2. Iterables and Pandas Data Frames#

2.1. House Keeping#

2.2. Assignment 1#

You can revise it to fix anything you learned today before I give feedback.

2.3. Closing Jupyter server.#

In the terminal use Ctrl+C (actually control, not command on mac).

It will ask you a question and give options, read and follow

or

do ctrl+C a second time.

A jupyter server typically runs at localhost:8888, but if you have multiple servers running the count increases.

Once I saw a student in office hours working on localhost:8894 asking why their code kept crashing.

Important

Remember to close your jupyter server

2.4. Using Pandas#

We will use data with a library called pandas. By convention, we import it like:

import pandas as pd

  • the import keyword is used for loading packages

  • pandas is the name of the package that is installed

  • as keyword allows us to assign an alias (nickname)

  • pd is the typical alias for pandas

2.5. Everything is Data#

Data we will see:

  • tabular data

  • websites as data

  • activity logs on websites

  • images

  • text

2.6. Why inspection in code?#

Some IDEs give you GUI based tools to inspect objects. We are going to do it programmatically inline with our analyses for two reasons.

  • (minor, logistical) it helps make for good notes

  • (most importantly) it helps build habits of data science

In data science, our code will be aiming to tell a story.

If you’re curious about something, try it out, see what happens. We’re going to use a lot of code inspection tools during class. These are helpful both for understanding what’s going on, but the advantage to knowing how to get this information programmatically even though a different IDE would give you inspection tools is that it helps you treat your code as data.

2.7. everything is an object#

let’s examine the type of some variables:

a=4
b = 'monday'
c =5.3
d = print

type(a)

int

` ints are a base python type, like they appear in other languages

strings are iterable type, meaning that theycan be indexed into, or their elements iterated over. For a more technical definition, see the official python glossary entry

type(b)

str

we can select one element

b[0]

'm'

or multiple, this is called slicing.

b[0:3]

'mon'

negative numbers count from the right.

b[-1]

'y'

type (c)

float

a variable can hold a whole function.

type(d)

builtin_function_or_method

functions are also objects like any other type in python

we can use the variable just like the function itself

d('hello')

hello

print(b)

monday

2.8. Tabular Data#

Structured data is easier to work with than other data.

We’re going to focus on tabular data for now. At the end of the course, we’ll examine images, which are structured, but more complex and text, which is much less structured.

2.9. Getting familiar with the datset#

We’re going to use a dataset about coffee quality today.

How was this dataset collected?

  • reviews added to DB

  • then scraped

Where did it come from?

  • coffee Quality Institute’s trained reviewers.

what format is it provided in?

  • csv (Comma Separated Values)

what other information is in this repository?

  • the code to scrape and clean the data

  • the data before cleaning

It’s important to always know where data came from and how it was collected.

This helps you know what is is useful for and what its limitations are.

Further Reading

An important research article on documenting datasets for machine learning is called Datasheets for Datasets these researchers also did a follow up study to better understand how practitioner use datasheets and decide how to use data.

If topics like this are interesting to you, let me know! my research is related to this and I have a lot of students who complete 310 do research in my lab.

2.10. Loading the Coffee Data#

Get raw url for the dataset click on the raw button on the csv page, then copy the url.

coffee_data_url = 'https://raw.githubusercontent.com/jldbc/coffee-quality-database/master/data/robusta_data_cleaned.csv'

Warning

This did not work in class, so I downloaded the data and dragged it to the same folder as my notebook

