<jupyter_start><jupyter_text>Customize your output  Overview Dataprep supports customizability for `plot()`, `plot_missing()`, `plot_correlation()` and `create_report()`. There are mainly two settings,`display` and `config`. 1. `display` is a list of names which controls the Tabs, Sections and Sessions you want to show.2. `config` is a dictionary that contains the customizable parameters and designated values.For your convenience, the input for `display` can directly be copied from the Tabs. You can save the computations by diaplaying less contents. And for `config`, we developed the `how-to guide` function to help you mange the frequently-use parameters. Click the `question mark icon` in the upper right corner of each plot, in the pop-up you will see the customizable parameters for that plot, parameters' brief descriptions and parameters' default settings. You can easily use the `Copy All Parameters` button to copy the parameters with their default settings into a dictionary. Then customize the settings and pass to the `config` argument.  Global and local parameter There are two types of parameters, global and local.1. Local parameters are plot-specified and the names are separated  by `.`. The portion before the first `.` is plot name and the portion after the `.` is parameter name. e.g. `bar.bars`. 2. Global parameter applies to all the plots which has that parameter. It is single-word. e.g. `ngroups` .When global and local parameter are both given, the global parameter will be overwrote by local parameters for specific plots. You can find more details about parameters in [parameter_configurations](parameter_configurations.ipynb).  Exmaple 1: Choose the Tabs, Sections and Sessions you want<jupyter_code>from dataprep.eda import plot,create_report
from dataprep.datasets import load_dataset
df = load_dataset('titanic')
plot(df, 'Pclass', display=['Stats', 'Bar Chart', 'Pie Chart'])
create_report(df,display=["Overview","Interactions"])
plot(df, display=["Stats", "Insights"])<jupyter_output><empty_output><jupyter_text>Example 2: Customize your plot<jupyter_code>plot(df, "Pclass", config={'bar.bars': 10, 'bar.sort_descending': True, 'bar.yscale': 'linear', 'height': 400, 'width': 450, })<jupyter_output><empty_output><jupyter_text>Example 3: Customize your Insights<jupyter_code>plot(df,config={'insight.missing.threshold':20, 'insight.duplicates.threshold':20})<jupyter_output><empty_output>
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