Indicators Guide

This comprehensive guide covers all 13 technical indicators available in Rhoa, including when to use each, parameter tuning, mathematical formulas, and best practices.

Overview

Rhoa provides 13 carefully selected technical indicators accessible through the .indicators accessor on both pandas DataFrame and Series objects. These indicators cover the four main categories:

Trend Indicators: SMA, EWMA, ADX, Parabolic SAR
Momentum Indicators: RSI, MACD, Stochastic, Williams %R
Volatility Indicators: ATR, EWMV, EWMSTD, Bollinger Bands
Oscillators: RSI, CCI, Stochastic, Williams %R

All indicators are implemented with:

Industry-standard default parameters
Full parameter customization
Comprehensive documentation
Type hints for IDE support
Proper NaN handling

Accessing Indicators

Rhoa offers two ways to access indicators:

DataFrame accessor (recommended for OHLC indicators) — auto-detects Close, High, and Low columns:

import pandas as pd
import rhoa

df = pd.read_csv('prices.csv')

# OHLC indicators — no need to pass columns manually
atr = df.rhoa.indicators.atr(window_size=14)
stoch = df.rhoa.indicators.stochastic(k_window=14)
adx_data = df.rhoa.indicators.adx(window_size=14)

# Single-series indicators default to the Close column
sma = df.rhoa.indicators.sma(window_size=20)
rsi = df.rhoa.indicators.rsi(window_size=14)

# Override auto-detection with explicit params
atr_custom = df.rhoa.indicators.atr(close=df['Adj Close'], window_size=14)

Series accessor — call on a single column for single-series indicators:

# Series-level: you choose which column to call on
sma = df['Close'].rhoa.indicators.sma(window_size=20)

Note

OHLC indicators (ATR, CCI, Stochastic, Williams %R, ADX, Parabolic SAR) are only available via the DataFrame accessor. Single-series indicators (SMA, EWMA, EWMV, EWMSTD, RSI, MACD, Bollinger Bands) work on both.

Column auto-detection is case-insensitive. For the close column, Adj Close is also checked as a fallback.

Quick Reference Table

Indicator	Category	Default Period	Primary Use Case
SMA	Trend	20	Identify trend direction, support/resistance
EWMA	Trend	20	Faster trend following than SMA
EWMV	Volatility	20	Measure variance with recent emphasis
EWMSTD	Volatility	20	Measure volatility for risk management
RSI	Momentum/Oscillator	14	Overbought/oversold conditions
MACD	Momentum	12/26/9	Trend changes and momentum shifts
Bollinger Bands	Volatility	20, 2σ	Volatility bands, mean reversion
ATR	Volatility	14	Position sizing, stop-loss placement
CCI	Oscillator	20	Cyclical turning points
Stochastic	Momentum/Oscillator	14, 3	Overbought/oversold, momentum
Williams %R	Momentum/Oscillator	14	Overbought/oversold conditions
ADX	Trend	14	Trend strength measurement
Parabolic SAR	Trend	0.02/0.2	Trailing stops, trend reversals

Trend Indicators

Simple Moving Average (SMA)

Purpose: Smooth price data to identify the underlying trend direction.

Mathematical Formula:

\[SMA_t = \frac{1}{n} \sum_{i=0}^{n-1} Price_{t-i}\]

Where: - \(n\) = window size - \(Price_t\) = price at time t

Parameters:

window_size (int, default=20): Number of periods to average
min_periods (int, optional): Minimum observations required
center (bool, default=False): Center the window

Code Example:

import pandas as pd
import rhoa

# Load price data
df = pd.read_csv('prices.csv')

# Basic SMA
sma_20 = df.rhoa.indicators.sma(window_size=20)

# Multiple SMAs for crossover strategy
sma_50 = df.rhoa.indicators.sma(window_size=50)
sma_200 = df.rhoa.indicators.sma(window_size=200)

# Golden cross: SMA 50 crosses above SMA 200
golden_cross = (sma_50 > sma_200) & (sma_50.shift(1) <= sma_200.shift(1))

When to Use:

Identifying long-term trend direction
Support and resistance levels
Crossover strategies (fast SMA crosses slow SMA)
Filtering out market noise

Best Practices:

Use 20-period for short-term, 50 for medium-term, 200 for long-term
Combine multiple SMAs for confirmation
SMA lags price, not suitable for fast trading
Works best in trending markets, less effective in sideways markets

Interpretation:

Price above SMA: Uptrend
Price below SMA: Downtrend
SMA slope: Trend strength (steep = strong)
SMA acting as support (uptrend) or resistance (downtrend)

Exponential Weighted Moving Average (EWMA)

Purpose: Moving average that gives more weight to recent prices, responding faster to changes than SMA.

