The number of items in a Series object can be determined by several techniques. To demonstrate this, we will use the following Series:
In [16]: # example series, which also contains a NaN s = pd.Series([0, 1, 1, 2, 3, 4, 5, 6, 7, np.nan]) s Out[16]: 0 0 1 1 2 1 3 2 4 3 5 4 6 5 7 6 8 7 9 NaN dtype: float64
The length can be determined using the len() function:
In [17]: # length of the Series len(s) Out[17]: 10
Alternately, the length can be determined using the .size property:
In [18]: # .size is also the # of items in the Series s.size Out[18]: 10
The .shape property returns a tuple where the first item is the number of items:
In [19]: # .shape is a tuple with one value s.shape Out[19]: (10,)
The number of the values that are not part of the NaN can be found by using the .count() method:
In [20]: # count() returns the number of non-NaN values s.count() Out[20]: 9
To determine all of the unique values in a Series, pandas provides the .unique() method:
In [21]: # all unique values s.unique() Out[21]: array([ 0., 1., 2., 3., 4., 5., 6., 7., nan])
Also, the count of each of the unique items in a Series can be obtained using .value_counts():
In [22]: # count of non-NaN values, returned max to min order s.value_counts() Out[22]: 1 2 7 1 6 1 5 1 4 1 3 1 2 1 0 1 dtype: int64
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