Merge pull request #126 from rdenise/main

Update python-pandas.qmd

Author: jfy133

python-pandas.qmd

@@ -1,6 +1,6 @@
 ---
 title: Introduction to Python and Pandas
-author: Kevin Nota, Robin Warner, and Maxime Borry
+author: Kevin Nota, Robin Warner, Rémi Denise, and Maxime Borry
 ---
 
 :::  {.callout-note} 
@@ -154,13 +154,13 @@ Hello World from bash!
 ```
 :::
 
-Stings or numbers can be stored as a variable by using the *=* sign.
+Strings or numbers can be stored as a variable by using the *=* sign.
 
 ```{.python eval=False}
 i = 0
 ```
 
-Ones a variable is set in one _code cell_ they are stored and can be accessed in other downstream _code cells_.
+Once a variable is set in a _code cell_, they are stored and can be accessed in another downstream _code cell_.
 
 To see what value a variable contains, the `print()` function can be used.
 
@@ -1336,10 +1336,10 @@ Looking at the data, can you figure out what might cause this?
 ::: {.callout-note collapse="true" title="Answer"}
 
 When we look at the highest earners, we see that somebody put _666666_ as their income.
-This is much higher than any other income, which makes the histogram very draged out to include this person.
+This is much higher than any other income, which makes the histogram very dragged out to include this person.
 
 ```{.python eval=False} 
-merged_df[merged_df["Income"].sort_values(ascending=False)]
+merged_df["Income"].sort_values(ascending=False)
 ```
 :::{.callout-note collapse="true"}
 ## Expand to see output
@@ -1488,7 +1488,7 @@ library_table_url
 ## Summary
 
 In this chapter, we have started exploring the basics of data analysis using `Python` with the versatile `Pandas` library.
-We wrote `Python` code and executed it in a Jupyter Notebook, with just a handful of functions such as `.read_csv()`, `.loc[]`, `drop()`, `merge()`, `.concat() and `plot()`, we have done data manipulation, calculated summary statistics, and plotted the data.
+We wrote `Python` code and executed it in a Jupyter Notebook, with just a handful of functions such as `.read_csv()`, `.loc[]`, `drop()`, `merge()`, `.concat()` and `plot()`, we have done data manipulation, calculated summary statistics, and plotted the data.
 
 The takeaway messages therefore are: