Count the frequency that a value occurs in a dataframe column

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Count the frequency that a value occurs in a dataframe column

I have a dataset

cat a
cat b
cat a

I’d like to be able to return something like (showing unique values and frequency)

category   freq
cat a       2
cat b       1

Answer #1:

Use groupby and count:

In [37]:
df = pd.DataFrame({'a':list('abssbab')})
a  2
b  3
s  2
[3 rows x 1 columns]

See the online docs:

Also value_counts() as @DSM has commented, many ways to skin a cat here

In [38]:
b    3
a    2
s    2
dtype: int64

If you wanted to add frequency back to the original dataframe use transform to return an aligned index:

In [41]:
df['freq'] = df.groupby('a')['a'].transform('count')
   a freq
0  a    2
1  b    3
2  s    2
3  s    2
4  b    3
5  a    2
6  b    3
[7 rows x 2 columns]
Answered By: EdChum

Answer #2:

If you want to apply to all columns you can use:


This will apply a column based aggregation function (in this case value_counts) to each of the columns.

Answered By: Arran Cudbard-Bell

Answer #3:


This short little line of code will give you the output you want.

If your column name has spaces you can use

Answered By: Satyajit Dhawale

Answer #4:


value_counts – Returns object containing counts of unique values

apply – count frequency in every column. If you set axis=1, you get frequency in every row

fillna(0) – make output more fancy. Changed NaN to 0

Answered By: Roman Kazakov

Answer #5:

In 0.18.1 groupby together with count does not give the frequency of unique values:

>>> df
0  a
1  b
2  s
3  s
4  b
5  a
6  b
>>> df.groupby('a').count()
Empty DataFrame
Columns: []
Index: [a, b, s]

However, the unique values and their frequencies are easily determined using size:

>>> df.groupby('a').size()
a    2
b    3
s    2

With df.a.value_counts() sorted values (in descending order, i.e. largest value first) are returned by default.

Answered By: Vidhya G

Answer #6:

If your DataFrame has values with the same type, you can also set return_counts=True in numpy.unique().

index, counts = np.unique(df.values,return_counts=True)

np.bincount() could be faster if your values are integers.

Answered By: user666

Answer #7:

Using list comprehension and value_counts for multiple columns in a df

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

Answered By: Shankar ARUL

Answer #8:

Without any libraries, you could do this instead:

def to_frequency_table(data):
    frequencytable = {}
    for key in data:
        if key in frequencytable:
            frequencytable[key] += 1
            frequencytable[key] = 1
    return frequencytable


>>> {1: 4, 2: 1, 3: 1, 4: 2}
Answered By: Timz95

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