Update doc/python/bar-charts.md

Author: LiamConnors

doc/python/bar-charts.md

@@ -313,14 +313,14 @@ Stacked bar charts are a powerful way to present results summarizing categories
 from plotly import graph_objects as go
 import pandas as pd
 
-#get one year of gapminder data
+# Get one year of gapminder data
 url = 'https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv'
 df = pd.read_csv(url)
 df = df[df['year']==2007]
 df["gdp"]=df["pop"]*df['gdpPercap']
 
 
-#build the summary of interest
+# Build the summary of interest
 df_summarized = df.groupby("continent", observed=True).agg("sum").reset_index()
 
 df_summarized["percent of world population"]=100*df_summarized["pop"]/df_summarized["pop"].sum()
@@ -332,17 +332,17 @@ df = df_summarized[["continent",
 "percent of world GDP",
 ]]
 
-#we now have a wide data frame, but it's in the opposite orientation from the one that px is designed to deal with.
-#transposing it and rebuilding the indexes is an option, but iterating through the DF using graph objects is more succinct. 
+# We now have a wide data frame, but it's in the opposite orientation from the one that px is designed to deal with.
+# Transposing it and rebuilding the indexes is an option, but iterating through the DF using graph objects is more succinct. 
 
 fig=go.Figure()
 for category in df_summarized["continent"].values:
     fig.add_trace(go.Bar(
             x=df.columns[1:],
-            #we need to get a pandas series that contains just the values to graph; 
-            #we do so by selecting the right row, selecting the right columns
-            #and then tranposing and using iloc to convert to a series
-            #here, I assume that the bar element category variable is in column 0
+            # We need to get a pandas series that contains just the values to graph; 
+            # We do so by selecting the right row, selecting the right columns
+            # and then transposing and using iloc to convert to a series
+            # Here, we assume that the bar element category variable is in column 0
             y=list(df.loc[df["continent"]==category][list(df.columns[1:])].transpose().iloc[:,0]),
             name=str(category)