Frequency table for a single variable

Question

One last newbie pandas question for the day: How do I generate a table for a single Series?

For example:

my_series = pandas.Series([1,2,2,3,3,3]) pandas.magical_frequency_function( my_series )  >> {      1 : 1,      2 : 2,       3 : 3    } 

Lots of googling has led me to Series.describe() and pandas.crosstabs, but neither of these does quite what I need: one variable, counts by categories. Oh, and it'd be nice if it worked for different data types: strings, ints, etc.

Answer 1

Maybe .value_counts()?

>>> import pandas >>> my_series = pandas.Series([1,2,2,3,3,3, "fred", 1.8, 1.8]) >>> my_series 0       1 1       2 2       2 3       3 4       3 5       3 6    fred 7     1.8 8     1.8 >>> counts = my_series.value_counts() >>> counts 3       3 2       2 1.8     2 fred    1 1       1 >>> len(counts) 5 >>> sum(counts) 9 >>> counts["fred"] 1 >>> dict(counts) {1.8: 2, 2: 2, 3: 3, 1: 1, 'fred': 1} 

Answer 2

You can use list comprehension on a dataframe to count frequencies of the columns as such

[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)] 

Breakdown:

my_series.select_dtypes(include=['O'])  

Selects just the categorical data

list(my_series.select_dtypes(include=['O']).columns)  

Turns the columns from above into a list

[my_series[c].value_counts() for c in list(my_series.select_dtypes(include=['O']).columns)]  

Iterates through the list above and applies value_counts() to each of the columns