whylogs

https://github.com/whylabs/whylogs
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### Install whylogs and dependencies

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

Install the necessary libraries to run the notebook examples.

```bash
!pip install whylogs whylabs-client pandas numpy scikit-learn
```

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

### Install dependencies

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

Install the required packages for the example project.

```bash
pip install -r requirements.txt
```

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

### Install Required Libraries

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

Install the necessary whylogs and ranking metrics libraries to get started.

```bash
pip install whylogs whylogs-container-ranking
```

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

### Install dependencies for training example

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

Install common data science libraries required for the training example.

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

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

### Install whylogs and dependencies

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

Install the necessary libraries to run the ranking metrics examples.

```bash
!pip install whylogs pandas scikit-learn
```

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

### Install and Import Dependencies

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

Install the necessary libraries and import them for use in the notebook.

```python
!pip install whylogs whylabs-client pandas numpy scikit-learn
```

```python
import pandas as pd
import numpy as np
from whylogs.experimental.core.metrics.udf_metric import register_metric
from whylogs.experimental.core.metrics.ranking_metrics import RankingMetric
import whylogs as why
from whylogs.core.schema import DatasetSchema
from whylogs.core.datatypes import String
from whylogs.core.resolvers import ResolverSpec
```

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

### Install whylogs

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

Install the whylogs library to begin working with custom metrics.

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

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

### Install Required Libraries

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

Install the necessary whylogs and ranking metrics libraries.

```bash
!pip install whylogs whylabs-client pandas scikit-learn
```

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

### Install dependencies

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

Install the necessary whylogs and datasets packages.

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

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

### Install Dependencies

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

Install the required whylogs and scikit-learn packages.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install whylogs scikit-learn==1.0.2
```

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

### Initialize Environment and Paths

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

Set up the necessary directories and import required libraries for the logging example.

```python
import os
from os import listdir
from os.path import isfile

import pandas as pd
import random
import time
import datetime
import whylogs as why

tmp_path = "example_output"
input_path = "mock_input"
cwd = os.getcwd()

if not os.path.isdir(tmp_path):
    os.makedirs(tmp_path)
    
if not os.path.isdir(input_path):
    os.makedirs(input_path)
```

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

### Install whylogs

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Profile_Store/LocalStore/LocalStore_with_Constraints.ipynb

Install the whylogs library using pip.

```python
%pip install whylogs
```

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

### Install Dependencies

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

Installs the necessary libraries for audio processing and whylogs profiling.

```python
%pip install whylogs pyAudioAnalysis eyed3 pydub
```

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

### Install whylogs and whylabs-client

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

Install the necessary packages to use whylogs and the WhyLabs client.

```bash
pip install whylogs whylabs-client
```

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

### Install required libraries

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

Install whylogs with visualization support, pandas-gbq for BigQuery integration, and tqdm.

```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 and Import Dependencies

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

Install the necessary libraries and import them for data manipulation and logging.

```python
!pip install whylogs whylabs-client pandas scikit-learn
import pandas as pd
import whylogs as why
from whylogs.core.metrics import MetricConfig
from whylogs.experimental.core.metrics.udf_metric import register_metric
from whylogs.core.preprocessing import PreprocessedColumn
```

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

### Install and Import Dependencies

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

Install the necessary libraries and import whylogs along with other required modules.

```python
!pip install whylogs whylabs-client pandas numpy scikit-learn

import pandas as pd
import numpy as np
import whylogs as why
from whylogs.experimental.core.metrics.udf_metric import register_metric
from whylogs.core.metrics.metrics import Metric, MetricConfig, OperationResult
from whylogs.core.preprocessing import Preprocessor
from whylogs.core.resolvers import Resolver
from whylogs.core.schema import DeclarativeSchema
from whylogs.core.segmentation_partition import segment_on_column
from whylogs.experimental.core.metrics.ranking_metrics import RankingMetric
```

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

### Install whylogs and dependencies

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

Install the necessary libraries to enable ranking metrics and WhyLabs integration.

```bash
!pip install whylogs[viz] pandas scikit-learn
```

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

### Install whylogs and pandas

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

Install the necessary libraries for data profiling and manipulation.

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

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

### Defining and Tracking Custom Performance Metrics

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

This example demonstrates the basic setup for creating a custom metric and integrating it into a whylogs profile.

```python
import whylogs as why
from whylogs.core.metrics import Metric, MetricConfig
from whylogs.core.resolvers import Resolver

# Define a custom metric class
class CustomPerformanceMetric(Metric):
    def __init__(self, config: MetricConfig = None):
        super().__init__(config)
        self.value = 0

    def add(self, data):
        # Custom logic to update the metric
        self.value += 1

# Usage example
profile = why.log(data={"feature": [1, 2, 3]})
print(profile.view().get_column("feature").get_metric("custom_metric"))
```

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

### Install Dependencies

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

Install the whylogs and kafka-python libraries required for the profiling workflow.

