string_stats

string_stats ¤

StringStatistics ¤

StringStatistics(
    dataframe: DataFrame, time_column: str = "systime"
)

Bases: Base

Provides class methods to calculate statistics on string columns in a pandas DataFrame.

count_unique `classmethod` ¤

count_unique(
    dataframe: DataFrame, value_column: str = "value_string"
) -> int

Returns the number of unique strings in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Number of distinct non-null values.

most_frequent `classmethod` ¤

most_frequent(
    dataframe: DataFrame, value_column: str = "value_string"
) -> str | None

Returns the most frequent string in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`str \| None`	The most frequent non-null string (the smallest on ties), or
`str \| None`	`None` when the column holds no non-null value.

count_most_frequent `classmethod` ¤

count_most_frequent(
    dataframe: DataFrame, value_column: str = "value_string"
) -> int

Returns the count of the most frequent string in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Occurrences of :meth:`most_frequent`; `0` when the column holds
`int`	no non-null value (it raised `IndexError` before).

count_null `classmethod` ¤

count_null(
    dataframe: DataFrame, value_column: str = "value_string"
) -> int

Returns the number of null (NaN) values in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Number of missing values.

average_string_length `classmethod` ¤

average_string_length(
    dataframe: DataFrame, value_column: str = "value_string"
) -> float

Returns the average length of strings in the column, excluding null values.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`float`	Mean length of the non-null strings; NaN when there is none.

longest_string `classmethod` ¤

longest_string(
    dataframe: DataFrame, value_column: str = "value_string"
) -> str | None

Returns the longest string in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`str \| None`	The longest non-null string, or `None` when the column holds no
`str \| None`	non-null value.

shortest_string `classmethod` ¤

shortest_string(
    dataframe: DataFrame, value_column: str = "value_string"
) -> str | None

Returns the shortest string in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`str \| None`	The shortest non-null string, or `None` when the column holds no
`str \| None`	non-null value.

string_length_summary `classmethod` ¤

string_length_summary(
    dataframe: DataFrame, value_column: str = "value_string"
) -> pd.DataFrame

Returns a summary of string lengths, including min, max, and average lengths.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`DataFrame`	One-row DataFrame with columns `Min Length`, `Max Length` and `Average Length` (null values excluded).

most_common_n_strings `classmethod` ¤

most_common_n_strings(
    dataframe: DataFrame,
    n: int,
    value_column: str = "value_string",
) -> pd.Series

Returns the top N most frequent strings in the column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`n`	`int`	Number of strings to return.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`Series`	Series of counts indexed by string, most frequent first.

contains_substring_count `classmethod` ¤

contains_substring_count(
    dataframe: DataFrame,
    substring: str,
    value_column: str = "value_string",
    *,
    regex: bool = False
) -> int

Counts how many strings contain the specified substring.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`substring`	`str`	Text to look for, matched literally by default, so characters such as `.`, `(` or `+` have no special meaning.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`
`regex`	`bool`	If False (default), match `substring` literally; if True, treat it as a regular expression.	`False`

Returns:

Type	Description
`int`	Number of non-null strings that contain `substring`.

starts_with_count `classmethod` ¤

starts_with_count(
    dataframe: DataFrame,
    prefix: str,
    value_column: str = "value_string",
) -> int

Counts how many strings start with the specified prefix.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`prefix`	`str`	Prefix to match.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Number of non-null strings that start with `prefix`.

ends_with_count `classmethod` ¤

ends_with_count(
    dataframe: DataFrame,
    suffix: str,
    value_column: str = "value_string",
) -> int

Counts how many strings end with the specified suffix.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`suffix`	`str`	Suffix to match.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Number of non-null strings that end with `suffix`.

uppercase_percentage `classmethod` ¤

uppercase_percentage(
    dataframe: DataFrame, value_column: str = "value_string"
) -> float

Returns the percentage of strings that are fully uppercase.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`float`	Percentage (0-100) of the non-null strings that are fully uppercase; NaN when the column holds no non-null value.

lowercase_percentage `classmethod` ¤

lowercase_percentage(
    dataframe: DataFrame, value_column: str = "value_string"
) -> float

Returns the percentage of strings that are fully lowercase.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`float`	Percentage (0-100) of the non-null strings that are fully lowercase; NaN when the column holds no non-null value.

contains_digit_count `classmethod` ¤

contains_digit_count(
    dataframe: DataFrame, value_column: str = "value_string"
) -> int

Counts how many strings contain digits.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze. Default `"value_string"`.	`'value_string'`

Returns:

Type	Description
`int`	Number of non-null strings with at least one digit.

summary_as_dict `classmethod` ¤

summary_as_dict(
    dataframe: DataFrame, value_column: str
) -> dict[str, int | str | float | None]

Returns a dictionary with comprehensive string statistics for the specified column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze.	required

Returns:

Type	Description
`dict[str, int \| str \| float \| None]`	Dict with keys `unique_values`, `most_frequent`, `count_most_frequent`, `count_null`, `average_string_length`, `longest_string`, `shortest_string`, `uppercase_percentage`, `lowercase_percentage`, `contains_digit_count`, `least_common` and `frequency_least_common`.

summary_as_dataframe `classmethod` ¤

summary_as_dataframe(
    dataframe: DataFrame, value_column: str
) -> pd.DataFrame

Returns a DataFrame with comprehensive string statistics for the specified column.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame.	required
`value_column`	`str`	Name of the string column to analyze.	required

Returns:

Type	Description
`DataFrame`	One-row DataFrame with the keys of `summary_as_dict` as columns.

string_stats

string_stats ¤

StringStatistics ¤

count_unique classmethod ¤

most_frequent classmethod ¤

count_most_frequent classmethod ¤

count_null classmethod ¤

average_string_length classmethod ¤

longest_string classmethod ¤

shortest_string classmethod ¤

string_length_summary classmethod ¤

most_common_n_strings classmethod ¤

contains_substring_count classmethod ¤

starts_with_count classmethod ¤

ends_with_count classmethod ¤

uppercase_percentage classmethod ¤

lowercase_percentage classmethod ¤

contains_digit_count classmethod ¤

summary_as_dict classmethod ¤

summary_as_dataframe classmethod ¤

count_unique `classmethod` ¤

most_frequent `classmethod` ¤

count_most_frequent `classmethod` ¤

count_null `classmethod` ¤

average_string_length `classmethod` ¤

longest_string `classmethod` ¤

shortest_string `classmethod` ¤

string_length_summary `classmethod` ¤

most_common_n_strings `classmethod` ¤

contains_substring_count `classmethod` ¤

starts_with_count `classmethod` ¤

ends_with_count `classmethod` ¤

uppercase_percentage `classmethod` ¤

lowercase_percentage `classmethod` ¤

contains_digit_count `classmethod` ¤

summary_as_dict `classmethod` ¤

summary_as_dataframe `classmethod` ¤