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+# Statistical Methods
+
+> Dataframe API specification for statistical methods.
+
+A conforming implementation of the dataframe API standard must provide and support the following methods adhering to the following conventions.
+
+-   Positional parameters must be [positional-only](https://www.python.org/dev/peps/pep-0570/) parameters. Positional-only parameters have no externally-usable name. When a method accepting positional-only parameters is called, positional arguments are mapped to these parameters based solely on their order.
+-   Optional parameters must be [keyword-only](https://www.python.org/dev/peps/pep-3102/) arguments.
+
+## Methods
+
+<!-- NOTE: please keep the methods in alphabetical order -->
+
+(method-cummax)=
+### cummax(/, *, skipna=True)
+
+Calculates the cumulative maximum value.
+
+#### Parameters
+
+-   **skipna**: _bool_
+
+    -   Exclude `NA` and `null` values. If an entire column is `NA`, the result must be `NA`. Default: `True`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing cumulative maximum values. The returned dataframe must have the same size as the original dataframe instance.
+
+(method-cummin)=
+### cummin(/, *, skipna=True)
+
+Calculates the cumulative minimum value.
+
+#### Parameters
+
+-   **skipna**: _bool_
+
+    -   Exclude `NA` and `null` values. If an entire column is `NA`, the result must be `NA`. Default: `True`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing cumulative minimum values. The returned dataframe must have the same size as the original dataframe instance.
+
+(method-cumsum)=
+### cumsum(/, *, skipna=True)
+
+Calculates the cumulative sum.
+
+#### Parameters
+
+-   **skipna**: _bool_
+
+    -   Exclude `NA` and `null` values. If an entire column is `NA`, the result must be `NA`. Default: `True`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing cumulative sums. The returned dataframe must have the same size as the original dataframe instance.
+
+(method-cumprod)=
+### cumprod(/, *, skipna=True)
+
+Calculates the cumulative product.
+
+#### Parameters
+
+-   **skipna**: _bool_
+
+    -   Exclude `NA` and `null` values. If an entire column is `NA`, the result must be `NA`. Default: `True`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing cumulative products. The returned dataframe must have the same size as the original dataframe instance.
+
+(method-max)=
+### max(/, *, axis=None)
+
+Calculates the maximum value.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which maximum values must be computed. If equal to `0`, the maximum values must be computed over the index. If equal to `1`, the maximum values must be computed over the columns. By default, maximum values must be computed over the index. Default: `None`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the maximum values.
+
+(method-mean)=
+### mean(/, *, axis=None)
+
+Calculates the arithmetic mean.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which arithmetic means must be computed. If equal to `0`, arithmetic means must be computed over the index. If equal to `1`, arithmetic means must be computed over the columns. By default, arithmetic means must be computed over the index. Default: `None`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the arithmetic means.
+
+(method-min)=
+### min(/, *, axis=None)
+
+Calculates the minimum value.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which minimum values must be computed. If equal to `0`, the minimum values must be computed over the index. If equal to `1`, the minimum values must be computed over the columns. By default, minimum values must be computed over the index. Default: `None`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the minimum values.
+
+(method-nlargest)=
+### nlargest(n, columns, /)
+
+Returns the first `n` rows having the largest values in `columns` and sorted in descending order.
+
+#### Parameters
+
+-   **n**: _int_
+
+    -   Number of rows.
+
+-   **columns**: _Any_
+
+    -   Column label(s) to order by. If provided a list of column labels, the first list element (label) determines the first `n` rows, the second label orders the `n` rows, the third label orders ties for the first two labels, and so on and so forth. In other words, ordering by label is applied sequentially.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the first `n` rows.
+
+(method-nsmallest)=
+### nsmallest(n, columns, /)
+
+Returns the first `n` rows having the smallest values in `columns` and sorted in ascending order.
+
+#### Parameters
+
+-   **n**: _int_
+
+    -   Number of rows.
+
+-   **columns**: _Any_
+
+    -   Column label(s) to order by. If provided a list of column labels, the first list element (label) determines the first `n` rows, the second label orders the `n` rows, the third label orders ties for the first two labels, and so on and so forth. In other words, ordering by label is applied sequentially.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the first `n` rows.
+
+(method-prod)=
+### prod(/, *, axis=None)
+
+Calculates the product.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which products must be computed. If equal to `0`, the products must be computed over the index. If equal to `1`, the products must be computed over the columns. By default, products must be computed over the index. Default: `None`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the products.
+
+(method-std)=
+### std(/, *, axis=None, correction=1.0)
+
+Calculates the standard deviation.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which standard deviations must be computed. If equal to `0`, standard deviations must be computed over the index. If equal to `1`, standard deviations must be computed over the columns. By default, standard deviations must be computed over the index. Default: `None`.
+
+-   **correction**: _Union\[ int, float ]_
+
+    -   degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the standard deviation according to `N-c` where `N` corresponds to the total number of elements over which the standard deviation is computed and `c` corresponds to the provided degrees of freedom adjustment. When computing the standard deviation of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the corrected sample standard deviation, setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction). Default: `1.0`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the standard deviations.
+
+(method-sum)=
+### sum(/, *, axis=None)
+
+Calculates the sum.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which sums must be computed. If equal to `0`, the sums must be computed over the index. If equal to `1`, the sums must be computed over the columns. By default, sums must be computed over the index. Default: `None`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the sums.
+
+(method-var)=
+### var(/, *, axis=None, correction=1.0)
+
+Calculates the variance.
+
+#### Parameters
+
+-   **axis**: _Optional\[ int ]_
+
+    -   axis along which variances must be computed. If equal to `0`, variances must be computed over the index. If equal to `1`, variances must be computed over the columns. By default, variances must be computed over the index. Default: `None`.
+
+-   **correction**: _Union\[ int, float ]_
+
+    -   degrees of freedom adjustment. Setting this parameter to a value other than `0` has the effect of adjusting the divisor during the calculation of the variance according to `N-c` where `N` corresponds to the total number of elements over which the variance is computed and `c` corresponds to the provided degrees of freedom adjustment. When computing the variance of a population, setting this parameter to `0` is the standard choice (i.e., the provided array contains data constituting an entire population). When computing the unbiased sample variance, setting this parameter to `1` is the standard choice (i.e., the provided array contains data sampled from a larger population; this is commonly referred to as Bessel's correction). Default: `1.0`.
+
+#### Returns
+
+-   **out**: _&lt;dataframe&gt;_
+
+    -   dataframe containing the variances.
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