Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Python: Find the Highest Value in a Group

DZone's Guide to

Python: Find the Highest Value in a Group

· Web Dev Zone ·
Free Resource

Bugsnag monitors application stability, so you can make data-driven decisions on whether you should be building new features, or fixing bugs. Learn more.

In my continued playing around with a "How I Met Your Mother" data set I needed to find out the last episode that happened in a season so that I could use it in a chart I wanted to plot.

I had this CSV file containing each of the episodes:

$ head -n 10 data/import/episodes.csv
NumberOverall,NumberInSeason,Episode,Season,DateAired,Timestamp
1,1,/wiki/Pilot,1,"September 19, 2005",1127084400
2,2,/wiki/Purple_Giraffe,1,"September 26, 2005",1127689200
3,3,/wiki/Sweet_Taste_of_Liberty,1,"October 3, 2005",1128294000
4,4,/wiki/Return_of_the_Shirt,1,"October 10, 2005",1128898800
5,5,/wiki/Okay_Awesome,1,"October 17, 2005",1129503600
6,6,/wiki/Slutty_Pumpkin,1,"October 24, 2005",1130108400
7,7,/wiki/Matchmaker,1,"November 7, 2005",1131321600
8,8,/wiki/The_Duel,1,"November 14, 2005",1131926400
9,9,/wiki/Belly_Full_of_Turkey,1,"November 21, 2005",1132531200

I started out by parsing the CSV file into a dictionary of (seasons -> episode ids):

import csv
from collections import defaultdict
 
seasons = defaultdict(list)
with open("data/import/episodes.csv", "r") as episodesfile:
    reader = csv.reader(episodesfile, delimiter = ",")
    reader.next()
    for row in reader:
        seasons[int(row[3])].append(int(row[0]))
 
print seasons

which outputs the following:

$ python blog.py
defaultdict(<type 'list'>, {
  1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], 
  2: [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44], 
  3: [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64], 
  4: [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88], 
  5: [89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112], 
  6: [113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136], 
  7: [137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160], 
  8: [161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184], 
  9: [185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208]})

It’s reasonably easy to transform that into a dictionary of (season -> max episode id) with the following couple of lines:

for season, episode_ids in seasons.iteritems():
    seasons[season] = max(episode_ids)
 
>>> print seasons
defaultdict(<type 'list'>, {1: 22, 2: 44, 3: 64, 4: 88, 5: 112, 6: 136, 7: 160, 8: 184, 9: 208})

This works fine but it felt very much like a dplyr problem to me so I wanted to see whether I could write something cleaner using pandas.

I started out by capturing the seasons and episode ids in separate lists and then building up a DataFrame:

import pandas as pd
from pandas import DataFrame
 
seasons, episode_ids = [], []
with open("data/import/episodes.csv", "r") as episodesfile:
    reader = csv.reader(episodesfile, delimiter = ",")
    reader.next()
    for row in reader:
        seasons.append(int(row[3]))
        episode_ids.append(int(row[0]))
 
df = DataFrame.from_items([('Season', seasons), ('EpisodeId', episode_ids)])
 
>>> print df.groupby("Season").max()["EpisodeId"]
Season
1          22
2          44
3          64
4          88
5         112
6         136
7         160
8         184
9         208

Or we can simplify that and read the CSV file directly into a DataFrame:

df = pd.read_csv('data/import/episodes.csv', index_col=False, header=0)
 
>>> print df.groupby("Season").max()["NumberOverall"]
Season
1          22
2          44
3          64
4          88
5         112
6         136
7         160
8         184
9         208

Pretty neat. I need to get more into pandas.

Monitor application stability with Bugsnag to decide if your engineering team should be building new features on your roadmap or fixing bugs to stabilize your application.Try it free.

Topics:

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}