<jupyter_start><jupyter_text>Exercise 1  Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data  Step 2. Download the dataset to your computer and unzip it.  Step 3. Use the tsv file and assign it to a dataframe called food<jupyter_code>import pandas as pd
import numpy as np
food = pd.read_csv('en.openfoodfacts.org.products.tsv', sep='\t')<jupyter_output><empty_output><jupyter_text>Step 4. See the first 5 entries<jupyter_code>food.head()<jupyter_output><empty_output><jupyter_text>Step 5. What is the number of observations in the dataset?<jupyter_code>food.shape[0]<jupyter_output><empty_output><jupyter_text>Step 6. What is the number of columns in the dataset?<jupyter_code>food.shape[1]<jupyter_output><empty_output><jupyter_text>Step 7. Print the name of all the columns.<jupyter_code>print(food.columns.values)<jupyter_output>['code' 'url' 'creator' 'created_t' 'created_datetime' 'last_modified_t'
 'last_modified_datetime' 'product_name' 'generic_name' 'quantity'
 'packaging' 'packaging_tags' 'brands' 'brands_tags' 'categories'
 'categories_tags' 'categories_en' 'origins' 'origins_tags'
 'manufacturing_places' 'manufacturing_places_tags' 'labels' 'labels_tags'
 'labels_en' 'emb_codes' 'emb_codes_tags' 'first_packaging_code_geo'
 'cities' 'cities_tags' 'purchase_places' 'stores' 'countries'
 'countries_tags' 'countries_en' 'ingredients_text' 'allergens'
 'allergens_en' 'traces' 'traces_tags' 'traces_en' 'serving_size'
 'no_nutriments' 'additives_n' 'additives' 'additives_tags' 'additives_en'
 'ingredients_from_palm_oil_n' 'ingredients_from_palm_oil'
 'ingredients_from_palm_oil_tags'
 'ingredients_that_may_be_from_palm_oil_n'
 'ingredients_that_may_be_from_palm_oil'
 'ingredients_that_may_be_from_palm_oil_tags' 'nutrition_grade_uk'
 'nutrition_grade_fr' 'pnns_groups_1' 'pnns_groups_2' 'states'
 'states_tags' [...]<jupyter_text>Step 8. What is the name of 105th column?<jupyter_code>print(food.columns.values[104])<jupyter_output>-glucose_100g<jupyter_text>Step 9. What is the type of the observations of the 105th column?<jupyter_code>food.iloc[:,104].dtype
### Step 10. How is the dataset indexed?
food.index<jupyter_output><empty_output><jupyter_text>Step 11. What is the product name of the 19th observation?<jupyter_code>food.loc[18,'product_name']<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1134020618556701, 'fraction_numerical': 0.01632302405498282, 'mean_word_length': 8.86864406779661, 'pattern_counts': {'":': 0, '<': 31, '<?xml version=': 0, '>': 31, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
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