#!/usr/bin/env python
# coding: utf-8

# >### 🚩 *Create a free WhyLabs account to get more value out of whylogs!*<br> 
# >*Did you know you can store, visualize, and monitor whylogs profiles with the [WhyLabs Observability Platform](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Getting_Started)? Sign up for a [free WhyLabs account](https://whylabs.ai/whylogs-free-signup?utm_source=whylogs-Github&utm_medium=whylogs-example&utm_campaign=Getting_Started) to leverage the power of whylogs and WhyLabs together!*

# # Getting Started

# [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/whylabs/whylogs/blob/mainline/python/examples/basic/Getting_Started.ipynb)

# 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`:

# In[1]:


# Note: you may need to restart the kernel to use updated packages.
get_ipython().run_line_magic('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:

# In[2]:


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()`:

# In[3]:


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:

# In[6]:


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

prof_df


# 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:

# In[7]:


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:

# In[8]:


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
#     - [Visualizing Profiles](https://whylogs.readthedocs.io/en/stable/examples/basic/Notebook_Profile_Visualizer.html) - Compare profiles to detect distribution shifts, visualize histograms and bar charts and explore your data
#     - [Logging Data](https://whylogs.readthedocs.io/en/stable/examples/basic/Logging_Different_Data.html) - See the different ways you can log your data with whylogs
#     - [Inspecting Profiles](https://whylogs.readthedocs.io/en/stable/examples/basic/Inspecting_Profiles.html) - A deeper dive on the metrics generated by whylogs
#     - [Schema Configuration for Tracking Metrics](https://whylogs.readthedocs.io/en/stable/examples/basic/Schema_Configuration.html) - Configure tracking metrics according to data type or column features
#     - [Data Constraints](https://whylogs.readthedocs.io/en/stable/examples/advanced/Metric_Constraints.html) - Set constraints to your data to ensure its quality
#     - [Merging Profiles](https://whylogs.readthedocs.io/en/stable/examples/basic/Merging_Profiles.html) - Merge your profiles logged across different computing instances, time periods or data segments
# - Integrations
#     - [WhyLabs](https://whylogs.readthedocs.io/en/stable/examples/integrations/writers/Writing_to_WhyLabs.html) - Monitor your profiles continuously with the WhyLabs Observability Platform
#     - [Pyspark](https://whylogs.readthedocs.io/en/stable/examples/integrations/Pyspark_Profiling.html) - Use whylogs with pyspark
#     - [Writing Profiles](https://whylogs.readthedocs.io/en/stable/examples/integrations/writers/Writing_Profiles.html) - See different ways and locations to output your profiles
#     - [Flask](https://whylogs.readthedocs.io/en/stable/examples/integrations/flask_streaming/flask_with_whylogs.html) - See how you can create a Flask app with whylogs and WhyLabs integration
#     - [Feature Stores](https://whylogs.readthedocs.io/en/stable/examples/integrations/Feature_Stores_and_whylogs.html) - Learn how to log features from your Feature Store with feast and whylogs
#     - [BigQuery](https://whylogs.readthedocs.io/en/stable/examples/integrations/BigQuery_Example.html) - Profile data queried from a Google BigQuery table
#     - [MLflow](https://whylogs.readthedocs.io/en/stable/examples/integrations/Mlflow_Logging.html) - Log your whylogs profiles to an MLflow environment
# 
# Or go to the [examples page](https://whylogs.readthedocs.io/en/stable/examples.html) for the complete list of examples!