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

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

### Install graphviz

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

Install graphviz to enable visualization of the task graph.

```python
%pip install -q graphviz
```

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

### Install Required Libraries

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

Install the necessary whylogs and whylabs-client libraries to enable metric logging.

```bash
!pip install whylogs whylabs-client pandas numpy
```

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

### Install and Import Dependencies

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

Install the necessary libraries and import the required modules for data handling and metric calculation.

```python
!pip install whylogs pandas scikit-learn
import pandas as pd
import whylogs as why
from whylogs.experimental.core.metrics.udf_metric import register_metric
from whylogs.core.metrics.metrics import Metric, MetricConfig
from whylogs.core.resolvers import ResolverSpec
from whylogs.core.schema import DeclarativeSchema
```

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

### Install whylogs library

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

Installs the whylogs library with visualization dependencies.

```python
%pip install -q 'whylogs[viz]'
```

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

### Install Required Packages

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

Install the necessary dependencies for Sagemaker, whylogs, and PyTorch.

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

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

### Install whylogs

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

Install the required whylogs library version.

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

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

### Install required packages

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

Install whylogs and scikit-learn to handle logging and metric calculation.

```bash
!pip install whylogs scikit-learn
```

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

### Install dependencies

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

Install the necessary packages for Dask and whylogs visualization.

```python
%pip install dask 'whylogs[viz]'
```

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

### Install Dependencies

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

Install the necessary libraries for data processing, machine learning, and web serving.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install pandas utils joblib scikit-learn Flask
%pip install whylogs
```

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

### Install dependencies

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

Install the required packages for the project, including the optional process-based logger for Mac/Linux.

```bash
pip install -r requirements.txt

# Install the proc extra as well if you're going to use the process based logger. This only suports Mac/Linux at the moment.
pip install whylogs[proc]
```

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

### Install required packages

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

Install or upgrade pip and the necessary libraries for Feast and whylogs.

```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
```

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

### v1 Programmatic Configuration

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

Example of the new v1 configuration style using Python code.

```python
import whylogs as why


```

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

### Install S3 dependencies

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

Install the required whylogs S3 extra and the moto library for mocking.

```python
%pip install -q 'whylogs[s3]' moto
```

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

### Install and Import Required Libraries

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

Install the necessary whylogs and whylabs-client libraries and import the required modules for data processing and logging.

```python
!pip install whylogs whylabs-client pandas numpy scikit-learn
import pandas as pd
import numpy as np
from whylogs.experimental.core.metrics.udf_metric import register_metric
from whylogs.core.metrics.metrics import Metric, MetricConfig
from whylogs.core.preprocessing import Preprocessor
from whylogs import DatasetProfile
import whylogs as why
```

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

### Install whylogs with visualization support

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

Install the necessary package to enable whylogs functionality and visualization.

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

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

### Install piexif dependency

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

Install the piexif library to facilitate EXIF tag manipulation.

```python
%pip install piexif
```

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

### Install whylogs with WhyLabs support

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

Install the whylogs library with the whylabs extra to enable platform integration.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'whylogs>=1.5.0'
```

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

### Describe example data

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

Displays summary statistics for the first loaded data batch.

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

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

### Setup Cardinality Constraints

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

Initialize a dataset profile and apply a cardinality constraint to a specific column.

```python
import whylogs as why
import pandas as pd
data = {
    "animal": ["cat", "snake", "snake", "cat", "mosquito"],
    "legs": [4, 2, 0, None, 6],
    "weight": [4.3, 1.8, 1.3, 4.1, 5.5e-6],
}

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

```python
builder = ConstraintsBuilder(dataset_profile_view=profile_view)
builder.add_constraint(distinct_number_in_range(column_name = "animal", lower = 3, upper = 6))

constraints = builder.build()

from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.constraints_report(constraints, cell_height=300)
```

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

### Install whylogs with Image Support

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

Install the necessary dependencies for image logging by including the image and whylabs extras.

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

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

### Initialization Invocation

Source: https://github.com/whylabs/whylogs/blob/mainline/python/whylogs/viz/html/templates/index-hbs-cdn-all-in-for-jupyter-notebook.html

Invokes core setup functions for the template.

```javascript
// Invoke functions -- keep in mind invokation order registerHandlebarHelperFunctions(); initHandlebarsTemplate(); initWebsiteScripts();
```

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

### Calculate Ranking Metrics

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

Example of calculating ranking metrics using scikit-learn.

```python
from sklearn.metrics import ndcg_score
import numpy as np

# Example ground truth and predicted scores
y_true = np.asarray([[1, 2, 3]])
y_score = np.asarray([[1, 3, 2]])

ndcg = ndcg_score(y_true, y_score)
print(f"NDCG: {ndcg}")
```

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

### Install whylogs library

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

Install the whylogs package using pip. A kernel restart may be required after installation.

```python
# Note: you may need to restart the kernel to use updated packages.
import shutil

%pip install whylogs
```

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

### Install whylogs datasets module

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

Installs the necessary dependencies for using whylogs datasets. A kernel restart may be required after installation.

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

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

### Install whylogs PySpark dependency

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

Installs the necessary extra dependencies for using whylogs with PySpark. A kernel restart may be required after installation.

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

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

### Initialize LocalStore

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/advanced/Profile_Store/LocalStore/LocalStore_with_Constraints.ipynb

Instantiate the LocalStore and list existing profiles to verify the store state.

