your profiles
# from whylogs import Profile
# ... (code to write profiles to files or other destinations)
```

```python
# Flask Integration
# Create a Flask app with whylogs and WhyLabs integration
# from flask import Flask
# from whylogs.api import Logger
# app = Flask(__name__)
# logger = Logger()
# ... (code for Flask app with whylogs)
```

```python
# Feature Stores Integration
# Log features from your Feature Store with feast and whylogs
# import feast
# from whylogs.api import Logger
# ... (code to integrate with feature stores)
```

```python
# BigQuery Integration
# Profile data queried from a Google BigQuery table
# from whylogs.api import Logger
# ... (code to profile BigQuery data)
```

```python
# MLflow Integration
# Log your whylogs profiles to an MLflow environment
# import mlflow
# from whylogs.api import Logger
# ... (code to log profiles with MLflow)
```

--------------------------------

### Load Example Data with Pandas

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Getting_Started_with_WhyLabsV1.ipynb

Loads example data from a public S3 bucket into pandas DataFrames. It iterates through a series of URLs to fetch CSV files and stores them in a list.

```python
import pandas as pd

pdfs = []
for i in range(1, 8):
    path = f"https://whylabs-public.s3.us-west-2.amazonaws.com/demo_batches/input_batch_{i}.csv"
    print(f"Loading data from {path}")
    df = pd.read_csv(path)
    pdfs.append(df)
```

--------------------------------

### Install whylogs and dependencies

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/BigQuery_Example.ipynb

Installs the whylogs library with visualization capabilities, pandas-gbq for BigQuery interaction, and tqdm for progress bars.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[viz]'
%pip install pandas-gbq
%pip install tqdm
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Constraints_Suite.ipynb

Provides the command to install the whylogs library using pip.

```python
pip install whylogs
```

--------------------------------

### Write Profile to Disk

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Saves the generated whylogs profile to a binary file named 'profile.bin' on the local filesystem.

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

--------------------------------

### Install whylogs datasets module

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/ecommerce.ipynb

Installs the whylogs library with the datasets extra, which is necessary for loading and using datasets like Ecommerce.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[datasets]'
```

--------------------------------

### Install Whylogs and Dependencies

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/NLP_Summarization.ipynb

Installs necessary Python packages for the document summarization example, including whylogs with embedding support, NLTK, and BeautifulSoup.

```python
%pip install nltk
%pip install bs4
%pip install whylogs[embeddings]
```

--------------------------------

### Initialize whylogs from Command Line

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Getting_Started_with_WhyLabsV1.ipynb

Initializes whylogs from the command line, which can be used to set up the configuration for the WhyLabs Platform integration or reset existing configurations.

```bash
python -m whylogs.api.whylabs.session.why_init
```

--------------------------------

### Install whylogs and datasets

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Image_Logging_Udf_Metric.ipynb

Installs the whylogs library with all extras and the datasets library using pip.

```python
%pip install 'whylogs[all]' datasets -q
```

--------------------------------

### Install whylogs Datasets Module

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/weather.ipynb

Installs the whylogs library with the datasets extra, which is necessary for loading and using the Weather Forecast Dataset. A kernel restart might be required after installation.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[datasets]'
```

--------------------------------

### Install whylogs Datasets Module

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/employee.ipynb

Installs the whylogs library with the datasets extra, which is necessary for using dataset modules like Employee. A kernel restart might be required after installation.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[datasets]'
```

--------------------------------

### Load Pandas DataFrame

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Creates a sample Pandas DataFrame with 'animal', 'legs', and 'weight' columns to demonstrate whylogs profiling.

```python
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)
```

--------------------------------

### Read Profile from Disk

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Loads a whylogs profile from a binary file ('profile.bin'). The loaded object is a Profile View, suitable for visualization and merging, but not for further tracking.

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

--------------------------------

### Profile DataFrame with whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Logs a Pandas DataFrame using whylogs and retrieves the generated profile object. This profile contains statistical summaries of the data.

```python
import whylogs as why

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

--------------------------------

### Initialize whylogs for WhyLabs Platform

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Getting_Started_with_WhyLabsV1.ipynb

Initializes the whylogs library for logging to the WhyLabs Platform. It enables uploading statistics upon logging and requires an organization ID and access token.

