<jupyter_start><jupyter_text>1. create an array and a list from exercise 26<jupyter_code>list_1 = [1,2,3] 
import numpy as np
array_1 = np.array(list_1)<jupyter_output><empty_output><jupyter_text>2. concat list_1 with itself.<jupyter_code>list_2 = list_1 + list_1
list_2<jupyter_output><empty_output><jupyter_text>3. concat array_1 with itself<jupyter_code>array_2 = array_1 + array_1 

array_2<jupyter_output><empty_output><jupyter_text>4. Load a csv file and concatinate it with itself.<jupyter_code>data = np.genfromtxt('numbers.csv', delimiter=',', names=True)
data = data.astype('float64')
data + data<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11635220125786164, 'fraction_numerical': 0.03773584905660377, 'mean_word_length': 8.231884057971014, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '163074', 'license': 'MIT', 'n_tokens_mistral': 262, 'n_tokens_neox': 254, 'n_words': 47, 'path': 'Chapter03/.ipynb_checkpoints/Exercise 27-checkpoint.ipynb', 'repo_name': 'arifmudi/The-Data-Wrangling-Workshop', 'size': '636'}