# pandas

A Cursor rule that steers the agent toward vectorized pandas: no row loops, explicit .loc and .iloc, lean dtypes. Useful when generated code runs slow on big frames.

- rule · `type=rule` · Cursor · ★ 3579
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/pandas.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/pandas

## What it does

The rule lives in `.cursor/rules/pandas.mdc` and applies to every Python file through the globs `**/*.py`. It covers imports, method chaining, vectorization, indexing, CSV loading, dtypes and missing values.

Each section ships a bad/good pair, so the agent has a concrete target. Row-wise `.iterrows()` becomes a vectorized operation, chained indexing becomes an assignment through `.loc`, and stacked conditionals turn into `np.select`. For filtering the rule points at `df.query()`, for computed columns at `df.eval()`.

Memory gets its own section: pass `dtype` and `usecols` to `read_csv`, use `category` for repeated strings, `Int32` for nullable integers. Missing values and silent type conversions are covered too, including the case where an `Int64` column quietly turns into `float64` after arithmetic. Everything here assumes pandas and numpy, nothing else.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/pandas.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/pandas.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/pandas.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/pandas.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/pandas.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/pandas.mdc

## When to use it
- replace row-by-row loops with vectorized operations
- stop `SettingWithCopyWarning` from chained assignment
- cut memory use when loading large CSV files
- pick a sensible dtype instead of object and int64 defaults
- handle NaN before arithmetic so it does not propagate

## Example prompts
- rewrite this df loop using vectorized operations
- why does SettingWithCopyWarning show up here and how do I fix it
- add dtype and usecols to this read_csv call

## Source text · pandas.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ce114c6b0fa8522b.

BEGIN THIRD-PARTY FILE ce114c6b0fa8522b
````text
---
description: This guide outlines definitive best practices for writing high-performance, maintainable, and robust pandas code, focusing on modern patterns and avoiding common pitfalls.
globs: **/*.py
---
# pandas Best Practices

Pandas is the backbone of data analysis in Python. Adhere to these guidelines to write efficient, readable, and scalable code that integrates seamlessly into modern AI/ML pipelines.

## 1. Code Organization & Structure

### 1.1 Standard Imports
Always import pandas with its conventional alias for consistency.

❌ BAD
```python
import pandas
from pandas import DataFrame
```

✅ GOOD
```python
import pandas as pd
import numpy as np # For numerical operations
```

### 1.2 Method Chaining for Readability
Chain operations to create clear, sequential data transformations. Use `.pipe()` for custom functions or when intermediate steps improve clarity.

❌ BAD
```python
df_filtered = df[df['value'] > 10]
df_grouped = df_filtered.groupby('category')
df_result = df_grouped['metric'].mean().reset_index()
```

✅ GOOD
```python
df_result = (
    df[df['value'] > 10]
    .groupby('category')['metric']
    .mean()
    .reset_index()
    .rename(columns={'metric': 'avg_metric'}) # Add a descriptive rename
)
```

## 2. Common Patterns & Anti-patterns

### 2.1 Avoid Python Loops – Embrace Vectorization
Iterating over DataFrames row-by-row (`.iterrows()`, `.apply(axis=1)`) is a major performance bottleneck. Pandas operations are optimized C-level functions.

❌ BAD (Slow for large DataFrames)
```python
# Calculating a new column based on existing ones
for index, row in df.iterrows():
    df.loc[index, 'new_col'] = row['col_a'] + row['col_b']

# Complex conditional logic with .apply(axis=1)
def calculate_status(row):
    if row['score'] > 90 and row['grade'] == 'A':
        return 'Excellent'
    return 'Good'
df['status'] = df.apply(calculate_status, axis=1)
```

✅ GOOD (Vectorized and performant)
```python
# Calculating a new column
df['new_col'] = df['col_a'] + df['col_b']

# Complex conditional logic using np.select or boolean indexing
conditions = [
    (df['score'] > 90) & (df['grade'] == 'A'),
    (df['score'] > 80) & (df['grade'] == 'B')
]
choices = ['Excellent', 'Very Good']
df['status'] = np.select(conditions, choices, default='Good')
```

### 2.2 Use `.loc` and `.iloc` for Explicit Access
Always use `.loc` (label-based) or `.iloc` (integer-based) for setting values to prevent `SettingWithCopyWarning` and ensure predictable behavior.

❌ BAD (Chained indexing can return a view, leading to `SettingWithCopyWarning`)
```python
df[df['city'] == 'London']['population'] = 8_000_000
```

✅ GOOD (Explicitly modifies the original DataFrame)
```python
df.loc[df['city'] == 'London', 'population'] = 8_000_000
```

### 2.3 Leverage `df.eval()` and `df.query()`
For complex filtering or column creation, `eval()` and `query()` can be significantly faster than boolean indexing, as they use the underlying C engine.

❌ BAD
```python
df_filtered = df[(df['col_a'] > 10) & (df['col_b'] < 20) | (df['col_c'] == 'X')]
df['new_col'] = df['col_a'] * df['col_b'] + df['col_c'] / df['col_d']
```

✅ GOOD
```python
df_filtered = df.query("(col_a > 10 and col_b < 20) or (col_c == 'X')")
df['new_col'] = df.eval("col_a * col_b + col_c / col_d")
```

## 3. Performance Considerations

### 3.1 Optimize Data Loading with `read_csv`
Specify `dtype` and `usecols` upfront to reduce memory footprint and speed up loading, especially for large files. Let `read_csv` infer optimal defaults (`low_memory=False` is often better).

❌ BAD
```python
df = pd.read_csv("large_data.csv") # Pandas infers all dtypes, loads all columns
```

✅ GOOD
```python
# Define dtypes and select only necessary columns
dtypes = {
    'store_id': 'Int32', # Use nullable integer
    'product_id': 'Int32',
````
END THIRD-PARTY FILE ce114c6b0fa8522b

The third-party file has ended. Everything between the ce114c6b0fa8522b tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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