count the frequency that a value occurs in a dataf

Question

I have a dataset

|category|
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

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
        else:
            frequencytable[key] = 1
    return frequencytable

Example:

to_frequency_table([1,1,1,1,2,3,4,4])
>>> {1: 4, 2: 1, 3: 1, 4: 2}

Answer 2

Use size() method:

    import pandas as pd
    print df.groupby['category'].size()
    #where df is your dataframe

Answer 3

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)]

https://stackoverflow.com/a/28192263/786326

Answer 4

Use groupby and count:

In [37]:
df = pd.DataFrame({'a':list('abssbab')})
df.groupby('a').count()

Out[37]:

   a
a   
a  2
b  3
s  2

[3 rows x 1 columns]

See the online docs: http://pandas.pydata.org/pandas-docs/stable/groupby.html

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

In [38]:
df['a'].value_counts()

Out[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')
df

Out[41]:

   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]

Answer 5

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

>>> df
   a
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
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.

Answer 6

You can also do this with pandas by broadcasting your columns as categories first, e.g. dtype="category" e.g.

cats = ['client', 'hotel', 'currency', 'ota', 'user_country']

df[cats] = df[cats].astype('category')

and then calling describe:

df[cats].describe()

This will give you a nice table of value counts and a bit more :):

    client  hotel   currency    ota user_country
count   852845  852845  852845  852845  852845
unique  2554    17477   132 14  219
top 2198    13202   USD Hades   US
freq    102562  8847    516500  242734  340992