I have a pandas data frame similar to:
ColA ColB 1 1 1 1 1 1 1 2 1 2 2 1 3 2 I want an output that has the same function as Counter. I need to know how many time each row appears (with all of the columns being the same.
In this case the proper output would be:
ColA ColB Count 1 1 3 1 2 2 2 1 1 3 2 1 I have tried something of the sort:
df.groupby(['ColA','ColB']).ColA.count() but this gives me some ugly output I am having trouble formatting
3 Answers
You can use size with reset_index:
print df.groupby(['ColA','ColB']).size().reset_index(name='Count') ColA ColB Count 0 1 1 3 1 1 2 2 2 2 1 1 3 3 2 1 I only needed to count the unique rows and have used the DataFrame.drop_duplicates alternative as below:
len(df[['ColA', 'ColB']].drop_duplicates()) It was twice as fast on my data than len(df.groupby(['ColA', 'ColB'])).
Since Pandas 1.1.0 the method pandas.DataFrame.value_counts is available, which does exactly, what you need. It creates a Series with the unique rows as multi-index and the counts as values:
df = pd.DataFrame({'ColA': [1, 1, 1, 1, 1, 2, 3], 'ColB': [1, 1, 1, 2, 2, 1, 2]}) pd.options.display.multi_sparse = False # option to print as requested print(df.value_counts()) # requires pandas >= 1.1.0 Output, where ColA and ColB are the multi-index and the third column contains the counts:
ColA ColB 1 1 3 1 2 2 3 2 1 2 1 1 dtype: int64 
