piper.verbs.drop_if¶
-
piper.verbs.drop_if(df: pandas.core.frame.DataFrame, value: Optional[Union[str, int, float]] = None, how: str = 'all') → pandas.core.frame.DataFrame[source]¶ drop columns containing blanks, zeros or na
Examples
df = dummy_dataframe() head(df, tablefmt='plain') zero_1 zero_2 zero_3 zero_4 zero_5 blank_1 blank_2 blank_3 blank_4 blank_5 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 3 0 0 0 0 0
df = drop_if(df) head(df, tablefmt='plain') zero_1 zero_2 zero_3 zero_4 zero_5 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 3 0 0 0 0 0
- Parameters
df – pandas dataframe
value –
Default is None. Drop a column if it contains a blank value in every row. Enter a literal string or numeric value to check against.
Special value - ‘isna’. If specified, depending on the ‘how’ parameter drop columns containing na / null values.
how –
{None, ‘any’, ‘all’}, default ‘all’. Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
’any’ : If any NA values are present, drop that row or column.
’all’ : If all values are NA, drop that row or column.
- Returns
- Return type
A pandas DataFrame