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# Navigate to your desired directory. Here I am using D | |
$ d: | |
# make the directory named 'ChromeCustomHomepage' | |
$ mkdir ChromeCustomHomepage | |
# Navigate to the newly created Directory | |
$ cd ChromeCustomHomepage | |
# install 'virtualenv' if you don't have it installed |
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# split those ingredients values | |
choco_data['ingredients'] = choco_data['ingredients'].str.strip(' ') | |
choco_data['num_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[0] | |
choco_data['main_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[1] | |
choco_data['main_ingredients'] = choco_data['main_ingredients'].str.strip(' ') | |
# encoding the values | |
ingre_encode = choco_data['main_ingredients'].str.get_dummies(sep=',') | |
# concatenating lecithin column with the main data. Containing lecithin denoted by 1 |
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# removing the misspelled word | |
taste_codo['nutty'] = taste_codo['nut'] + taste_codo['nuts'] + taste_codo['nutty'] | |
taste_codo['woody'] = taste_codo['woodsy'] + taste_codo['woody'] | |
taste_codo['earthy'] = taste_codo['earth'] + taste_codo['earthy'] | |
taste_codo.drop(['nut', 'nuts', 'woodsy', 'earth'], axis=1, inplace=True) | |
# making the taste dictionary | |
tasty_dict = {} | |
tasty_list = list(taste_codo.columns) | |
for taste in tasty_list: |
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# filtering those chocolates which are manufactured by Soma Chocomaker | |
soma_choco_data = choco_data[choco_data['manufacturer'].isin(['Soma'])] | |
# creating the dictionary | |
bean_dict = {} | |
bean_origins = list(soma_choco_data['bean_origin']) | |
for origin in bean_origins: | |
if origin in bean_dict: | |
bean_dict[origin] += 1 | |
else: |
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# taking the all cocoa percentages uses in chocolates of soma chocomaker | |
cocoa_list = list(soma_choco_data['cocoa_percent']) | |
# creating a dictionary | |
cocoa_percent_dict = {} | |
for cocoa_percent in cocoa_list: | |
if str(cocoa_percent) in cocoa_percent_dict: | |
cocoa_percent_dict[str(cocoa_percent)] += 1 | |
else: | |
cocoa_percent_dict[str(cocoa_percent)] = 1 |
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# removing those manufacturer whose count is less than 10 | |
choco_data_with_sec_count = count_df(choco_data, 'manufacturer') | |
choco_data_mod2 = choco_data_with_sec_count[choco_data_with_sec_count['count'] > 10] | |
# grouping the data by manufacturer and calculating the avg. mean for each of them | |
avg_rating_by_company = choco_data_mod2.groupby('manufacturer')['rating'].mean() | |
avg_rating_by_company_df = avg_rating_by_company.rename_axis('Company').reset_index(name='Rating') | |
avg_rating_by_company_df_sorted = avg_rating_by_company_df.sort_values(by='Rating', ascending=False).head(10) | |
# adding title and plotting the data |
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# listing all the tastes | |
tastes = list(taste_encode.columns) | |
taste_dict = {} | |
# taking the sum of the values of those taste columns to understand how many people are agreed with that taste | |
for taste in tastes: | |
taste_dict[taste] = sum(taste_encode[taste]) | |
# sorting the taste dictionary in decending order | |
taste_dict = sort_sliced_dict(taste_dict, is_reverse=True, item_count=8) |
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# filtering the taste of Kokoa Kamili and separating the values | |
taste_encode = choco_data[choco_data['bar_name'].isin(['Kokoa Kamili'])]['taste'].str.get_dummies(sep=', ') | |
# fixing some of the values whose pronunciation is wrong | |
taste_encode['nuts'] = taste_encode['nut'] + taste_encode['nuts'] | |
taste_encode['rich_cocoa'] = taste_encode['rich'] + taste_encode['rich cocoa'] + taste_encode['rich cooa'] | |
# dropping the columns containing wrong pronunciation | |
taste_encode.drop(['nut', 'rich', 'rich cooa'], axis=1, inplace=True) |
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# filtering those companies where Kokoa Kamili is manufactured | |
company_loc_list = list(choco_data[choco_data['bar_name'].isin(['Kokoa Kamili'])]['company_loc']) | |
company_loc_dict = {i:company_loc_list.count(i) for i in company_loc_list} | |
# plotting the data | |
fig = px.pie(values=list(company_loc_dict.values()), | |
names=list(company_loc_dict.keys()), | |
title='Most Common Location where Kokoa Kamili is Manufactured', | |
color_discrete_sequence=px.colors.sequential.Aggrnyl) |
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import plotly.express as px | |
import plotly.graph_objects as go | |
# removing those chocolates which has a count less than 10 | |
choco_data_with_count = count_df(choco_data, 'bar_name') | |
choco_data_mod = choco_data_with_count[choco_data_with_count['count'] >= 10] | |
# grouping chocolates according to bar_name and calculating mean | |
avg_rating_by_bar = choco_data_mod.groupby('bar_name')['rating'].mean() | |
avg_rating_by_bar_df = avg_rating_by_bar.rename_axis('bar_name').reset_index(name='rating') |
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