Pandas pd.Series() Function

Pandas Series API 手册Pandas Series API Manual

pd.Series()It is a core function in the Pandas library, used to create one-dimensional arrays (similar to lists or NumPy arrays), but it is more powerful than ordinary arrays.SeriesIt can store any data type (such as integers, strings, floating-point numbers, etc.), and each element has an associated index, which makes data operations more flexible.

Basic syntax of pd.Series()

pd.Series(data, index=None, dtype=None, name=None, copy=False)

Parameter description:

  1. data: data, can be a list, dictionary, NumPy array, etc.
  2. index: index, used to label data. If not specified, it defaults to starting from 0.
  3. dtype: data type, such asint、float、stretc.
  4. name: the name of the Series, usually used to label data.
  5. copy: whether to copy data, defaults toFalse。

How to use pd.Series()?

Example 1: Create a Series from a list

Example

import pandas as pd

# Create a simple Series
data = [10, 20, 30, 40]
s = pd.Series(data)
print(s)

Output:

0    10
1    20
2    30
3    40
dtype: int64

Explanation:

  • data[10, 20, 30, 40]is converted to a Series.
  • The default index starts from 0.

Example 2: Specify index

Example

# Create a Series and specify the index
data = [10, 20, 30, 40]
index = ['a', 'b', 'c', 'd']
s = pd.Series(data, index=index)
print(s)

Output:

a    10
b    20
c    30
d    40
dtype: int64

Explanation:

  • The index is specified as['a', 'b', 'c', 'd'], instead of the default 0, 1, 2, 3.

Example 3: Create a Series from a dictionary

Example

# Create a Series from a dictionary
data = {'a': 10, 'b': 20, 'c': 30, 'd': 40}
s = pd.Series(data)
print(s)

Output:

a    10
b    20
c    30
d    40
dtype: int64

Explanation:

  • The dictionary's keys automatically become the Series indices, and the values become the data.

Example 4: Specify data type

Example

# Create a Series and specify the data type as float
data = [10, 20, 30, 40]
s = pd.Series(data, dtype=float)
print(s)

Output:

0    10.0
1    20.0
2    30.0
3    40.0
dtype: float64

Explanation:

  • The data type is specified asfloat, so all values are displayed as floating-point numbers.

Common operations of Series

1. Access data

You can access data in a Series by index.

Example

s = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print(s['b'])  # Output: 20

2. Modify data

You can modify data in a Series by index.

Example

s['b'] = 25
print(s)

Output:

a    10
b    25
c    30
d    40
dtype: int64

3. Slicing operations

You can perform slicing operations on a Series.

Example

print(s['b':'d'])

Output:

b    25
c    30
d    40
dtype: int64

4. Mathematical operations

You can perform mathematical operations on a Series.

Example

print(s * 2)

Output:

a    20
b    50
c    60
d    80
dtype: int64

Pandas Series API 手册Pandas Series API Manual

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