<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>chipo = 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>chipo.head(10)<jupyter_output><empty_output><jupyter_text>Step 5. What is the number of observations in the dataset?<jupyter_code># Solution 1
rows = chipo.shape[0]
rows
# Solution 2
rows = len(chipo.iloc[:])
rows<jupyter_output><empty_output><jupyter_text>Step 6. What is the number of columns in the dataset?<jupyter_code>columns = chipo.shape[1]
columns<jupyter_output><empty_output><jupyter_text>Step 7. Print the name of all the columns.<jupyter_code>print(chipo.columns)<jupyter_output>Index(['order_id', 'quantity', 'item_name', 'choice_description',
       'item_price'],
      dtype='object')<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().sort_values(ascending=False, by="quantity").iloc[0].name<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().sort_values(ascending=False, by="quantity")["quantity"][0]<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().sort_values(ascending=False, by="quantity").iloc[0].name<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<jupyter_code># Solution 1
chipo["item_price"] = chipo["item_price"].apply(lambda col: col.replace("$", "")).astype("float")
# Solution 2
# chipo["item_price"].str.replace("$", "").astype("float")<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"]

chipo["revenue"] = revenue
chipo["revenue"].sum()<jupyter_output><empty_output><jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>chipo.groupby("order_id").sum().shape[0]<jupyter_output><empty_output><jupyter_text>Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1
chipo.groupby("order_id").aggregate(np.sum)["revenue"].mean()
# Solution 2
chipo.pivot_table(index="order_id", values="revenue",  aggfunc=np.sum).mean().iloc[0]<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>chipo["item_name"].unique().shape[0]<jupyter_output><empty_output>
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