semvec/pypi-packages huggingface dataset:
MissingValuesGraph 4.0.0
Find the missing values for any dataframe/dataset

# MissingValuesGraph
Find the missing values for any dataframe/dataset

## Installation

You can install the Missing Values Graph package from [PyPI](https://pypi.org/project/MissingValuesGraph/):

    python -m pip install MissingValuesGraph

The MissingValuesGraph is supported on Python 3.7 and above. Older versions of Python, including Python 2.7, are supported by version 1.0.0 of the reader.

## How to use

The MissingValuesGraph package is used in the pandas dataframes to find all the null values and display them in a graph with missing percentage

We need to have the object created for the class MissingValues

    df_miss = MissingValues(df)

where df_miss is the object name and df is the pandas dataframe.

Once the object is created, the function findMissingValues() needs to be invoked

    df_miss.findMissingValues()
  
## Output

![image](https://user-images.githubusercontent.com/90926526/158602355-0beced43-1203-4a9c-9ba1-97c6318c81bc.png)

![image](https://user-images.githubusercontent.com/90926526/158602440-0414b4c1-ad6c-46be-b6ef-9de777696ceb.png)
sst-crispy 1.0.2
A Python package for using data from the Swedish 1 m Solar Telescope's CRisp Imaging SpectroPolarimeter instrument.
Requires Python: >3.6

# crispy: A Python Package for Using Imaging Spectropolatimetric Data in Solar Physics

Author: John A. Armstrong, *Univeristy of Glasgow*

The following repository contains the source code for the `crispy` Python package, aptly named due to my working with the CRisp Imagining SpectroPolarimeter (CRISP) instrument mounted at the Swedish 1-m Solar Telescope (SST) in La Palma, Spain. (However, the code with work *any* imaging spectropolarimetric data regardless of instrument so long as it follows normal FITS standards or a custom zarr standard I have grown accustomed to using, see the docs for more details).

## Why does this exist?

``crispy`` started its life as the base code for data viewing/augmentation/analysis for my PhD as the tools for data exploration and exploitation of this kind of solar physics data did not previously exist.

## What is imaging spectropolarimetric data?

The type of data that ``crispy`` is built to deal with is **optical** imaging spectropolarimetry data. This kind of data consists of measurements of a number of Stokes profiles at specific narrowband wavelength points over an extended field-of-view over a given length of time. What this means is that we have five-dimensional data structures ordered (t, stokes, &lambda;, y, x). This kind of data is very powerful for exploring the lower solar atmopshere due to having relatively high spectral, polarimetric and spatial resolutions. The time resolution can be not shabby too.

## How do I get this moderately cool Python package?

``crispy`` can be found on PyPI using:

```
pip install sst-crispy
```

or can be installed from source using:

```
git clone https://github.com/bionic-toucan/crispy
cd crispy
python setup.py install --user
```

## Acknowledgements
I would like to thank Chris Osborne (https://github.com/Goobley) for the improved rotation algorithm used for data rotated in the images plane to coincide with the helioprojective plane and also for listening to me complaining all the time and telling me about the wonders of properties.