Python Pyforest Library - GeeksforGeeks

19 Feb, 2020
Sometimes, it happens that we spent a huge amount of time importing some common libraries like NumPy, pandas, matplotlib, seaborn, nltk and many more. To remove this headache of importing such libraries manually, we have pyforest library.
It is that library which helps you to work directly without importing other libraries separately.It itself adds up some of the highly usable libraries used in DataScience while we are using it.
Functions of pyforest :
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.
Installing Library:
pip install pyforest
Let’s see the usage of pyforest with various libraries.
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:# 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
Example:
# 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: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
Example:
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:# 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
Example:
# 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:# 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
Example:
# 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
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