7.14.1 pandas Series

• An enhanced one-dimensional array
• Supports custom indexing, including even non-integer indices like strings
• Offers additional capabilities that make them more convenient for many data-science oriented tasks
  • Series may have missing data
  • Many Series operations ignore missing data by default

Creating a Series with Default Indices

• By default, a Series has integer indices numbered sequentially from 0

import pandas as pd

grades = pd.Series([87, 100, 94])

Creating a Series with All Elements Having the Same Value

• Second argument is a one-dimensional iterable object (such as a list, an array or a range) containing the Series’ indices
• Number of indices determines the number of elements

pd.Series(98.6, range(3))

0    98.6
1    98.6
2    98.6
dtype: float64

Accessing a Series’ Elements

grades[0]

87

Producing Descriptive Statistics for a Series

• Series provides many methods for common tasks including producing various descriptive statistics
• Each of these is a functional-style reduction

grades.count()

3

grades.mean()

93.66666666666667

grades.min()

87

grades.max()

100

grades.std()

6.506407098647712

• Series method describe produces all these stats and more
• The 25%, 50% and 75% are quartiles:
  • 50% represents the median of the sorted values.
  • 25% represents the median of the first half of the sorted values.
  • 75% represents the median of the second half of the sorted values.
• For the quartiles, if there are two middle elements, then their average is that quartile’s median

grades.describe()

count      3.000000
mean      93.666667
std        6.506407
min       87.000000
25%       90.500000
50%       94.000000
75%       97.000000
max      100.000000
dtype: float64

Creating a Series with Custom Indices

Can specify custom indices with the index keyword argument

grades = pd.Series([87, 100, 94], index=['Wally', 'Eva', 'Sam'])

grades

Wally     87
Eva      100
Sam       94
dtype: int64

Dictionary Initializers

• If you initialize a Series with a dictionary, its keys are the indices, and its values become the Series’ element values

grades = pd.Series({'Wally': 87, 'Eva': 100, 'Sam': 94})

grades

Wally     87
Eva      100
Sam       94
dtype: int64

Accessing Elements of a Series Via Custom Indices

• Can access individual elements via square brackets containing a custom index value

grades['Eva']

100

• If custom indices are strings that could represent valid Python identifiers, pandas automatically adds them to the Series as attributes

grades.Wally

87

• dtype attribute returns the underlying array’s element type

grades.dtype

dtype('int64')

• values attribute returns the underlying array

grades.values

array([ 87, 100,  94])

Creating a Series of Strings

• In a Series of strings, you can use str attribute to call string methods on the elements

hardware = pd.Series(['Hammer', 'Saw', 'Wrench'])

hardware

0    Hammer
1       Saw
2    Wrench
dtype: object

• Call string method contains on each element
• Returns a Series containing bool values indicating the contains method’s result for each element
• The str attribute provides many string-processing methods that are similar to those in Python’s string type
  • https://pandas.pydata.org/pandas-docs/stable/api.html#string-handling

hardware.str.contains('a')

0     True
1     True
2    False
dtype: bool

• Use string method upper to produce a new Series containing the uppercase versions of each element in hardware

hardware.str.upper()

0    HAMMER
1       SAW
2    WRENCH
dtype: object

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