How to Create Series in pandas: Simple Guide with Examples

You can create a Series in pandas by passing a list, array, or dictionary to pd.Series(). Optionally, you can specify an index to label the data points.

Syntax

The basic syntax to create a Series is pd.Series(data, index=index), where:

• data can be a list, array, or dictionary containing the values.
• index is an optional list of labels for the data points.

python

import pandas as pd

# Basic syntax
series = pd.Series(data, index=index)

Example

This example shows how to create a Series from a list with custom index labels.

python

import pandas as pd

# Create a Series from a list
data = [10, 20, 30, 40]
index = ['a', 'b', 'c', 'd']
series = pd.Series(data, index=index)
print(series)

Output

a    10
b    20
c    30
d    40
dtype: int64

Common Pitfalls

Common mistakes include:

• Not providing an index when you want custom labels, which results in default numeric indexes.
• Passing a dictionary but expecting the order to be preserved (order depends on Python version and pandas version).
• Mixing data types unintentionally, which can cause dtype changes.

python

import pandas as pd

# Wrong: expecting custom index but not providing it
series_wrong = pd.Series([1, 2, 3])
print(series_wrong)

# Right: providing index
series_right = pd.Series([1, 2, 3], index=['x', 'y', 'z'])
print(series_right)

Output

0    1
1    2
2    3
dtype: int64
x    1
y    2
z    3
dtype: int64

Quick Reference

Parameter	Description
data	List, array, or dictionary of values
index	Optional list of labels for the Series
dtype	Optional data type for the Series
name	Optional name for the Series

Key Takeaways

Use pd.Series() with data and optional index to create a Series.

Providing an index lets you label each data point clearly.

Data can be a list, array, or dictionary for flexible input.

Watch out for default numeric indexes if you don't specify labels.

Mixed data types can affect the Series data type automatically.