Python Pandas Series

Pandas is a widely-used Python library that offers powerful data manipulation and analysis tools. Among its core components is the Pandas Series, a key data structure that enables efficient handling of one-dimensional data. In this article, we will dive into the concept of a Series, explaining how to create, access, and manipulate it with practical examples.

What is a Pandas Series?

A Pandas Series is essentially a one-dimensional labeled array that can hold a variety of data types, such as integers, strings, floats, and even custom Python objects. It is similar to a column in a spreadsheet or a database table, but with the added advantage of being able to index and manipulate data efficiently.

Each Series has two main components:

1. Data: The actual values stored in the Series.
2. Index: A label that is associated with each data element. If no index is specified, Pandas automatically generates a default integer-based index.

How to Create a Pandas Series

There are multiple ways to create a Pandas Series, each suitable for different data sources. Here are a few common methods:

1. Create a Series from an Array

To create a Series from a NumPy array, you need to import Pandas and NumPy, and then pass the array to the pd.Series() constructor. If no index is provided, the Series will automatically use a default numeric index starting from 0.

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import pandas as pd
import numpy as np

data = np.array([10, 20, 30, 40, 50])
series = pd.Series(data)
print(series)

Output:

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0    10
1    20
2    30
3    40
4    50
dtype: int64

2. Create a Series from a List

Creating a Series from a Python list is simple—just pass the list to the pd.Series() function. Like arrays, the default index will be numeric.

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import pandas as pd

data = [10, 20, 30, 40, 50]
series = pd.Series(data)
print(series)

Output:

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0    10
1    20
2    30
3    40
4    50
dtype: int64

3. Create a Series from a Dictionary

If you want to create a Series with custom index labels, you can use a dictionary. In this case, the keys of the dictionary become the index labels, and the values become the data.

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import pandas as pd

data = {'a': 10, 'b': 20, 'c': 30, 'd': 40, 'e': 50}
series = pd.Series(data)
print(series)

Output:

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a    10
b    20
c    30
d    40
e    50
dtype: int64

4. Create a Series from a Scalar Value

A Series can also be created from a scalar value, where the scalar will be repeated for each index label. You must provide an index in this case.

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import pandas as pd

scalar = 10
index = ['a', 'b', 'c', 'd', 'e']
series = pd.Series(scalar, index=index)
print(series)

Output:

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a    10
b    10
c    10
d    10
e    10
dtype: int64

Access Elements of a Pandas Series

Once a Series is created, you can access its elements in different ways: by position or by label.

1. Access Elements by Position

You can access elements by their position using the index operator []. The position is zero-based, meaning that the first element is at position 0.

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import pandas as pd

data = [10, 20, 30, 40, 50]
series = pd.Series(data)

# Accessing the first element
print("First element:", series[0])

# Accessing a range of elements
print("Subset of elements:")
print(series[1:4])

Output:

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First element: 10
Subset of elements:
1    20
2    30
3    40
dtype: int64

2. Access Elements by Label

If you use a custom index, you can access elements using the corresponding labels.

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import pandas as pd

data = [10, 20, 30, 40, 50]
index = ['a', 'b', 'c', 'd', 'e']
series = pd.Series(data, index=index)

# Accessing an element by label
print(series['b'])

# Accessing multiple elements by labels
print(series[['a', 'c', 'e']])

Output:

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a    10
c    30
e    50
dtype: int64

Manipulate a Pandas Series

Pandas provides a variety of operations to manipulate and transform Series data. Below are some of the most common operations.

1. Mathematical Operations on Series

You can perform element-wise mathematical operations such as addition, subtraction, multiplication, and division between Series objects.

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import pandas as pd

data1 = pd.Series([1, 2, 3, 4, 5])
data2 = pd.Series([10, 20, 30, 40, 50])

# Addition
result = data1 + data2
print(result)

# Subtraction
result = data1 - data2
print(result)

# Multiplication
result = data1 * data2
print(result)

# Division
result = data1 / data2
print(result)

Output:

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0    11
1    22
2    33
3    44
4    55
dtype: int64
0    -9
1   -18
2   -27
3   -36
4   -45
dtype: int64
0    10
1    40
2    90
3   160
4   250
dtype: int64
0    0.1
1    0.1
2    0.1
3    0.1
4    0.1
dtype: float64

2. Filter and Selection on Series

You can filter elements based on conditions using boolean indexing.

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import pandas as pd

data = pd.Series([1, 2, 3, 4, 5])

# Filtering based on a condition
result = data[data > 2]
print(result)

# Selection based on index
result = data[data.index.isin([0, 2, 4])]
print(result)

Output:

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2    3
3    4
4    5
dtype: int64
0    1
2    3
4    5
dtype: int64

3. Aggregation on Series

Common aggregation operations include calculating the sum, mean, minimum, maximum, and count of elements in a Series.

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import pandas as pd

data = pd.Series([10, 20, 30, 40, 50])

# Sum
print("Sum:", data.sum())

# Mean
print("Mean:", data.mean())

# Minimum
print("Minimum:", data.min())

# Maximum
print("Maximum:", data.max())

# Count
print("Count:", data.count())

Output:

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Sum: 150
Mean: 30.0
Minimum: 10
Maximum: 50
Count: 5

Conclusion

In this article, we explored the Pandas Series in detail. We covered how to create Series from various data sources like arrays, lists, dictionaries, and scalar values. We also looked at different ways to access and manipulate the data within a Series, including mathematical operations, filtering, and aggregation.