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

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 pd
import numpy as np

my_series = pd.Series([1,2,2,3,3,3])

print(my_series.value_counts())
3    3
2    2
1    1
dtype: 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    1
2    2
3    3
dtype: int64

Answer 2

 import pandas as pd
 my_series = pd.Series([1,2,2,3,3,3, "fred", 1.8, 1.8])
 my_series.value_counts()

This will generate output as in below:

3       3
1.8     2
2       2
fred    1
1       1
dtype: int64

Answer 3

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 4

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