Series
Learn about the pandas Series object for 1-D data.
We'll cover the following
Chapter Goals
- Learn about the pandas Series object and its basic utilities
- Write code to create several Series objects
A. 1-D data
Similar to NumPy, pandas frequently deals with 1-D and 2-D data. However, we use two separate objects to deal with 1-D and 2-D data in pandas. For 1-D data, we use the pandas.Series objects, which we'll refer to simply as a Series.
A Series is created through the pd.Series constructor, which takes in no required arguments but does have a variety of keyword arguments.
The first keyword argument is data, which specifies the elements of the Series. If data is not set, pd.Series returns an empty Series. Since the data keyword argument is almost always used, we treat it like a regular first argument (i.e. skip the data= prefix).
Similar to the np.array constructor, pd.Series also takes in the dtype keyword argument for manual casting.
The code below shows how to create pandas Series objects using pd.Series.
import pandas as pdimport numpy as np# Creating an empty series, will result in DeprecationWarning#series = pd.Series()# Passing dtype as a parameter to Series for an empty series to avoid DeprecationWarning# Creating an empty seriesseries = pd.Series(dtype='float64')# Newline to separate series print statementsprint('{}\n'.format(series))series = pd.Series(5)print('{}\n'.format(series))series = pd.Series([1, 2, 3])print('{}\n'.format(series))series = pd.Series([1, 2.2]) # upcastingprint('{}\n'.format(series))arr = np.array([1, 2])series = pd.Series(arr, dtype=np.float32)print('{}\n'.format(series))series = pd.Series([[1, 2], [3, 4]])print('{}\n'.format(series))
In our examples, we initialized each Series with its values by setting the first argument using a scalar, list, or NumPy array. Note that pd.Series upcasts values in the same way as np.array. Furthermore, since Series objects are 1-D, the ser variable represents a Series with lists as elements, rather than a 2-D matrix.
B. Index
In the previous examples, you may have noticed the zero-indexed integers to the left of the elements in each Series. These integers are collectively referred to as the index of a Series, and each individual index element is referred to as a label.
The default index is integers from 0 to n - 1, where n is the number of elements in the Series. However, we can specify a custom index via the index keyword argument of pd.Series.
The code below shows how to use the index keyword argument with pd.Series.
series = pd.Series([1, 2, 3], index=['a', 'b', 'c'])print('{}\n'.format(series))series = pd.Series([1, 2, 3], index=['a', 8, 0.3])print('{}\n'.format(series))
The index keyword argument needs to be a list or array with the same length as the data argument for pd.Series. The values in the index list can be any hashable type (e.g. integer, float, string).
C. Dictionary input
Another way to set the index of a Series is by using a Python dictionary for the data argument. The keys of the dictionary represent the index of the Series, while each individual key is the label for its corresponding value.
The code below shows how to use pd.Series with a Python dictionary as the first argument. In our example, we set 'a', 'b', and 'c' as the Series index, with corresponding values 1, 2, and 3.
series = pd.Series({'a':1, 'b':2, 'c':3})print('{}\n'.format(series))series = pd.Series({'b':2, 'a':1, 'c':3})print('{}\n'.format(series))
Time to Code!
The coding exercise for this chapter involves creating various pandas Series objects.
The first Series we create will contain basic floating point numbers. The list we use to initialize the Series is [1, 3, 5.2].
Set s1 equal to pd.Series with the specified list as the only argument.
# CODE HERE
The second Series we create comes from performing elemental multiplication on s1 using a separate list of floating point numbers.
Set s2 equal to s1 multiplied by [0.1, 0.2, 0.3].
# CODE HERE
We'll create another Series, this time with integers. The list we use to initialize this Series is [1, 3, 8, np.nan]. This Series will also have row labels, which will be ['a', 'b', 'c', 'd'].
Set s3 equal to pd.Series with the specified list of integers as the first argument and the list of labels as the index keyword argument.
# CODE HERE
The final Series we create will be initialized from a Python dictionary. The dictionary will have key-value pairs 'a':0, 'b':1, and 'c':2.
Set s4 equal to pd.Series with a dictionary of the specified key-value pairs as the only argument.
# CODE HERE
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