What is Pandas method chaining and how does df.pipe() support it?

Method chaining is the style of writing data transformations as a single expression where each step's result is the input to the next. It avoids creating intermediate variables, reads like a pipeline, and makes the data flow explicit from top to bottom.

import pandas as pd

# --- Without chaining (intermediate variables) ---
df1 = pd.read_csv('raw.csv')
df2 = df1.dropna(subset=['revenue'])
df3 = df2.rename(columns={'rev': 'revenue'})
df4 = df3[df3['revenue'] > 0]
df5 = df4.assign(log_revenue=lambda d: d['revenue'].apply(np.log1p))
result = df5.groupby('region')['log_revenue'].mean()

# --- With method chaining ---
import numpy as np

result = (
    pd.read_csv('raw.csv')
    .dropna(subset=['revenue'])
    .rename(columns={'rev': 'revenue'})
    .query('revenue > 0')
    .assign(log_revenue=lambda d: np.log1p(d['revenue']))
    .groupby('region')['log_revenue']
    .mean()
)

# --- df.pipe() for custom functions ---
def remove_outliers(df, col, n_std=3):
    mean, std = df[col].mean(), df[col].std()
    return df[(df[col] - mean).abs() < n_std * std]

def add_rank(df, col):
    df = df.copy()
    df['rank'] = df[col].rank(ascending=False)
    return df

result = (
    pd.read_csv('raw.csv')
    .pipe(remove_outliers, col='revenue')
    .pipe(add_rank, col='revenue')
)
# pipe passes the DataFrame as the first argument to the function

df.pipe(func, *args, **kwargs) calls func(df, *args, **kwargs), inserting the DataFrame at the front of the argument list. This lets you write standalone functions and use them inline in a method chain without breaking the fluent style.

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