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)`

*chevron_right**filter_none***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)`

*chevron_right**filter_none***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))`

*chevron_right**filter_none***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()`

*chevron_right**filter_none***Output:**

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

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