semvec/pypi-packages huggingface dataset:
dmapi 0.0.1
A Python client for Dados de Mercado
Requires Python: >=3.6

# Dados de Mercado

This is a Python Client for
[Dados de Mercado](https://www.dadosdemercado.com/)'s API.

## Instalation

```
pip3 install dmapi
```

## Usage

You will need a token to access the API endpoints. Go to the
[documentation page](https://www.dadosdemercado.com/api) (in portuguese)
to generate one.

### Usage example

```python
from dmapi import DMAPI

dm = DMAPI(token='c8cad35b0376c8f6bcb46614f80d9443')  # Set your token here

print(dm.companies())
```

### Available calls

Please refer to the [documentation](https://www.dadosdemercado.com/api)
for more details on the parameters available on each call.

- `dm.companies()`
- `dm.company(company_slug)`
- `dm.financials(company_slug, [type, offset, limit])`
- `dm.ratios(company_slug, [type, offset, limit, market])`
- `dm.tickers()`
- `dm.ticker(ticker, [from_date, to_date])`
- `dm.price_indexes([from_date, to_date])`
- `dm.selic()`
dvpipe 0.0.4
A small Python utility that pipes data from function to function in sequencial order.
Requires Python: >=3.0

# dvpipe

> A small Python utility for piping data from function to function in sequential order.

dvpipe allows you to pass data from function to function sequentially as you would in
tradional method chaining. You can use dvpipe to transform any type of data not
just DataFrames as with [DataFrame.pipe()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pipe.html)

![Version](https://img.shields.io/pypi/v/dvpipe)
![License](https://img.shields.io/github/license/chrisdiana/dvpipe.svg)

### Installation
```
$ pip install dvpipe
```

### Usage
```python
processed_data = pipe(data, func_a, func_b, func_c, ...)
```

### Example
```python
from dvpipe import pipe

data = (pipe(data,
             clean,
             transform,
             aggregate))
```

### Why?
dvpipe attempts to help solve this classic problem seen in many data tranformation applications to allow for cleaner, more understandable code.

```python
data = clean(data)
data = transform(data)
data = aggregate(data)
```

### Functions with Arguments
Use Python tuples for functions with parameters.
```python
df = pipe(df, (replace_foo, 'bar'))
```

### Full Example
```python
from dvpipe import pipe

raw_data = {'foo': 1, 'bar': 2}

def subtract_foo(data):
    data['foo'] = data['foo'] - 1
    return data

def add_bar(data):
    data['bar'] = data['bar'] + 1
    return data

def add_entry(data, entry):
    data.update(entry)
    return data

data = (pipe(raw_data,
             subtract_foo,
             add_bar,
             (add_entry, {'foobar': 5})))
```