I have a large (about 12M rows) dataframe df with say:
df.columns = ['word','documents','frequency']
So the following ran in a timely fashion:
word_grouping = df[['word','frequency']].groupby('word')
MaxFrequency_perWord = word_grouping[['frequency']].max().reset_index()
MaxFrequency_perWord.columns = ['word','MaxFrequency']
However, this is taking an unexpected long time to run:
Occurrences_of_Words = word_grouping[['word']].count().reset_index()
What am I doing wrong here? Is there a better way to count occurences in a large dataframe?
df.word.describe()
ran pretty well, so I really did not expect this Occurrences_of_Words dataframe to take very long to build.
ps: If the answer is obvious and you feel the need to penalize me for asking this question, please include the answer as well. thank you.
I think df['word'].value_counts() should serve. By skipping the groupby machinery, you'll save some time. I'm not sure why count should be much slower than max. Both take some time to avoid missing values. (Compare with size.)
In any case, value_counts has been specifically optimized to handle object type, like your words, so I doubt you'll do much better than that.
When you want to count the frequency of categorical data in a column in pandas dataFrame use: df['Column_Name'].value_counts()
-Source.
Just an addition to the previous answers. Let's not forget that when dealing with real data there might be null values, so it's useful to also include those in the counting by using the option dropna=False (default is True)
An example:
>>> df['Embarked'].value_counts(dropna=False)
S 644
C 168
Q 77
NaN 2
Source: Stackoverflow.com