semvec/pypi-packages huggingface dataset:
end2endML 0.9.3.dev0
Automated data analysis pipelines on tabular data for data analyst
Requires Python: >=3.6, <4
Keywords: data analysis,machine learning,automation

# end2endML package

The end2endML Python package is designed for data analysts to do automated data analysis on tabular data using machine learning algorithms. The package implemented all the components, data preprocessing, data splitting, model selection, model fitting and model evaluation, required for defining pipelines to automatically analyze tabular data. 

### Installation

Install end2endML package by running:

```
pip install end2endML
```

on the command line of a Linux system or the Anaconda Prompt on a Windows system. If you don't have root privileges, sometimes you need to add `--user` after the above commands, then pip will install the package in your home directory.

If you want to install a specific version, please visit https://pypi.org/project/end2endML/ to find the exact version number. For example, you can install version 0.9.3 by `pip install end2endML==0.9.3`. 

### User guide

The user guide is available at https://end2endml.readthedocs.io/en/latest/.
lr35902dis 1.0.2
lr35902 (GameBoy CPU) disassembler library

# lr35902dis

A python disassembler library for the LR35902 CPU (the CPU of the GameBoy, similar to a Z80 processor).
This is an adaptation of [z80dis](https://github.com/lwerdna/z80dis). Any errors are probably mine, PRs welcome :).

# Use

```
>>> from lr35902dis import lr35902
>>> lr35902.disasm(b'\xCB\xE7', 0)
'set 4,a'
```

Or, if you'd like access to the instruction internals, like opcode identifier, length, and operands:

```
>>> decoded = lr35902.decode(b'\xCB\xE7', 0)
>>> decoded.op
<OP.SET: 58>
>>> decoded.operands[0]
(<OPER_TYPE.IMM: 45>, 4)
>>> decoded.operands[1]
(<OPER_TYPE.REG_A: 1>,)
>>> decoded.len
2
```

The decoded structure can be supplied to disasm() to make a string:

```
>>> lr35902.disasm(decoded)
'set 4,a'
```
AlmaIndicator 0.0.4
A small library for computing ALMA indicator for stocks
Requires Python: >=3.6

# Arnaud Legoux Moving Average (ALMA) in Python

This is a small Technical Analysis library for the calculation of Arnaud Legoux Moving Average (ALMA). It is built in Pandas and Numpy and uses [TA](https://github.com/bukosabino/ta).

## Description

The Arnaud Legoux Moving Average (ALMA) indicator is a superior moving average as compared to the Exponential Moving and Simple Moving Averages. Arnaud Legoux and Dimitrios Kouzis Loukas developed the indicator in the year 2009. The objective of ALMA is to minimize the noise and produce a more reliable signal than the conventional moving averages. The indicator (ALMA) removes small price fluctuations and also enhances the trend by applying a moving average (MA) twice, once from left to right, and once from right to left.

## Key Points on ALMA Indicator

- ALMA indicator works on the principle of the Moving Average (MA), but the calculation formula is more perfect.
- The main difference in regards to conventional moving averages is its minimal lag.
- The classic EMA, SMA, SMMA and other Moving Average lines have a significant minus – signal lag.
- The MA ALMA in this regard is more perfect. In a volatile market, this tool shows very good trading results, even without the use of auxiliary filters.

The Arnaud Legoux moving average attempts to bridge the gap and thus is expected to show both responsiveness and smoothness at the same time. Generally, the Arnaud Legoux Moving Average indicator applies the moving average twice, once from left to right and the other from right from left with the process said to eliminate price lag or phase shift significantly, a problem that is common to the traditional moving averages.

Source: Read more on [Stock Maniacs](https://www.stockmaniacs.net/arnaud-legoux-moving-average-indicator/)

## How to Use

```python
# import the package
from AlmaIndicator import ALMAIndicator

# define a Pandas dataframe which should contain closing price of the stocks
df = ...

# initialize the indicator
# you only need to provide a Pandas series of the closing price of the stock
alma_indicator = ALMAIndicator(close=df['close'])
# add alma to dataframe by calling the alma function
df['alma'] = alma_indicator.alma()
```

## Sample Result

![spy - data](https://user-images.githubusercontent.com/29625844/92999031-54f8c600-f550-11ea-974c-ae96fdb8e623.png)

Note that there will be `NaN` values at the beginning of the ALMA series since there is a window of analysis which is set to 9. In other words, your dataset must at least contain 9 entries.

## Sample Figure

![spy - alma](https://user-images.githubusercontent.com/29625844/92998867-1d3d4e80-f54f-11ea-919c-dd48c8927172.png)