Frequency table for a single variable

Maybe .value_counts()?

>>> import pandas>>> my_series = pandas.Series([1,2,2,3,3,3, "fred", 1.8, 1.8])>>> my_series0       11       22       23       34       35       36    fred7     1.88     1.8>>> counts = my_series.value_counts()>>> counts3       32       21.8     2fred    11       1>>> len(counts)5>>> sum(counts)9>>> counts["fred"]1>>> dict(counts){1.8: 2, 2: 2, 3: 3, 1: 1, 'fred': 1}

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

The answer provided by @DSM is simple and straightforward, but I thought I'd add my own input to this question. If you look at the code for pandas.value_counts, you'll see that there is a lot going on.

If you need to calculate the frequency of many series, this could take a while. A faster implementation would be to use numpy.unique with return_counts = True

Here is an example:

import pandas as pdimport numpy as npmy_series = pd.Series([1,2,2,3,3,3])print(my_series.value_counts())3    32    21    1dtype: int64

Notice here that the item returned is a pandas.Series

In comparison, numpy.unique returns a tuple with two items, the unique values and the counts.

vals, counts = np.unique(my_series, return_counts=True)print(vals, counts)[1 2 3] [1 2 3]

You can then combine these into a dictionary:

results = dict(zip(vals, counts))print(results){1: 1, 2: 2, 3: 3}

And then into a pandas.Series

print(pd.Series(results))1    12    23    3dtype: int64