module Crysda

Overview

CrysDAis a **{Crys}**tal shard for **{D}**ata **{A}**nalysis. Provides you modern functional-style API for data manipulation to filter, transform, aggregate and reshape tabular data. Core of the library isCrysDA::DataFrame` an immutable data structure interface.

Features

• [X] Filter, transform, aggregate and reshape tabular data
• [X] Modern, user-friendly and easy-to-learn data-science API
• [X] Reads from plain and compressed tsv, csv, json, or any delimited format with or without header from local or remote.
• [X] Supports grouped operations
• [X] Supports reading data from DB
• [X] Tables can contain atomic columns (Number, Float, Bool, String) as well as object columns
• [X] Reshape tables from wide to long and back
• [X] Table joins (left, right, semi, inner, outer)
• [X] Cross tabulation
• [X] Descriptive statistics (mean, min, max, median, ...)
• [X] Functional API inspired by dplyr, pandas
• [X] many more...

Defined in:

Constant Summary

MISSING_VALUE = "NA"

PRINT_MAX_DIGITS = 3

PRINT_MAX_ROWS = 10

PRINT_MAX_WIDTH = 100

PRINT_ROW_NUMBERS = true

VERSION = {{ (`shards version \"/srv/crystaldoc.info/github-naqvis-CrysDA-v0.1.3/src\"`).chomp.stringify }}

Class Method Summary

• .bind_cols(left : DataFrame, right : DataFrame, rename_duplicates = true) : DataFrame

  Binds dataframes by column.

• .bind_rows(*dfs : DataFrame) : DataFrame

  Adds new rows.

• .column_types(df : DataFrame) : Array(ColSpec)

  return column types as an array of ColSpec struct

• .dataframe_of(rows : Iterable(Hash(String, Any)))

  Creates a new data-frame from Array of {} of String => Any

• .dataframe_of(rows : Iterable(DataFrameRow))

  Creates a new data-frame from array of DataFrameRow

• .dataframe_of(cols : Iterable(DataCol))

  Creates a data-frame from Array of DataCol

• .dataframe_of(*rows : Hash(String, Any))

  Creates a new data-frame from {} of String => Any

• .dataframe_of(*header : String)

  Creates a new dataframe in place.

• .dataframe_of(*rows : DataFrameRow)

  Creates a new data-frame from records encoded as key-value maps Column types will be inferred from the value types

• .dataframe_of(*cols : DataCol)

  Create a new data-frame from a list of DataCol instances

• .empty_df

  Creates an empty dataframe with 0 observation

• .from(resultset : DB::ResultSet)

  build a data-frame from a DB::ResultSet

• .from_json(json : String)

  builds a data-frame from a JSON string

• .read_csv(file : String | IO, separator : Char = ',', quote_char : Char = '"', skip_blank_lines : Bool = true, skip : Int32 = 0, comment : Char | Nil = '#', header : Int32 | Nil = 0, na_value : String = MISSING_VALUE, true_values = ["T", "TRUE"], false_values = ["F", "FALSE"])

  reads a comma separated value file/io into a dataframe.

• .read_json(file : String | IO)

  reads a json file or URL

• .selector(&block : ColumnSelector)

  helper method to return the block as Proc.

Class Method Detail

df = dataframe_of("quarter", "sales", "location").values(1, 300.01, "london", 2, 290, "chicago")

df.select(
  Crysda::ColumnSelector.new { |s| ... },
  Crysda::ColumnSelector.new { |s| ... }
)

df.select(
 Crysda.selector{|e| ....},
 Crysda.selector{|e| ....},
)