A Pythonic Data Explorer.

For Python 3.8+:

pip install dx>=1.0.3

The dx library currently enables DEX media type visualization of pandas DataFrames in two ways:

• individual calls to dx.display()
• updating the current IPython display formatter for a session

import dx

dx.display() will display a single dataset using the DEX media type. It currently supports:

• pandas DataFrame objects

  import pandas as pd
  import random
  
  df = pd.DataFrame({
      'random_ints': [random.randint(0, 100) for _ in range(500)],
      'random_floats': [random.random() for _ in range(500)],
  })
  dx.display(df)

  

• tabular data as dict or list types

  dx.display([
    [1, 5, 10, 20, 500],
    [1, 2, 3, 4, 5],
    [0, 0, 0, 0, 1]
  ])

  

• .csv or .json filepaths

  

dx will update the current IPython display formatters to allow DEX media type visualization of pandas DataFrame objects for an entire notebook / kernel session instead of the default DataFrame display output.

Note: this only affects pandas DataFrames; it does not affect the display of .csv/.json file data, or dict/list outputs

• dx.register()

  import pandas as pd
  
  # enable DEX display outputs from now on
  dx.register()
  
  df = pd.read_csv("examples/sample_data.csv")
  df

  df2 = pd.DataFrame(
      [
          [1, 5, 10, 20, 500],
          [1, 2, 3, np.nan, 5],
          [0, 0, 0, np.nan, 1]
      ],
      columns=['a', 'b', 'c', 'd', 'e']
  )
  df2

  

• dx.deregister()

  df2 = pd.DataFrame(
      [
          [1, 5, 10, 20, 500],
          [1, 2, 3, np.nan, 5],
          [0, 0, 0, np.nan, 1]
      ],
      columns=['a', 'b', 'c', 'd', 'e']
  )
  df2

  dx.deregister()
  df2

  

git clone https://github.com/noteable-io/dx
cd ./dx
pip install -e .

We follow the noteable.io code of conduct.

See LICENSE.md.