timeseries_shaper.filter.custom_filter

View Source

 1from ..base import Base
 2import pandas as pd
 3
 4class CustomFilter(Base):
 5    @classmethod
 6    def filter_custom_conditions(cls, dataframe: pd.DataFrame, conditions: str) -> pd.DataFrame:
 7        """
 8        Filters the DataFrame based on a set of user-defined conditions passed as a string.
 9
10        This method allows for flexible data filtering by evaluating a condition or multiple conditions
11        specified in the 'conditions' parameter. The conditions must be provided as a string
12        that can be interpreted by pandas' DataFrame.query() method.
13
14        Args:
15        - dataframe (pd.DataFrame): The DataFrame to apply the filter on.
16        - conditions (str): A string representing the conditions to filter the DataFrame.
17                            The string should be formatted according to pandas query syntax.
18
19        Returns:
20        - pd.DataFrame: A DataFrame containing only the rows that meet the specified conditions.
21
22        Example:
23        --------
24        # Given a DataFrame 'df' containing columns 'age' and 'score':
25        >>> filtered_data = CustomFilter.filter_custom_conditions(df, "age > 30 and score > 80")
26        >>> print(filtered_data)
27
28        Note:
29        - Ensure that the column names and values used in conditions match those in the DataFrame.
30        - Complex expressions and functions available in pandas query syntax can also be used.
31        """
32        return dataframe.query(conditions)

class CustomFilter(timeseries_shaper.base.Base): View Source

 5class CustomFilter(Base):
 6    @classmethod
 7    def filter_custom_conditions(cls, dataframe: pd.DataFrame, conditions: str) -> pd.DataFrame:
 8        """
 9        Filters the DataFrame based on a set of user-defined conditions passed as a string.
10
11        This method allows for flexible data filtering by evaluating a condition or multiple conditions
12        specified in the 'conditions' parameter. The conditions must be provided as a string
13        that can be interpreted by pandas' DataFrame.query() method.
14
15        Args:
16        - dataframe (pd.DataFrame): The DataFrame to apply the filter on.
17        - conditions (str): A string representing the conditions to filter the DataFrame.
18                            The string should be formatted according to pandas query syntax.
19
20        Returns:
21        - pd.DataFrame: A DataFrame containing only the rows that meet the specified conditions.
22
23        Example:
24        --------
25        # Given a DataFrame 'df' containing columns 'age' and 'score':
26        >>> filtered_data = CustomFilter.filter_custom_conditions(df, "age > 30 and score > 80")
27        >>> print(filtered_data)
28
29        Note:
30        - Ensure that the column names and values used in conditions match those in the DataFrame.
31        - Complex expressions and functions available in pandas query syntax can also be used.
32        """
33        return dataframe.query(conditions)

@classmethod

def filter_custom_conditions( cls, dataframe: pandas.core.frame.DataFrame, conditions: str) -> pandas.core.frame.DataFrame: View Source

 6    @classmethod
 7    def filter_custom_conditions(cls, dataframe: pd.DataFrame, conditions: str) -> pd.DataFrame:
 8        """
 9        Filters the DataFrame based on a set of user-defined conditions passed as a string.
10
11        This method allows for flexible data filtering by evaluating a condition or multiple conditions
12        specified in the 'conditions' parameter. The conditions must be provided as a string
13        that can be interpreted by pandas' DataFrame.query() method.
14
15        Args:
16        - dataframe (pd.DataFrame): The DataFrame to apply the filter on.
17        - conditions (str): A string representing the conditions to filter the DataFrame.
18                            The string should be formatted according to pandas query syntax.
19
20        Returns:
21        - pd.DataFrame: A DataFrame containing only the rows that meet the specified conditions.
22
23        Example:
24        --------
25        # Given a DataFrame 'df' containing columns 'age' and 'score':
26        >>> filtered_data = CustomFilter.filter_custom_conditions(df, "age > 30 and score > 80")
27        >>> print(filtered_data)
28
29        Note:
30        - Ensure that the column names and values used in conditions match those in the DataFrame.
31        - Complex expressions and functions available in pandas query syntax can also be used.
32        """
33        return dataframe.query(conditions)

Filters the DataFrame based on a set of user-defined conditions passed as a string.

This method allows for flexible data filtering by evaluating a condition or multiple conditions specified in the 'conditions' parameter. The conditions must be provided as a string that can be interpreted by pandas' DataFrame.query() method.

Args:

dataframe (pd.DataFrame): The DataFrame to apply the filter on.
conditions (str): A string representing the conditions to filter the DataFrame. The string should be formatted according to pandas query syntax.

Returns:

pd.DataFrame: A DataFrame containing only the rows that meet the specified conditions.

Example:

Given a DataFrame 'df' containing columns 'age' and 'score':

>>> filtered_data = CustomFilter.filter_custom_conditions(df, "age > 30 and score > 80")
>>> print(filtered_data)

Note:

Ensure that the column names and values used in conditions match those in the DataFrame.
Complex expressions and functions available in pandas query syntax can also be used.

Inherited Members

timeseries_shaper.base.Base: Base; dataframe; get_dataframe