🥫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 = ketchup_np.sort('price.heinz', descending=True) ketchup_np.show(10)

##Question 2: Group by ketchup choice, then show only the ketchup choice and average prices of each brand q2 = ketchup_np.group('Ketchup.choice', collect=np.mean).drop(['Unnamed: 0 mean', 'Ketchup.hid mean', 'Ketchup.id mean']) q2.show()

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 = ketchup_pd.sort_values(by=['price.heinz', 'price.hunts', 'price.delmonte', 'price.stb']) ketchup_pd.head()

Unnamed: 0int64

Ketchup.hidint64

269

270

594

595

229

906

907

335

949

950

347

1043

1044

383

# 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_pd[['Ketchup.choice']].value_counts()[[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 ketchup_pd[ketchup_pd['Ketchup.choice'] == 'heinz'].sort_values(by='price.heinz').drop(['price.hunts', 'price.delmonte', 'price.stb'], axis=1)

Unnamed: 0int64

1 - 4953

Ketchup.hidint64

1 - 1953

269

270

4913

4914

1919

4914

4915

1920

4928

4929

1931

4930

4931

1933

4937

4938

1939

141

142

171

172

549

550

217

687

688

259

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

Economics and Data Science are the most common double majors at UC Berkeley. However, the rise of double majors in economics is correlated with the rise of Data Science. There are many jobs that can take advantage of the skillsets from two majors. Even though the job title might be similar, the job skillsets differ depending on the fields and the companies. Additionally, communication skill is very important for anyone wanting to pursue a Data Analyst career.

[DSS DeCal][SPR'23] Mini-Lecture: Intro to Pandas

🥫HOMEWORK 1: Intro to NumPy & Pandas🥫

Questions based on Mini-Lecture 2️⃣

Dataset to use: click me!

1. NumPy #️⃣🥧

2. Pandas!! 🐼

3. Reading! 📚

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

.css-ytumd6{-webkit-text-decoration:none;text-decoration:none;}🥫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! 😃

🥫HOMEWORK 1: Intro to NumPy & Pandas🥫