semvec/pypi-packages huggingface dataset:
pypi-tdb 0.0.6
tdb: text based database with tagging
Requires Python: >=3.11

# tdb

A text based database with tagging.

```
Usage: py -m tdb [add | edit | rm | template | show | open | listen] [text | options]
----------------------------------------------------------------
Commands:
add:            Make a record when text is supplied. Otherwise, open an editor to write one.
edit:           Open an editor with some view of the database, see options.
rm:         Move matching records to the archive.
template:       Open an editor to write a record with the passed template file as a basis.
open:           Open tdbs files: tdb open ['archive', 'config', 'db']
listen:         Starts a server listening on passed port.
----------------------------------------------------------------
Options:
span:           The records to select, example: span:7d is the last 7 days.
as:             The format to see the records in. Only valid for show currently. [html, json, list, tags]
@{tag}:         This tag or any others must be included, example: @notes @school, records must have either.
+@{tag}:        This tag and any others must be included. i.e. +@notes +@school, records must have both.
-@{tag}:        This tag must not be included. i.e. -@notes @school, records for school, no notes.
{text}:         This text is optional.
+{text}:        This text must be included.
-{text}:        This text must not be included

Note, text must be quoted if there are spaces.
```
pandas-datatypes 1.2.0
A library for getting correct datatype from pandas DataFrame

# pandas_datatypes

## infer_data_types

![GitHub](https://img.shields.io/github/license/yourusername/infer_data_types)
![GitHub release (latest by date)](https://img.shields.io/github/v/release/yourusername/infer_data_types)

A Python function for inferring data types of columns in a pandas DataFrame.

## Overview

`infer_data_types` is a utility function designed to assist in the process of inferring data types of columns within a pandas DataFrame. It offers functionality to handle numeric columns with NaN values by optionally filling them with zeros, and it can automatically convert columns to datetime objects where appropriate.

## Usage

```python
import pandas as pd
import pandas_datatypes 

# Create a DataFrame
data = {
    'A': [1, 2, 3],
    'B': ['2022-01-01', '2022-02-01', '2022-03-01'],
    'C': [4.0, None, 6.0],
}

df = pd.DataFrame(data)

# Infer data types
data_types = pandas_datatypes.infer_data_types(df)

print(data_types)
```
# Function Description

```
def infer_data_types(df: pd.DataFrame, fill_numeric_zeros: bool = True) -> dict:
```
 
    Infer data types of columns in a DataFrame.

    Args:
        df (pd.DataFrame): Input DataFrame.
        fill_numeric_zeros (bool, optional): Whether to fill NaN values in numeric columns with zeros. Defaults to True.

    Returns:
        dict: Dictionary containing column names as keys and inferred data types as values.


df: Input DataFrame for which data types are to be inferred.

fill_numeric_zeros: Optional boolean parameter to indicate whether to fill NaN values in numeric columns with zeros. Defaults to True.

Returns a dictionary containing column names as keys and inferred data types as values.