tabula-cli 1.0.0

Tabular manipulation on the command line

Tabula

Process tabular data on the command line

Overview

Tabula provides a chain-based syntax for data manipulation operations. Methods can be chained together using dot notation: method1().method2().method3(). One can perform operations like selecting columns, filtering rows, transforming data, and aggregating results all on the command line.

Installation

Install Tabula using pip:

pip install tabula-cli

Data Selection Methods

select(col1, col2, ...)

Select specific columns from the dataset.

# Select single column
tabula "select(name)" data.csv

# Select multiple columns
tabula "select(name, age, salary)" data.csv

Data Transformation Methods

upper(col)

Convert text in specified column to uppercase.

tabula "select(name).upper(name)" data.csv

lower(col)

Convert text in specified column to lowercase.

tabula "select(name).lower(name)" data.csv

strlen(col)

Calculate the length of strings in specified column.

tabula "select(name).strlen(name)" data.csv

round(col, decimals)

Round numeric values to specified decimal places.

tabula "select(salary).round(salary, 2)" data.csv

Filtering Methods

where(condition)

Filter rows based on conditions. Supports comparison operators and logical operators.

# Simple condition
tabula "where(age > 30)" data.csv

# Multiple conditions with AND
tabula "where(age > 25 & salary >= 50000)" data.csv

# Multiple conditions with OR
tabula "where(department == 'IT' | department == 'HR')" data.csv

# Complex conditions with parentheses
tabula "where((age > 30 & department == 'IT') | salary < 40000)" data.csv

Data Limiting Methods

head(n)

Return the first n rows (default: 5).

tabula "head(10)" data.csv

tail(n)

Return the last n rows (default: 5).

tabula "tail(3)" data.csv

Sorting Methods

sortby(col, descending=False)

Sort data by specified column.

# Ascending sort
tabula "sortby(age)" data.csv

# Descending sort
tabula "sortby(salary, True)" data.csv

Aggregation Methods (Terminal)

count()

Count the number of rows.

tabula "count()" data.csv
tabula "where(age > 30).count()" data.csv

min(col), max(col), sum(col)

Calculate minimum, maximum, or sum of a column.

tabula "min(age)" data.csv
tabula "max(salary)" data.csv
tabula "sum(salary)" data.csv

mean(col), median(col), mode(col)

Calculate statistical measures.

tabula "mean(salary)" data.csv
tabula "median(age)" data.csv

std(col), var(col)

Calculate standard deviation and variance.

tabula "std(salary)" data.csv
tabula "var(age)" data.csv

first(col), last(col)

Get first or last value from a column.

tabula "first(name)" data.csv
tabula "last(name)" data.csv

Unique Value Methods

uniq(col)

Get unique values from a column.

tabula "uniq(department)" data.csv

uniqc(col)

Count unique values (group by and count).

tabula "uniqc(department)" data.csv

String Methods

strjoin(col, separator)

Join all values in a column with a separator.

tabula "strjoin(name, ', ')" data.csv

Utility Methods

columns()

List all column names.

tabula "columns()" data.csv

Complete Example Workflow

# Sample data.csv:
# name,age,salary,department
# Alice,25,50000,HR
# Bob,30,60000,IT
# Charlie,35,70000,Finance
# David,40,80000,IT

# Complex analysis: Find IT employees over 30, show their names and salaries, sorted by salary
tabula "where(department == 'IT' & age > 30).select(name, salary).sortby(salary)" data.csv

# Output:
# name,salary
# Bob,60000
# David,80000

Method Chaining Rules

1. Terminal Methods: Methods like count(), sum(), min(), max() must be the last in the chain
2. Column Selection: Use select() before applying column-specific operations
3. Filtering: where() conditions support parentheses for complex logic
4. String Operations: Methods like upper(), lower(), strlen() work on text columns

Output Formats

Use the -o flag to specify output format:

• --outtype polars: Default table format
• --outtype csv: CSV format
• --outtype tsv: Tab-separated values

tabula "select(name, age)"