Python Pyforest Library

• active_imports(): It will return all the libraries which have been used in the program.
• lazy_imports(): It will return all the libraries available in pyforest.

pip install pyforest

• Numpy: NumPy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. Example: Python3

  # here we have not import  
  # 'numpy as np' by explicitly 
  a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) 
  
  print(a)
  

  Output:

  [[1 2 3]
   [4 5 6]
   [7 8 9]]
  

  Note: For more information, refer to NumPy in Python
• Pandas: Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns. Example: Python3

  d = {'A':[1, 2, 3], 'B':[4, 5, 6], 'C':[7, 8, 9]}
  
  # here we have not import
  # 'pandas as pd' by ourself .
  df = pd.DataFrame(d)  
  
  print(df)
  

  Output:

     A  B  C
  0  1  4  7
  1  2  5  8
  2  3  6  9
  

  Note: For more information, refer to Python | Pandas DataFrame
• NLTK: The NLTK module is a massive tool kit, aimed at helping you with the entire Natural Language Processing (NLP) methodology. Example: Python3

  # here we do not import
  # ' Nltk library' by ourself
  # but only the class of nltk .
  from nltk.tokenize import word_tokenize
  
  data = "All apples are red in colour"
  
  print(word_tokenize(data))
  

  Output:

  ['All', 'apples', 'are', 'red', 'in', 'colour']

  Note: For more information, refer to Tokenize text using NLTK in python
• Matplotlib: Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. Example: Python3

  # here we have not imported 
  # 'matplotlib.pyplot as plt' by ourself.
  
  x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  y = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 
  
  plt.plot(x, y)  
  plt.show()
  

  Output: Note: For more information, refer to Introduction to Matplotlib