Computation#

 Compute absolute values. Compute the element-wise value for the inverse cosine. Compute the element-wise value for the inverse hyperbolic cosine. Compute the element-wise value for the inverse sine. Compute the element-wise value for the inverse hyperbolic sine. Compute the element-wise value for the inverse tangent. Compute the element-wise value for the inverse hyperbolic tangent. Get index values where Boolean Series evaluate True. Get unique index as Series. Compute the element-wise value for the cosine. 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. Compute the exponential, element-wise. `Series.hash`([seed, seed_1, seed_2, seed_3]) Hash the Series. `Series.is_between`(lower_bound, upper_bound) Get a boolean mask of the values that fall between the given start/end values. `Series.kurtosis`(*[, fisher, bias]) Compute the kurtosis (Fisher or Pearson) of a dataset. `Series.log`([base]) Compute the logarithm to a given base. Compute the base 10 logarithm of the input array, element-wise. Compute the natural logarithm of the input array plus one, element-wise. `Series.map_dict`(remapping, *[, default, ...]) Replace values in the Series using a remapping dictionary. Computes percentage change between values. Get a boolean mask of the local maximum peaks. Get a boolean mask of the local minimum peaks. `Series.rank`([method, descending, seed]) 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. Find indices where elements should be inserted to maintain order. Compute the element-wise indication of the sign. Compute the element-wise value for the sine. Compute the element-wise value for the hyperbolic sine. `Series.skew`(*[, bias]) Compute the sample skewness of a data set. Compute the square root of the elements. Compute the element-wise value for the tangent. Compute the element-wise value for the hyperbolic tangent.