```python
import whylogs as why

# Create a model in the dashboard and use that model id as the default dataset id in the prompt here. It will be
# saved in your whylogs conifg for future use. You can optionally supply reinit=True to reset your conifg. 
why.init(upload_on_log=True)
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Writing_Profiles.ipynb

Installs the whylogs library using pip. A kernel restart might be necessary after installation.

```python
import shutil

%pip install whylogs
```

--------------------------------

### Get Production Data by Number of Batches

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/employee.ipynb

Retrieves a specified number of production data batches starting from a given date. This method allows for processing data over a period.

```python
import whylogs as why
batches = dataset.get_production_data(number_batches=5)

for batch in batches:
  print("logging batch of size {} for {}".format(len(batch.data),batch.timestamp))
  profile = why.log(batch.data).profile()
  profile.set_dataset_timestamp(batch.timestamp)
  profile.view().write("batch_{}".format(batch.timestamp))
```

--------------------------------

### Python Data Logging with whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Demonstrates basic data logging using whylogs in Python. This snippet shows how to create a logger, log data, and view the results. It's a fundamental example for getting started with whylogs.

```python
from whylogs import Log

# Create a logger instance
logger = Log()

# Log a dictionary of data
data = {
    "name": "Alice",
    "age": 30,
    "city": "New York"
}
logger.log(data)

# Log another record
data2 = {
    "name": "Bob",
    "age": 25,
    "city": "Los Angeles"
}
logger.log(data2)

# View the logged data (e.g., print a summary)
print(logger.view().to_summary())
```

--------------------------------

### whylogs Configuration and Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Demonstrates how to configure and initialize whylogs, including setting up logging destinations and custom configurations.

```python
from whylogs import Profile, logger
from whylogs.core.config import YamlConfig

# Load configuration from a YAML file
config = YamlConfig.load('whylogs_config.yaml')

# Initialize logger with custom configuration
logger = logger.init(config=config)

# Create a profile using the initialized logger
profile = logger.profile()
profile.log_data(data={'col1': 1})

print(profile.view().get_column('col1').get_statistics())

```

--------------------------------

### whylogs Configuration and Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Shows how to configure and initialize whylogs, including setting up custom configurations and integrating with different backends.

```python
from whylogs import Log
from whylogs.core.config import YamlConfigLoader

# Load configuration from a YAML file
config_loader = YamlConfigLoader()
config = config_loader.load_config("path/to/your/whylogs.yaml")

# Initialize Log with custom configuration
log_with_config = Log(config=config)
log_with_config.log_data(value=20, name='configured_metric')

print(f"Configured Log JSON: {log_with_config.to_json()}")
```

--------------------------------

### Basic whylogs Usage

Source: https://github.com/whylabs/whylogs/blob/mainline/python/docs/examples.md

Demonstrates the fundamental steps of initializing whylogs, creating a dataset profile, and logging data.

```python
from whylogs import Log

# Initialize whylogs
log = Log()

# Create a dataset profile
profile = log.profile()

# Log data
profile.log_data(data={'col1': 1, 'col2': 'a'})
profile.log_data(data={'col1': 2, 'col2': 'b'})

# View the profile
print(profile.view_profile())

```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Installs the whylogs library using pip. This is the first step before using any whylogs functionalities.

```python
%pip install whylogs
```

--------------------------------

### Set up Kaggle and Download Dataset

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/whylogs_Audio_examples.ipynb

Installs the Kaggle CLI, uploads Kaggle credentials, and downloads the Free Spoken Digit Dataset (FSDD) from Kaggle. This dataset is used for audio sample data.

```shell
! pip install -q kaggle
from google.colab import files
files.upload()
```

```shell
! kaggle datasets download -d joserzapata/free-spoken-digit-dataset-fsdd
```

```shell
!unzip -q /content/free-spoken-digit-dataset-fsdd.zip -d .
```

--------------------------------

### Install MLflow and supporting libraries

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/Mlflow_Logging.ipynb

Installs MLflow and other essential libraries like pandas, scikit-learn, and matplotlib. These are used for data manipulation, model training, and visualization in the example.

```python
%pip install -q scikit-learn matplotlib pandas mlflow-skinny
```

--------------------------------

### Get Feast Version

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/Feature_Stores_and_whylogs.ipynb

Prints the installed version of the Feast library.

```python
import feast

feast.__version__
```

--------------------------------

### Install whylogs with WhyLabs extra

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Writing_Reference_Profiles_to_WhyLabs.ipynb

Installs the whylogs library, including the necessary components for interacting with the WhyLabs platform.