pd.read_csv(coffee_data_url)
Unnamed: 0 Species Owner Country.of.Origin Farm.Name Lot.Number Mill ICO.Number Company Altitude ... Color Category.Two.Defects Expiration Certification.Body Certification.Address Certification.Contact unit_of_measurement altitude_low_meters altitude_high_meters altitude_mean_meters
0 1 Robusta ankole coffee producers coop Uganda kyangundu cooperative society NaN ankole coffee producers 0 ankole coffee producers coop 1488 ... Green 2 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1488.0 1488.0 1488.0
1 2 Robusta nishant gurjer India sethuraman estate kaapi royale 25 sethuraman estate 14/1148/2017/21 kaapi royale 3170 ... NaN 2 October 31st, 2018 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3170.0 3170.0 3170.0
2 3 Robusta andrew hetzel India sethuraman estate NaN NaN 0000 sethuraman estate 1000m ... Green 0 April 29th, 2016 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
3 4 Robusta ugacof Uganda ugacof project area NaN ugacof 0 ugacof ltd 1212 ... Green 7 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1212.0 1212.0 1212.0
4 5 Robusta katuka development trust ltd Uganda katikamu capca farmers association NaN katuka development trust 0 katuka development trust ltd 1200-1300 ... Green 3 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1200.0 1300.0 1250.0
5 6 Robusta andrew hetzel India NaN NaN (self) NaN cafemakers, llc 3000' ... Green 0 February 28th, 2013 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3000.0 3000.0 3000.0
6 7 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Green 0 May 15th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
7 8 Robusta nishant gurjer India sethuraman estate kaapi royale 7 sethuraman estate 14/1148/2017/18 kaapi royale 3140 ... Bluish-Green 0 October 25th, 2018 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3140.0 3140.0 3140.0
8 9 Robusta nishant gurjer India sethuraman estate RKR sethuraman estate 14/1148/2016/17 kaapi royale 1000 ... Green 0 August 17th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
9 10 Robusta ugacof Uganda ishaka NaN nsubuga umar 0 ugacof ltd 900-1300 ... Green 6 August 5th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 900.0 1300.0 1100.0
10 11 Robusta ugacof Uganda ugacof project area NaN ugacof 0 ugacof ltd 1095 ... Green 1 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1095.0 1095.0 1095.0
11 12 Robusta nishant gurjer India sethuraman estate kaapi royale RC AB sethuraman estate 14/1148/2016/12 kaapi royale 1000 ... Green 0 August 23rd, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
12 13 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Green 1 May 19th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
13 14 Robusta kasozi coffee farmers association Uganda kasozi coffee farmers NaN NaN 0 kasozi coffee farmers association 1367 ... Green 7 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1367.0 1367.0 1367.0
14 15 Robusta ankole coffee producers coop Uganda kyangundu coop society NaN ankole coffee producers coop union ltd 0 ankole coffee producers coop 1488 ... Green 2 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1488.0 1488.0 1488.0
15 16 Robusta andrew hetzel India sethuraman estate NaN NaN 0000 sethuraman estate 1000m ... Green 0 April 29th, 2016 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
16 17 Robusta andrew hetzel India sethuraman estates NaN sethuraman estates NaN cafemakers, llc 750m ... Blue-Green 0 June 3rd, 2014 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
17 18 Robusta kawacom uganda ltd Uganda bushenyi NaN kawacom 0 kawacom uganda ltd 1600 ... Green 1 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1600.0 1600.0 1600.0
18 19 Robusta nitubaasa ltd Uganda kigezi coffee farmers association NaN nitubaasa 0 nitubaasa ltd 1745 ... Green 2 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1745.0 1745.0 1745.0
19 20 Robusta mannya coffee project Uganda mannya coffee project NaN mannya coffee project 0 mannya coffee project 1200 ... Green 1 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1200.0 1200.0 1200.0
20 21 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Bluish-Green 1 May 19th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
21 22 Robusta andrew hetzel India sethuraman estates NaN sethuraman estates NaN cafemakers, llc 750m ... Green 0 June 20th, 2014 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
22 23 Robusta andrew hetzel United States sethuraman estates NaN sethuraman estates NaN cafemakers, llc 3000' ... Green 0 February 28th, 2013 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3000.0 3000.0 3000.0
23 24 Robusta luis robles Ecuador robustasa Lavado 1 our own lab NaN robustasa NaN ... Blue-Green 1 January 18th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN
24 25 Robusta luis robles Ecuador robustasa Lavado 3 own laboratory NaN robustasa 40 ... Blue-Green 0 January 18th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 40.0 40.0 40.0
25 26 Robusta james moore United States fazenda cazengo NaN cafe cazengo NaN global opportunity fund 795 meters ... NaN 6 December 23rd, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 795.0 795.0 795.0
26 27 Robusta cafe politico India NaN NaN NaN 14-1118-2014-0087 cafe politico NaN ... Green 1 August 25th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN
27 28 Robusta cafe politico Vietnam NaN NaN NaN NaN cafe politico NaN ... NaN 9 August 25th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN

28 rows × 44 columns

If the file is local, you can load it this way. The parameter of the function is the path to the dataset, that can be relative, like below, absolute (a full address on your computer) or a URL like above.

pd.read_csv('robusta_data_cleaned.csv')

This read in the data and printed it out because it is the last line on the cell. If we do something else after, it will read it in, but not print it out.