```python
from whylogs.api.store import LocalStore

store = LocalStore()
store.list()
```

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

### Initialize WhyLabs Client

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

Set up the WhyLabs client with your credentials to enable data logging.

```python
import os
from whylabs.client import WhyLabsClient

# Set your credentials
os.environ["WHYLABS_API_KEY"] = "YOUR_API_KEY"
os.environ["WHYLABS_DEFAULT_ORG_ID"] = "YOUR_ORG_ID"
os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "YOUR_DATASET_ID"

client = WhyLabsClient()
```

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

### Start Kafka Services and Create Topic

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

Commands to initialize the local Kafka cluster and create a target topic for data ingestion.

```bash
$ docker-compose up -d

% docker exec -ti kafka bash

root@kafka: kafka-topics --create --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1 --topic whylogs-stream
```

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

### Install whylogs library

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

Install the whylogs package via pip.

```python
!pip install whylogs
```

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

### Install whylogs

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

Use pip to install the whylogs package in your terminal.

```bash
pip install whylogs
```

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

### Prepare Data and Log Metrics

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

Create a sample dataset and log the ranking metrics using a WhyLabs profile.

```python
data = {
    "query_id": [1, 1, 1, 2, 2, 2],
    "relevance": [3, 1, 2, 0, 2, 1],
    "prediction": [0.9, 0.8, 0.7, 0.1, 0.6, 0.5]
}
df = pd.DataFrame(data)

profile = why.log(df)
profile.view().get_column("relevance").get_metric("ranking")
```

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

### Initialize NotebookProfileVisualizer

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

Sets up the visualizer with the target and reference profile views.

```python
from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.set_profiles(target_profile_view=prof_view, reference_profile_view=prof_view_ref)
```

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

### Initialize NotebookProfileVisualizer

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

Sets up the visualizer with target and reference profile views for drift analysis.

```python
from whylogs.viz import NotebookProfileVisualizer

visualization = NotebookProfileVisualizer()
visualization.set_profiles(target_profile_view=target_profile_2.view(), reference_profile_view=reference_profile.view())
```

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

### Run the API server

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

Start the FastAPI server using uvicorn after configuring WhyLabs variables.

```bash
uvicorn api:app --reload
```

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

### Install whylogs datasets

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

Install the necessary dependencies for using the datasets module.

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

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

### Install pandas dependency

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

Install the pandas library if it is not already present in the environment.

```python
# Install pandas if you don't have it already
%pip install pandas
```

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

### Install GCS dependencies

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

Install the necessary emulator and whylogs GCS integration packages.

```python
%pip install -q gcp-storage-emulator 'whylogs[gcs]'
```

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

### Install whylogs library

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

Installs the required version of whylogs for 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'
```

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

### Initialize whylogs and WhyLabs writer

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

Set up the whylogs session and configure the WhyLabs writer to send profiles to the platform.

```python
import whylogs as why
from whylogs.api.writer.whylabs import WhyLabsWriter
import os

# Set your WhyLabs credentials
os.environ["WHYLABS_DEFAULT_ORG_ID"] = "org-xxxx"
os.environ["WHYLABS_DEFAULT_DATASET_ID"] = "model-xxxx"
os.environ["WHYLABS_DEFAULT_API_KEY"] = "xxxx"

writer = WhyLabsWriter()
profile = why.log(data={"ranking_score": 0.85})
writer.write(file=profile.view())
```

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

### Install whylogs MLflow integration

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

Install the whylogs package with MLflow support enabled.

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

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

### Initialize GCS emulator

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

Start a local GCS emulator server and configure the storage client to point to it.

```python
import random
import socket


def find_free_port(preferred_port, min_port, max_port):
    def is_port_free(port):
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
            try:
                s.bind(('localhost', port))
                return True
            except OSError:
                return False

    if is_port_free(preferred_port):
        return preferred_port
    else:
        while True:
            port = random.randint(min_port, max_port)
            if is_port_free(port):
                return port

HOST = "localhost"
PORT = find_free_port(9023, 9000, 9100)
GCS_BUCKET = "test-bucket"

server = create_server(HOST, PORT, in_memory=True, default_bucket=GCS_BUCKET)
server.start()

os.environ["STORAGE_EMULATOR_HOST"] = f"http://{HOST}:{PORT}"
client = storage.Client()
bucket = client.bucket(GCS_BUCKET)

for blob in bucket.list_blobs():
    content = blob.download_as_bytes()
    print(f"Blob [{blob.name}]: {content}")
```

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

### Install required packages

Source: https://github.com/whylabs/whylogs/blob/mainline/python/examples/benchmarks/KS_Profiling.ipynb

Installs the necessary libraries for data visualization and statistical analysis.

```python
# Note: you may need to restart the kernel to use updated packages.
%pip install 'matplotlib' 'numpy' 'seaborn==0.12.1'
%pip install 'scipy==1.7.3' 'whylogs[viz]' 'typing_extensions'
```
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