Package parallax on DUB

To use this package, run the following command in your project's root directory:

Parallax

Parallax is a powerful pandas/polars-like DataFrames library.

It has good functionality for data analysis, transformation, and time series manipulation.

Usage

import parallax.dataframes;

void main()
{
    auto df = createDataFrame(
        ["name", "age", "salary"],
        ["Alice", "Bob", "Charlie"],
        [25, 30, 35],
        [50000.0, 60000.0, 70000.0]
    );

    df.show();
    auto head = df.head(2);
    auto selected = df.select("name", "salary");
    auto summary = df.describe();
    summary.showPivot();
}

Will output:

DataFrame(3 rows, 3 columns)
┌────────────┬────────────┬────────────┐
│ name       │ age        │ salary     │
├────────────┼────────────┼────────────┤
│ Alice      │ 25         │ 50000      │
│ Bob        │ 30         │ 60000      │
│ Charlie    │ 35         │ 70000      │
└────────────┴────────────┴────────────┘
DataFrame(8 rows, 3 columns)
┌────────────┬────────────┬──────────────┐
│            │ age        │ salary       │
├────────────┼────────────┼──────────────┤
│ count      │ 3          │ 3            │
│ mean       │ 30.000000  │ 60000.000000 │
│ std        │ 5.000000   │ 10000.000000 │
│ min        │ 25.000000  │ 50000.000000 │
│ 25%        │ 27.500000  │ 55000.000000 │
│ 50%        │ 30.000000  │ 60000.000000 │
│ 75%        │ 32.500000  │ 65000.000000 │
│ max        │ 35.000000  │ 70000.000000 │
└────────────┴────────────┴──────────────┘

Core Components

Function	Description
`create(...)`	Construct a DataFrame from columns
`opIndex(...)`	Access column(s) or row(s) via indexing
`opIndexAssign(...)`	Assign values via index
`rows`	Number of rows
`cols`	Number of columns
`columns()`	Returns list of column names
`shape()`	Tuple of (rows, cols)
`opSlice(...)`	Slice the DataFrame
`head(n)`	Return the first `n` rows
`tail(n)`	Return the last `n` rows
`select(...)`	Select one or more columns
`where(predicate)`	Filter rows by custom condition
`groupBy(col)`	Group by one or more columns
`pivotTable(...)`	Create a pivot table
`melt(...)`	Reshape from wide to long format
`meltAllExcept(...)`	Melt all columns except the specified ones
`meltMultiple(...)`	Melt multiple sets of columns
`unmelt(...)`	Reshape from long to wide format
`stack()`	Stack columns into a single column
`unstack()`	Unstack a stacked column back to wide
`crosstab(...)`	Cross-tabulate two columns
`rolling(window)`	Rolling window statistics
`pctChange()`	Compute percent change
`shift(n)`	Shift rows forward/backward
`ffill()`	Forward fill missing values
`bfill()`	Backward fill missing values
`createLags(n)`	Create lagged versions of columns
`describe()`	Summary statistics
`valueCounts(col)`	Count frequency of values in a column
`fillna(value)`	Replace missing values
`drop(cols...)`	Drop specified columns
`dropna()`	Drop rows with missing data
`rename(old => new)`	Rename columns
`apply(fn)`	Apply a custom function to each row
`show()`	Print the DataFrame
`showPivot()`	Display a pivot table nicely
`readCsv(path)`	Read a CSV file into a DataFrame
`toCsv(path)`	Write a DataFrame to CSV
`copy()`	Deep copy the DataFrame
`merge(other)`	Merge two DataFrames (default is inner join)
`sortValues(by)`	Sort by a specific column
`sum()`	Sum across numeric columns
`mean()`	Mean across numeric columns
`max()`	Max across numeric columns
`min()`	Min across numeric columns
`opBinary(...)`	Binary operations between DataFrames or columns

Time Series & Dates

Function / Feature	Description
`toDatetime(col)`	Convert string column to datetime
`dt(col)`	Return datetime column accessor
`setDatetimeIndex(col)`	Use datetime column as an index
`resample(freq)`	Resample time series by frequency
`betweenDates(start, end)`	Filter rows between two datetime values
`forYear(year)`	Filter for a specific year
`forMonth(month)`	Filter for a specific month
`rollup()`	Aggregate over time hierarchy

Datetime Accessors (`dt(...)`)

Accessor	Description
`dt_year()`	Extract year
`dt_month()`	Extract month
`dt_day()`	Extract day
`dt_dayofweek()`	Extract day of the week (0–6)
`dt_strftime(fmt)`	Format datetime as string
`dt_floor(freq)`	Round down to nearest freq
`dt_ceil(freq)`	Round up to nearest freq

Modules

Module	Purpose
`parallax.dataframes`	Core DataFrame class and utilities
`parallax.columns`	Column types and column management
`parallax.csv`	CSV reader/writer
`parallax.values`	Generic value abstraction (`DataValue`)
`parallax.datetime`	Datetime parsing and time logic

0.0.9	2025-Jul-25
0.0.8	2025-Jul-25
0.0.7	2025-Jul-25
0.0.6	2025-Jul-25
0.0.5	2025-Jul-24