In order to use it, we save the output to a variable.

coffee_df = pd.read_csv(coffee_data_url)

we choose this name so that related variables will all use coffee and then have other parts after _ to describe them in terms of type and content. In Python, for variables, the typical convention is to use _ to join words, not CamelCase, which is used for classes, like DataFrame

we can look at it again using the jupyter display

coffee_df
Unnamed: 0 Species Owner Country.of.Origin Farm.Name Lot.Number Mill ICO.Number Company Altitude ... Color Category.Two.Defects Expiration Certification.Body Certification.Address Certification.Contact unit_of_measurement altitude_low_meters altitude_high_meters altitude_mean_meters
0 1 Robusta ankole coffee producers coop Uganda kyangundu cooperative society NaN ankole coffee producers 0 ankole coffee producers coop 1488 ... Green 2 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1488.0 1488.0 1488.0
1 2 Robusta nishant gurjer India sethuraman estate kaapi royale 25 sethuraman estate 14/1148/2017/21 kaapi royale 3170 ... NaN 2 October 31st, 2018 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3170.0 3170.0 3170.0
2 3 Robusta andrew hetzel India sethuraman estate NaN NaN 0000 sethuraman estate 1000m ... Green 0 April 29th, 2016 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
3 4 Robusta ugacof Uganda ugacof project area NaN ugacof 0 ugacof ltd 1212 ... Green 7 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1212.0 1212.0 1212.0
4 5 Robusta katuka development trust ltd Uganda katikamu capca farmers association NaN katuka development trust 0 katuka development trust ltd 1200-1300 ... Green 3 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1200.0 1300.0 1250.0
5 6 Robusta andrew hetzel India NaN NaN (self) NaN cafemakers, llc 3000' ... Green 0 February 28th, 2013 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3000.0 3000.0 3000.0
6 7 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Green 0 May 15th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
7 8 Robusta nishant gurjer India sethuraman estate kaapi royale 7 sethuraman estate 14/1148/2017/18 kaapi royale 3140 ... Bluish-Green 0 October 25th, 2018 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3140.0 3140.0 3140.0
8 9 Robusta nishant gurjer India sethuraman estate RKR sethuraman estate 14/1148/2016/17 kaapi royale 1000 ... Green 0 August 17th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
9 10 Robusta ugacof Uganda ishaka NaN nsubuga umar 0 ugacof ltd 900-1300 ... Green 6 August 5th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 900.0 1300.0 1100.0
10 11 Robusta ugacof Uganda ugacof project area NaN ugacof 0 ugacof ltd 1095 ... Green 1 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1095.0 1095.0 1095.0
11 12 Robusta nishant gurjer India sethuraman estate kaapi royale RC AB sethuraman estate 14/1148/2016/12 kaapi royale 1000 ... Green 0 August 23rd, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
12 13 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Green 1 May 19th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
13 14 Robusta kasozi coffee farmers association Uganda kasozi coffee farmers NaN NaN 0 kasozi coffee farmers association 1367 ... Green 7 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1367.0 1367.0 1367.0
14 15 Robusta ankole coffee producers coop Uganda kyangundu coop society NaN ankole coffee producers coop union ltd 0 ankole coffee producers coop 1488 ... Green 2 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1488.0 1488.0 1488.0
15 16 Robusta andrew hetzel India sethuraman estate NaN NaN 0000 sethuraman estate 1000m ... Green 0 April 29th, 2016 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
16 17 Robusta andrew hetzel India sethuraman estates NaN sethuraman estates NaN cafemakers, llc 750m ... Blue-Green 0 June 3rd, 2014 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
17 18 Robusta kawacom uganda ltd Uganda bushenyi NaN kawacom 0 kawacom uganda ltd 1600 ... Green 1 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1600.0 1600.0 1600.0
18 19 Robusta nitubaasa ltd Uganda kigezi coffee farmers association NaN nitubaasa 0 nitubaasa ltd 1745 ... Green 2 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1745.0 1745.0 1745.0
19 20 Robusta mannya coffee project Uganda mannya coffee project NaN mannya coffee project 0 mannya coffee project 1200 ... Green 1 June 27th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1200.0 1200.0 1200.0
20 21 Robusta andrew hetzel India sethuraman estates NaN NaN NaN cafemakers 750m ... Bluish-Green 1 May 19th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
21 22 Robusta andrew hetzel India sethuraman estates NaN sethuraman estates NaN cafemakers, llc 750m ... Green 0 June 20th, 2014 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 750.0 750.0 750.0
22 23 Robusta andrew hetzel United States sethuraman estates NaN sethuraman estates NaN cafemakers, llc 3000' ... Green 0 February 28th, 2013 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3000.0 3000.0 3000.0
23 24 Robusta luis robles Ecuador robustasa Lavado 1 our own lab NaN robustasa NaN ... Blue-Green 1 January 18th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN
24 25 Robusta luis robles Ecuador robustasa Lavado 3 own laboratory NaN robustasa 40 ... Blue-Green 0 January 18th, 2017 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 40.0 40.0 40.0
25 26 Robusta james moore United States fazenda cazengo NaN cafe cazengo NaN global opportunity fund 795 meters ... NaN 6 December 23rd, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 795.0 795.0 795.0
26 27 Robusta cafe politico India NaN NaN NaN 14-1118-2014-0087 cafe politico NaN ... Green 1 August 25th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN
27 28 Robusta cafe politico Vietnam NaN NaN NaN NaN cafe politico NaN ... NaN 9 August 25th, 2015 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m NaN NaN NaN

