<jupyter_start><jupyter_text>Describe This function will analysis the data and outputs the following artifacts per    column within the data frame (based on data types):    histogram matrix chart    histogram per feature chart    violin chart    correlation-matrix chart    correlation-matrix csv    imbalance pie chart    imbalance-weights-vec csv  analyse  Docs Parameters:* **`context`**: `mlrun.MLClientCtx` - The MLRun function execution context* **`name`**: `str` - Key of the dataset to database ("dataset" for default).* **`table`**: `DataItem = None` - MLRun input pointing to pandas dataframe (csv/parquet file path)* **`label_column`**: `str = None` - Ground truth column label* **`plots_dest`**: `str = "plots"` - Destination folder of summary plots (relative to artifact_path)* **`frac`**: `float = 0.1` -  when the table has more than 5000 samples, the function will execute on random frac from the data.  DEMO Set-up<jupyter_code>import pandas as pd
import mlrun
import os
from sklearn.datasets import make_classification

# Set our project's name:
project_name = "new-describe-project"

# Create the project:
project = mlrun.get_or_create_project(name=project_name, context="./", user_project=True)<jupyter_output>> 2022-03-07 15:57:33,482 [info] loaded project new-describe-project from MLRun DB<jupyter_text>Loading datasetWe will use make_classification to generate random dataset<jupyter_code>n_features=5
X, y = make_classification(n_samples=100, n_features=n_features, n_classes=3, random_state = 18,
                                     class_sep=2, n_informative=3)
df = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(n_features)])
df['label'] = y
try:
    os.mkdir('artifacts')
except:
    pass
df.to_parquet("artifacts/random_dataset.parquet")<jupyter_output><empty_output><jupyter_text>Run the function on new data setImport the describe MLRun function with analysis handler and run it.After we run the function you can see the created artifacts by click on the run uid and go -> artifacts<jupyter_code>describe_func = mlrun.import_function("function.yaml")
describe_func.apply(mlrun.platforms.auto_mount())
describe_run = describe_func.run(
            name="task-describe",
            handler='analysis',
            inputs={"table": os.path.abspath("artifacts/random_dataset.parquet")},
            params={"label_column": "label"},
            local=True
        )<jupyter_output>> 2022-03-07 15:58:36,721 [info] starting run task-describe uid=2933ae26f64c4c1a91f43dd0f220de44 DB=http://mlrun-api:8080<jupyter_text>Run the function on alredy loaded data setlog new data set to the project<jupyter_code>context = mlrun.get_or_create_ctx(project_name)
df = pd.read_parquet(os.path.abspath("artifacts/random_dataset.parquet"))
context.log_dataset(key="dataset", db_key="dataset1", stats=True, df=df)<jupyter_output><empty_output><jupyter_text>Import the describe MLRun function with analysis handler and run it.After we run the function you can see the created artifacts by click on the run uid and go -> artifacts<jupyter_code>describe_func = mlrun.import_function("function.yaml")
describe_func.apply(mlrun.platforms.auto_mount())
describe_run = describe_func.run(
            name="task-describe",
            handler='analysis',
            inputs={"table": os.path.abspath("artifacts/random_dataset.parquet")},
            params={"name": "dataset1", "label_column": "label"},
            local=True
        )<jupyter_output>> 2022-03-07 16:31:05,060 [info] starting run task-describe uid=512a02a6152f4b4bb92f3b7301cc439c DB=http://mlrun-api:8080
{'fraction_non_alphanumeric': 0.10925306577480491, 'fraction_numerical': 0.03260869565217391, 'mean_word_length': 4.816855753646678, 'pattern_counts': {'":': 5, '<': 18, '<?xml version=': 0, '>': 23, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '322524', 'license': 'Apache-2.0', 'n_tokens_mistral': 1189, 'n_tokens_neox': 1095, 'n_words': 304, 'path': 'describe/describe.ipynb', 'repo_name': 'EyalDanieliIgu/functions', 'size': '3588'}