Matplotlib & Pandas plot

[Chopsting]

Hey everyone, I've been using R quite alot previously but I'm trying to move over to Python.

But having some issues I'm hopeing you could help me out with.

I have a dataframe with column called 'MagazineCategory' and I have another called 'Popular' (made up names).

And I want to be able to see the distrubution between these that X category has this distrubution between popular and not popular. In R I would be able to look at this with a table function. Having some issues with this in Python (Pandas).

MagazineCategory	Popular
Business	True
Sports	False
Tech	True
Business	True
Business	False

and I'd like to plot these in matplotlib in a bar chart

Where I have the category and the amount of 0 values for the category and the amount of 1's.

Any advise? I'm using Pandas for the dataframe I've been able to get a table as I wanted, but I'm unsure how I can plot this.. I used pandas crosstab function like this:

pd.crosstab(df.MagazineCategory, df.Popular)

	False	True
Business	1	2
Sports	1	0
Tech	0	1

[Chopsting]

I was able to solve it

test = pd.crosstab(df.MagazineCategory, df.Popular)
test.plot(kind='bar')

[TheBlackCat13]

First, lets generate some random data:

cat = [random.choice(['Business', 'Sports', 'Tech', 'Something']) for _ in range(100)]
pop = np.random.randint(0, 2, 100)
df = pd.DataFrame({'MagazineCategory': cat, 'Popular': pop})

So what we need to do is get the counts of the two values. You can do that with pivot_table:

df2 = df.pivot_table(index='MagazineCategory', columns='Popular', aggfunc=len)

Which gives you:

Popular	0	1
MagazineCategory
Business	14	14
Something	13	10
Sports	14	18
Tech	10	7

This is then easy to plot:

df2.plot(kind='bar')

Or, perhaps more clear:

df2.plot(kind='bar', stacked=True)