Module `phc.easy.util.frame`

Expand source code

import pandas as pd
from functools import reduce
from typing import List, Optional


def search(frame: pd.DataFrame, query: Optional[str] = None):
    if query is None:
        return frame

    return frame[
        frame.select_dtypes(include=[object, "string"])
        .apply(
            lambda column: column.str.contains(
                query, regex=False, case=False, na=False
            )
        )
        .any(axis=1)
    ]


def combine_first(frame: pd.DataFrame, columns: List[str], column_name: str):
    if columns[0] not in frame.columns:
        return frame

    return frame.assign(
        **{
            column_name: reduce(
                lambda series, k: (
                    series.combine_first(frame[k])
                    if k in frame.columns
                    else series
                ),
                columns,
                frame[columns[0]],
            )
        }
    )


def drop(frame: pd.DataFrame, columns: List[str]):
    return frame.drop([c for c in columns if c in frame.columns], axis=1)

Functions

def combine_first(frame: pandas.core.frame.DataFrame, columns: List[str], column_name: str)

Expand source code

def combine_first(frame: pd.DataFrame, columns: List[str], column_name: str):
    if columns[0] not in frame.columns:
        return frame

    return frame.assign(
        **{
            column_name: reduce(
                lambda series, k: (
                    series.combine_first(frame[k])
                    if k in frame.columns
                    else series
                ),
                columns,
                frame[columns[0]],
            )
        }
    )

def drop(frame: pandas.core.frame.DataFrame, columns: List[str])

Expand source code

def drop(frame: pd.DataFrame, columns: List[str]):
    return frame.drop([c for c in columns if c in frame.columns], axis=1)

def search(frame: pandas.core.frame.DataFrame, query: Optional[str] = None)

Expand source code

def search(frame: pd.DataFrame, query: Optional[str] = None):
    if query is None:
        return frame

    return frame[
        frame.select_dtypes(include=[object, "string"])
        .apply(
            lambda column: column.str.contains(
                query, regex=False, case=False, na=False
            )
        )
        .any(axis=1)
    ]