01_Getting_&_Knowing_Your_Data -> World Food Facts

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

import pandas as pd

food = pd.read_csv(
    "./file/en.openfoodfacts.org.products.tsv", sep="\t", low_memory=False
) #使用low_memory=False这个参数会告诉 Pandas 在读取文件时使用更多的内存，从而避免某些类型的推断错误。

Step 4. See the first 5 entries

# 显示前5条数据，Display the first 5 rows of data
food.head(5)
code url creator created_t created_datetime last_modified_t last_modified_datetime product_name generic_name quantity ... fruits-vegetables-nuts_100g fruits-vegetables-nuts-estimate_100g collagen-meat-protein-ratio_100g cocoa_100g chlorophyl_100g carbon-footprint_100g nutrition-score-fr_100g nutrition-score-uk_100g glycemic-index_100g water-hardness_100g
0 0000000003087 http://world-en.openfoodfacts.org/product/0000... openfoodfacts-contributors 1474103866 2016-09-17T09:17:46Z 1474103893 2016-09-17T09:18:13Z Farine de blé noir NaN 1kg ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 0000000004530 http://world-en.openfoodfacts.org/product/0000... usda-ndb-import 1489069957 2017-03-09T14:32:37Z 1489069957 2017-03-09T14:32:37Z Banana Chips Sweetened (Whole) NaN NaN ... NaN NaN NaN NaN NaN NaN 14.0 14.0 NaN NaN
2 0000000004559 http://world-en.openfoodfacts.org/product/0000... usda-ndb-import 1489069957 2017-03-09T14:32:37Z 1489069957 2017-03-09T14:32:37Z Peanuts NaN NaN ... NaN NaN NaN NaN NaN NaN 0.0 0.0 NaN NaN
3 0000000016087 http://world-en.openfoodfacts.org/product/0000... usda-ndb-import 1489055731 2017-03-09T10:35:31Z 1489055731 2017-03-09T10:35:31Z Organic Salted Nut Mix NaN NaN ... NaN NaN NaN NaN NaN NaN 12.0 12.0 NaN NaN
4 0000000016094 http://world-en.openfoodfacts.org/product/0000... usda-ndb-import 1489055653 2017-03-09T10:34:13Z 1489055653 2017-03-09T10:34:13Z Organic Polenta NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 163 columns

Step 5. What is the number of observations in the dataset?

# 获取行数，Get the number of rows
num_observations = food.shape[0]
print(f"There are {num_observations} observation records in the dataset.")
There are 356027 observation records in the dataset.

Step 6. What is the number of columns in the dataset?

# 获取列数，Get the number of columns
num_columns = food.shape[1]
print(f"There are {num_columns} columns in the dataset.")
There are 163 columns in the dataset.

Step 7. Print the name of all the columns.

print(food.columns.tolist())
['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', 'states_en', 'main_category', 'main_category_en', 'image_url', 'image_small_url', 'energy_100g', 'energy-from-fat_100g', 'fat_100g', 'saturated-fat_100g', '-butyric-acid_100g', '-caproic-acid_100g', '-caprylic-acid_100g', '-capric-acid_100g', '-lauric-acid_100g', '-myristic-acid_100g', '-palmitic-acid_100g', '-stearic-acid_100g', '-arachidic-acid_100g', '-behenic-acid_100g', '-lignoceric-acid_100g', '-cerotic-acid_100g', '-montanic-acid_100g', '-melissic-acid_100g', 'monounsaturated-fat_100g', 'polyunsaturated-fat_100g', 'omega-3-fat_100g', '-alpha-linolenic-acid_100g', '-eicosapentaenoic-acid_100g', '-docosahexaenoic-acid_100g', 'omega-6-fat_100g', '-linoleic-acid_100g', '-arachidonic-acid_100g', '-gamma-linolenic-acid_100g', '-dihomo-gamma-linolenic-acid_100g', 'omega-9-fat_100g', '-oleic-acid_100g', '-elaidic-acid_100g', '-gondoic-acid_100g', '-mead-acid_100g', '-erucic-acid_100g', '-nervonic-acid_100g', 'trans-fat_100g', 'cholesterol_100g', 'carbohydrates_100g', 'sugars_100g', '-sucrose_100g', '-glucose_100g', '-fructose_100g', '-lactose_100g', '-maltose_100g', '-maltodextrins_100g', 'starch_100g', 'polyols_100g', 'fiber_100g', 'proteins_100g', 'casein_100g', 'serum-proteins_100g', 'nucleotides_100g', 'salt_100g', 'sodium_100g', 'alcohol_100g', 'vitamin-a_100g', 'beta-carotene_100g', 'vitamin-d_100g', 'vitamin-e_100g', 'vitamin-k_100g', 'vitamin-c_100g', 'vitamin-b1_100g', 'vitamin-b2_100g', 'vitamin-pp_100g', 'vitamin-b6_100g', 'vitamin-b9_100g', 'folates_100g', 'vitamin-b12_100g', 'biotin_100g', 'pantothenic-acid_100g', 'silica_100g', 'bicarbonate_100g', 'potassium_100g', 'chloride_100g', 'calcium_100g', 'phosphorus_100g', 'iron_100g', 'magnesium_100g', 'zinc_100g', 'copper_100g', 'manganese_100g', 'fluoride_100g', 'selenium_100g', 'chromium_100g', 'molybdenum_100g', 'iodine_100g', 'caffeine_100g', 'taurine_100g', 'ph_100g', 'fruits-vegetables-nuts_100g', 'fruits-vegetables-nuts-estimate_100g', 'collagen-meat-protein-ratio_100g', 'cocoa_100g', 'chlorophyl_100g', 'carbon-footprint_100g', 'nutrition-score-fr_100g', 'nutrition-score-uk_100g', 'glycemic-index_100g', 'water-hardness_100g']

Step 8. What is the name of 105th column?

# 获取第105个列的名称，get 105th column name
column_name_105 = food.columns[
    104
]  # 索引从0开始，所以第105个列的索引是104，The index starts from 0, so the index of the 105th column is 104
print(column_name_105)
-glucose_100g

Step 9. What is the type of the observations of the 105th column?

# 获取第105列的数据类型, get type of the 105th column
column_type = food.dtypes.iloc[104]
print(f"dtype('{column_type}')")
dtype('float64')

Step 10. How is the dataset indexed?

# 查看索引类型，View index type
print(food.index)
RangeIndex(start=0, stop=356027, step=1)

Step 11. What is the product name of the 19th observation?

# 获取第19个观察值的产品名称，Obtain the product name for the 19th observation value
product_name_19th = food.iloc[18]['product_name']
print(product_name_19th)
Lotus Organic Brown Jasmine Rice