Seven moves: practice

Name the moveYou missed this one earlier. Try it again.

Here is schools before and after one move.

`schools`
school	district	students	absent
Adams	North	400	60
Baker	North	250	50
Cole	North	150	45
Diaz	South	600	60
Evans	South	100	30
Ford	East	300	75
Grant	East	200	NaN
Hale	NaN	350	70

After

school	district	students	absent
Adams	North	400	60
Baker	North	250	50
Cole	North	150	45

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics[clinics["visits"] > 100]

Predict: how many rows will come out?

Name the moveYou missed this one earlier. Try it again.

Here is schools before and after one move.

`schools`
school	district	students	absent
Adams	North	400	60
Baker	North	250	50
Cole	North	150	45
Diaz	South	600	60
Evans	South	100	30
Ford	East	300	75
Grant	East	200	NaN
Hale	NaN	350	70

After

school	absent
Adams	60
Baker	50
Cole	45
Diaz	60
Evans	30
Ford	75
Grant	NaN
Hale	70

Which move turned the first table into the last one?

Pick the codeYou missed this one earlier. Try it again.

“Keep only the clinics in East county.”

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

The result should look like this

clinic	county	visits	staff
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4

Which line produces that result?

Name the moveYou missed this one earlier. Try it again.

Here is clinics before and after one move.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

After

clinic	county	visits	staff
Cedar	East	200	5
Elm	East	150	3
Ash	West	120	4
Fir	East	100	4
Birch	West	80	2
Oak	North	60	2

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics.sort_values("visits")

Predict: which clinic will be in the first row?

Name the moveYou missed this one earlier. Try it again.

Here is clinics before and after one move.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

After

clinic	county	visits	staff	visits_per_staff
Ash	West	120	4	30
Birch	West	80	2	40
Cedar	East	200	5	40
Elm	East	150	3	50
Fir	East	100	4	25
Oak	North	60	2	30

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics["visits_per_staff"] = clinics["visits"] / clinics["staff"]

Predict: what will Cedar's visits_per_staff be?

Pick the codeYou missed this one earlier. Try it again.

“Show just each clinic's name and county.”

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

The result should look like this

clinic	county
Ash	West
Birch	West
Cedar	East
Elm	East
Fir	East
Oak	North

Which line produces that result?

Name the moveYou missed this one earlier. Try it again.

Here is schools before and after one move.

`schools`
school	district	students	absent
Adams	North	400	60
Baker	North	250	50
Cole	North	150	45
Diaz	South	600	60
Evans	South	100	30
Ford	East	300	75
Grant	East	200	NaN
Hale	NaN	350	70

After

district	students
East	300
North	400
South	600

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics.groupby("county")["visits"].sum()

Predict: how many rows will come out?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics.groupby("county")["visits"].sum()

Predict: what will East's value in the result be?

Name the moveYou missed this one earlier. Try it again.

Here is clinics, a second table county_pop, and the result of one move.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

`county_pop`
county	population
West	50,000
East	80,000
North	30,000
South	40,000

After

clinic	county	visits	staff	population
Ash	West	120	4	50,000
Birch	West	80	2	50,000
Cedar	East	200	5	80,000
Elm	East	150	3	80,000
Fir	East	100	4	80,000
Oak	North	60	2	30,000

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`schools`
school	district	students	absent
Adams	North	400	60
Baker	North	250	50
Cole	North	150	45
Diaz	South	600	60
Evans	South	100	30
Ford	East	300	75
Grant	East	200	NaN
Hale	NaN	350	70

`funding`
district	year	per_pupil
North	2025	12,000
South	2024	9,500
South	2025	9,800
East	2025	10,200

schools.merge(funding[funding["year"] == 2025], on="district", how="left")

funding[funding["year"] == 2025], inside the parentheses, is itself a move.

Predict: how many rows will come out?

Name the moveYou missed this one earlier. Try it again.

Here is clinics before and after one move.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

After

clinic	county	variable	value
Ash	West	visits	120
Birch	West	visits	80
Cedar	East	visits	200
Elm	East	visits	150
Fir	East	visits	100
Oak	North	visits	60
Ash	West	staff	4
Birch	West	staff	2
Cedar	East	staff	5
Elm	East	staff	3
Fir	East	staff	4
Oak	North	staff	2

Which move turned the first table into the last one?

PredictYou missed this one earlier. Try it again.

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

clinics.melt(id_vars=["clinic", "county"], value_vars=["visits", "staff"])

Predict: after this line, what does one row represent?

PredictYou missed this one earlier. Try it again.

`county_pop`
county	population
West	50,000
East	80,000
North	30,000
South	40,000

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

county_pop.merge(clinics, on="county", how="left")

Predict: how many rows will come out?

Pick the codeYou missed this one earlier. Try it again.

“Calculate each clinic's visits per staff member.”

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

The result should look like this

clinic	county	visits	staff	visits_per_staff
Ash	West	120	4	30
Birch	West	80	2	40
Cedar	East	200	5	40
Elm	East	150	3	50
Fir	East	100	4	25
Oak	North	60	2	30

Which line produces that result?

Pick the codeYou missed this one earlier. Try it again.

“Find the largest number of visits at any clinic in each county.”

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

The result should look like this

county	visits
East	200
North	60
West	120

Which line produces that result?

Pick the codeYou missed this one earlier. Try it again.

“List the East clinics from most visits to fewest.”

`clinics`
clinic	county	visits	staff
Ash	West	120	4
Birch	West	80	2
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4
Oak	North	60	2

The result should look like this

clinic	county	visits	staff
Cedar	East	200	5
Elm	East	150	3
Fir	East	100	4

Which line produces that result?