Python Data Pipeline: From Raw CSV to Dashboard in 10 Minutes

Building a complete data pipeline is easier than you think.

Step 1: Ingest

import pandas as pd

# Load any file format automatically
def load_data(filepath):
    ext = filepath.split(".")[-1].lower()
    loaders = {
        "csv": pd.read_csv,
        "json": pd.read_json,
        "xlsx": pd.read_excel,
        "tsv": lambda f: pd.read_csv(f, sep="\t"),
    }
    return loaders.get(ext, pd.read_csv)(filepath)

df = load_data("sales_data.csv")
print(f"Loaded {len(df)} rows, {len(df.columns)} columns")

Step 2: Clean

def clean_data(df):
    # Remove duplicates
    df = df.drop_duplicates()
    # Handle missing values
    for col in df.columns:
        if df[col].dtype in ["float64", "int64"]:
            df[col] = df[col].fillna(df[col].median())
        else:
            df[col] = df[col].fillna("N/A")
    # Standardize dates
    for col in df.columns:
        if "date" in col.lower() or "time" in col.lower():
            df[col] = pd.to_datetime(df[col], errors="coerce")
    return df

df = clean_data(df)

Step 3: Analyze

summary = df.describe()
top_categories = df.groupby("category").size().sort_values(ascending=False).head(10)

Step 4: Visualize

import matplotlib.pyplot as plt

df["category"].value_counts().head(10).plot(kind="bar", title="Top Categories")
plt.savefig("dashboard.png")

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