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https://github.com/arv-anshul/easy-analysis
A python package to perform Data Analysis easily. (Not Recommended)
https://github.com/arv-anshul/easy-analysis
arv-dumped data-analysis data-science easy-analysis eda pypi pypi-package python3
Last synced: 12 days ago
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A python package to perform Data Analysis easily. (Not Recommended)
- Host: GitHub
- URL: https://github.com/arv-anshul/easy-analysis
- Owner: arv-anshul
- License: mit
- Created: 2023-10-01T04:53:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-17T14:13:08.000Z (about 1 year ago)
- Last Synced: 2024-11-30T03:35:48.266Z (about 1 month ago)
- Topics: arv-dumped, data-analysis, data-science, easy-analysis, eda, pypi, pypi-package, python3
- Language: Python
- Homepage: https://pypi.org/p/arv-easy-analysis
- Size: 80.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Easy Analysis
Use this package to perform **Data Analysis** easily.
### Installation
```sh
pip install arv-easy-analysis
```
### Examples
For examples, we are working on **tips** dataset from `seaborn` library.
```python
import seaborn as sns
tips = sns.load_dataset("tips")
```
1. `data.summary` module.
Get the custom and more insightful summary of your data.
```python
from easy_analysis.data import summary
summary.custom_describe(tips[["total_bill", "tip", "size"]])
```
**Output:**
| | total_bill | tip | size |
| :------- | ---------: | ----: | ----: |
| count | 244 | 244 | 244 |
| mean | 19.786 | 2.998 | 2.57 |
| std | 8.902 | 1.384 | 0.951 |
| min | 3.07 | 1 | 1 |
| 1% | 7.25 | 1 | 1 |
| 5% | 9.558 | 1.44 | 2 |
| 25% | 13.348 | 2 | 2 |
| 50% | 17.795 | 2.9 | 2 |
| 75% | 24.127 | 3.562 | 3 |
| 95% | 38.061 | 5.196 | 4 |
| 99% | 48.227 | 7.214 | 6 |
| max | 50.81 | 10 | 6 |
| skew | 1.133 | 1.465 | 1.448 |
| kurtosis | 1.218 | 3.648 | 1.732 |
| notnull | 244 | 244 | 244 |
| isnull | 0 | 0 | 0 |
2. `data.outliers` module
This function extract the outliers from the given dataset using **IQR** method and returns the dataframe.
```python
from easy_analysis.data import outliers
outliers.extract_outliers(tips[["total_bill", "tip", "size"]], "total_bill")
```
**Output:**
| total_bill | tip | size |
| ---------: | ---: | ---: |
| 50.81 | 10 | 3 |
| 48.33 | 9 | 4 |
| 48.27 | 6.73 | 4 |
| 48.17 | 5 | 6 |
| 45.35 | 3.5 | 3 |
| 44.3 | 2.5 | 3 |
| 43.11 | 5 | 4 |
| 41.19 | 5 | 5 |
| 40.55 | 3 | 2 |
3. `plots.univariate` module
Use it for comprehensive univariate plots.
```python
from easy_analysis.plots import univariate
univariate.visualize_feature(tips["total_bill"])
```
4. `plots.insights`
Use `insights.null_plot` function to get overview of null values in a graph.
```python
from easy_analysis.plots import insights
insights.null_plot(tips) # Output: A graph which gives a overview of null values.
```
### Issues
If you have any issue or query related to this project you can raise [here](https://github.com/arv-anshul/easy-analysis/issues).
### Authors
- [@arv-anshul](https://github.com/arv-anshul)