Exporting processed data

So far, we have used pandas to load data, inspect it, and apply simple transformations. That work is only valuable if the results can move forward. In real systems, processed data rarely stays inside a notebook or script. It is usually written out so that another program, agent, or tool can pick it up and continue the pipeline.

This lesson orients us to exporting tabular data so it can be reused reliably in downstream steps.

Writing tabular data back to CSV files

Once data has been cleaned or transformed, the most common next step is to write it back to disk. pandas supports this directly from a DataFrame.

Writing to CSV preserves the tabular structure in a form that many tools understand.

import pandas as pd

df = pd.DataFrame({
    "planet": ["Mercury", "Venus", "Earth"],
    "moons": [0, 0, 1]
})

df.to_csv("planets.csv", index=False)

The resulting file can be opened by other Python programs, scripts in different languages, or spreadsheet tools.

Exporting transformed data for reuse

Exporting is usually done after deliberate changes have been applied. Filters, new columns, or renamed fields are all common reasons to save a new version of a dataset.

The exported file becomes an artifact that represents a specific processing step.

import pandas as pd

df = pd.read_csv("planets.csv")
filtered = df[df["moons"] > 0]

filtered.to_csv("planets_with_moons.csv", index=False)

This pattern keeps transformation logic in code and the resulting data in a reusable form.

Preserving data structure and column meanings

When exporting data, column names and their meanings matter. Downstream programs rely on these names to interpret the data correctly.

Simple choices, like stable column names and consistent ordering, make exported files easier to consume and harder to misuse.

import pandas as pd

df = pd.DataFrame({
    "body_name": ["Earth", "Mars"],
    "moon_count": [1, 2]
})

df.to_csv("bodies_summary.csv", index=False)

Clear structure is often more important than compactness.

Using exported data as input to other programs

An exported CSV is often the handoff point between programs. One script produces it, and another script reads it later as input.

This allows programs to stay loosely coupled while still sharing results.

import pandas as pd

data = pd.read_csv("bodies_summary.csv")
print(data)

The file acts as a boundary between processing stages.

Sharing processed data between tools and agents

In agent-based systems, exported data is often the simplest coordination mechanism. One agent writes a file, and another agent consumes it when needed.

Because CSV is durable and widely supported, it works well as a shared format between tools with different responsibilities.

The key idea is that exported data is no longer internal state. It is a contract between steps in a larger workflow.

Conclusion

By exporting processed data, we turn pandas work into something durable and reusable. The results can flow into other programs, tools, or agents without carrying the original code along with them.

At this point, we are oriented to treating data files as deliberate outputs of our processing steps, not just temporary byproducts.