CODEWITHSUNDEEP
pythonpandas
JD2775
JD2775

Reputation: 3801

Finding top 10 in a dataframe in Pandas

I have a dataframe (df) with approx 800 rows with data like this:

Name:Jason
Age: 45 Ticket:1

Name:Kim
Age: 30 Ticket:0

1 = has a ticket 0 = does not have a ticket

(sorry, that didn't format very well. It's basically 3 columns in the dataframe: Name, Age and Ticket)

Using Pandas, I am wondering what the syntax is for find the Top 10 oldest people who HAVE a ticket

So far I have:

df.sort_values('Age',ascending=False,inplace=True)(data.Ticket==1)
(data.head(10))

I know that's not correct but it shows what the parameters are that I am looking for. Any ideas? Thanks

Upvotes: 21

Views: 133809

Answers (3)

Teoretic
Teoretic

Reputation: 2533

One of the common ways to do this is to use nlargest method:

df[df.Ticket == 1].nlargest(10, 'Age')['Names']

This way you don't need to do sorting explicitly

Upvotes: 26

piRSquared
piRSquared

Reputation: 294218

mask, sort, head

df[df.Ticket == 1].sort_values('Age').head(10)

Upvotes: 5

bigbounty
bigbounty

Reputation: 17368

If you only want names of the old people,then

df[df['Ticket'] == 1].sort_values('Age')['Names'].head(10)

Upvotes: 30

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