<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).<jupyter_code>url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'

url_data = pd.read_table(url,sep='\t')
url_data<jupyter_output><empty_output><jupyter_text>Step 3. Assign it to a variable called chipo.<jupyter_code>chipo = url_data<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.describe()
# Solution 2
len(chipo)<jupyter_output><empty_output><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>for col in chipo.columns:
    print(col)
chipo.columns.values<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>item_order_counts = chipo.groupby('item_name')['quantity'].count()
item_order_counts.idxmax()<jupyter_output><empty_output><jupyter_text>Step 10. For the most-ordered item, how many items were ordered?<jupyter_code>chipo.groupby('item_name')['quantity'].count().max()<jupyter_output><empty_output><jupyter_text>Step 11. What was the most ordered item in the choice_description column?<jupyter_code>choice_descriptions = chipo.groupby('choice_description')['quantity'].count()
choice_descriptions.idxmax()<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>type(chipo['item_price'].max())<jupyter_output><empty_output><jupyter_text>Step 13.b. Create a lambda function and change the type of item price<jupyter_code>from re import sub
from decimal import Decimal


chipo['item_price'] = chipo['item_price'].apply(lambda x: Decimal(sub(r'[^\d.]', '', x)))
chipo<jupyter_output><empty_output><jupyter_text>Step 13.c. Check the item price type<jupyter_code>type(chipo['item_price'].max())<jupyter_output><empty_output><jupyter_text>Step 14. How much was the revenue for the period in the dataset?<jupyter_code>chipo['item_price'].sum()<jupyter_output><empty_output><jupyter_text>Step 15. How many orders were made in the period?<jupyter_code>chipo['order_id'].count()<jupyter_output><empty_output><jupyter_text>Step 16. What is the average revenue amount per order?<jupyter_code># Solution 1
revenue = chipo['item_price'].sum()
total_orders = chipo['order_id'].count()

average_revenue = revenue/total_orders
average_revenue
# Solution 2

# Nah son<jupyter_output><empty_output><jupyter_text>Step 17. How many different items are sold?<jupyter_code>chipo.groupby(['item_name']).nunique().count()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11932479627473806, 'fraction_numerical': 0.012223515715948778, 'mean_word_length': 8.627450980392156, 'pattern_counts': {'":': 0, '<': 77, '<?xml version=': 0, '>': 77, '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}}
{'chain_length': '19', 'dir': 'jupyter-structured-clean-dedup', 'id': '465362', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 1249, 'n_tokens_neox': 1195, 'n_words': 246, 'path': '01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb', 'repo_name': 'mtzupan/pandas_exercises', 'size': '3436'}