Mathematical Formula:

\[EWMA_t = \alpha \cdot Price_t + (1 - \alpha) \cdot EWMA_{t-1}\]

Where: - \(\alpha = \frac{2}{span + 1}\) (smoothing factor) - \(span\) = window_size

Parameters:

window_size (int, default=20): Span of the exponential window
adjust (bool, default=False): Normalize weights by sum
min_periods (int, optional): Minimum observations required

Code Example:

# Basic EWMA
ewma_20 = df.rhoa.indicators.ewma(window_size=20)

# Multiple EMAs for trend confirmation
ema_12 = df.rhoa.indicators.ewma(window_size=12)
ema_26 = df.rhoa.indicators.ewma(window_size=26)

# EMA crossover strategy
bullish = (ema_12 > ema_26) & (ema_12.shift(1) <= ema_26.shift(1))

When to Use:

When you need faster response to price changes than SMA
Short-term trading strategies
As a component of MACD
Dynamic support/resistance levels

Best Practices:

Use shorter periods (12, 26) for active trading
EWMA responds faster but is more sensitive to noise
Combine with other indicators for confirmation
More suitable than SMA for volatile markets

Interpretation:

Same as SMA but more responsive
Crossovers happen earlier than SMA
Better for catching trend changes
Can generate more false signals in choppy markets

Average Directional Index (ADX)

Purpose: Measure the strength of a trend, regardless of direction.

Mathematical Formula:

\[ \begin{align}\begin{aligned}+DM = High_t - High_{t-1} \text{ (if positive and greater than -DM)}\\-DM = Low_{t-1} - Low_t \text{ (if positive and greater than +DM)}\\+DI = 100 \times \frac{EMA(+DM)}{ATR}\\-DI = 100 \times \frac{EMA(-DM)}{ATR}\\DX = 100 \times \frac{|+DI - -DI|}{+DI + -DI}\\ADX = EMA(DX)\end{aligned}\end{align} \]

Parameters:

high (Series): High prices
low (Series): Low prices
window_size (int, default=14): Smoothing period

Code Example:

# Calculate ADX
adx_data = df.rhoa.indicators.adx(window_size=14)

adx = adx_data['ADX']
plus_di = adx_data['+DI']
minus_di = adx_data['-DI']

# Strong trend identification
strong_trend = adx > 25

# Trend direction
uptrend = (plus_di > minus_di) & strong_trend
downtrend = (minus_di > plus_di) & strong_trend

When to Use:

Determining if a market is trending or ranging
Filtering trades (only trade in strong trends)
Confirming breakouts
Avoiding choppy, directionless markets

Best Practices:

ADX > 25: Strong trend (good for trend-following strategies)
ADX < 20: Weak trend or ranging (good for mean reversion)
Use +DI/-DI crossovers for direction, ADX for strength
Rising ADX confirms trend strengthening
Falling ADX indicates trend weakening

Interpretation:

0-20: Weak or absent trend
20-25: Emerging trend
25-50: Strong trend
50-75: Very strong trend
75-100: Extremely strong trend (rare)

Parabolic SAR

Purpose: Identify potential reversal points and provide trailing stop levels.

Parameters:

high (Series): High prices
low (Series): Low prices
af_start (float, default=0.02): Initial acceleration factor
af_increment (float, default=0.02): AF increase per new extreme
af_maximum (float, default=0.2): Maximum AF value

Code Example:

# Calculate Parabolic SAR
sar = df.rhoa.indicators.parabolic_sar(
    af_start=0.02,
    af_increment=0.02,
    af_maximum=0.2
)

# Trend identification
uptrend = df['Close'] > sar
downtrend = df['Close'] < sar

# Detect trend reversals
reversal_up = (df['Close'] > sar) & (df['Close'].shift(1) <= sar.shift(1))
reversal_down = (df['Close'] < sar) & (df['Close'].shift(1) >= sar.shift(1))

