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`Series` Data Structure

A Series in pandas is a one-dimensional array-like object that can hold various data types, similar to a list in Python, but with additional features. It combines elements of lists and dictionaries, storing items in order and allowing access via labels (index).

  • List-like: Ordered collection of items.
  • Dictionary-like: Access items using labels.

Structure​

A Series consists of two main components:

  • Index: Similar to keys in a dictionary.
  • Data: Actual values stored in the series.

The data column has a label that can be retrieved using the .name attribute, which is useful for operations like merging multiple columns of data.

Creating a Series​

To start, import pandas:

import pandas as pd

From a List​

You can create a Series by passing a list of values. Pandas automatically assigns an index starting with zero and sets the name of the series to None.

Example​

students = ['Alice', 'Jack', 'Molly']
series_students = pd.Series(students)

Data Types​

  • String List: The Series type is set to object.

    students = ['Alice', 'Jack', 'Molly']
    pd.Series(students)

  • Integer List: The Series type is set to int64.

    numbers = [1, 2, 3]
    pd.Series(numbers)

Handling Missing Data​

  • Strings with None: Pandas uses the type object.

    students = ['Alice', 'Jack', None]
    pd.Series(students)

  • Numbers with None: Pandas converts None to NaN and sets the type to float64.

    numbers = [1, 2, None]
    pd.Series(numbers)

NaN vs. None​

  • NaN is not equivalent to None. Using equality tests, the result is False.

    import numpy as np
    np.nan == None  # False
    np.nan == np.nan  # False
    np.isnan(np.nan)  # True

Creating Series from Dictionaries​

A Series can also be created from dictionary data, where the keys become the index values.

Example​

students_scores = {'Alice': 'Physics', 'Jack': 'Chemistry', 'Molly': 'English'}
s = pd.Series(students_scores)

Index and Data Types​

  • The index can be accessed using the .index attribute.
  • The data type (dtype) of the series and index is inferred automatically.

Example​

s.index

Complex Data Types​

You can store complex data types like tuples in a Series.

Example​

students = [("Alice", "Brown"), ("Jack", "White"), ("Molly", "Green")]
pd.Series(students)

Custom Index​

You can explicitly pass an index when creating a Series.

Example​

s = pd.Series(['Physics', 'Chemistry', 'English'], index=['Alice', 'Jack', 'Molly'])

Handling Mismatched Index and Dictionary Keys​

If the index provided does not match the dictionary keys, pandas will only include the provided index values, filling missing values with None or NaN.

Example​

students_scores = {'Alice': 'Physics', 'Jack': 'Chemistry', 'Molly': 'English'}
s = pd.Series(students_scores, index=['Alice', 'Molly', 'Sam'])