semvec/pypi-packages huggingface dataset:
prettierjson 1.0.1
Generate prettier and more compact JSON dumps
Requires Python: >=3.9,<4.0

# prettierjson

[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.svg?style=flat-square)](https://www.python.org/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/psf/black)
[![Github release](https://img.shields.io/badge/release-1.0.1-blue.svg?style=flat-square)](https://github.com/brianburwell11/prettierjson/releases/tag/1.0.1)
[![PyPi](https://img.shields.io/badge/-PyPi-white.svg?logo=pypi&labelColor=4B8BBE&logoColor=FFD43B&style=flat-square)](https://pypi.org/project/prettierjson/)
[![Documentation](https://img.shields.io/badge/-Documentation-2980b9.svg?logo=readthedocs&labelColor=2980b9&logoColor=FFFFFF&style=flat-square)][documentation]

Generate prettier and more compact JSON dumps

## Installation

**prettierjson** can be installed using one of these commands:

```sh
pip install prettierjson
```

```sh
poetry add prettierjson
```

## Usage

### in python scripts

prettierjson offers one function `prettierjson.dumps()` which is intended to be used as a drop-in replacement for `json.dumps()`

```python
from prettierjson import dumps

my_dictionary = {"foo": bar}

with open("foobar.json", "w") as f:
    f.write(dumps(my_dictionary))
```

If prettierjson needs to exist within the same module as the built-in json package _without overriding_ the default `json.dumps()`, the entire package should be imported in order to avoid namespace collisions
```python
import json
import prettierjson

my_dictionary = {"foo": bar}

with open("builtin.json", "w") as f:
    f.write(json.dumps(my_dictionary))
with open("prettierjson.json", "w") as f:
    f.write(prettierjson.dumps(my_dictionary))
```

See [the documentation][documentation] for more details.


### as a command line interface

prettierjson has a `__main__` module which allows it to be called directly when installed with the command `python -m prettierjson`.

In this way, prettierjson can be used to "prettify" one or multiple JSON files in-place by passing them as arguments
```sh
$ python -m prettierjson PATH/TO/JSON/FILE1.json PATH/TO/JSON/FILE2.json
```

Indent size and max line length can be set with the `--indent` and `--line-length` flags
```sh
$ python -m prettierjson --indent=2 --line-length=88 PATH/TO/JSON/FILE.json
```

Run `python -m prettierjson -h` for more command line usage details.


<!-- links -->
[poetry]: https://python-poetry.org/docs/
[changelog]: docs/CHANGELOG.md
[documentation]: https://github.com/brianburwell11/prettierjson/wiki
EasyReport 0.0.7
Generate a quick EDA report of a pandas DataFrame
Requires Python: >=3.6
Keywords: EDA,Exploratory Data Analysis,Easy Report,Dataset Report


# Easy Report
Generates EDA reports from a pandas DataFrame.

[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://www.python.org)

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)  

## Features of the package
The pandas df.describe() function is great but a little 
basic, for serious exploratory data analysis for any 
classification or regression problem EasyReport helps the 
user for quick data analysis.

For each column the following statistics (if relevant for 
the column type) are presented:

- **Type inference:** detect the types of columns in a dataframe
- **Essentials:** type, unique values, missing values, outliers
- **Quantile statistics:** minimum value, Q1, median, Q3, maximum, interquartile range, upper, lower
- **Descriptive statistics:** mean, variance, standard deviation, coefficient of variation, kurtosis, skewness
- **Graphs:** Histogram, boxplot, countplot, pie, scatterplot
- **Correlations:** correlation matrix, heatmap
- **Duplicates:** total number of duplicate row and number of times they are repated  
- **Missing value:** number of missing values, dendrogram of missing values




## Installation
You can install using the pip package manager by running
```bash
pip install EasyReport
```
    
## Examples
### For Classification Problem
```python
import pandas as pd
from EasyReport.easy_report import EdaReport

#Read the dataset
df = pd.read_csv("Dataset")

report = EdaReport(data = df,target_column = 'target_column_name')
report.summary()
```

### For Regression Problem
```python
import pandas as pd
from EasyReport.easy_report import EdaReport

#Read the dataset
df = pd.read_csv("Dataset")

report = EdaReport(data = df,target_column = 'target_column_name',regression = True)
report.summary()
```
## Author

- [@Sumegh20](https://www.linkedin.com/in/sumegh-sen/)