# Use as trailing stop
stop_loss = sar

When to Use:

Identifying trend direction
Setting trailing stop-loss levels
Spotting potential reversal points
Trend-following strategies

Best Practices:

SAR works best in trending markets
Generates many false signals in sideways markets
Combine with ADX (trade SAR only when ADX > 25)
Use conservative AF values for long-term trends
Increase AF for more responsive signals (but more whipsaws)

Interpretation:

Dots below price: Uptrend (long position)
Dots above price: Downtrend (short position)
SAR flip: Potential trend reversal
Use SAR dots as stop-loss levels

Momentum Indicators

Relative Strength Index (RSI)

Purpose: Measure momentum and identify overbought/oversold conditions.

Mathematical Formula:

\[ \begin{align}\begin{aligned}RS = \frac{EMA(Gains)}{EMA(Losses)}\\RSI = 100 - \frac{100}{1 + RS}\end{aligned}\end{align} \]

Parameters:

window_size (int, default=14): Period for calculation
edge_case_value (float, default=100.0): Value when no losses occur

Code Example:

# Basic RSI
rsi = df.rhoa.indicators.rsi(window_size=14)

# Identify overbought/oversold
overbought = rsi > 70
oversold = rsi < 30

# RSI divergence (advanced)
# Price makes new high but RSI doesn't
price_high = df['Close'] == df['Close'].rolling(20).max()
rsi_lower = rsi < rsi.shift(20)
bearish_divergence = price_high & rsi_lower

When to Use:

Identifying overbought/oversold conditions
Spotting momentum divergences
Confirming trend strength
Mean reversion strategies

Best Practices:

70/30 levels for general use
80/20 levels for strong trends
60/40 levels for ranging markets
Wait for RSI to exit extreme zones before trading
Combine with price action for confirmation

Interpretation:

0-30: Oversold (potential buy signal)
30-70: Neutral zone
70-100: Overbought (potential sell signal)
Above 50: Bullish momentum
Below 50: Bearish momentum

MACD (Moving Average Convergence Divergence)

Purpose: Identify trend changes and momentum shifts through moving average relationships.

Mathematical Formula:

\[ \begin{align}\begin{aligned}MACD = EMA_{12} - EMA_{26}\\Signal = EMA_9(MACD)\\Histogram = MACD - Signal\end{aligned}\end{align} \]

Parameters:

short_window (int, default=12): Fast EMA period
long_window (int, default=26): Slow EMA period
signal_window (int, default=9): Signal line EMA period

Code Example:

# Calculate MACD
macd_data = df.rhoa.indicators.macd(
    short_window=12,
    long_window=26,
    signal_window=9
)

macd_line = macd_data['macd']
signal_line = macd_data['signal']
histogram = macd_data['histogram']

# Bullish crossover
bullish = (macd_line > signal_line) & \
          (macd_line.shift(1) <= signal_line.shift(1))

# Bearish crossover
bearish = (macd_line < signal_line) & \
          (macd_line.shift(1) >= signal_line.shift(1))

# Histogram growing = strengthening trend
momentum_increasing = histogram > histogram.shift(1)

When to Use:

Identifying trend reversals
Confirming trend direction
Measuring momentum strength
Finding divergences

Best Practices:

Most reliable in trending markets
Use histogram for early momentum signals
Look for MACD-price divergences
Combine with other indicators for confirmation
Adjust periods for different timeframes

Interpretation:

MACD > Signal: Bullish momentum
MACD < Signal: Bearish momentum
MACD crosses above Signal: Buy signal
MACD crosses below Signal: Sell signal
Histogram expanding: Momentum strengthening
Histogram contracting: Momentum weakening

Stochastic Oscillator

Purpose: Compare closing price to price range over time to identify momentum.

