Will need to use pd.to_numeric here.

Actually row 99.

This should be "regions" - you should sort the list of unique regions that you have just generated.

This should be .xcor(), not .x

Perhaps - "that can be known about the object" (an object doesn't know about itself).

Variance of the x (i.e., diameters)

Do this in Activity 5, not Activity 4.

This phrase should be "(this should use compute_y)" - i.e., in parentheses.

One will need to preprocess the data to determine the totals first before feeding it into Altair here (or use color).

When you add the color parameter, it should be a scatter plot (mark_point) rather than a line plot.

Rewrite: you are provided with a list years of integers; create another list is_leap_year of boolean variables telling whether the corresponding integers in years are leap years or not. You don't need to ask the user for input.

You may need to consult https://en.wikipedia.org/wiki/Standard_deviation for some definitions.

Kind of a vague question with a vague answer.

As a list

We don't use this now.

This should be the test and the predicted data, not the training and test data.

Note that 9.14.2 and 9.14.3 are just comments about what you will be doing in 9.14.4, below.

Upload the bikeshare.db file into Colab first, then use the following:

import pandas as pd stations = pd.read_sql_query("""select * from bikeshare_stations where latitude is not NULL""",'sqlite:///bikeshare.db') stations.head()

9.9

The link is broken; just use the code above to create the stations DataFrame.

Don't include the "64"

That is, the long form of the data (not the pivot table).

Below, they suggest limiting yourself to 12 related countries.

Leave as a decimal value, rounded to the hundredths.

Need to "import networkx as nx" first.

On Colab, I needed to run the following:

import networkx as nx

from networkx.drawing.nx_agraph import graphviz_layout

!apt install libgraphviz-dev

!pip install pygraphviz

graphviz, not graphvix

I think analyzer.polarity_scores does this already

If running in Colab, use the following code alt.data_transformers.disable_max_rows() alt.Chart(undf[['year','sentiment']]).mark_bar().encode(x=alt.X('sentiment', bin=True), y='count()')

The answer here is probably incorrect.

'start with "M"', not "have a country code that starts with "M"'.

Of the three possible answers that I got, only two of them were counted as correct here.

Same comment: ir, not il.

Typo: should be an "ir" (interest rate) not "il".

There's a missing underscore here: Starting_a_Business_score

No need to do this - just paste the code below into Jupyter lab. (Pasting this in the browser will retrieve the same data that you retrieve in the code block below.)

The output does not seem to be correct here.

top-ranked (in the starting a business score)

The two-letter country codes can be found in the "Administrative Divisions" file.

The book's answer might be wrong here.

this should be my_votes, not my_vote

list-like

You may need to do this in two lines of code.

If you get 881, you've used "original_title" as your index. Instead use "title".

Using the code below

Note that your file path will depend on where you downloaded your file.

sections (3.00-3.03)

Note that both words in the titles need to be capitalized (even if not capitalized in the sheet).

Must be in a particular order (the reverse order is marked as wrong)

players (not all are cheaters)

CSS class

typo

Run "pip install vega_datasets" on the command line

These two modules don't appear to be used in this section.

Has to be >= 10 and <= 180 (or <180) to get an answer that the books considers correct

Use the 'title' rather than the 'original_title' in order to get the correct count later on.

Of the 2017 data

Runestone marks incorrect if it's in the "reverse" order.

Must be put in order (i.e., if in "reverse" order, Runestone marks it as incorrect).