Quarto mod2 (#73)

* remove files not needed for quarto

* add basic environment file

* quarto file structure and update style

* module 2

* remove sklearn specific style

* update quiz background color

* update pandas to 2.2.3 and other minor fixes

* fix pyodide error

* minor fix

* update error related contents

Author: zmx721

_quarto.yml

@@ -79,6 +79,37 @@ website:
           - text: '   12.1. Exercises'
             href: modules/module1/module1-32-practicing_bar_charts.qmd
           - href: modules/module1/module1-34-what_did_we_just_learn.qmd
+      - section: "**M2. Not So Scary Wrangling (Table Manipulation and Chaining)**"
+        contents:
+          - href: modules/module2/module2-00-module_learning_outcomes.qmd
+          - href: modules/module2/module2-01-reading_in_different_file_types.qmd
+          - text: '   1.1. Exercises'
+            href: modules/module2/module2-02-delimiter.qmd
+          - href: modules/module2/module2-06-arguments_for_reading_data.qmd
+          - text: '   2.1. Exercises'
+            href: modules/module2/module2-07-name_that_argument.qmd
+          - href: modules/module2/module2-09-column_renaming_and_dropping.qmd
+          - text: '   3.1. Exercises'
+            href: modules/module2/module2-10-column_editing_questions.qmd
+          - href: modules/module2/module2-13-column_arithmetic_and_creation.qmd
+          - text: '   4.1. Exercises'
+            href: modules/module2/module2-14-column_arithmetic_questions.qmd
+          - href: modules/module2/module2-16-data_filtering.qmd
+          - text: '   5.1. Exercises'
+            href: modules/module2/module2-17-filtering_question.qmd
+          - href: modules/module2/module2-20-conditional_value_replacement.qmd
+          - text: '   6.1. Exercises'
+            href: modules/module2/module2-21-practice_replacing_values.qmd
+          - href: modules/module2/module2-22-chaining_notation.qmd
+          - text: '   7.1. Exercises'
+            href: modules/module2/module2-23-chaining_true_false.qmd
+          - href: modules/module2/module2-25-grouping_and_aggregating.qmd
+          - text: '   8.1. Exercises'
+            href: modules/module2/module2-26-fruit_salad_grouping_and_aggregating.qmd
+          - href: modules/module2/module2-29-plotting_with_altair.qmd
+          - text: '   9.1. Exercises'
+            href: modules/module2/module2-30-plotting_a_groupby_object.qmd
+          - href: modules/module2/module2-31-what_did_we_just_learn.qmd
 
 # Since we are declaring options for two formats here (html and revealjs)
 # each qmd file needs to include a yaml block including which format to use for that file.

data/candybars-h.csv

@@ -0,0 +1 @@
+This dataset was created by Hayley Boyce in February 2020.,,,,,,,,,,Note this is not a complete dataset and there are many other candybars that are in existance ,,,,,,,,,,name,weight,chocolate,peanuts,caramel,nougat,cookie_wafer_rice,coconut,white_chocolate,multi,available_canada_americaCoffee Crisp,50,1,0,0,0,1,0,0,0,CanadaButterfinger,184,1,1,1,0,0,0,0,0,AmericaSkor,39,1,0,1,0,0,0,0,0,BothSmarties,45,1,0,0,0,0,0,0,1,CanadaTwix,58,1,0,1,0,1,0,0,1,BothReeses Peanutbutter Cups ,43,1,1,0,0,0,0,0,1,Both3 Musketeers,54,1,0,0,1,0,0,0,0,AmericaKinder Surprise,20,1,0,0,0,0,0,1,0,CanadaM & M,48,1,1,0,0,0,0,0,1,BothGlosettes,50,1,0,0,0,0,0,0,1,CanadaKitKat,45,1,0,0,0,1,0,0,1,BothBabe Ruth,60,1,1,1,1,0,0,0,0,AmericaCaramilk,52,1,0,1,0,0,0,0,0,CanadaAero,42,1,0,0,0,0,0,0,0,CanadaMars,51,1,0,1,1,0,0,0,0,BothPayday,52,0,1,1,0,0,0,0,0,AmericaSnickers,48,1,1,1,1,0,0,0,0,BothCrunchie,26,1,0,0,0,0,0,0,0,CanadaWonderbar ,58,1,1,1,0,0,0,0,0,Canada100 Grand ,43,1,0,1,0,1,0,0,0,AmericaTake 5,43,1,1,1,0,1,0,0,0,AmericaWhatchamacallits,45,1,1,0,0,1,0,0,0,AmericaAlmond Joy,46,1,0,0,0,0,1,0,0,AmericaOh Henry,51,1,1,1,0,0,0,0,0,BothCookies and Cream,43,0,0,0,0,1,0,1,0,Both

