<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>url = "https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv"
chipotle = pd.read_csv("https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv", sep='\t')<jupyter_output><empty_output><jupyter_text>Step 4. See the first 10 entries<jupyter_code>chipotle.head(10)<jupyter_output><empty_output><jupyter_text>Step 5. What is the number of observations in the dataset?<jupyter_code># Solution 1
print(len(chipotle))
# Solution 2
chipotle.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>chipotle.keys<jupyter_output><empty_output><jupyter_text>Step 7. Print the name of all the columns.<jupyter_code>for col in chipotle.columns:
    print(col)
chipotle.columns<jupyter_output>order_id
quantity
item_name
choice_description
item_price<jupyter_text>Step 8. How is the dataset indexed?<jupyter_code>chipotle.index<jupyter_output><empty_output><jupyter_text>Step 9. Which was the most-ordered item?<jupyter_code>chip_top = chipotle.groupby(['item_name'])
chipotle["qtySum"] = chip_top[["quantity"]].transform(sum)
chipotle.sort_values(by="qtySum", ascending=False).head(1)
#DataFrame["sum_column"] = grouped_DataFrame[["column2"]].transform(sum).
#.agg({'quantity':['sum']}).sort_values(by=['quantity'])
#print(chip_top)<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>chipotle.sort_values(by="qtySum", ascending=False).head(1).qtySum<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>choices = chipotle.groupby('choice_description').sum()
choices = choices.sort_values(['quantity'],ascending=False)
choices.head(10)<jupyter_output><empty_output><jupyter_text>Step 12. How many items were orderd in total?<jupyter_code>chipotle.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>chipotle['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>priceFloat = lambda x: float(x[1:-1])
chipotle['item_price'] = chipotle.item_price.apply(priceFloat)<jupyter_output><empty_output><jupyter_text>Step 13.c. Check the item price type<jupyter_code>chipotle.item_price.dtype<jupyter_output><empty_output><jupyter_text>Step 14. How much was the revenue for the period in the dataset?<jupyter_code>totalRevenue = (chipotle.quantity*chipotle.item_price).sum()<jupyter_output><empty_output><jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>totalOrders = chipotle.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
totalRevenue/totalOrders

# Solution 2
chipotle['orderTotal'] = chipotle.quantity * chipotle.item_price
chipotle.groupby(by=['order_id']).sum().mean()['orderTotal']
chipotle.head(10)<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>chipotle.item_name.value_counts().count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11969839773798303, 'fraction_numerical': 0.023327049952874646, 'mean_word_length': 6.306368330464716, 'pattern_counts': {'":': 0, '<': 72, '<?xml version=': 0, '>': 72, 'https://': 4, '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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