semvec/pypi-packages huggingface dataset:
auto-bots 0.0.4
Automated time-series forecasting

# auto-bots

`auto-bots` is an easy-to-use time series forecasting package that does all the model selection work for you.

# Installation
You can install `auto-bots` with a simple
```commandline
pip install auto-bots
```

Then, to use the AutoTS model in your code, import it like so:
```python
from auto_bots.AutoTS import AutoTS
```


# Quickstart

AutoTS follows sci-kit learn's `model.fit()`/`model.predict()` paradigm. The only requirement of your
data is that it must be a pandas dataframe with a datetime index. Given such a dataframe, here is how
to train your model and make predictions:

```python
model = AutoTS()

model.fit(data, series_column_name='passengers')
model.predict(start=pd.to_datetime('1960-1-1'), end=pd.to_datetime('1960-12-1'))
```

### Tips/Tricks/Things to know
- Since you provide the name of the time series column during fit, the dataframe provided 
during fit can contain as many extra columns as you like and the model will ignore them. No need to do
a bunch of filtering before training!
- You can have the model predict in-sample by setting the `start_date` equal to a date inside the data given during fit.


For a more thorough introduction, check out [this example](examples/airline_passengers/airline_example.ipynb)
matgrab 0.1.0
search and extract field names or variables from MATLAB .mat files and return a pandas Dataframe
Requires Python: >=3.6

# matgrab
Search and extract field names or variables from MATLAB .mat files and return a pandas Dataframe

# installation

## PYPI
```
pip install matgrab
```

# usage

## python
```
import matgrab
df = matgrab.mat2df("PATH","VARS")
```
PATH may be a file path, directory path, or list of either
VARS may be a string or list of strings that correspond to field names of desired matlab variables

if your desired data is nested within another named data structure or array, that structure name must proceed the desired variable separated by a "."
```
EEG.mat
├── dataset_description {string}
└── sub-D0048 {struct}
    ├── anat {struct}
    │   ├── T1w {string}
    │   └── CT {float}
    └── ieeg {array}
        ├── channels {column}
        └── values {column}
```
To retrieve the values from the above mat file, the command would be:
```
matgrab.mat2df("EEG.mat",["sub-D0048.anat.T1w","sub-D0048.anat.CT","sub-D0048.ieeg.channels","sub-D0048.ieeg.values"])
```
c-digo-ff-gr-tis 2021
código ff grátis
Requires Python: >=3.6

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# código ff grátis
```bash
pip3 código ff grátis