Metadata-Version: 2.1
Name: vizedax
Version: 1.0.3
Summary: EDA, Cleaning and Visualizations in an Instant!
Home-page: https://github.com/Shreyas-Madhav/Vizeda
Author: Shreyas Madhav AV
Author-email: shreyas.madhav@gmail.com
Project-URL: Repository, https://github.com/Shreyas-Madhav/Vizeda
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# Vizeda: Instant EDA and Data Visualization for Python
Author: Shreyas Madhav A V (avshreyasmadhav@gmail.com)

Vizeda is a powerful Python library designed to make exploratory data analysis (EDA) a breeze. With just a few lines of code, you can perform comprehensive EDA on your clean CSV datasets and create stunning visualizations of all attributes. Say goodbye to the hassle of manually inspecting and plotting data – Vizeda has got you covered!

## aboutit() Function
The aboutit() function reads a CSV file and displays the following information about the dataset:

#### Name of the file
#### Size of the file
#### Number of rows and columns
#### Column names and their data types
#### Top 5 rows
#### Last 5 rows
#### Summary statistics 

```python
import vizeda as vz

#For a CSV
vz.aboutit("example.csv") 

#For a Dataframe
vz.aboutit(df)

```


## cleanit() Function
The cleanit() function takes a DataFrame or a CSV file as its argument. It performs the following cleaning tasks:

#### Replaces null values based on the provided argument (zero, average, previous reading; default is average of the column).
#### Deletes duplicate rows.
#### Drops columns with more than 50% null values.
#### Detects categorical columns and performs one-hot encoding.

```python
import vizeda as vz

#For a CSV
# null strategy can be on of {average ,null ,previous}
vz.cleanit("example.csv", null_strategy) 

#For a Dataframe
vz.cleanit(df,null_strategy)

```

## graphit() Function
The graphit() function reads a CSV file and generates exploratory data analysis visualizations:

#### Correlation matrix
#### Various graphs for all columns of the dataset

```python
import vizeda as vz

#For a CSV
# null strategy can be on of {average ,null ,previous}
vz.graphit("example.csv") 

#For a Dataframe
vz.graphit(df)

```