28 rows × 44 columns

Next we examine the type

type(coffee_df)

pandas.core.frame.DataFrame

This is a new type provided by the pandas library, called a dataframe

We can also exmaine its parts. It consists of several; first the column headings

coffee_df.columns

Index(['Unnamed: 0', 'Species', 'Owner', 'Country.of.Origin', 'Farm.Name',
       'Lot.Number', 'Mill', 'ICO.Number', 'Company', 'Altitude', 'Region',
       'Producer', 'Number.of.Bags', 'Bag.Weight', 'In.Country.Partner',
       'Harvest.Year', 'Grading.Date', 'Owner.1', 'Variety',
       'Processing.Method', 'Fragrance...Aroma', 'Flavor', 'Aftertaste',
       'Salt...Acid', 'Bitter...Sweet', 'Mouthfeel', 'Uniform.Cup',
       'Clean.Cup', 'Balance', 'Cupper.Points', 'Total.Cup.Points', 'Moisture',
       'Category.One.Defects', 'Quakers', 'Color', 'Category.Two.Defects',
       'Expiration', 'Certification.Body', 'Certification.Address',
       'Certification.Contact', 'unit_of_measurement', 'altitude_low_meters',
       'altitude_high_meters', 'altitude_mean_meters'],
      dtype='object')

These are a special type called Index that is also provided by pandas.

It also tells us that the actual headings are of dtype object. object is used for strings or columns with mixed types

the dtype is slightly different from base Python types and is how pandas classifies but roughly is the same idea as a type.

type(coffee_df.columns)

pandas.core.indexes.base.Index

We can look at the first 5 rows with head

coffee_df.head()
Unnamed: 0 Species Owner Country.of.Origin Farm.Name Lot.Number Mill ICO.Number Company Altitude ... Color Category.Two.Defects Expiration Certification.Body Certification.Address Certification.Contact unit_of_measurement altitude_low_meters altitude_high_meters altitude_mean_meters
0 1 Robusta ankole coffee producers coop Uganda kyangundu cooperative society NaN ankole coffee producers 0 ankole coffee producers coop 1488 ... Green 2 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1488.0 1488.0 1488.0
1 2 Robusta nishant gurjer India sethuraman estate kaapi royale 25 sethuraman estate 14/1148/2017/21 kaapi royale 3170 ... NaN 2 October 31st, 2018 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 3170.0 3170.0 3170.0
2 3 Robusta andrew hetzel India sethuraman estate NaN NaN 0000 sethuraman estate 1000m ... Green 0 April 29th, 2016 Specialty Coffee Association ff7c18ad303d4b603ac3f8cff7e611ffc735e720 352d0cf7f3e9be14dad7df644ad65efc27605ae2 m 1000.0 1000.0 1000.0
3 4 Robusta ugacof Uganda ugacof project area NaN ugacof 0 ugacof ltd 1212 ... Green 7 July 14th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1212.0 1212.0 1212.0
4 5 Robusta katuka development trust ltd Uganda katikamu capca farmers association NaN katuka development trust 0 katuka development trust ltd 1200-1300 ... Green 3 June 26th, 2015 Uganda Coffee Development Authority e36d0270932c3b657e96b7b0278dfd85dc0fe743 03077a1c6bac60e6f514691634a7f6eb5c85aae8 m 1200.0 1300.0 1250.0

5 rows × 44 columns

Some notes:

  • columns is an attribute, something that the DataFrame object stores directly so we access it as is

  • head is a method, it does something to the content and can rely on parameters (here n=5 can be changed to show different numbers of rows)