Mathematical Formula:

\[ \begin{align}\begin{aligned}\%K = 100 \times \frac{Close - Lowest\_Low}{Highest\_High - Lowest\_Low}\\\%D = SMA_3(\%K)\end{aligned}\end{align} \]

Parameters:

high (Series): High prices
low (Series): Low prices
k_window (int, default=14): %K calculation period
d_window (int, default=3): %D smoothing period

Code Example:

# Calculate Stochastic
stoch = df.rhoa.indicators.stochastic(
    k_window=14,
    d_window=3
)

k_percent = stoch['%K']
d_percent = stoch['%D']

# Overbought/oversold
overbought = k_percent > 80
oversold = k_percent < 20

# Crossover signals
bullish = (k_percent > d_percent) & \
          (k_percent.shift(1) <= d_percent.shift(1)) & \
          (k_percent < 50)

When to Use:

Identifying overbought/oversold conditions
Confirming trend reversals
Finding divergences
Range-bound markets

Best Practices:

80/20 levels for general use
Look for %K crossing %D in extreme zones
More reliable in ranging markets
Can give premature signals in strong trends
Use with trend confirmation

Interpretation:

0-20: Oversold zone
80-100: Overbought zone
%K above %D: Bullish
%K below %D: Bearish
Crossovers in extreme zones: Strong signals

Williams %R

Purpose: Momentum indicator measuring overbought/oversold levels (inverted Stochastic).

Mathematical Formula:

\[Williams\%R = -100 \times \frac{Highest\_High - Close}{Highest\_High - Lowest\_Low}\]

Parameters:

high (Series): High prices
low (Series): Low prices
window_size (int, default=14): Lookback period

Code Example:

# Calculate Williams %R
wr = df.rhoa.indicators.williams_r(window_size=14)

# Identify conditions
overbought = wr > -20
oversold = wr < -80

# Exit signals
exit_overbought = (wr < -20) & (wr.shift(1) >= -20)
exit_oversold = (wr > -80) & (wr.shift(1) <= -80)

When to Use:

Quick overbought/oversold identification
Short-term trading signals
Confirming price momentum
Similar use cases to Stochastic

Best Practices:

-20/-80 are traditional levels
Look for exits from extreme zones
Works well for short-term reversals
Combine with price action
Can stay extreme during strong trends

Interpretation:

0 to -20: Overbought
-20 to -80: Normal range
-80 to -100: Oversold
Rising from oversold: Bullish
Falling from overbought: Bearish

Volatility Indicators

Exponentially Weighted Moving Variance (EWMV)

Purpose: Measure variance with more weight on recent observations.

Parameters:

window_size (int, default=20): Span for weighting
adjust (bool, default=True): Normalize weights
min_periods (int, optional): Minimum observations

Code Example:

# Calculate EWMV
ewmv = df.rhoa.indicators.ewmv(window_size=20)

# Identify high volatility periods
mean_variance = ewmv.rolling(50).mean()
high_volatility = ewmv > mean_variance * 1.5

# Volatility regime changes
volatility_increasing = ewmv > ewmv.shift(5)

When to Use:

Risk measurement
Volatility regime detection
Position sizing
Option pricing adjustments

Best Practices:

Use relative comparisons (current vs. historical)
Pair with price action
Higher variance = higher risk
Consider different timeframes

Exponentially Weighted Moving Standard Deviation (EWMSTD)

Purpose: Measure volatility with exponential weighting (square root of EWMV).

Parameters:

window_size (int, default=20): Span for weighting
adjust (bool, default=True): Normalize weights
min_periods (int, optional): Minimum observations

Code Example:

# Calculate EWMSTD
ewmstd = df.rhoa.indicators.ewmstd(window_size=20)

# Position sizing based on volatility
base_position = 10000
target_volatility = 0.02
position_size = (base_position * target_volatility) / ewmstd

# Stop-loss based on volatility
stop_distance = 2 * ewmstd

When to Use:

Position sizing
Stop-loss calculation
Risk management
Volatility-adjusted strategies

Best Practices:

Use for ATR-style position sizing
Scale positions inversely with volatility
Combine with price levels for stops
Monitor for volatility spikes

Bollinger Bands

Purpose: Volatility bands showing standard deviation channels around moving average.

