<jupyter_start><jupyter_text><jupyter_code>import matplotlib.pyplot as plt
import numpy as np
arrx = np.random.rand(3,1)
print (arrx)

a=np.array([1,2,3])
print(type(a))

print(a.shape)
b= np.arange(12)
b
b.reshape(3,4)
import pandas as pd

df = pd.DataFrame(np.random.randn(6,4),index=list(range(6)),columns=list("ABCD"))
df
df.describe()
df.shape
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(10)
N=30
x = np.random.rand(N)
y = np.random.rand(N)
colors= np.random.rand(N)
area =(30 * np.random.rand(N))**2
plt.scatter(x, y, s=area, c=colors ,alpha =0.4)
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17795275590551182, 'fraction_numerical': 0.03307086614173228, 'mean_word_length': 6.066666666666666, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '639760', 'license': 'MIT', 'n_tokens_mistral': 291, 'n_tokens_neox': 283, 'n_words': 56, 'path': '1Assignment.ipynb', 'repo_name': 'Baisalis/DS-Unit-1-Sprint-1-Dealing-With-Data', 'size': '635'}