<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<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")
chipo<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[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>len(chipo.columns)<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>c9 = chipo.groupby("item_name")
c9 = c9.sum()
c9 = c9.sort_values(["quantity"], ascending=False)
c9<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>c9.max()<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>c11 = chipo.groupby("choice_description").sum()
c11 = c11.sort_values(["quantity"], ascending=False)
c11<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>c13 = chipo["item_price"].replace("[\$,]", "", regex=True).astype(float)
c13<jupyter_output><empty_output><jupyter_text>Step 13.b. Create a lambda function and change the type of item price<jupyter_code>c13b = lambda x: float(x[1:-1])
c13b_solution = chipo["item_price"].apply(c13b)
c13b_solution<jupyter_output><empty_output><jupyter_text>Step 13.c. Check the item price type<jupyter_code>c13b_solution.dtype<jupyter_output><empty_output><jupyter_text>Step 14. How much was the revenue for the period in the dataset?<jupyter_code>c14 = chipo.copy()
c14["revenue"] = c14["quantity"] * c13
c14<jupyter_output><empty_output><jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>c15 = c14["order_id"].max()
c15<jupyter_output><empty_output><jupyter_text>Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1
c16a = c14["revenue"].sum() / c15
c16a
# Solution 2
c16b = c14["revenue"].mean()
c16b<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>c17a = len(chipo["item_name"].unique())
c17b = chipo["item_name"].value_counts().count()

print(c17a)
print(c17b)<jupyter_output>50
50
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