If we forget the () on a method, it looks weird in the output

coffee_df.head

<bound method NDFrame.head of     Unnamed: 0  Species                              Owner Country.of.Origin  \
0            1  Robusta       ankole coffee producers coop            Uganda   
1            2  Robusta                     nishant gurjer             India   
2            3  Robusta                      andrew hetzel             India   
3            4  Robusta                             ugacof            Uganda   
4            5  Robusta       katuka development trust ltd            Uganda   
5            6  Robusta                      andrew hetzel             India   
6            7  Robusta                      andrew hetzel             India   
7            8  Robusta                     nishant gurjer             India   
8            9  Robusta                     nishant gurjer             India   
9           10  Robusta                             ugacof            Uganda   
10          11  Robusta                             ugacof            Uganda   
11          12  Robusta                     nishant gurjer             India   
12          13  Robusta                      andrew hetzel             India   
13          14  Robusta  kasozi coffee farmers association            Uganda   
14          15  Robusta       ankole coffee producers coop            Uganda   
15          16  Robusta                      andrew hetzel             India   
16          17  Robusta                      andrew hetzel             India   
17          18  Robusta                 kawacom uganda ltd            Uganda   
18          19  Robusta                      nitubaasa ltd            Uganda   
19          20  Robusta              mannya coffee project            Uganda   
20          21  Robusta                      andrew hetzel             India   
21          22  Robusta                      andrew hetzel             India   
22          23  Robusta                      andrew hetzel     United States   
23          24  Robusta                        luis robles           Ecuador   
24          25  Robusta                        luis robles           Ecuador   
25          26  Robusta                        james moore     United States   
26          27  Robusta                      cafe politico             India   
27          28  Robusta                      cafe politico           Vietnam   

                             Farm.Name Lot.Number  \
0        kyangundu cooperative society        NaN   
1       sethuraman estate kaapi royale         25   
2                    sethuraman estate        NaN   
3                  ugacof project area        NaN   
4   katikamu capca farmers association        NaN   
5                                  NaN        NaN   
6                   sethuraman estates        NaN   
7       sethuraman estate kaapi royale          7   
8                    sethuraman estate        RKR   
9                               ishaka        NaN   
10                 ugacof project area        NaN   
11      sethuraman estate kaapi royale      RC AB   
12                  sethuraman estates        NaN   
13               kasozi coffee farmers        NaN   
14              kyangundu coop society        NaN   
15                   sethuraman estate        NaN   
16                  sethuraman estates        NaN   
17                            bushenyi        NaN   
18   kigezi coffee farmers association        NaN   
19               mannya coffee project        NaN   
20                  sethuraman estates        NaN   
21                  sethuraman estates        NaN   
22                  sethuraman estates        NaN   
23                           robustasa   Lavado 1   
24                           robustasa   Lavado 3   
25                     fazenda cazengo        NaN   
26                                 NaN        NaN   
27                                 NaN        NaN   

                                      Mill         ICO.Number  \
0                  ankole coffee producers                  0   
1                        sethuraman estate    14/1148/2017/21   
2                                      NaN               0000   
3                                   ugacof                  0   
4                 katuka development trust                  0   
5                                   (self)                NaN   
6                                      NaN                NaN   
7                        sethuraman estate    14/1148/2017/18   
8                        sethuraman estate    14/1148/2016/17   
9                             nsubuga umar                  0   
10                                  ugacof                  0   
11                       sethuraman estate    14/1148/2016/12   
12                                     NaN                NaN   
13                                     NaN                  0   
14  ankole coffee producers coop union ltd                  0   
15                                     NaN               0000   
16                      sethuraman estates                NaN   
17                                 kawacom                  0   
18                               nitubaasa                  0   
19                   mannya coffee project                  0   
20                                     NaN                NaN   
21                      sethuraman estates                NaN   
22                      sethuraman estates                NaN   
23                             our own lab                NaN   
24                          own laboratory                NaN   
25                            cafe cazengo                NaN   
26                                     NaN  14-1118-2014-0087   
27                                     NaN                NaN   