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

--------------------------------

### whylogs Configuration and Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

This snippet illustrates how to configure and initialize whylogs, including setting up custom configurations and integrating with other systems. It highlights the flexibility in setting up the logging environment.

```python
from whylogs import Log
from whylogs.core.config import YamlConfig

# Load configuration from a YAML file
config = YamlConfig('whylogs_config.yaml')

# Initialize whylogs with custom configuration
log = Log(config=config)

# Log data using the configured logger
data = {
    "user_id": 123,
    "event": "click"
}
log.log(data)

```

--------------------------------

### Get Baseline Timestamp

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/weather.ipynb

Retrieves the baseline timestamp from a dataset object.

```python
baseline = dataset.get_baseline()
baseline.timestamp
```

--------------------------------

### Describe DataFrame

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Getting_Started_with_WhyLabsV1.ipynb

Generates descriptive statistics for the first DataFrame in the loaded list using the .describe() method.

```python
pdfs[0].describe()
```

--------------------------------

### Install Feast and whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/Feature_Stores_and_whylogs.ipynb

Installs the necessary packages for the tutorial, including Feast and whylogs with visualization capabilities. It's recommended to restart the kernel after installation.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install --upgrade pip -qq
```

```python
%pip install feast==0.22.4 Pygments 'whylogs[viz]' -qqU
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Transaction_Examples.ipynb

Installs the whylogs library using pip.

```python
!pip install whylogs
```

--------------------------------

### Whylogs: Value Counts Example

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/tutorials/Data_Validation_Tutorial.ipynb

Demonstrates how to get value counts for a column in a Pandas DataFrame using Whylogs.

```python
df_reference['room_type'].value_counts()
```

--------------------------------

### OpenAI Model Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Demonstrates the initialization of an OpenAI model, specifying the model name and optionally the provider.

```python
OpenAIModel:
  __init__(model_name: str, provider: str = 'openai')
    model_name: The name of the OpenAI model to use
    provider: The provider to use (defaults to 'openai')
```

--------------------------------

### Access Baseline Timestamp

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/employee.ipynb

Accesses the timestamp attribute of the baseline data object, which indicates the start time of the batch.

```python
baseline.timestamp
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/whylogs_UDF_examples.ipynb

Installs the whylogs library using pip. This is the first step to using whylogs and its UDF features.

```python
%pip install whylogs
```

--------------------------------

### Get Baseline After Setting Parameters

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/ecommerce.ipynb

Retrieves the baseline data again after setting parameters, reflecting the updated timestamp configuration.

```python
baseline = dataset.get_baseline()
baseline.timestamp
```

--------------------------------

### Get Inference Data by Date

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/weather.ipynb

Retrieves a single inference data batch for a specific target date.

```python
batch = dataset.get_inference_data(target_date=now)

# Accessing batch attributes:
batch.timestamp
batch.data
batch.prediction.head()
```

--------------------------------

### Access Baseline Timestamp

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/ecommerce.ipynb

Accesses the timestamp attribute of the baseline object, indicating the start time of the baseline batch.

```python
baseline.timestamp
```

--------------------------------

### Load Pandas DataFrame

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started_with_UDFs.ipynb

Creates a sample Pandas DataFrame with 'animal', 'legs', and 'weight' columns to be used for whylogs profiling.

```python
import pandas as pd
data = {
    "animal": ["cat", "hawk", "clam", "cat", "mongoose", "octopus"],
    "legs": [4, 2, 1, 4, 4, 8],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.4, 3.2],
}

df = pd.DataFrame(data)
```

--------------------------------

### Install whylogs with Datasets and WhyLabs extras

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/writers/Writing_Regression_Performance_Metrics_to_WhyLabs.ipynb

Installs the whylogs library with the necessary extras for datasets and WhyLabs integration. This command ensures all required dependencies are available for the tutorial.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs[datasets]'
```

--------------------------------

### Install Dependencies

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/whylogs_Audio_examples.ipynb

Installs the necessary Python packages: whylogs, pyAudioAnalysis, eyed3, and pydub. This is a prerequisite for running the subsequent code.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install whylogs pyAudioAnalysis eyed3 pydub
```

--------------------------------

### Custom Metrics

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/README.md

Guides on creating your own custom metrics and metric components. This allows for highly specific data analysis tailored to unique requirements.

