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import numpy as np

#creating an array wiith np.array
new_matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])

print(new_matrix)

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import pandas as pd

df = pd.DataFrame({"A":[1, 10, 100, 1000, 10000],
                   "B":[2, 20, 200, 2000, 20000], 
                   "C":[3, 30, 300, 3000, 30000],
                   "D":[4, 40, 400, 4000, 40000]})

#return the mean absolute deviation of the values for the requested axis
df.mad(axis = 0)

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from scipy import special

#exponential Function
x = special.exp10(1)

print(x)

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from sklearn import cluster, datasets

# load data
iris = datasets.load_iris()

# K-means clustering: create clusters for k=3
k=3
k_means = cluster.KMeans(k)

# fit data

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import tensorflow as tf

# Create a Tensor.
hello = tf.constant("hello world")
print(hello)

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import tensorflow as tf
from tensorflow import keras

#load dataset
mnist = tf.keras.datasets.mnist

#Build a machine learning model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(100, activation='relu'),

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import torch

p_Tensor = torch.ones((2, 2))

#size of a Tensor
print(p_Tensor.size())

#resizing 2x2 Tensor to 4x1 
p_Tensor = p_Tensor.view(4) 

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from nltk.stem import PorterStemmer
#from nltk.tokenize import sent_tokenize, word_tokenize

ps = PorterStemmer()

text_words = ["tech","technology","technologized","techy", "Technologization"]

for x in text_words:
    print(ps.stem(x))

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npm install danfojs-node

import * as dfd from "danfojs-node"

#creating a DataFrame/Series
s = new dfd.Series([1, 3, 5, undefined, 6, 8])
s.print()

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pip install pandasai