Counting particular occurrences in python in csv file

Question

I have a csv file with 4 columns {Tag, User, Quality, Cluster_id}. Using python I would like to do the following: For every cluster_id (from 1 to 500), I want to see for eac

Answer 1

Since someone's already posted a defaultdict solution, I'm going to give a pandas one, just for variety. pandas is a very handy library for data processing. Among other nice features, it can handle this counting problem in one line, depending on what kind of output is required. Really:

df = pd.read_csv("cluster.csv")
counted = df.groupby(["Cluster_id", "User", "Quality"]).size()
df.to_csv("counted.csv")

--

Just to give a trailer for what pandas makes easy, we can load the file -- the main data storage object in pandas is called a "DataFrame":

>>> import pandas as pd
>>> df = pd.read_csv("cluster.csv")
>>> df
<class 'pandas.core.frame.DataFrame'>
Int64Index: 500000 entries, 0 to 499999
Data columns:
Tag           500000  non-null values
User          500000  non-null values
Quality       500000  non-null values
Cluster_id    500000  non-null values
dtypes: int64(1), object(3)

We can check that the first few rows look okay:

>>> df[:5]
   Tag  User Quality  Cluster_id
0  bbb  u001     bad          39
1  bbb  u002     bad          36
2  bag  u003    good          11
3  bag  u004    good           9
4  bag  u005     bad          26

and then we can group by Cluster_id and User, and do work on each group:

>>> for name, group in df.groupby(["Cluster_id", "User"]):
...     print 'group name:', name
...     print 'group rows:'
...     print group
...     print 'counts of Quality values:'
...     print group["Quality"].value_counts()
...     raw_input()
...     
group name: (1, 'u003')
group rows:
        Tag  User Quality  Cluster_id
372002  xxx  u003     bad           1
counts of Quality values:
bad    1

group name: (1, 'u004')
group rows:
           Tag  User Quality  Cluster_id
126003  ground  u004     bad           1
348003  ground  u004    good           1
counts of Quality values:
good    1
bad     1

group name: (1, 'u005')
group rows:
           Tag  User Quality  Cluster_id
42004   ground  u005     bad           1
258004  ground  u005     bad           1
390004  ground  u005     bad           1
counts of Quality values:
bad    3
[etc.]

If you're going to be doing a lot of processing of csv files, it's definitely worth having a look at.

Answer 2

collections.defaultdict should be a great help here:

# WARNING: Untested
from collections import defaultdict

auto_vivificator = lambda: defaultdict(auto_vivificator)

data = auto_vivificator()

# open your csv file

for tag, user, quality, cluster in csv_file:
    user = data[cluster].setdefault(user, defaultdict(int))
    if is_good(quality):
        user["good"] += 1
    else:
        user["bad"] += 1

for cluster, users in enumerate(data):
    print "Cluster:", cluster
    for user, quality_metrics in enumerate(users):
       print "User:", user
       print quality_metrics
       print  # A blank line