Keyword - how¶

The how parameter in join() specifies the type of join, controlling which rows are included based on index matching.

Left Join (Default)¶

Keep all rows from the calling DataFrame.

1. Default Behavior¶

import pandas as pd
import yfinance as yf

def download(ticker):
    return yf.Ticker(ticker).history(period="max")

tickers = ['META', 'AAPL', 'AMZN', 'NFLX', 'GOOG']

for i, ticker in enumerate(tickers):
    if i == 0:
        df = download(ticker).rename(columns={'Close': ticker})[[ticker]]
    else:
        dg = download(ticker).rename(columns={'Close': ticker})[[ticker]]
        df = df.join(dg, how="left")  # Default

print(df.head(3))

2. Preserves Left Index¶

All dates from the first stock are kept.

3. NaN for Missing¶

Stocks without data for certain dates have NaN.

Right Join¶

Keep all rows from the passed DataFrame.

1. Right Join Example¶

for i, ticker in enumerate(tickers):
    if i == 0:
        df = download(ticker).rename(columns={'Close': ticker})[[ticker]]
    else:
        dg = download(ticker).rename(columns={'Close': ticker})[[ticker]]
        df = df.join(dg, how="right")

print(df.head(3))

2. Preserves Right Index¶

All dates from the joined stock are kept.

3. Use Case¶

When the right DataFrame has the authoritative index.

Inner Join¶

Keep only rows with matching indices.

1. Inner Join Example¶

for i, ticker in enumerate(tickers):
    if i == 0:
        df = download(ticker).rename(columns={'Close': ticker})[[ticker]]
    else:
        dg = download(ticker).rename(columns={'Close': ticker})[[ticker]]
        df = df.join(dg, how="inner")

print(df.head(3))
print(df.tail(3))

2. Intersection of Indices¶

Only dates present in all stocks.

3. No Missing Values¶

Inner join produces complete data without NaN.

Outer Join¶

Keep all rows from both DataFrames.

1. Outer Join Example¶

for i, ticker in enumerate(tickers):
    if i == 0:
        df = download(ticker).rename(columns={'Close': ticker})[[ticker]]
    else:
        dg = download(ticker).rename(columns={'Close': ticker})[[ticker]]
        df = df.join(dg, how="outer")

print(df.head(3))
print(df.tail(3))

2. Union of Indices¶

All dates from any stock are included.

3. Most Missing Values¶

Outer join may have many NaN values.

Comparison¶

Summary of join types.

1. Row Counts¶

# Given df1 (100 dates) and df2 (80 dates) with 60 overlap:
# how='left':  100 rows (all from df1)
# how='right':  80 rows (all from df2)
# how='inner':  60 rows (intersection)
# how='outer': 120 rows (union)

2. Best Practices¶

# Inner: When you need complete data
# Left: When preserving primary DataFrame structure
# Outer: When you need all dates for analysis

3. Financial Context¶

# Inner join for synchronized analysis
# Outer join for data completeness check
# Left join for preserving benchmark dates