data/candybars-text.txt

@@ -0,0 +1,26 @@
+name	weight	chocolate	peanuts	caramel	nougat	cookie_wafer_rice	coconut	white_chocolate	multi	available_canada_america
+Coffee Crisp	50	1	0	0	0	1	0	0	0	Canada
+Butterfinger	184	1	1	1	0	0	0	0	0	America
+Skor	39	1	0	1	0	0	0	0	0	Both
+Smarties	45	1	0	0	0	0	0	0	1	Canada
+Twix	58	1	0	1	0	1	0	0	1	Both
+Reeses Peanutbutter Cups 	43	1	1	0	0	0	0	0	1	Both
+3 Musketeers	54	1	0	0	1	0	0	0	0	America
+Kinder Surprise	20	1	0	0	0	0	0	1	0	Canada
+M & M	48	1	1	0	0	0	0	0	1	Both
+Glosettes	50	1	0	0	0	0	0	0	1	Canada
+KitKat	45	1	0	0	0	1	0	0	1	Both
+Babe Ruth	60	1	1	1	1	0	0	0	0	America
+Caramilk	52	1	0	1	0	0	0	0	0	Canada
+Aero	42	1	0	0	0	0	0	0	0	Canada
+Mars	51	1	0	1	1	0	0	0	0	Both
+Payday	52	0	1	1	0	0	0	0	0	America
+Snickers	48	1	1	1	1	0	0	0	0	Both
+Crunchie	26	1	0	0	0	0	0	0	0	Canada
+Wonderbar 	58	1	1	1	0	0	0	0	0	Canada
+100 Grand 	43	1	0	1	0	1	0	0	0	America
+Take 5	43	1	1	1	0	1	0	0	0	America
+Whatchamacallits	45	1	1	0	0	1	0	0	0	America
+Almond Joy	46	1	0	0	0	0	1	0	0	America
+Oh Henry	51	1	1	1	0	0	0	0	0	Both
+Cookies and Cream	43	0	0	0	0	1	0	1	0	Both

data/foods.xlsx

@@ -9,7 +9,6 @@ dependencies:
     - scipy
     - matplotlib
     - jupyter
-    - quarto
+    - quarto=1.6.43
     - pip
-
-
+    - openpyxl

environment.yaml

@@ -77,34 +77,27 @@ Now we can use `.value_counts()` on this `mfr_column` variable to reference it,
 ---
 
 ```{python}
-mfr_col_wrong = cereal[['mfr']]
-mfr_col_wrong
+mfr_col_dataframe = cereal[['mfr']]
+mfr_col_dataframe
 ```
 
 <br>
 
-```python
-mfr_col_wrong.value_counts()
+```{python}
+mfr_col_dataframe.value_counts()
 ```
 
-```out
-AttributeError: 'DataFrame' object has no attribute 'value_counts'
-
-Detailed traceback: 
-  File "<string>", line 1, in <module>
-  File "/usr/local/lib/python3.7/site-packages/pandas/core/generic.py", line 5274, in __getattr__
-    return object.__getattribute__(self, name)
-```
 
 :::{.notes} 
-If we did instead use double square brackets with `pd.value_counts()`, we would get an error. So it is important to take care and remember when you are using `value_counts()`, you only use one set of square brackets.
+`value_counts()` can also be called on a DataFrame to count unique rows.
 :::
 
 ---
 
 ## Saving a dataframe
 
 ```{python}
+# | eval: false
 mfr_freq.to_csv('data/mfr_frequency.csv', index=False)
 ```
 

modules/module1/slides/module1_29.qmd

@@ -0,0 +1,29 @@
+---
+format:
+  html:
+    page-layout: full
+---
+
+# 0. Module Learning Outcomes
+
+::: {.panel-tabset .nav-pills}
+
+## Video
+
+<iframe
+    class="video"
+    src="https://www.youtube.com/embed/rn5t62Utn4U?start=0&end=29&rel=0" 
+    title="Module 2 Video - Module Learning Outcomes"
+    frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowfullscreen
+></iframe>
+
+## Slides
+
+<iframe
+    class="slide-deck"
+    src="slides/module2_00.html"
+></iframe>
+
+:::

modules/module2/module2-00-module_learning_outcomes.qmd

@@ -0,0 +1,29 @@
+---
+format:
+  html:
+    page-layout: full
+---
+
+# 1. Reading in Different File Types
+
+::: {.panel-tabset .nav-pills}
+
+## Video
+
+<iframe
+    class="video"
+    src="https://www.youtube.com/embed/WCWi1R2CQsY?start=0&end=270&rel=0" 
+    title="Module 2 Video - Reading in Different File Types"
+    frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowfullscreen
+></iframe>
+
+## Slides
+
+<iframe
+    class="slide-deck"
+    src="slides/module2_01.html"
+></iframe>
+
+:::