# Visualization Utilities
Use the utilities below to visualize the comparison between real and synthetic data. You can access these from the `sdmetrics.visualization` module.
{% hint style="success" %}
**Tip!** All visualizations are interactive. If you're using an iPython notebook, you can zoom, pan, toggle legends and take screenshots.
{% endhint %}
### Compare a synthetic column & real column (1D)
**get\_column\_plot**
Use this utility to visualize a real column against the same synthetic column. You can plot any column of type: `boolean`, `categorical`, `datetime` or `numerical`.
* (required) `real_data`: A [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the table of your real data. *To skip plotting the real data, input `None`.*
* (required) `synthetic_data`: A [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the synthetic data. *To skip plotting the synthetic data, input `None`.*
* (required) `column_name`: The name of the column you want to plot.
* `plot_type`: The type of plot to create
* (default) `None`: Determine the type of plot to create based on the data.
* `'distplot'`: Plot the data as a smooth, continuous distribution. Use this for continuous columns.
* `'bar'`: Plot the data as discrete bars. Use this for discrete columns.
Returns: A [plotly.Figure](https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html) object
```python
from sdmetrics.visualization import get_column_plot
fig = get_column_plot(
real_data=real_table,
synthetic_data=synthetic_table,
column_name='high_perc',
plot_type='distplot'
)
fig.show()
```
### Compare a pair of synthetic columns & real columns (2D)
**utils.get\_column\_pair\_plot**
Use this utility to visualize the trends between a pair of columns for real and synthetic data. You can plot any 2 columns of type: `boolean`, `categorical`, `datetime` or `numerical`. The columns do not have to the be the same type.
* (required) `real_data`: A [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the table of your real data. *To skip plotting the real data, input `None`.*
* (required) `synthetic_data`: A [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the synthetic data. *To skip plotting the synthetic data, input `None`.*
* (required) `column_names`: A list containing the names of the 2 columns you want to plot.
* `plot_type`: The type of plot to create
* (default) `None`: Determine the type of plot to create based on the data.
* `'scatter'`: Plot each data point in 2D space as a scatter plot. Use this to compare a pair of continuous columns.
* `'box'`: Plot the data as one or more box plot. Use this to compare a continuous column with a discrete column.
* `'violin'`: Create a violin plot to show distribution of a continuous column broken down by a discrete column. This is an alternative to using `'box'` .
* `'heatmap'`: Plot a side-by-side headmap of the data's categories. Use this to compare a pair of discrete columns.
Returns: A [plotly.Figure](https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html) object
```python
from sdmetrics.visualization import get_column_pair_plot
fig = get_column_pair_plot(
real_data=real_table,
synthetic_data=synthetic_table,
column_names=['mba_perc', 'degree_perc'],
plot_type='scatter'
)
fig.show()
```
Various types of plots are possible based on the types of data you provide
### Visualize the cardinality of a relationship
**utils.get\_cardinality\_plot**
Use this utility to visualize the cardinality of parent-child relationship. The cardinality is the # of children that each parent row has. Your cardinality may be fixed (eg. every parent has exactly 2 children) or variable (eg. every parent has 1-3 children).
* (required) `real_data`: A dictionary mapping the name of each table to a [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the real data for that table. *To skip plotting the real data, input `None`.*
* (required) `synthetic_data`: A dictionary mapping the name of each table to a [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) containing the synthetic data for that table. *To skip plotting the synthetic data, input `None`.*
* (required) `parent_table_name`: The string name of the parent table in the relationship
* (required) `child_table_name`: The string name of the child table in the relationship
* (required) `parent_primary_key`: The string name of the parent table's primary key
* (required) `child_foreign_key`: The string name of the column in the child table that refers to the parent's primary key
* `plot_type`: The type of plot to create
* (default) `None`: Determine the type of plot to create based on the data.
* `'distplot'`: Plot the data as a smooth, continuous distribution
* `'bar'`: Plot the data as discrete bars
Returns: A [plotly.Figure](https://plotly.com/python-api-reference/generated/plotly.graph_objects.Figure.html) object
```python
from sdmetrics.visualization import get_cardinality_plot
fig = get_cardinality_plot(
real_data=real_tables,
synthetic_data=synthetic_tables,
parent_table_name='users',
child_table_name='sessions',
parent_primary_key='user_id',
child_foreign_key='user_id',
plot_type='bar'
)
fig.show()
```
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