tidypolars_extra.stats

Functions

abs(x)	Absolute value
cor(x, y[, method])	Find the correlation of two columns
count(x)	Number of observations in each group
cov(x, y)	Find the covariance of two columns
cume_dist(x)	Compute cumulative distribution (proportion of values <= current value)
cummax(x)	Cumulative maximum
cummin(x)	Cumulative minimum
cumprod(x)	Cumulative product
cumsum(x)	Cumulative sum
first(x)	Get first value
floor(x)	Round numbers down to the lower integer
iqr(x)	Compute the interquartile range (Q3 - Q1)
last(x)	Get last value
length(x)	Number of observations in each group.
log(x)	Compute the natural logarithm of a column
log10(x)	Compute the base 10 logarithm of a column
mad(x)	Compute the median absolute deviation
max(x)	Get column max
mean(x)	Get column mean
median(x)	Get column median
min(x)	Get column minimum
mode(x)	Compute the statistical mode (most frequent value)
n()	Number of observations in each group
ntile(x, n)	Divide values into n roughly equal groups
percent_rank(x)	Compute percent rank (values between 0 and 1)
quantile(x[, quantile])	Get number of distinct values in a column
rank(x[, method])	Assigns a minimum rank to each element in the input list, handling ties by
scale(x)	Standardize the input by scaling it to a mean of 0 and a standard deviation of 1.
sd(x)	Get column standard deviation
sqrt(x)	Get column square root
sum(x)	Get column sum
var(x)	Get column variance
weighted_mean(x, w)	Compute weighted mean
zscore(x)	Standardize to z-scores (alias for scale)

Module Contents

tidypolars_extra.stats.abs(x)

Absolute value

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(abs_x = tp.abs('x'))
>>> df.mutate(abs_x = tp.abs(col('x')))

tidypolars_extra.stats.cor(x, y, method='pearson')

Find the correlation of two columns

Parameters:

• x (Expr) – A column

• y (Expr) – A column

• method (str) – Type of correlation to find. Either ‘pearson’ or ‘spearman’.

Examples

>>> df.summarize(cor = tp.cor(col('x'), col('y')))

tidypolars_extra.stats.count(x)

Number of observations in each group

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(count = tp.count(col('x')))

tidypolars_extra.stats.cov(x, y)

Find the covariance of two columns

Parameters:

• x (Expr) – A column

• y (Expr) – A column

Examples

>>> df.summarize(cor = tp.cov(col('x'), col('y')))

tidypolars_extra.stats.cume_dist(x)

Compute cumulative distribution (proportion of values <= current value)

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(cd = tp.cume_dist('x'))

tidypolars_extra.stats.cummax(x)

Cumulative maximum

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(cmax = tp.cummax('x'))

tidypolars_extra.stats.cummin(x)

Cumulative minimum

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(cmin = tp.cummin('x'))

tidypolars_extra.stats.cumprod(x)

Cumulative product

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(cprod = tp.cumprod('x'))

tidypolars_extra.stats.cumsum(x)

Cumulative sum

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(csum = tp.cumsum('x'))

tidypolars_extra.stats.first(x)

Get first value

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(first_x = tp.first('x'))
>>> df.summarize(first_x = tp.first(col('x')))

tidypolars_extra.stats.floor(x)

Round numbers down to the lower integer

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(floor_x = tp.floor(col('x')))

tidypolars_extra.stats.iqr(x)

Compute the interquartile range (Q3 - Q1)

Use in summarize() context only. Not suitable for mutate().

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(iqr_val = tp.iqr('x'))

tidypolars_extra.stats.last(x)

Get last value

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(last_x = tp.last('x'))
>>> df.summarize(last_x = tp.last(col('x')))

tidypolars_extra.stats.length(x)

Number of observations in each group.

Alias for count().

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(length = tp.length(col('x')))

tidypolars_extra.stats.log(x)

Compute the natural logarithm of a column

Parameters:

x (Expr) – Column to operate on

Examples

>>> df.mutate(log = tp.log('x'))

tidypolars_extra.stats.log10(x)

Compute the base 10 logarithm of a column

Parameters:

x (Expr) – Column to operate on

Examples

>>> df.mutate(log = tp.log10('x'))

tidypolars_extra.stats.mad(x)

Compute the median absolute deviation

Use in summarize() context only. Not suitable for mutate().

