<jupyter_start><jupyter_text>Ex2 - Getting and Knowing your Data This time we are going to pull data directly from the internet.Special thanks to: https://github.com/justmarkham for sharing the dataset and materials. Step 1. Import the necessary libraries<jupyter_code>import numpy as np
import pandas as pd<jupyter_output><empty_output><jupyter_text>Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv).   Step 3. Assign it to a variable called chipo.<jupyter_code>url = '../../data/chipotle.tsv'
chipo = pd.read_csv(url, sep='\t')<jupyter_output><empty_output><jupyter_text>Step 4. See the first 10 entries<jupyter_code>chipo.head(10)<jupyter_output><empty_output><jupyter_text>Step 5. What is the number of observations in the dataset?<jupyter_code># Solution 1
# len(chipo)
chipo.shape[0]
# Solution 2
chipo.info()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 4622 entries, 0 to 4621
Data columns (total 5 columns):
order_id              4622 non-null int64
quantity              4622 non-null int64
item_name             4622 non-null object
choice_description    3376 non-null object
item_price            4622 non-null object
dtypes: int64(2), object(3)
memory usage: 180.6+ KB<jupyter_text>Step 6. What is the number of columns in the dataset?<jupyter_code># len(chipo.columns)
chipo.shape[1]<jupyter_output><empty_output><jupyter_text>Step 7. Print the name of all the columns.<jupyter_code>chipo.columns<jupyter_output><empty_output><jupyter_text>Step 8. How is the dataset indexed?<jupyter_code>chipo.index<jupyter_output><empty_output><jupyter_text>Step 9. Which was the most-ordered item?<jupyter_code>chipo.groupby('item_name').sum().nlargest(1, 'quantity')
chipo.groupby('item_name').sum().sort_values(['quantity'], ascending=False).head(1)<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>chipo.groupby('item_name').sum().nlargest(1, 'quantity')<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>chipo.groupby('choice_description').sum().nlargest(1, 'quantity')<jupyter_output><empty_output><jupyter_text>Step 12. How many items were orderd in total?<jupyter_code>chipo['quantity'].sum()
chipo.quantity.sum()<jupyter_output><empty_output><jupyter_text>Step 13. Turn the item price into a float  Step 13.a. Check the item price type<jupyter_code>chipo['item_price'].dtype
chipo.item_price.dtype<jupyter_output><empty_output><jupyter_text>Step 13.b. Create a lambda function and change the type of item price<jupyter_code># chipo['item_price'] = chipo.apply(lambda x:x['item_price'].replace('$', ''), axis=1).astype('float')
chipo.item_price = chipo.item_price.apply(lambda x:float(x[1:-1]))<jupyter_output><empty_output><jupyter_text>Step 13.c. Check the item price type<jupyter_code>chipo['item_price'].dtype<jupyter_output><empty_output><jupyter_text>Step 14. How much was the revenue for the period in the dataset?<jupyter_code>print('Revenue was: $' + str((chipo['quantity'] * chipo['item_price']).sum()))
revenue = (chipo['quantity'] * chipo['item_price']).sum()
print('Revenue was: $' + str(np.round(revenue,2)))<jupyter_output>Revenue was: $39237.02<jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>chipo['order_id'].drop_duplicates().count()
chipo.order_id.value_counts().count()<jupyter_output><empty_output><jupyter_text>Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1
(chipo['item_price'] * chipo['quantity']).sum()/chipo['order_id'].drop_duplicates().count()

chipo['revenue'] = chipo['item_price'] * chipo['quantity']
order_grouped = chipo.groupby('order_id').sum()
order_grouped.mean()['revenue']
# Solution 2
chipo.groupby('order_id').apply(lambda x:(x['quantity'] * x['item_price']).sum()).mean()<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>chipo['item_name'].drop_duplicates().count()
chipo.item_name.value_counts().count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13851351351351351, 'fraction_numerical': 0.02413127413127413, 'mean_word_length': 7.373737373737374, 'pattern_counts': {'":': 0, '<': 72, '<?xml version=': 0, '>': 72, 'https://': 2, '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}}
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