pandas.Series.value_counts#

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[source]#

Return a Series containing counts of unique values.

The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

Parameters:
normalizebool, default False

If True then the object returned will contain the relative frequencies of the unique values.

sortbool, default True

Sort by frequencies when True. Preserve the order of the data when False.

ascendingbool, default False

Sort in ascending order.

binsint, optional

Rather than count values, group them into half-open bins, a convenience for `pd.cut`, only works with numeric data.

dropnabool, default True

Donâ€™t include counts of NaN.

Returns:
Series

Series containing counts of unique values.

`Series.count`

Number of non-NA elements in a Series.

`DataFrame.count`

Number of non-NA elements in a DataFrame.

`DataFrame.value_counts`

Equivalent method on DataFrames.

Examples

```>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0    2
1.0    1
2.0    1
4.0    1
Name: count, dtype: int64
```

With normalize set to True, returns the relative frequency by dividing all values by the sum of values.

```>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(normalize=True)
3.0    0.4
1.0    0.2
2.0    0.2
4.0    0.2
Name: proportion, dtype: float64
```

bins

Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins.

```>>> s.value_counts(bins=3)
(0.996, 2.0]    2
(2.0, 3.0]      2
(3.0, 4.0]      1
Name: count, dtype: int64
```

dropna

With dropna set to False we can also see NaN index values.

```>>> s.value_counts(dropna=False)
3.0    2
1.0    1
2.0    1
4.0    1
NaN    1
Name: count, dtype: int64
```