<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
import seaborn as sns<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'
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
chipo.shape
# Solution 2
chipo.info<jupyter_output><empty_output><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>pop = chipo.groupby('item_name')
pop = pop.sum()
pop = pop.sort_values(['quantity'], ascending=False)
pop.head(1)<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>pop.head(1)<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>desc = chipo.groupby('choice_description')
desc = desc.sum()
desc = desc.sort_values(['quantity'], ascending = False)
desc.head(1)<jupyter_output><empty_output><jupyter_text>Step 12. How many items were orderd in total?<jupyter_code>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<jupyter_output><empty_output><jupyter_text>Step 13.b. Create a lambda function and change the type of item price  Step 13.c. Check the item price type  Step 14. How much was the revenue for the period in the dataset?  Step 15. How many orders were made in the period?  Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1


# Solution 2<jupyter_output><empty_output>
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