https://github.com/queirozfcom/python-ds-util

Collection of useful helper methods for interactive data science work in python. Usually on jupyter notebooks, using the basic python scientific stack.
https://github.com/queirozfcom/python-ds-util

data-science matplotlib pandas python scikit-learn utils

Last synced: 26 days ago
JSON representation

Collection of useful helper methods for interactive data science work in python. Usually on jupyter notebooks, using the basic python scientific stack.

• Host: GitHub
• URL: https://github.com/queirozfcom/python-ds-util
• Owner: queirozfcom
• License: mit
• Created: 2018-07-11T01:18:26.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-11-01T02:37:53.000Z (about 3 years ago)
• Last Synced: 2024-09-28T13:22:42.942Z (about 1 month ago)
• Topics: data-science, matplotlib, pandas, python, scikit-learn, utils
• Language: Python
• Homepage:
• Size: 909 KB
• Stars: 4
• Watchers: 2
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# python-ds-util

[![PyPI version](https://badge.fury.io/py/dsutil.png)](https://badge.fury.io/py/dsutil)

## Installation

The library is available on **pip**:

`pip install dsutil`

## Examples

For full usage examples see notebooks under [the examples directory](https://github.com/queirozfcom/python-ds-util/tree/master/examples).

### Plotting

Full examples for plotting [here](https://github.com/queirozfcom/python-ds-util/tree/master/examples/examples-plotting.ipynb)

- `add_grid()`: reasonable default grid settings, with weak grey lines, light alpha, etc.

   

  ```python

  import numpy as np

  import matplotlib.pyplot as plt

  from dsutil.plotting import add_grid

  

  x = np.linspace(0.0,100,10)

  y = np.random.uniform(low=0,high=10,size=10)

  plt.bar(x,y)

  

  add_grid()

  ```

   



  

  



- `add_value_labels()` annotates barplots, line plots and scatter plots with values for the coordinates

  ```python

  import numpy as np

  import matplotlib.pyplot as plt

  from dsutil.plotting import add_value_labels

  

  x = np.linspace(0.0,100,10)

  y = np.random.uniform(low=0,high=10,size=10)

  plt.bar(x,y)

  

  add_value_labels()

  ```

   



  

  



- `format_yaxis_percentage()`: turns values between 0 and 1 in y-axis into percentages

  ```python

  import numpy as np

  import matplotlib.pyplot as plt

  from dsutil.plotting import format_yaxis_percentage

  

  x = np.linspace(0.0,100,10)

  y = np.random.uniform(low=0,high=1,size=10)

    

  plt.bar(x,y)

  plt.yticks(np.arange(0,1.01,0.1))

  format_yaxis_percentage()

  ```

  



  

  



- `format_yaxis_thousands()`: uses commas as thousands separator in the y-axis labels

  ```python

  import numpy as np

  import matplotlib.pyplot as plt

  from dsutil.plotting import format_yaxis_thousands

  

  x = np.linspace(0.0,100,10)

  y = np.random.uniform(low=10000,high=100000,size=10)

  plt.bar(x,y)

  plt.yticks(np.arange(0,100001,10000))

  format_yaxis_thousands()

  ```