Introduction to Pandas
Chapter 2: Series
Accessing Series elements
Accessing elements in a Series is straightforward.
You can access elements in a Series just like in a NumPy array. Slicing also works.
>>> series = pd.Series(data=["UK", "France", "Italy"])
>>> print(series)
0 UK
1 France
2 Italy
dtype: object
>>> print(series[0])
UK
>>> print(series[1:])
1 France
2 Italy
dtype: object
You can also access elements by their index labels.
>>> series = pd.Series(data={"a": "UK", "b": "France", "c": "Italy"})
>>> print(series)
a UK
b France
c Italy
dtype: object
>>> print(series["a"])
UK
>>> print(series[["c", "b"]])
c Italy
b France
dtype: object
If you need a NumPy array representation of your data, you can access it using the series.to_numpy() method. To convert it to a Python list, you can use list(series) or series.tolist().
>>> series = pd.Series(data={"a": "UK", "b": "France", "c": "Italy"})
>>> series_array = series.to_numpy()
>>> print(type(series_array))
<class 'numpy.ndarray'>
>>> print(series_array)
['UK' 'France' 'Italy']
>>> series_list = list(series) # or series.tolist()
>>> print(type(series_list))
<class 'list'>
>>> print(series_list)
['UK', 'France', 'Italy']
We will not spend any more time discussing Series. I believe that you are already well-versed enough to read the documentation yourself and see what attributes/methods are available for Series instances.