Skip to content
Tutorial Reference

Python Pandas: How to Convert a Pandas Series to a Python List

Learn how to convert a pandas Series to a Python list using tolist(), handle different data types (numeric, strings, mixed), and manage NaN values for JSON serialization or API integration.

Converting a pandas Series to a Python list is a common operation when you need to pass data to functions that expect standard Python types, serialize data for APIs, or simply work with list-based operations. The tolist() method provides a clean and efficient way to make this conversion.

In this guide, you'll learn how to convert a pandas Series to a list using tolist(), handle different data types, and understand when this conversion is useful.

Using tolist() to Convert a Series

The tolist() method converts a pandas Series into a standard Python list. It works with any data type - integers, floats, strings, or mixed types.

Converting a Numeric Series

import pandas as pd

numbers = pd.Series([2, 4, 6, 8, 10])

print("Pandas Series:")
print(numbers)
print(f"Type: {type(numbers)}")

numbers_list = numbers.tolist()

print("\nPython list:")
print(numbers_list)
print(f"Type: {type(numbers_list)}")

Output:

Pandas Series:
0     2
1     4
2     6
3     8
4    10
dtype: int64
Type: <class 'pandas.core.series.Series'>

Python list:
[2, 4, 6, 8, 10]
Type: <class 'list'>

Converting a String Series

import pandas as pd
import numpy as np

animals = pd.Series(
    np.array(['ant', 'bat', 'cat', 'dog']),
    index=[100, 101, 102, 103]
)

print("Pandas Series:")
print(animals)

animal_list = animals.tolist()

print(f"\nPython list: {animal_list}")
print(f"Type: {type(animal_list)}")

Output:

Pandas Series:
100    ant
101    bat
102    cat
103    dog
dtype: object

Python list: ['ant', 'bat', 'cat', 'dog']
Type: <class 'list'>

Converting a Series Created from a Dictionary

import pandas as pd

marks = {'Maths': 100.0, 'Physics': 90.0, 'Chemistry': 85.0}

series = pd.Series(marks)

print("Pandas Series:")
print(series)

marks_list = series.tolist()

print(f"\nValues as list: {marks_list}")
print(f"Index as list:  {series.index.tolist()}")

Output:

Pandas Series:
Maths        100.0
Physics       90.0
Chemistry     85.0
dtype: float64

Values as list: [100.0, 90.0, 85.0]
Index as list:  ['Maths', 'Physics', 'Chemistry']

Converting a Slice of a Series

You can slice a Series first and then convert the subset to a list:

import pandas as pd

scores = pd.Series(
    [100, 90, 80, 90, 85],
    index=['Student1', 'Student2', 'Student3', 'Student4', 'Student5']
)

top_three = scores[:3]

print("First 3 students:")
print(top_three)

print(f"\nAs list: {top_three.tolist()}")
print(f"Type: {type(top_three.tolist())}")

Output:

First 3 students:
Student1    100
Student2     90
Student3     80
dtype: int64

As list: [100, 90, 80]
Type: <class 'list'>

Alternative: Using list() Constructor

You can also use Python's built-in list() function to convert a Series:

import pandas as pd

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

result1 = series.tolist()

result2 = list(series)

print(f"tolist(): {result1}")
print(f"list():   {result2}")
print(f"Equal:    {result1 == result2}")

Output:

tolist(): [10, 20, 30, 40]
list():   [10, 20, 30, 40]
Equal:    True

Here is the corrected version of your section:

Handling Missing Values (NaN)

When a Series contains NaN values, tolist() converts them to Python's float('nan') or None depending on the dtype:

import pandas as pd
import numpy as np

numeric = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])
print(f"Numeric with NaN: {numeric.tolist()}")

strings = pd.Series(['apple', None, 'cherry'])
print(f"Strings with None: {strings.tolist()}")

Output:

Numeric with NaN: [1.0, nan, 3.0, nan, 5.0]
Strings with None: ['apple', None, 'cherry']

To replace NaN values before converting, use fillna():

import pandas as pd
import numpy as np

series = pd.Series([1.0, np.nan, 3.0, np.nan, 5.0])

clean_list = series.fillna(0).tolist()
print(f"Cleaned list: {clean_list}")

Output:

Cleaned list: [1.0, 0.0, 3.0, 0.0, 5.0]

Converting DataFrame Columns to Lists

Since each DataFrame column is a Series, tolist() works on columns too:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Score': [88.5, 92.3, 76.8]
})

names = df['Name'].tolist()
ages = df['Age'].tolist()

print(f"Names: {names}")
print(f"Ages:  {ages}")

Output:

Names: ['Alice', 'Bob', 'Charlie']
Ages:  [25, 30, 35]

Practical Example: Preparing Data for JSON

A common use case for tolist() is preparing pandas data for JSON serialization, since JSON doesn't support NumPy types:

import pandas as pd
import json

series = pd.Series([100, 200, 300], index=['a', 'b', 'c'])

data = {
    'values': series.tolist(),
    'labels': series.index.tolist()
}

json_string = json.dumps(data)
print(json_string)

Output:

{"values": [100, 200, 300], "labels": ["a", "b", "c"]}

Summary

Method Returns Native Python Types* Preserves Index
series.tolist() ✅ Yes ❌ No
list(series) ✅ Yes (for standard dtypes) ❌ No
series.to_dict() ✅ Yes ✅ Yes (as dict keys)
series.values.tolist() ✅ Yes ❌ No

Conclusion

Converting a pandas Series to a Python list is simple and versatile:

  • Use series.tolist() for the cleanest conversion - it returns a standard Python list with native Python types, making it ideal for JSON serialization, API calls, and interoperability with non-pandas code.
  • Use list(series) when NumPy types are acceptable and you want a quick conversion.
  • Use series.index.tolist() when you also need the index values as a list.
  • Handle NaN values with fillna() before converting if your downstream code doesn't support missing values.

For most use cases, tolist() is the recommended and most reliable method.

Navigation