semvec/pypi-packages huggingface dataset:
dfbridge 0.0.2
Schematized pipeline operations on dataframes

# dfbridge

## A Schematized dataframe formatter.

We often have need to reformat a base dataframe to create a dataframe following a schema, applying a combination of renaming some columns, applying functions to others, and doing groupby/transform operations.
These steps introduce a lot of boilerplate, but here we can assign it as a dictionary schema.
The original dataframe is unchanged, and all of the operations take place only on the original dataframe.

Let's say we want the output dataframe to have columns `final_name1`, `final_name2`, and `final_name3`, with one of them a simple rename from an input dataframe, one the result of some fucntion applied to the input dataframe, and one some groupby transform operation.
We can even remap values to other values in the process.
Setting `fill_missing` to True lets one add the column and set it as full of pandas NA values.

The schema to do this looks like:

```python
schema = {
    "final_name1`": {
        "type": "rename",
        "from": "original_name",
        "fill_missing": True,
        "column_type": None,
        'remap_dict': {'orig_val': 'new_val'}, # Remaps elements with original val to new val. Set to None or ignore to not use.
        'strict_remap': True, # If True, values not in the remap_dict are made pd.NA, else are passed through intact.
    },
    "final_name2": {
        "type": "apply",
        "func": function, # Expects the whole row of the original dataframe, so use row['col] style arguments.
        "fill_missing": True,
        "column_type": None,
        'remap_dict': None, # Remaps elements with original val to new val. Set to None or ignore to not use.
    },
    "final_name3": {
        "type": "transform",
        "groupby": "groupby_column",
        "column": "return_column",
        "action`": "mean", # (or anything that works in df.groupby().transform())
        "fill_missing": True,
        "column_type": None,
    },
}
```
ptext 0.0.2
Pacakage for text mining
Keywords: textmining

Keywords based text mining library.


neighbourhood_words function: extract the keyword along with left and right neghbouthood words.
left_texts function extract the left part of the keyword in a given sentence.
right_texts function extract the right part of the keyword in a given sentence.
keyword_position function extract the all matched keyword's start and end positions.
neighbourhood_chr function extract the keyword's along with left and right neghbouthood charecters.
dataframe_keyword_remover function remove the keyword from the dataframe. Non alphanumeric charecters need to be write in regex format.
text_keyword_remover function remove the keyword along with nonalphanumerics from a long text.


Change Log
==========


0.0.1 (20/01/2022) - First Release 

0.0.2 (24/01/2022) - Second Release