pdataframe #

A DataFrame library for storing, manipulating, and analyzing data, using an interface similar to Python’s pandas. Designed for large datasets; key features include fast label-based access (O(k)) and support for duplicate labels.

Usage examples: #

Create a DataFrame

// List input; each inner List is a row of data 
final dfList = DataFrame([[1,2,3],[4,5,6],[7,8,9]]); 

// Map input; keys are the column names and the value List's are the associated column of data
final dfMap = DataFrame({0:[1,4,7], 1:[2,5,8], 2:[3,6,9]});

// List input Using parameters to set column/row names
final df = DataFrame([[1,2,3.0],[4,5,6],[7,'hi',9]], index:['Dog','Dog','Cat'], columns:['M','W','F']); 

print(df);
//     | M |   W  |  F
// --------------------  
// Dog | 1 |   2  | 3.0   
// Dog | 4 |   5  |  6
// Cat | 7 | 'no' |  9

Verify column types

print(df.dtypes); // [int, Object, Double]

Note: For simplicity, dtypes does not distinguish between nullable and non-nullable types (e.g. int? shown as int).

Access a column of data

print(df['a']); // [1,4,7]

Retrieve a row of data

print(df2.iloc(row: 1)); // [1, 2, 3.0]

Edit data (multiple methods)

// Edit data via columns
df['a'][2] = [30];

// Edit row using iloc(); the row is called using it's integer index
df.iloc(row: 2, col:'a', edit: 30); 

// Edit row using loc(); the row is called using it's row name
df.iloc(row: 'Cat', col:'a', edit: 30); 

// Edit row using editRow(); the row data modified using [] operators
df.editRow['Cat']['a'] = 30; 

Add Data

// Add one DataFrame to another DataFrame using concat()
var newDf = concat([dfList, dfMap], axis:0); // 'axis:0' combines the columns 

// Append a new row to a DataFrame 
var newDf = df.append([[1,2,3]], columns:['M','W','F']); 

Note: Use parameter inplace:true to modify the current DataFrame

Delete Data

// Drop a row
var newDf = drop('Cat', axis:0); // 'axis:0' specifies a row operation ('axis:1' would be a column)  

Apply a math function to a column

// Apply '*2' to every value in column a
df.m('a',(a)=>a*2);

csv import/export support

// Import data from data.csv file
var file = await DataFrame.read_csv('lib/files/data.csv');

// Create csv file called dataOutput.csv and store df data in it
df.to_csv(file:'lib/files/dataOutput.csv', index: true);

Timestamp (basic functionality)

// Create a Timestamp
var ts1 = Timestamp("February 9, 1999");

Miscellaneous Functions #

These functions provide additional flexibility and allow for fine-tuning specific behaviors.

DataFrame Utilities

• reset_index() – Resets the row index of the DataFrame to a default integer-based index.
• sort() – Sorts the DataFrame rows by a specified column.
• head() – Returns the first N rows of the DataFrame.
• info() – Prints and returns a summary of the DataFrame, including the column index, name, non-null count, and data type.
• table() – Converts the entire DataFrame to a list of rows, optionally including the row index as the first column.

Data Cleaning & Filtering

filterNulls(), countNulls(), countZeros(), interpolate(), knnImputer()

Descriptive Statistics

sumCol(), mean(), max(), min(), rollingMad(), weightedQuantile(), weightedHistogram()

Rolling & Expanding Windows

rollingSum(), rollingMean(), rollingStd(), rollingApply(), expandingMin(), expandingMax(), expandingMean(), expandingVar()

Grouping & Reshaping

groupBy(), pivotTable(), melt(), resample()

Time Series Analysis

autocorrelation(), partialAutocorrelation(), seasonalDecompose(), exponentialSmoothing()

Statistical Tests

tTest(), anova(), chiSquare(), bootstrap()

Linear Algebra & Decomposition

covarianceMatrix(), pca(), svd()

Signal Processing

fft(), convolve(), crossCorrelate()

Outlier Detection

outlierIQR(), outlierZScore(), localOutlierFactor()

For more details, refer to the full documentation or the code comments.