Metadata

Dataset metadata

When sharing reports with coworkers or publishing online, you might want to include metadata of the dataset, such as author, copyright holder or a description. The supported properties are inspired by https://schema.org/Dataset. Currently supported are: “description”, “creator”, “author”, “url”, “copyright_year”, “copyright_holder”.

The following example generates a report with a “description”, “copyright_holder” and “copyright_year”, “creator” and “url”. You can find these properties in the “Overview” section under the “About” tab.

report = df.profile_report(
    title="Masked data",
    dataset={
        "description": "This profiling report was generated using a sample of 5% of the original dataset.",
        "copyright_holder": "StataCorp LLC",
        "copyright_year": "2020",
        "url": "http://www.stata-press.com/data/r15/auto2.dta",
    },
)
report.to_file(Path("stata_auto_report.html"))

Descriptions per variable

In addition to providing dataset details, users often would like to include column-specific descriptions when sharing reports with team members and stakeholders. This section provides two code examples how to do this in pandas-profiling.

Generate a report with descriptions per variable

profile = df.profile_report(
    variables={
        "descriptions": {
            "files": "Files in the filesystem",
            "datec": "Creation date",
            "datem": "Modification date",
        }
    }
)

profile.to_file("report.html")

This alternative example demonstrates how you could load the definitions from a json file. By default, the descriptions are presented in the overview tab and next to each variable.

dataset_column_definition.json

   {
       "column name 1": "column 1 definition",
       "column name 2": "column 2 definition"
   }

Generate a report with descriptions per variable from a definitions file

import json
import pandas as pd
import pandas_profiling

definition_file = "dataset_column_definition.json"

# Read the variable descriptions
with open(definition_file, "r") as f:
    definitions = json.load(f)

# By default, the descriptions are presented in the overview tab and next to each variable
report = df.profile_report(variable={"descriptions": definitions})

# We can disable showing the descriptions next to each variable
report = df.profile_report(
    variable={"descriptions": definitions}, show_variable_description=False
)

report.to_file('report.html')