Morph allows you to perform complex data analysis using Python.

To write a Python function, simply add the @morph.func annotation to a normal Python function and run it in Morph.

@morph.func
@morph.load_data("example_sql_cell")
def get_data_from_database(context):
	sql_result_df = context.data["example_sql_cell"]
	return pd.DataFrame({})

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About return values

Python return values can be dataframe, visualisation, markdown or json. Create a value that matches one of these and return it as the return value.

If dataframe is specified, it will be displayed as is as a Morph dataframe.

If visualisation is specified, it will be displayed as is as a Morph visualisation. In this case, you can use libraries such as Plotly or Matplotlib to visualise it.

If markdown is specified, it is displayed as is as a Morph Markdown visualization.

If json is specified, it will be displayed as Morph’s JSON as is.

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About visualisation

For visualisation, implement a function that returns a visualisation object using a library such as Plotly or Matplotlib as follows. Here, take care not to create and return html in the function.

Plotly

import plotly.express as px

import morph
from morph import MorphGlobalContext

@morph.func(
    name="${MORPH_NAME}",
    description="${MORPH_DESCRIPTION}",
)
@morph.load_data("${MORPH_PARENT_NAME}")
def main(context: MorphGlobalContext) -> px.line:
    data = context.data["${MORPH_PARENT_NAME}"]
    # This is where you write your code.
    # The `px.line` function creates a line plot using Plotly Express.
    fig = px.line(data, x="X Axis", y="Y Axis", markers=True)
    fig.update_layout(title="Plotly Plot")
    return fig

Matplotlib

import matplotlib.pyplot as plt

import morph
from morph import MorphGlobalContext

@morph.func(
    name="${MORPH_NAME}",
    description="${MORPH_DESCRIPTION}",
)
@morph.load_data("${MORPH_PARENT_NAME}")
def main(context: MorphGlobalContext) -> plt.Figure:
    data = context.data["${MORPH_PARENT_NAME}"]
    # This is where you write your code.
    # The `plot` function creates a line plot using Matplotlib.
    fig, ax = plt.subplots()
    ax.plot(data["X-Axis"], data["Y-Axis"], marker="o")
    ax.set_title("Matplotlib Plot")
    ax.set_xlabel("X-Axis")
    ax.set_ylabel("Y-Axis")
    return fig