As shown in Table 2, the previous code has created a new pandas DataFrame, where all rows with one or multiple NaN values have been deleted. Example 2: Drop Rows of pandas DataFrame that Contain a Missing Value
The `edited_cells` dictionary is now called `edited_rows` and uses a different format (`{0: {"column name": "edited value"}}` instead of `{"0:1": "edited value"}`). You may need to adjust the code if your app uses `st.experimental_data_editor` in combination with `st.session...
merge_cells: bool = True, encoding: Optional[str] = None, encoding: str | None = None, inf_rep: str = "inf", verbose: bool = True, freeze_panes: Optional[Tuple[int, int]] = None, freeze_panes: tuple[int, int] | None = None, ) -> None: from pandas.io.formats.excel import...
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html pandas.DataFrame.drop_duplicates. ¶. Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes are ignored. Only … woblink ...