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Contents
module Polars
module Functions
# Convert categorical variables into dummy/indicator variables.
#
# @param df [DataFrame]
# DataFrame to convert.
# @param columns [Array, nil]
# A subset of columns to convert to dummy variables. `nil` means
# "all columns".
#
# @return [DataFrame]
def get_dummies(df, columns: nil)
df.to_dummies(columns: columns)
end
# Aggregate to list.
#
# @return [Expr]
def to_list(name)
col(name).list
end
# Compute the spearman rank correlation between two columns.
#
# Missing data will be excluded from the computation.
#
# @param a [Object]
# Column name or Expression.
# @param b [Object]
# Column name or Expression.
# @param ddof [Integer]
# Delta degrees of freedom
# @param propagate_nans [Boolean]
# If `True` any `NaN` encountered will lead to `NaN` in the output.
# Defaults to `False` where `NaN` are regarded as larger than any finite number
# and thus lead to the highest rank.
#
# @return [Expr]
def spearman_rank_corr(a, b, ddof: 1, propagate_nans: false)
corr(a, b, method: "spearman", ddof: ddof, propagate_nans: propagate_nans)
end
# Compute the pearson's correlation between two columns.
#
# @param a [Object]
# Column name or Expression.
# @param b [Object]
# Column name or Expression.
# @param ddof [Integer]
# Delta degrees of freedom
#
# @return [Expr]
def pearson_corr(a, b, ddof: 1)
corr(a, b, method: "pearson", ddof: ddof)
end
end
end
Version data entries
39 entries across 39 versions & 1 rubygems