pandas-term 0.0.1

CLI bringing pandas operations to the command line

pandas-term

pandas-term is a CLI bringing pandas operations to the command line.

Note: Still in early experimental development and may change

Installation

pipx install pandas-term

Or with pip:

pip install pandas-term

Command Reference

CLI Command	Pandas Function	Description
pd select	df[columns]	Select columns
pd drop	df.drop()	Drop columns
pd rename	df.rename()	Rename columns
pd sort	df.sort_values()	Sort by columns
pd dedup	df.drop_duplicates()	Remove duplicate rows
pd merge	pd.merge()	Merge two dataframes
pd concat	pd.concat()	Concatenate dataframes
pd batch	df.iloc[]	Split dataframe into batches
pd query	df.query()	Filter using query expressions
pd head	df.head()	Get first n rows
pd tail	df.tail()	Get last n rows
pd dropna	df.dropna()	Drop rows with null values
pd describe	df.describe()	Descriptive statistics
pd unique	df[col].unique()	Unique values in column
pd shape	df.shape	Dimensions (rows, columns)
pd columns	df.columns	Column names
pd value-counts	df[col].value_counts()	Count unique values
pd groupby	df.groupby().agg()	Group by and aggregate

Usage

All commands accept an input file path (or - for stdin) and an optional -o/--output flag for the output file (or - for stdout).

Transform commands

# Select columns (comma-separated)
pd select name,age data.csv

# Drop columns (comma-separated)
pd drop unwanted_column data.csv

# Sort by columns (comma-separated for multiple)
pd sort age data.csv --ascending
pd sort "age,name" data.csv --descending

# Remove duplicate rows
pd dedup data.csv
pd dedup --subset name,email data.csv

# Rename columns
pd rename "name:full_name" data.csv
pd rename "name:full_name,age:years" data.csv

# Merge two dataframes
pd merge left.csv right.csv --on user_id --how inner
pd merge left.csv right.csv --left-on id --right-on user_id --how left

# Concatenate multiple dataframes
pd concat file1.csv file2.csv file3.csv

# Split dataframe into batches
pd batch data.csv --sizes 100 -o "batch_{}.csv"
pd batch data.csv --sizes 1,2,10,50 -o "batch_{}.csv"  # variable sizes, last repeats

Filter commands

# Filter using pandas query expressions
pd query "age > 30 and city == 'NYC'" data.csv

# First N rows
pd head --n 100 data.csv

# Last N rows
pd tail --n 50 data.csv

# Drop rows with null values in any column
pd dropna data.csv

# Drop rows with null values in specific column
pd dropna --column column_name data.csv

Stats commands

# Descriptive statistics
pd describe data.csv

# Unique values in a column
pd unique country data.csv

# Dimensions (rows, columns)
pd shape data.csv

# Column names
pd columns data.csv

Aggregate commands

# Count unique values
pd value-counts city data.csv
pd value-counts department data.csv --normalize

# Group by and aggregate (comma-separated for multiple group columns)
pd groupby department data.csv --col salary --agg sum
pd groupby "city,department" data.csv --col age --agg mean

Piping

All commands support piping through stdin/stdout. When piping, you can omit the input file argument (it defaults to stdin):

cat data.csv | pd head --n 100 | pd select name,age | pd query "age > 30"

# Or chain commands directly
pd sort age data.csv --descending | pd head --n 10 | pd select name,age

Output Formats

When writing to a file, the format is determined by the file extension:

pd select name,age data.csv -o output.xlsx
pd query "age > 30" data.json -o filtered.parquet

For stdout output (default is CSV), use --json to output as JSON:

pd head --n 10 data.csv --json
pd query "age > 30" data.csv --json | jq '.[] | .name'

Supported formats: CSV, XLSX, JSON, Parquet (file output) / CSV, JSON (stdout)

Development

Requires uv

Create virtual environment and install dependencies:

uv sync

Dev commands

Command	Description
make format	Format code with ruff
make lint	Run linting checks (ruff + type checking)
make test	Run pytest tests
make check	Format, lint, and run tests
make coverage	Run tests with coverage report