bulwark.checks¶
Each function in this module should:
- take a pd.DataFrame as its first argument, with optional additional arguments,
- make an assert about the pd.DataFrame, and
- return the original, unaltered pd.DataFrame
Functions
custom_check (check_func, df, *args, **kwargs) |
Assert that check(df, *args, **kwargs) is true. |
has_columns (df, columns[, exact_cols, …]) |
Asserts that df has columns |
has_dtypes (df, items) |
Asserts that df has dtypes |
has_no_infs (df[, columns]) |
Asserts that there are no np.infs in df. |
has_no_nans (df[, columns]) |
Asserts that there are no np.nans in df. |
has_no_neg_infs (df[, columns]) |
Asserts that there are no np.infs in df. |
has_no_nones (df[, columns]) |
Asserts that there are no Nones in df. |
has_no_x (df[, values, columns]) |
Asserts that there are no user-specified values in df’s columns. |
has_set_within_vals (df, items) |
Asserts that all given values are found in columns’ values. |
has_unique_index (df) |
Asserts that df’s index is unique. |
is_monotonic (df[, items, increasing, strict]) |
Asserts that the df is monotonic. |
is_same_as (df, df_to_compare, **kwargs) |
Asserts that two pd.DataFrames are equal. |
is_shape (df, shape) |
Asserts that df is of a known row x column shape. |
multi_check (df, checks[, warn]) |
Asserts that all checks pass. |
one_to_many (df, unitcol, manycol) |
Asserts that a many-to-one relationship is preserved between two columns. |
unique (df[, columns]) |
Asserts that columns in df only have unique values. |
within_n_std (df[, n]) |
Asserts that every value is within n standard deviations of its column’s mean. |
within_range (df[, items]) |
Asserts that df is within a range. |
within_set (df[, items]) |
Asserts that df is a subset of items. |