Data analysis agent pipeline in Agentic Ai

Introduction

A data analysis agent pipeline helps organize steps to explore and understand data automatically.

When you want to automate data cleaning, analysis, and reporting.

When you have many data files to process in the same way.

When you want to break down data tasks into clear steps for easier debugging.

When you want to reuse the same analysis steps on new data quickly.

Syntax

Agentic_ai

agent_pipeline = AgentPipeline(steps=[step1, step2, step3])
results = agent_pipeline.run(data)

AgentPipeline organizes multiple steps into one flow.

Each step is a function or agent that does part of the analysis.

Examples

This example shows cleaning data first, then summarizing it.

Agentic_ai

def clean_data(data):
    # remove missing values
    return data.dropna()

def analyze_data(data):
    # simple summary
    return data.describe()

pipeline = AgentPipeline(steps=[clean_data, analyze_data])
result = pipeline.run(raw_data)

Here, missing values are filled with zero, then the mean is calculated.

Agentic_ai

step1 = lambda data: data.fillna(0)
step2 = lambda data: data.mean()
pipeline = AgentPipeline(steps=[step1, step2])
result = pipeline.run(raw_data)

Sample Program

This program creates a simple pipeline that cleans data by removing rows with missing values, then calculates the average of each column.

Agentic_ai

import pandas as pd

class AgentPipeline:
    def __init__(self, steps):
        self.steps = steps

    def run(self, data):
        for step in self.steps:
            data = step(data)
        return data

# Step 1: Clean data by dropping missing values
def clean_data(data):
    return data.dropna()

# Step 2: Analyze data by calculating mean of each column
def analyze_data(data):
    return data.mean()

# Sample raw data with missing values
raw_data = pd.DataFrame({
    'age': [25, 30, None, 22],
    'score': [88, None, 92, 85]
})

# Create pipeline with two steps
pipeline = AgentPipeline(steps=[clean_data, analyze_data])

# Run pipeline on raw data
result = pipeline.run(raw_data)

print(result)

Important Notes

Each step should take data as input and return transformed data.

Order of steps matters: cleaning should happen before analysis.

You can add more steps like visualization or exporting results.

Summary

A data analysis agent pipeline organizes multiple steps into one flow.

It helps automate and reuse data tasks easily.

Steps run in order, each changing the data for the next step.