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(mad_val = tp.mad('x'))

tidypolars_extra.stats.max(x)

Get column max

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(max_x = tp.max('x'))
>>> df.summarize(max_x = tp.max(col('x')))

tidypolars_extra.stats.mean(x)

Get column mean

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(mean_x = tp.mean('x'))
>>> df.summarize(mean_x = tp.mean(col('x')))

tidypolars_extra.stats.median(x)

Get column median

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(median_x = tp.median('x'))
>>> df.summarize(median_x = tp.median(col('x')))

tidypolars_extra.stats.min(x)

Get column minimum

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(min_x = tp.min('x'))
>>> df.summarize(min_x = tp.min(col('x')))

tidypolars_extra.stats.mode(x)

Compute the statistical mode (most frequent value)

Returns the first mode if there are ties (non-deterministic for ties). Use in summarize() context.

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(m = tp.mode('x'))

tidypolars_extra.stats.n()

Number of observations in each group

Examples

>>> df.summarize(count = tp.n())

tidypolars_extra.stats.ntile(x, n)

Divide values into n roughly equal groups

Parameters:

• x (Expr, Series) – Column to operate on

• n (int) – Number of groups

Examples

>>> df.mutate(quartile = tp.ntile('x', 4))

tidypolars_extra.stats.percent_rank(x)

Compute percent rank (values between 0 and 1)

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(prank = tp.percent_rank('x'))

tidypolars_extra.stats.quantile(x, quantile=0.5)

Get number of distinct values in a column

Parameters:

• x (Expr, Series) – Column to operate on

• quantile (float) – Quantile to return

Examples

>>> df.summarize(quantile_x = tp.quantile('x', .25))

tidypolars_extra.stats.rank(x, method='dense')

Assigns a minimum rank to each element in the input list, handling ties by assigning the same (lowest) rank to tied values. The next distinct value’s rank is increased by the number of tied values before it.

Parameters:

• x (str) – Column to operate on

• method (str) – dense (default): Assigns ranks in a consecutive manner, without gaps, even for ties. average : Assigns the average rank to tied values. min: Assigns the minimum rank to tied values. max: Assigns the maximum rank to tied values. ordinal: Assigns a distinct rank to each value based on its order of appearance.

Returns:

A list of ranks corresponding to the elements of x.

Return type:

list of int

Examples

>>> rank([10, 20, 20, 30])
[1, 2, 2, 3]
>>> rank([3, 1, 2])
[3, 1, 2]  # since sorted order is 1,2,3 => ranks are assigned as per their order
>>> rank(["b", "a", "a", "c"])
[2, 1, 1, 3]

tidypolars_extra.stats.scale(x)

Standardize the input by scaling it to a mean of 0 and a standard deviation of 1.

Parameters:

x (Expr) – Column to operate on

Returns:

The standardized version of the input data.

Return type:

array-like

tidypolars_extra.stats.sd(x)

Get column standard deviation

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(sd_x = tp.sd('x'))
>>> df.summarize(sd_x = tp.sd(col('x')))

tidypolars_extra.stats.sqrt(x)

Get column square root

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(sqrt_x = tp.sqrt('x'))

tidypolars_extra.stats.sum(x)

Get column sum

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.summarize(sum_x = tp.sum('x'))
>>> df.summarize(sum_x = tp.sum(col('x')))

tidypolars_extra.stats.var(x)

Get column variance

Parameters:

x (Expr) – Column to operate on

Examples

>>> df.summarize(sum_x = tp.var('x'))
>>> df.summarize(sum_x = tp.var(col('x')))

tidypolars_extra.stats.weighted_mean(x, w)

Compute weighted mean

Parameters:

• x (Expr, Series) – Column of values

• w (Expr, Series) – Column of weights

Examples

>>> df.summarize(wm = tp.weighted_mean('x', 'w'))

tidypolars_extra.stats.zscore(x)

Standardize to z-scores (alias for scale)

Parameters:

x (Expr, Series) – Column to operate on

Examples

>>> df.mutate(z = tp.zscore('x'))