<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 pandas as pd
import numpy as np<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>data_set_url = "https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv"
chipo = pd.read_csv(data_set_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
print("rows in dataset: " + str(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):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   order_id            4622 non-null   int64 
 1   quantity            4622 non-null   int64 
 2   item_name           4622 non-null   object
 3   choice_description  3376 non-null   object
 4   item_price          4622 non-null   object
dtypes: int64(2), object(3)
memory usage: 180.7+ KB<jupyter_text>Step 6. What is the number of columns in the dataset?<jupyter_code>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>most_ordered = chipo.groupby('item_name')
most_ordered = most_ordered.sum()
most_ordered = most_ordered.sort_values(['quantity'], ascending=False)
most_ordered.head(1)<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>most_ordered.iloc[0]['quantity']<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>mcDesc = chipo.groupby('choice_description').sum()
mcDesc = mcDesc.sort_values(['quantity'], ascending=False)
mcDesc.head(1)<jupyter_output><empty_output><jupyter_text>Step 12. How many items were orderd in total?<jupyter_code>total_orders = chipo['quantity'].sum()
total_orders<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<jupyter_output><empty_output><jupyter_text>Step 13.b. Create a lambda function and change the type of item price<jupyter_code>cvtToFloat = lambda x: float(x[1:-1])
chipo.item_price = chipo.item_price.apply(cvtToFloat)<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>revenue = (chipo.item_price * chipo.quantity).sum()
print("The total revenue for the period was: $" + str(revenue))<jupyter_output>The total revenue for the period was: $39237.02<jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>orders = len(chipo.order_id.value_counts())
orders<jupyter_output><empty_output><jupyter_text>Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1
chipo['revenue'] = chipo['item_price']*chipo['quantity']
order_group = chipo.groupby('order_id').sum()
order_group.mean()['revenue']
# Solution 2<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>chipo.item_name.value_counts().count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11384152457372117, 'fraction_numerical': 0.025827482447342027, 'mean_word_length': 5.961605584642234, 'pattern_counts': {'":': 0, '<': 72, '<?xml version=': 0, '>': 72, 'https://': 3, '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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