pandas-term 0.0.6

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

uv tool install pandas-term

Usage

All commands accept an input file path (or - for stdin) and support -o/--output for file output (default: stdout).

Command Reference

Command	Pandas Equivalent	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 into batches
pd query	df.query()	Filter with query expr
pd head	df.head()	First n rows
pd tail	df.tail()	Last n rows
pd dropna	df.dropna()	Drop rows with nulls
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 dtypes	df.dtypes	Column data types
pd value-counts	df.value_counts()	Count unique values
pd groupby	df.groupby().agg()	Group by and aggregate

Transform

# Select columns
pd select name,age data.csv

# Drop, sort & rename
pd drop unwanted_column data.csv
pd sort age data.csv --descending
pd rename "name:full_name,age:years" data.csv

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

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

# Concatenate files (supports glob patterns)
pd concat file1.csv file2.csv
pd concat "data_*.csv"

# Split into batches
pd batch data.csv --sizes 100 -o "batch_{}.csv"

Filter

# Query expression
pd query "age > 30 and city == 'NYC'" data.csv

# First/last N rows
pd head --n 100 data.csv
pd tail --n 50 data.csv

# Drop rows with nulls
pd dropna data.csv
pd dropna --subset "name,age" data.csv

Stats

pd describe data.csv
pd unique country data.csv
pd shape data.csv
pd columns data.csv
pd dtypes data.csv

Aggregate

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

# Group by and aggregate
pd groupby department data.csv --col salary --agg sum
pd groupby "city,department" data.csv --col salary,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 query "age > 30" | pd select name,age

pd sort age data.csv --descending | pd head --n 10 | pd select name,age

Output Formats

Use -f/--format for stdout format (default: csv):

pd head --n 10 data.csv -f json
pd head --n 10 data.csv -f tsv
pd head --n 10 data.csv -f md

--json/-j is shorthand for -f json.

File output format is determined by extension:

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

Supported: csv, tsv, json, xlsx, parquet, md

For other extensions, use redirection: pd select name data.csv -f csv > output.txt

Developer setup

Requires uv

# Create venv & install dependencies
uv sync

Command	Description
make check	Format, lint, and test
make test	Run tests
make format	Format code
make lint	Linting only
make coverage	Tests with coverage