semvec/pypi-packages huggingface dataset:
cricbuzz-py 0.1.2
A package to retrive cricket scores
Requires Python: >=3.6



# CRICBUZZ - PY



This package retrieves scores from https://www.cricbuzz.com/match-api/matches.json.

This python package uses : Requests: HTTP for Humans™

**Installing the pip package :**  


    pip install cricbuzz-py



**Importing cricbuzz-py:**  


    import cbpy as cb

[How to use cricbuzz-py ?](https://akst99.blogspot.com/2019/09/fetch-live-cricket-scores-using-python.html)
codabl 0.1.0
Codabl Community API
Keywords: Swagger,Codabl Community API

The Codabl API is a collection of analytics tools and data integrations made for blockchain developers. In particular we offer Natural Language Processing and Anomaly detection algorithms that have been fine tune to understand text data and time series in the domain speficic setting of cryptocurrency and blockchain technology. This technology is intended to be use as building blocks to bigger applications, we offer examples on how to use them for Trading Backtesting and Smart Contract anomaly monitoring.  # noqa: E501
multisig-hmac-AmalieDue 0.0.3
multisig HMAC

# multisig-hmac-python-version

> Multisig scheme for HMAC authentication

Work in progress

## Usage
Key management can happen in either of two modes, either by storing every of the component keys, or by storing a single master seed and using that to derive keys ad hoc.

Using stored keys:

```python
m = MultisigHMAC()

# generate keys which need to be stored securely and need to be shared securely with each party
k1 = m.keygen()
k2 = m.keygen()
k3 = m.keygen()

# sign by each client with 2-of-3
data = b'Hello world'

s1 = m.sign(k1, data)
s3 = m.sign(k3, data)

out = m.combine([s1, s3])

sent = (out[0], base64.urlsafe_b64encode(out[1]))

# --- network ---

received = (sent[0], base64.urlsafe_b64decode(sent[1]))

# verify on the server
threshold = 2
keys = [k1, k2, k3]
signature = received

m.verify(keys, signature, data, threshold)

```

Using a derived master key:

```python
m = MultisigHMAC()

# generate a master seed which needs to be stored securely
# this seed must NOT be shared with any other party
seed = m.seedgen()

k1 = m.keygen()
k2 = m.keygen()
k3 = m.keygen()

# sign by each client with 2-of-3
data = b'Hello world'

s1 = m.sign(k1, data)
s3 = m.sign(k3, data)

out = m.combine([s1, s3])

sent = (out[0], base64.urlsafe_b64encode(out[1]))

# --- network ---

received = (sent[0], base64.urlsafe_b64decode(sent[1]))

# verify on the server, but now keys are dynamically derived
threshold = 2
keys = [k1, k2, k3]
signature = received

m.verifyDerived(keys, signature, data, threshold)

```
MICTI 0.2.0
Feature extraction approach in single-cell gene expression profiling for cell-type marker identification.

MICTI- Marker gene Identification for Cell Type Identity
========================================================

Recent advances in single-cell gene expression profiling technology have revolutionized the understanding of molecular processes underlying developmental cell and tissue differentiation, enabling the discovery of novel cell types and molecular markers that characterize developmental trajectories.  Common approaches for identifying marker genes are based on pairwise statistical testing for differential gene expression between cell types in heterogeneous cell populations, which is challenging due to unequal sample sizes and variance between groups resulting in little statistical power and inflated type I errors. 

Overview
--------

We developed an alternative feature extraction method, *Marker gene Identification for Cell Type Identity* (**MICTI**), that encodes the cell-type specific expression information to each gene in every single cell. This approach identifies features (genes) that are cell-type specific for a given cell-type in heterogeneous cell population.


Installation
------------

To install the current release:

	pip install MICTI

How to use MICTI
----------------

Import MICTI:

	from MICTI import MARKER

Creating MICTI object for known cell-type cluster label:

	mictiObject=MARKER.MICTI(datamatrix, geneName, cellName, cluster_assignment=cell_type, k=None, th=0, ensembel=False, organisum="hsapiens")

2D visualisation with tSNE:

	mictiObject.get_Visualization(dim=2, method="tsne")

Get MICTI marker genes:

        cluster_1_markers=mictiObject.get_markers_by_Pvalues_and_Zscore(1, threshold_pvalue=.01,threshold_z_score=0)

Markers heatmap plots:

	mictiObject.heatMap()

Markers Radar plots:

	mictiObject.get_Radar_plot()

Gene Ontology enrichment analysis for cell-type marker genes in each of cell-type clusters

	enrechment_table=mictiObject.get_gene_list_over_representation_analysis(list(cluster_1_markers.index))
	enrechment_table #gene-list enrichment analysis result for the cell-type marker genes for cluster-1


Licence
-------

[MICTI LICENCE](./LICENSE)
missingValues-kvarshney-101703295 1.0.3
A python package to handle Missing Values using SimpleImputer Class
Requires Python: >=3.6

# Handling Missing Values using SimpleImputer Class

**Project 3 : UCS633**


Submitted By: **Kshitiz Varshney 101703295**

***
pypi: <https://pypi.org/project/missingValues-kvarshney-101703295/>
***

## SimpleImputer Class

SimpleImputer is a scikit-learn class which is helpful in handling the missing data in the predictive model dataset.
It replaces the NaN values with a specified placeholder.
It is implemented by the use of the SimpleImputer() method which takes the following arguments:
<br>
missing_data : The missing_data placeholder which has to be imputed. By default is NaN.
<br>
stategy : The data which will replace the NaN values from the dataset. The strategy argument can take the values – ‘mean'(default), ‘median’, ‘most_frequent’ and ‘constant’.
<br>
fill_value : The constant value to be given to the NaN data using the constant strategy.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install missingValues-kvarshney-101703295.

```bash
pip install missingValues-kvarshney-101703295
```
<br>

## How to use this package:

missingValues-kvarshney-101703295 can be run as shown below:


### In Command Prompt
```
>> missingValues dataset.csv
```
<br>


## Input dataset



| a   | b   | c |
|-----|-----|---|
| 0   | NaN | 4 |
| 2   | NaN | 4 |
| 1   | 7   | 0 |
| 1   | 3   | 9 |
| 7   | 4   | 9 |
| 2   | 6   | 9 |
| 9   | 6   | 4 |
| 3   | 0   | 9 |
| 9   | 0   | 1 |


<br>


## Output Dataset after Handling the Missing Values

a | b | c 
:------------: | :-------------: | :-------------:
0	|4|	4
2	|4|4
1	|7|	0
1	|3|	9
7	|4	|9
2	|6|9
9	|6|	4
3	|0|	9
9	|0|	1


<br>

It is clearly visible that the rows,columns containing Null Values have been Handled Successfully using median values.


## License
[MIT](https://choosealicense.com/licenses/mit/)