Mathematical Formula:

\[ \begin{align}\begin{aligned}Middle = SMA(Close, n)\\Upper = Middle + (k \times \sigma)\\Lower = Middle - (k \times \sigma)\end{aligned}\end{align} \]

Where: - \(n\) = window_size - \(k\) = num_std - \(\sigma\) = standard deviation

Parameters:

window_size (int, default=20): SMA and std period
num_std (float, default=2.0): Number of standard deviations
min_periods (int, optional): Minimum observations
center (bool, default=False): Center the window

Code Example:

# Calculate Bollinger Bands
bb = df.rhoa.indicators.bollinger_bands(
    window_size=20,
    num_std=2.0
)

upper = bb['upper_band']
middle = bb['middle_band']
lower = bb['lower_band']

# Band squeeze (low volatility)
band_width = upper - lower
squeeze = band_width < band_width.rolling(50).quantile(0.25)

# Mean reversion signals
oversold = df['Close'] <= lower
overbought = df['Close'] >= upper

# Trend following: price riding upper band
strong_uptrend = (df['Close'] > middle) & (df['Close'].shift(1) > middle.shift(1))

When to Use:

Mean reversion strategies
Volatility analysis
Breakout identification
Overbought/oversold conditions

Best Practices:

20-period, 2 std is standard
Narrow bands = low volatility (squeeze)
Wide bands = high volatility (expansion)
Price at bands is not necessarily reversal signal
Look for band squeeze followed by expansion
Combine with other indicators

Interpretation:

Price touching upper band: Strong upward momentum
Price touching lower band: Strong downward momentum
Band squeeze: Consolidation, potential breakout ahead
Band expansion: High volatility, strong trending
Walking the bands: Very strong trend

Average True Range (ATR)

Purpose: Measure market volatility using true range.

Mathematical Formula:

\[ \begin{align}\begin{aligned}True\ Range = max[(High - Low), |High - Close_{prev}|, |Low - Close_{prev}|]\\ATR = SMA(True\ Range, n)\end{aligned}\end{align} \]

Parameters:

high (Series): High prices
low (Series): Low prices
window_size (int, default=14): Averaging period

Code Example:

# Calculate ATR
atr = df.rhoa.indicators.atr(window_size=14)

# Position sizing
risk_per_trade = 1000  # $1000 risk per trade
atr_multiple = 2  # Stop at 2x ATR
position_size = risk_per_trade / (atr_multiple * atr)

# Dynamic stop-loss
stop_loss_long = df['Close'] - (2 * atr)
stop_loss_short = df['Close'] + (2 * atr)

# Profit targets
profit_target_long = df['Close'] + (3 * atr)

When to Use:

Position sizing
Stop-loss placement
Profit target setting
Volatility filtering

Best Practices:

Use 1.5-3x ATR for stops
Use 2-5x ATR for targets
Scale position size inversely with ATR
Higher ATR = wider stops needed
Compare current ATR to historical average

Interpretation:

High ATR: High volatility, wider stops needed
Low ATR: Low volatility, tighter stops possible
Rising ATR: Volatility increasing
Falling ATR: Volatility decreasing

Commodity Channel Index (CCI)

Purpose: Identify cyclical trends and overbought/oversold conditions.

Mathematical Formula:

\[ \begin{align}\begin{aligned}Typical\ Price = \frac{High + Low + Close}{3}\\CCI = \frac{Typical\ Price - SMA(Typical\ Price)}{0.015 \times Mean\ Deviation}\end{aligned}\end{align} \]

Parameters:

high (Series): High prices
low (Series): Low prices
window_size (int, default=20): Calculation period

Code Example:

# Calculate CCI
cci = df.rhoa.indicators.cci(window_size=20)

# Traditional signals
overbought = cci > 100
oversold = cci < -100

# Extreme signals
very_overbought = cci > 200
very_oversold = cci < -200

# Zero-line crosses
bullish = (cci > 0) & (cci.shift(1) <= 0)
bearish = (cci < 0) & (cci.shift(1) >= 0)

When to Use:

Identifying cyclical turning points
Overbought/oversold detection
Trend strength confirmation
Divergence analysis

Best Practices:

+100/-100 are traditional levels
Can exceed 100 in strong trends
Look for exits from extreme zones
Use with price action confirmation
Works well in cyclical markets

Interpretation:

CCI > +100: Overbought, strong uptrend
CCI < -100: Oversold, strong downtrend
CCI near 0: Neutral
Rising CCI: Increasing momentum
Falling CCI: Decreasing momentum

Combining Indicators

Multi-Indicator Strategy Example

import pandas as pd
import rhoa

# Load data
df = pd.read_csv('stock_data.csv')

# Calculate multiple indicators
sma_50 = df.rhoa.indicators.sma(50)
sma_200 = df.rhoa.indicators.sma(200)
rsi = df.rhoa.indicators.rsi(14)
macd_data = df.rhoa.indicators.macd()
adx_data = df.rhoa.indicators.adx(window_size=14)
atr = df.rhoa.indicators.atr(window_size=14)

