🥫HOMEWORK 1: Intro to NumPy & Pandas🥫

Questions based on Mini-Lecture 2️⃣

To work on this HW, duplicate this Notebook and edit it in your workspace!

import pandas as pd import numpy as np from datascience import *

Dataset to use: click me!

https://www.kaggle.com/datasets/ulrikthygepedersen/which-ketchup-is-best

🍅 Background info/context: I LOVE Ketchup! (who doesn't?) Unfortunately, there's too many brands out there and in this economy I can't afford to buy every bottle to taste every brand. To help me solve this personal crisis, please take a look at this survey dataset containing a survey of people's opinions on different ketchup brands, their prices, and other information!

1. NumPy #️⃣🥧

# importing dataset as a NUMPY dataframe ketchup_np = Table().read_table("Ketchup.csv") ketchup_np

##Question 1: Sort the prices of heinz ketchup in ketchup_np in ascending order, return top 10 ketchup_np.sort("price.heinz").show(10)

##Question 2: Group by ketchup choice, then show only the ketchup choice and average prices of each brand ketchup_np.drop('Unnamed: 0', 'Ketchup.hid', 'Ketchup.id').group('Ketchup.choice', np.mean)

2. Pandas!! 🐼

# importing dataset as a PANDAS dataframe ketchup_pd = pd.read_csv("Ketchup.csv") ketchup_pd

Unnamed: 0int64

1 - 4956

Ketchup.hidint64

1 - 1956

# Question 3: Select the price of the different ketchup brands. Sort in the order of: heinz, hunts, delmonte, and stb. # Hint: if you are stuck, check this out! https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html ketchup_pd[['price.heinz', 'price.hunts', 'price.delmonte', 'price.stb']].sort_values(by=['price.heinz', 'price.hunts', 'price.delmonte', 'price.stb'])

price.heinzfloat64

0.79 - 1.47

price.huntsfloat64

0.79 - 1.53

269

0.79

1.43

594

0.79

1.43

906

0.79

1.43

949

0.79

1.43

1043

0.79

1.43

1460

0.79

1.43

1766

0.79

1.43

1884

0.79

1.43

1899

0.79

1.43

1944

0.79

1.43

# Question 4: Find the brand chosen the most in the data frame. Your answer should output one line. # Hint: Try using .value_counts!! # Hint 2: After you have your SERIES of value counts, use a double bracket to get the first line! ketchup_choice = ketchup_pd['Ketchup.choice'].value_counts() ketchup_choice.get([0])

# QUESTION 5: Within the Ketchup.choice column, select all the people who chose heinz. Then sort in DESCENDING order by price. # Hint:Confused on boolean conditioning? Check this out (DATA 100 content!): https://docs.google.com/presentation/d/1EyNlu_BDGSW3TiIxPkdwC2tSBwjp-XWENpFxC3X7F8I/edit#slide=id.g8a only_heinz = ketchup_pd[ketchup_pd['Ketchup.choice'].isin(['heinz'])] only_heinz.sort_values(by=['price.heinz', 'price.hunts', 'price.delmonte', 'price.stb'], ascending=False)

Unnamed: 0int64

1 - 4953

Ketchup.hidint64

1 - 1953

578

579

221

108

109

241

242

730

731

271

968

969

355

1159

1160

430

1377

1378

510

1610

1611

599

1751

1752

640

1873

1874

698

3. Reading! 📚

Read this article: click me! Give a 5 sentence summary of the article or something that you've learned from it.

https://medium.com/@dssberkeley/behind-berkeleys-most-popular-double-major-how-applicable-is-ds-x-econ-in-industry-64ea4dcca327

Studying both data science and economics has become more common and valuable in the past few years. This is due to the rise in demand for companies to understand their users and the risks of actions that may impact their profit. The daily skills and knowledge needed for data science jobs vary. Many data science jobs use econ knowledge and it may be helpful to have a solid understanding of both subjects. That being said, having a degree in economics may not be a requirement, but it can be very helpful if you are looking to get a data-related job.

🥫HOMEWORK 1: Intro to NumPy & Pandas🥫

Questions based on Mini-Lecture 2️⃣

1. NumPy #️⃣🥧

2. Pandas!! 🐼

3. Reading! 📚

Congrats!! That's it! You've just finished your first HW in the DeCal! 😃