Pandas Pro

What it does

When your agent uses it

• Cleaning a messy DataFrame with missing values, duplicates and wrong types
• Joining DataFrames on several keys and checking the result
• Building pivot tables and groupby summaries
• Resampling and filling gaps in time-series data
• Cutting memory use on a large dataset

Example prompts

• “Merge orders.csv and customers.csv on customer_id and date, then check that no rows were duplicated.”
• “Replace this iterrows loop with vectorized pandas code.”
• “Resample the sensor readings to daily means and interpolate the missing days.”
• “This DataFrame uses too much memory, so convert suitable columns to categoricals and read the file in chunks.”

Requirements

• Python with pandas

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Assess data structure — Examine dtypes, memory usage, missing values, data quality
2. 2Design transformation — Plan vectorized operations, avoid loops, identify indexing strategy
3. 3Implement efficiently — Use vectorized methods, method chaining, proper indexing
4. 4Validate results — Check dtypes, shapes, null counts, and row counts
5. 5Optimize — Profile memory, apply categorical types, use chunking if needed

What it can do on your machine

Read from SKILL.md and the folder at commit 1be15d8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • synergetic.solutions
  • jeffallan.github.io

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pandas Pro loads about 1.5k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 295 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~117

When it runs · the whole SKILL.md, loaded when a task matches

~1.5k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~18k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Jeffallan/claude-skills at commit 1be15d8, republished under its MIT licence (© Jeffallan). 295 words, ~1,539 tokens.

name

pandas-pro

description

Performs pandas DataFrame operations for data analysis, manipulation, and transformation. Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation tasks such as joining DataFrames on multiple keys, pivoting tables, resampling time series, handling NaN values with interpolation or forward-fill, groupby aggregations, type conversion, or performance optimization of large datasets.

license

MIT

metadata.author

https://github.com/Jeffallan

metadata.company

https://synergetic.solutions

metadata.version

1.1.0

metadata.domain

data-ml

metadata.triggers

pandas, DataFrame, data manipulation, data cleaning, aggregation, groupby, merge, join, time series, data wrangling, pivot table, data transformation

metadata.role

expert

metadata.scope

implementation

metadata.output-format

code

metadata.related-skills

python-pro

# ❌ AVOID: row-by-row iteration
for i, row in df.iterrows():
    df.at[i, 'tax'] = row['price'] * 0.2

# ✅ USE: vectorized assignment
df['tax'] = df['price'] * 0.2

# ❌ AVOID: chained indexing triggers SettingWithCopyWarning
df['A']['B'] = 1

# ✅ USE: .loc[] with explicit copy when mutating a subset
subset = df.loc[df['status'] == 'active', :].copy()
subset['score'] = subset['score'].fillna(0)

summary = (
    df.groupby(['region', 'category'], observed=True)
    .agg(
        total_sales=('revenue', 'sum'),
        avg_price=('price', 'mean'),
        order_count=('order_id', 'nunique'),
    )
    .reset_index()
)

merged = pd.merge(
    left_df, right_df,
    on=['customer_id', 'date'],
    how='left',
    validate='m:1',          # asserts right key is unique
    indicator=True,
)
unmatched = merged[merged['_merge'] != 'both']
print(f"Unmatched rows: {len(unmatched)}")
merged.drop(columns=['_merge'], inplace=True)

# Forward-fill then interpolate numeric gaps
df['price'] = df['price'].ffill().interpolate(method='linear')

# Fill categoricals with mode, numerics with median
for col in df.select_dtypes(include='object'):
    df[col] = df[col].fillna(df[col].mode()[0])
for col in df.select_dtypes(include='number'):
    df[col] = df[col].fillna(df[col].median())

daily = (
    df.set_index('timestamp')
    .resample('D')
    .agg({'revenue': 'sum', 'sessions': 'count'})
    .fillna(0)
)

pivot = df.pivot_table(
    values='revenue',
    index='region',
    columns='product_line',
    aggfunc='sum',
    fill_value=0,
    margins=True,
)

# Downcast numerics and convert low-cardinality strings to categorical
df['category'] = df['category'].astype('category')
df['count'] = pd.to_numeric(df['count'], downcast='integer')
df['score'] = pd.to_numeric(df['score'], downcast='float')
print(df.memory_usage(deep=True).sum() / 1e6, "MB after optimization")

© Jeffallan, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/pandas-pro of Jeffallan/claude-skills.

• SKILL.md
• references/aggregation-groupby.md
• references/data-cleaning.md
• references/dataframe-operations.md
• references/merging-joining.md
• references/performance-optimization.md

Open the folder on GitHubat commit 1be15d8

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Jeffallan/claude-skills, which our catalogue first saw on October 7, 2026.

• cortega26/chile-hub.agents/skills/pandas-pro