Computation#

Series.abs()

Compute absolute values.

Series.arccos()

Compute the element-wise value for the inverse cosine.

Series.arccosh()

Compute the element-wise value for the inverse hyperbolic cosine.

Series.arcsin()

Compute the element-wise value for the inverse sine.

Series.arcsinh()

Compute the element-wise value for the inverse hyperbolic sine.

Series.arctan()

Compute the element-wise value for the inverse tangent.

Series.arctanh()

Compute the element-wise value for the inverse hyperbolic tangent.

Series.arg_true()

Get index values where Boolean Series evaluate True.

Series.arg_unique()

Get unique index as Series.

Series.cos()

Compute the element-wise value for the cosine.

Series.cosh()

Compute the element-wise value for the hyperbolic cosine.

Series.cummax([reverse])

Get an array with the cumulative max computed at every element.

Series.cummin([reverse])

Get an array with the cumulative min computed at every element.

Series.cumprod([reverse])

Get an array with the cumulative product computed at every element.

Series.cumsum([reverse])

Get an array with the cumulative sum computed at every element.

Series.cumulative_eval(expr[, min_periods, ...])

Run an expression over a sliding window that increases 1 slot every iteration.

Series.diff([n, null_behavior])

Calculate the n-th discrete difference.

Series.dot(other)

Compute the dot/inner product between two Series.

Series.entropy([base, normalize])

Computes the entropy.

Series.ewm_mean([com, span, half_life, ...])

Exponentially-weighted moving average.

Series.ewm_std([com, span, half_life, ...])

Exponentially-weighted moving standard deviation.

Series.ewm_var([com, span, half_life, ...])

Exponentially-weighted moving variance.

Series.exp()

Compute the exponential, element-wise.

Series.hash([seed, seed_1, seed_2, seed_3])

Hash the Series.

Series.kurtosis([fisher, bias])

Compute the kurtosis (Fisher or Pearson) of a dataset.

Series.log([base])

Compute the logarithm to a given base.

Series.log10()

Compute the base 10 logarithm of the input array, element-wise.

Series.pct_change([n])

Computes percentage change between values.

Series.peak_max()

Get a boolean mask of the local maximum peaks.

Series.peak_min()

Get a boolean mask of the local minimum peaks.

Series.rank([method, reverse])

Assign ranks to data, dealing with ties appropriately.

Series.rolling_apply(function, window_size)

Apply a custom rolling window function.

Series.rolling_max(window_size[, weights, ...])

Apply a rolling max (moving max) over the values in this array.

Series.rolling_mean(window_size[, weights, ...])

Apply a rolling mean (moving mean) over the values in this array.

Series.rolling_median(window_size[, ...])

Compute a rolling median.

Series.rolling_min(window_size[, weights, ...])

Apply a rolling min (moving min) over the values in this array.

Series.rolling_quantile(quantile[, ...])

Compute a rolling quantile.

Series.rolling_skew(window_size[, bias])

Compute a rolling skew.

Series.rolling_std(window_size[, weights, ...])

Compute a rolling std dev.

Series.rolling_sum(window_size[, weights, ...])

Apply a rolling sum (moving sum) over the values in this array.

Series.rolling_var(window_size[, weights, ...])

Compute a rolling variance.

Series.search_sorted(element)

Find indices where elements should be inserted to maintain order.

Series.sign()

Compute the element-wise indication of the sign.

Series.sin()

Compute the element-wise value for the sine.

Series.sinh()

Compute the element-wise value for the hyperbolic sine.

Series.skew([bias])

Compute the sample skewness of a data set.

Series.sqrt()

Compute the square root of the elements.

Series.tan()

Compute the element-wise value for the tangent.

Series.tanh()

Compute the element-wise value for the hyperbolic tangent.