                              Company    Altitude  ...         Color  \
0        ankole coffee producers coop        1488  ...         Green   
1                        kaapi royale        3170  ...           NaN   
2                   sethuraman estate       1000m  ...         Green   
3                          ugacof ltd        1212  ...         Green   
4        katuka development trust ltd   1200-1300  ...         Green   
5                     cafemakers, llc       3000'  ...         Green   
6                          cafemakers        750m  ...         Green   
7                        kaapi royale        3140  ...  Bluish-Green   
8                        kaapi royale        1000  ...         Green   
9                          ugacof ltd    900-1300  ...         Green   
10                         ugacof ltd        1095  ...         Green   
11                       kaapi royale        1000  ...         Green   
12                         cafemakers        750m  ...         Green   
13  kasozi coffee farmers association        1367  ...         Green   
14       ankole coffee producers coop        1488  ...         Green   
15                  sethuraman estate       1000m  ...         Green   
16                    cafemakers, llc        750m  ...    Blue-Green   
17                 kawacom uganda ltd        1600  ...         Green   
18                      nitubaasa ltd        1745  ...         Green   
19              mannya coffee project        1200  ...         Green   
20                         cafemakers        750m  ...  Bluish-Green   
21                    cafemakers, llc        750m  ...         Green   
22                    cafemakers, llc       3000'  ...         Green   
23                          robustasa         NaN  ...    Blue-Green   
24                          robustasa          40  ...    Blue-Green   
25            global opportunity fund  795 meters  ...           NaN   
26                      cafe politico         NaN  ...         Green   
27                      cafe politico         NaN  ...           NaN   

   Category.Two.Defects           Expiration  \
0                     2      June 26th, 2015   
1                     2   October 31st, 2018   
2                     0     April 29th, 2016   
3                     7      July 14th, 2015   
4                     3      June 26th, 2015   
5                     0  February 28th, 2013   
6                     0       May 15th, 2015   
7                     0   October 25th, 2018   
8                     0    August 17th, 2017   
9                     6     August 5th, 2015   
10                    1      June 26th, 2015   
11                    0    August 23rd, 2017   
12                    1       May 19th, 2015   
13                    7      July 14th, 2015   
14                    2      July 14th, 2015   
15                    0     April 29th, 2016   
16                    0       June 3rd, 2014   
17                    1      June 27th, 2015   
18                    2      June 27th, 2015   
19                    1      June 27th, 2015   
20                    1       May 19th, 2015   
21                    0      June 20th, 2014   
22                    0  February 28th, 2013   
23                    1   January 18th, 2017   
24                    0   January 18th, 2017   
25                    6  December 23rd, 2015   
26                    1    August 25th, 2015   
27                    9    August 25th, 2015   

                     Certification.Body  \
0   Uganda Coffee Development Authority   
1          Specialty Coffee Association   
2          Specialty Coffee Association   
3   Uganda Coffee Development Authority   
4   Uganda Coffee Development Authority   
5          Specialty Coffee Association   
6          Specialty Coffee Association   
7          Specialty Coffee Association   
8          Specialty Coffee Association   
9   Uganda Coffee Development Authority   
10  Uganda Coffee Development Authority   
11         Specialty Coffee Association   
12         Specialty Coffee Association   
13  Uganda Coffee Development Authority   
14  Uganda Coffee Development Authority   
15         Specialty Coffee Association   
16         Specialty Coffee Association   
17  Uganda Coffee Development Authority   
18  Uganda Coffee Development Authority   
19  Uganda Coffee Development Authority   
20         Specialty Coffee Association   
21         Specialty Coffee Association   
22         Specialty Coffee Association   
23         Specialty Coffee Association   
24         Specialty Coffee Association   
25         Specialty Coffee Association   
26         Specialty Coffee Association   
27         Specialty Coffee Association   

                       Certification.Address  \
0   e36d0270932c3b657e96b7b0278dfd85dc0fe743   
1   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
2   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
3   e36d0270932c3b657e96b7b0278dfd85dc0fe743   
4   e36d0270932c3b657e96b7b0278dfd85dc0fe743   
5   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
6   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
7   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
8   ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
9   e36d0270932c3b657e96b7b0278dfd85dc0fe743   
10  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
11  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
12  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
13  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
14  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
15  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
16  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
17  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
18  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
19  e36d0270932c3b657e96b7b0278dfd85dc0fe743   
20  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
21  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
22  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
23  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
24  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
25  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
26  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   
27  ff7c18ad303d4b603ac3f8cff7e611ffc735e720   

                       Certification.Contact unit_of_measurement  \
0   03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
1   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
2   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
3   03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
4   03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
5   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
6   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
7   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
8   352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
9   03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
10  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
11  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
12  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
13  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
14  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
15  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
16  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
17  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
18  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
19  03077a1c6bac60e6f514691634a7f6eb5c85aae8                   m   
20  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
21  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
22  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
23  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
24  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
25  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
26  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   
27  352d0cf7f3e9be14dad7df644ad65efc27605ae2                   m   

