In this post I will explore the dataset relate to Kaggle’s competition “What’s cooking? to get understanding what kind of data we are dealing with. Getting know your data helps to clean-up the data in preprocessing in order to prepare dataset for classification.

In [9]:

import pandas as pd

Reading the data¶

In [10]:

df_train = pd.read_json("train.json")

In [11]:

df_train.head()

Out[11]:

	cuisine	id	ingredients
0	greek	10259	[romaine lettuce, black olives, grape tomatoes…
1	southern_us	25693	[plain flour, ground pepper, salt, tomatoes, g…
2	filipino	20130	[eggs, pepper, salt, mayonaise, cooking oil, g…
3	indian	22213	[water, vegetable oil, wheat, salt]
4	indian	13162	[black pepper, shallots, cornflour, cayenne pe…

How the different cuisines are represented in dataset?¶

In [12]:

%matplotlib inline
import matplotlib.pyplot as plt

plt.style.use("fivethirtyeight")

In [18]:

df_train["cuisine"].value_counts().plot(kind="barh", figsize=(8, 6));

What are the most common ingredients?¶

In [19]:

from collections import Counter

In [20]:

counters = {}
for cuisine in df_train["cuisine"].unique():
    counters[cuisine] = Counter()
    indices = df_train["cuisine"] == cuisine
    for ingredients in df_train[indices]["ingredients"]:
        counters[cuisine].update(ingredients)

What are the most used ingredients in different cuisines?¶

In [21]:

counters["italian"].most_common(10)

Out[21]:

[('salt', 3454),
 ('olive oil', 3111),
 ('garlic cloves', 1619),
 ('grated parmesan cheese', 1580),
 ('garlic', 1471),
 ('ground black pepper', 1444),
 ('extra-virgin olive oil', 1362),
 ('onions', 1240),
 ('water', 1052),
 ('butter', 1030)]

In [22]:

top10 = pd.DataFrame(
    [[items[0] for items in counters[cuisine].most_common(10)] for cuisine in counters],
    index=[cuisine for cuisine in counters],
    columns=["top{}".format(i) for i in range(1, 11)],
)
display(top10.head(8))

	top1	top2	top3	top4	top5	top6	top7	top8	top9	top10
greek	salt	olive oil	dried oregano	garlic cloves	feta cheese crumbles	extra-virgin olive oil	fresh lemon juice	ground black pepper	garlic	pepper
southern_us	salt	butter	all-purpose flour	sugar	large eggs	baking powder	water	unsalted butter	milk	buttermilk
filipino	salt	garlic	water	onions	soy sauce	pepper	oil	sugar	carrots	ground black pepper
indian	salt	onions	garam masala	water	ground turmeric	garlic	cumin seed	ground cumin	vegetable oil	oil
jamaican	salt	onions	water	garlic	ground allspice	pepper	scallions	dried thyme	black pepper	garlic cloves
spanish	salt	olive oil	garlic cloves	extra-virgin olive oil	onions	water	tomatoes	ground black pepper	red bell pepper	pepper
italian	salt	olive oil	garlic cloves	grated parmesan cheese	garlic	ground black pepper	extra-virgin olive oil	onions	water	butter
mexican	salt	onions	ground cumin	garlic	olive oil	chili powder	jalapeno chilies	sour cream	avocado	corn tortillas

In [23]:

df_train["all_ingredients"] = df_train["ingredients"].map(";".join)

In [24]:

df_train.head()

Out[24]:

	cuisine	id	ingredients	all_ingredients
0	greek	10259	[romaine lettuce, black olives, grape tomatoes…	romaine lettuce;black olives;grape tomatoes;ga…
1	southern_us	25693	[plain flour, ground pepper, salt, tomatoes, g…	plain flour;ground pepper;salt;tomatoes;ground…
2	filipino	20130	[eggs, pepper, salt, mayonaise, cooking oil, g…	eggs;pepper;salt;mayonaise;cooking oil;green c…
3	indian	22213	[water, vegetable oil, wheat, salt]	water;vegetable oil;wheat;salt
4	indian	13162	[black pepper, shallots, cornflour, cayenne pe…	black pepper;shallots;cornflour;cayenne pepper…

Check garlic cloves as differentiator¶

In [29]:

indices = df_train["all_ingredients"].str.contains("garlic cloves")
df_train[indices]["cuisine"].value_counts().plot(
    kind="barh", title="garlic cloves per cuisine", figsize=(8, 6)
);

In [32]:

relative_freq = (
    df_train[indices]["cuisine"].value_counts() / df_train["cuisine"].value_counts()
)
relative_freq.sort_values(inplace=True)
relative_freq.plot(kind="barh", figsize=(8, 6));

In [35]:

import numpy as np

unique = np.unique(top10.values.ravel())
unique

Out[35]:

array(['all-purpose flour', 'avocado', 'baking powder', 'baking soda',
       'black pepper', 'butter', 'buttermilk', 'cachaca',
       'cajun seasoning', 'carrots', 'cayenne pepper', 'chili powder',
       'coconut milk', 'corn starch', 'corn tortillas', 'cumin seed',
       'dried oregano', 'dried thyme', 'eggs', 'extra-virgin olive oil',
       'feta cheese crumbles', 'fish sauce', 'fresh lemon juice',
       'fresh lime juice', 'garam masala', 'garlic', 'garlic cloves',
       'ginger', 'grated parmesan cheese', 'green bell pepper',
       'green onions', 'ground allspice', 'ground black pepper',
       'ground cinnamon', 'ground cumin', 'ground ginger',
       'ground turmeric', 'jalapeno chilies', 'large eggs', 'lime',
       'milk', 'mirin', 'oil', 'olive oil', 'onions', 'paprika', 'pepper',
       'potatoes', 'red bell pepper', 'rice vinegar', 'sake', 'salt',
       'scallions', 'sesame oil', 'sesame seeds', 'shallots',
       'sour cream', 'soy sauce', 'sugar', 'tomatoes', 'unsalted butter',
       'vegetable oil', 'water'], dtype=object)

In [59]:

fig, axes = plt.subplots(2, 2, figsize=(20, 20))
for ingredient, ax_index in zip(unique, range(4)):
    indices = df_train["all_ingredients"].str.contains(ingredient)
    relative_freq = (
        df_train[indices]["cuisine"].value_counts() / df_train["cuisine"].value_counts()
    )
    relative_freq.plot(
        kind="barh", ax=axes.ravel()[ax_index], fontsize=24, title=ingredient
    );

Out[1]:

Reading the data¶

How the different cuisines are represented in dataset?¶

What are the most common ingredients?¶

What are the most used ingredients in different cuisines?¶

Check garlic cloves as differentiator¶

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