There are three ways to change the settings listed in Available settings:
through a custom configuration file
through environment variables
# Create the ProfileReport object without specifying a DataFrame profile = df.profile_report(title="Pandas Profiling Report", pool_size=1) # Change the configuration as desired profile.config.html.minify_html = False # Specify a DataFrame and trigger the report's computation profile.to_file("output.html")
Some related settings are grouped together in configuration shorthands, making it easy to selectively enable or disable certain report sections or functionality:
samples: control whether the dataset preview is shown.
correlation: control whether correlation computations are executed.
missing_diagrams: control whether missing value analysis is executed.
duplicates: control whether duplicate rows are previewed.
interactions: control whether interactions are computed.
# Disable samples, correlations, missing diagrams and duplicates at once r = ProfileReport( samples=None, correlations=None, missing_diagrams=None, duplicates=None, interactions=None, )
Through a custom configuration file
pandas-profiling through a custom file, you can start with one of the sample configuration files below:
default configuration file (default)
minimal configuration file (minimal computation, optimized for performance)
Change the configuration to your liking and point towards that configuration file when computing the report:
from pandas_profiling import ProfileReport profile = ProfileReport(df, config_file="your_config.yml") profile.to_file("report.html")
Through environment variables
Any configuration setting can also be read from environment variables. For example:
from pandas_profiling import ProfileReport profile = ProfileReport(df, title="My Custom Pandas Profiling Report")
is equivalent to setting the title as an environment variable
export PROFILE_TITLE="My Custom Pandas Profiling Report"
and then running
from pandas_profiling import ProfileReport profile = ProfileReport(df)