   altitude_low_meters altitude_high_meters altitude_mean_meters  
0               1488.0               1488.0               1488.0  
1               3170.0               3170.0               3170.0  
2               1000.0               1000.0               1000.0  
3               1212.0               1212.0               1212.0  
4               1200.0               1300.0               1250.0  
5               3000.0               3000.0               3000.0  
6                750.0                750.0                750.0  
7               3140.0               3140.0               3140.0  
8               1000.0               1000.0               1000.0  
9                900.0               1300.0               1100.0  
10              1095.0               1095.0               1095.0  
11              1000.0               1000.0               1000.0  
12               750.0                750.0                750.0  
13              1367.0               1367.0               1367.0  
14              1488.0               1488.0               1488.0  
15              1000.0               1000.0               1000.0  
16               750.0                750.0                750.0  
17              1600.0               1600.0               1600.0  
18              1745.0               1745.0               1745.0  
19              1200.0               1200.0               1200.0  
20               750.0                750.0                750.0  
21               750.0                750.0                750.0  
22              3000.0               3000.0               3000.0  
23                 NaN                  NaN                  NaN  
24                40.0                 40.0                 40.0  
25               795.0                795.0                795.0  
26                 NaN                  NaN                  NaN  
27                 NaN                  NaN                  NaN  

[28 rows x 44 columns]>

We can see more about why this happens with type.

type(coffee_df.head)

method

Without the parenthesis, it is the literal function object.

type(coffee_df.head())

pandas.core.frame.DataFrame

With the parenthesis, it runs the function and type examines what it returns, the DataFrame object.

2.11. Assignment 1 tips#

2.11.1. Pythonic Loops#

In Python, we call good style ‘pythonic’, for loops that means making a sensible loop variable. Let’s firs tmake a list object we can iterate over

coffee_cols_list = list(coffee_df.columns)
coffee_cols_list

['Unnamed: 0',
 'Species',
 'Owner',
 'Country.of.Origin',
 'Farm.Name',
 'Lot.Number',
 'Mill',
 'ICO.Number',
 'Company',
 'Altitude',
 'Region',
 'Producer',
 'Number.of.Bags',
 'Bag.Weight',
 'In.Country.Partner',
 'Harvest.Year',
 'Grading.Date',
 'Owner.1',
 'Variety',
 'Processing.Method',
 'Fragrance...Aroma',
 'Flavor',
 'Aftertaste',
 'Salt...Acid',
 'Bitter...Sweet',
 'Mouthfeel',
 'Uniform.Cup',
 'Clean.Cup',
 'Balance',
 'Cupper.Points',
 'Total.Cup.Points',
 'Moisture',
 'Category.One.Defects',
 'Quakers',
 'Color',
 'Category.Two.Defects',
 'Expiration',
 'Certification.Body',
 'Certification.Address',
 'Certification.Contact',
 'unit_of_measurement',
 'altitude_low_meters',
 'altitude_high_meters',
 'altitude_mean_meters']

Now we will write a loop to clean up the .

clean_cols = []
for col in coffee_cols_list:
    clean_cols.append(col.replace('.','_'))

clean_cols

['Unnamed: 0',
 'Species',
 'Owner',
 'Country_of_Origin',
 'Farm_Name',
 'Lot_Number',
 'Mill',
 'ICO_Number',
 'Company',
 'Altitude',
 'Region',
 'Producer',
 'Number_of_Bags',
 'Bag_Weight',
 'In_Country_Partner',
 'Harvest_Year',
 'Grading_Date',
 'Owner_1',
 'Variety',
 'Processing_Method',
 'Fragrance___Aroma',
 'Flavor',
 'Aftertaste',
 'Salt___Acid',
 'Bitter___Sweet',
 'Mouthfeel',
 'Uniform_Cup',
 'Clean_Cup',
 'Balance',
 'Cupper_Points',
 'Total_Cup_Points',
 'Moisture',
 'Category_One_Defects',
 'Quakers',
 'Color',
 'Category_Two_Defects',
 'Expiration',
 'Certification_Body',
 'Certification_Address',
 'Certification_Contact',
 'unit_of_measurement',
 'altitude_low_meters',
 'altitude_high_meters',
 'altitude_mean_meters']