# Define trading conditions
# 1. Trend: Price above 50 SMA, 50 SMA above 200 SMA
uptrend = (df['Close'] > sma_50) & (sma_50 > sma_200)

# 2. Momentum: MACD bullish, RSI not overbought
momentum_good = (macd_data['macd'] > macd_data['signal']) & \
                (rsi < 70) & (rsi > 30)

# 3. Trend strength: ADX confirms strong trend
strong_trend = adx_data['ADX'] > 25

# 4. Combined entry signal
buy_signal = uptrend & momentum_good & strong_trend

# 5. Position sizing and risk management
risk_amount = 1000  # Risk $1000 per trade
stop_distance = 2 * atr
position_size = risk_amount / stop_distance

# 6. Exit signals
stop_loss = df['Close'] - stop_distance
profit_target = df['Close'] + (3 * stop_distance)  # 3:1 reward:risk

Best Practices Summary

General Guidelines

Never Use a Single Indicator - Always confirm with multiple indicators - Combine trend, momentum, and volatility - Use indicators from different categories
Understand Market Context - Trending vs. ranging markets - High vs. low volatility environments - Different indicators work in different conditions
Adjust Parameters - Shorter periods for active trading - Longer periods for position trading - Test different parameters on your data
Handle NaN Values Properly - All indicators create NaN for initial periods - Use dropna() or fillna() appropriately - Consider min_periods parameter
Backtest Everything - Never trade on indicators alone - Backtest your indicator combinations - Use proper walk-forward validation

Common Mistakes to Avoid

Indicator Overload - Using too many correlated indicators - All momentum indicators will agree - 3-5 indicators maximum
Ignoring Market Regime - RSI works great in ranges, poor in trends - MACD works great in trends, poor in ranges - Match indicator to market condition
Not Considering Timeframes - 14-period RSI on 1-minute chart ≠ daily chart - Adjust parameters for your timeframe - Use multiple timeframe analysis
Forgetting Transaction Costs - More signals = more commissions - Slippage on every trade - Factor costs into backtest
Curve Fitting - Over-optimizing parameters - Use standard parameters first - Test on out-of-sample data

Performance Tips

For Large Datasets

# Pre-calculate and store
df['SMA_50'] = df.rhoa.indicators.sma(50)
df['RSI'] = df.rhoa.indicators.rsi(14)

# Rather than recalculating each time
# sma = df.rhoa.indicators.sma(50)  # Recalculates

Using with NumPy

# Indicators return pandas Series
rsi = df.rhoa.indicators.rsi(14)

# Convert to numpy for faster operations
rsi_array = rsi.values

# Or use pandas optimized operations
above_70 = (rsi > 70).sum()  # Fast pandas operation

Common Questions

Q: Which indicator is best?: No single “best” indicator exists. Use combinations based on market conditions and strategy.
Q: Why do my indicators give different values than TradingView?: Small differences may occur due to calculation methods, especially for exponential averages. Rhoa uses industry-standard formulas.
Q: Can I use indicators on intraday data?: Yes, indicators work on any timeframe. Adjust parameters accordingly (shorter periods for intraday).
Q: How do I choose parameter values?: Start with defaults, then optimize on training data. Validate on separate test data. Avoid overfitting.
Q: Should I normalize indicator values?: Some indicators (RSI, Stochastic) are already normalized (0-100). Others benefit from normalization for ML.

Indicators Guide

Overview

Accessing Indicators

Quick Reference Table

Trend Indicators

Simple Moving Average (SMA)

Exponential Weighted Moving Average (EWMA)

Average Directional Index (ADX)

Parabolic SAR

Momentum Indicators

Relative Strength Index (RSI)

MACD (Moving Average Convergence Divergence)

Stochastic Oscillator

Williams %R

Volatility Indicators

Exponentially Weighted Moving Variance (EWMV)

Exponentially Weighted Moving Standard Deviation (EWMSTD)

Bollinger Bands

Average True Range (ATR)

Commodity Channel Index (CCI)

Combining Indicators

Multi-Indicator Strategy Example

Best Practices Summary

General Guidelines

Common Mistakes to Avoid

Performance Tips

For Large Datasets

Using with NumPy

Further Reading

Common Questions