```python
from whylogs.core.metrics import Metric, Metrics
from whylogs.core.metrics.number import NumberType

# Example usage (actual code would be in the linked notebook)
class MyCustomMetric(Metric):
    def __init__(self):
        super().__init__(NumberType.INT)

    def merge(self, other: Metric) -> Metric:
        # Implementation for merging custom metrics
        pass

    def serialize(self) -> dict:
        # Implementation for serializing custom metrics
        pass

# ... register and use custom metric ...
# Metrics.register_metric("my_custom_metric", MyCustomMetric)
# profile.add_metric("my_custom_metric", MyCustomMetric())

```

--------------------------------

### Install whylogs and kafka-python

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/kafka-example/Kafka_Example.ipynb

Installs the necessary Python packages for whylogs and Kafka integration.

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

--------------------------------

### Log Data and Get Profile View

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Constraints_Suite.ipynb

Logs a Pandas DataFrame using whylogs and retrieves a profile view for constraint building.

```python
import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "hawk", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, 4, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

results = why.log(pd.DataFrame(data))
profile_view = results.view()
```

--------------------------------

### Initialize whylogs Logger

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/NLP_Summarization.ipynb

Initializes a PersistentLogger with a DeclarativeSchema for processing text data. This setup is used for logging article and summary data.

```python
schema = DeclarativeSchema(resolvers)
why = PersistentLogger(schema=schema)
```

--------------------------------

### OpenAI Model Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Demonstrates how to initialize an OpenAI model using its name and an optional provider. This is a fundamental step for utilizing OpenAI's services.

```python
OpenAIModel:
  __init__(model_name: str, provider: str = 'openai')
    model_name: The name of the OpenAI model to use.
    provider: The provider to use (defaults to 'openai').
```

--------------------------------

### Install Dependencies

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/sagemaker_whylabs_example/WhyLabs_Sagemaker-PyTorch.ipynb

Installs the necessary libraries: sagemaker, whylogs, and torch.

```python
%pip install sagemaker whylogs torch torchvision
```

--------------------------------

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/experimental/performance_estimation.ipynb

Installs the whylogs library along with necessary extras for dataset operations. A kernel restart might be required after installation.

```python
import sys

# Note: you may need to restart the kernel to use updated packages.
if sys.version_info >= (3, 11):
    # Use tomllib for Python 3.11+
    import tomllib
else:
    # Use tomli for older Python versions
    import tomli as tomllib

# Example of how to use tomllib or tomli if needed for configuration parsing
# with open("pyproject.toml", "rb") as f:
#     data = tomllib.load(f)

%pip install 'whylogs[datasets]'
```

--------------------------------

### Prepare Requirements File for SageMaker

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/integrations/sagemaker_whylabs_example/WhyLabs_Sagemaker-PyTorch.ipynb

Creates a requirements.txt file with whylogs installed, suitable for SageMaker deployment. It uses a temporary virtual environment.

```bash
! mkdir -p code 
! bash -c "virtualenv ./code/.venv && source ./code/.venv/bin/activate && pip install whylogs[image,proc]==1.3.8 && pip freeze > code/requirements.txt"
! rm -rf ./code/.venv

```

--------------------------------

### Whylogs Configuration and Initialization

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Basics_of_Custom_Performance_Metrics.ipynb

Shows how to configure and initialize whylogs, including setting up logging destinations and custom configurations. This is crucial for directing where and how logs are stored.

```python
import whylogs

# Initialize whylogs with default configuration
whylogs.init()

# Initialize with custom configuration (e.g., specifying a logger)
# from whylogs.core.config import WhylogsConfig
# config = WhylogsConfig(log_level='INFO', log_destination='file')
# whylogs.init(config=config)

```

--------------------------------

### Access Baseline Timestamp

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/datasets/weather.ipynb

Accesses the timestamp associated with the baseline data batch. This timestamp indicates the start time of the data batch.

```python
baseline.timestamp
```

--------------------------------

### Analyze and View Profile

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb

Converts the whylogs profile into a Profile View and then into a Pandas DataFrame for easy inspection of statistics like counters, inferred types, and distribution metrics.

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

prof_df
```

--------------------------------

### Profile DataFrame with Metric UDF

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started_with_UDFs.ipynb

Initializes whylogs and logs the DataFrame using the custom schema containing the metric UDF. The results are then viewed as a Pandas DataFrame.

```python
import whylogs as why

why.init(force_local=True)

results = why.log(df, name="udf_demo", schema=custom_schema)
results.view().to_pandas()
```