This is equivalent, but easier to read than:

clean_cols = []
for i in range(len(coffee_cols_list)):
    clean_cols.append(coffee_cols_list[i].replace('.','_'))

clean_cols

['Unnamed: 0',
 'Species',
 'Owner',
 'Country_of_Origin',
 'Farm_Name',
 'Lot_Number',
 'Mill',
 'ICO_Number',
 'Company',
 'Altitude',
 'Region',
 'Producer',
 'Number_of_Bags',
 'Bag_Weight',
 'In_Country_Partner',
 'Harvest_Year',
 'Grading_Date',
 'Owner_1',
 'Variety',
 'Processing_Method',
 'Fragrance___Aroma',
 'Flavor',
 'Aftertaste',
 'Salt___Acid',
 'Bitter___Sweet',
 'Mouthfeel',
 'Uniform_Cup',
 'Clean_Cup',
 'Balance',
 'Cupper_Points',
 'Total_Cup_Points',
 'Moisture',
 'Category_One_Defects',
 'Quakers',
 'Color',
 'Category_Two_Defects',
 'Expiration',
 'Certification_Body',
 'Certification_Address',
 'Certification_Contact',
 'unit_of_measurement',
 'altitude_low_meters',
 'altitude_high_meters',
 'altitude_mean_meters']

In this version the loop variable i is a number we have to use to access what we want, where in the first one the col loop variable is the thing we want. Simpler and easier to read, which is better by definition in Python.

To make it better than in class, without a lot of extra logic we can do the ... first then the single ones:

clean_cols = []
for col in coffee_cols_list:
    clean_cols.append(col.replace('...','_').replace('.','_'))

clean_cols

['Unnamed: 0',
 'Species',
 'Owner',
 'Country_of_Origin',
 'Farm_Name',
 'Lot_Number',
 'Mill',
 'ICO_Number',
 'Company',
 'Altitude',
 'Region',
 'Producer',
 'Number_of_Bags',
 'Bag_Weight',
 'In_Country_Partner',
 'Harvest_Year',
 'Grading_Date',
 'Owner_1',
 'Variety',
 'Processing_Method',
 'Fragrance_Aroma',
 'Flavor',
 'Aftertaste',
 'Salt_Acid',
 'Bitter_Sweet',
 'Mouthfeel',
 'Uniform_Cup',
 'Clean_Cup',
 'Balance',
 'Cupper_Points',
 'Total_Cup_Points',
 'Moisture',
 'Category_One_Defects',
 'Quakers',
 'Color',
 'Category_Two_Defects',
 'Expiration',
 'Certification_Body',
 'Certification_Address',
 'Certification_Contact',
 'unit_of_measurement',
 'altitude_low_meters',
 'altitude_high_meters',
 'altitude_mean_meters']

This shows that we can chain string operations (this will coem in handy at other times).

The above is a good form for all for loops in Python, but since it was specifically making a list with append, we could make it more concise with a list comprehension.

clean_cols_alt = [clean_cols.append(col.replace('...','_').replace('.','_')) for col in coffee_cols_list]

these two ways are the same

clean_cols_alt == clean_cols

False

2.12. Conditionals Evaluate in order#

recall we set this variable

a

4

If we write conditions where they can be both true, but we want the larger one to act, if we put them in this order it never sees the second, because the first is true.

if a >1:
    print('greater 1')
elif a >2:
    print('greater 2')

greater 1

This one works.

if a >2:
    print('greater 2')
elif a >1:
    print('greater 1')

greater 2

2.13. Questions After Class#

2.13.1. What is the name of the inline iteration/loop again in Python?#

list comprehension

2.13.2. I just want to know more about github In general as to me, although it’s new so it will take some time to get used to, it’s still pretty confusing to me#

I will hold an optional session for a bit more GitHub. You can also take CSC311 for a lot more detail

2.13.3. when will we learn about the portfolio?#

After A2 feedback, which will be the first time it makes sense for you to work on it.

2.13.4. How would you attain a level 3 on any given skill?#

There are example ideas in the Portfolio section of the website, but it will make more sense after Assignment 2 and then I’ll spend more time on it in class again.

2.13.5. Im confused on what this “pandas.core.frame.DataFrame” is#

It is the main data type provided by pandas, that represents a table of data. We will keep working with and inspecting them. For a technical description see the api docs, for a high level description, see the getting started tutorial

2.13.6. would like to get feedback on my homework so I can fix any errors I have#

You will get feedback and have a chance to revise later.

2.13.7. Can you use any dataset from github using the raw URL and importing it? Can you use any dataset URL or only github?#

You can use any URL that has a compatible type of data.

2.13.8. if level 1 is determined by attendence and participation, how can I assure I am getting my lesson 1s fulfilled every class#

I am removing the prismia grading this semester, but it seems I missed one reference of that in the syllabus.

2.13.9. If we get an achievement are we gonna see those on github or do we have to keep track of all the achievements we have to see our grade?#

in your feedback you will get a table with your current standing each time work is assessed

2.13.10. Can you slow down a little, some times it gets hard to follow along#

I will try a little, but also please either message on prismia or raise your hand if you ever fall behind.