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Python Pandas: How to Convert a Pandas DataFrame into a List

Converting a pandas DataFrame into a Python list is a common operation when you need to pass data to functions that expect lists, serialize data for APIs, export to non-pandas formats, or simply work with standard Python data structures.

Pandas provides several ways to perform this conversion, and the right method depends on what structure you need - a flat list from a single column, a nested list of rows, a list of columns, or a list that includes column headers. In this guide, you'll learn all these approaches with clear examples.

Setting Up the Example DataFrame​

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})

print(df)

Output:

      Name  Age  Score
0    Alice   25   88.5
1      Bob   30   92.3
2  Charlie   35   76.8
3    Diana   28   95.1

Converting a Single Column to a List​

Use tolist() on a column (Series) to get a flat list of its values:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})


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

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

Output:

Names: ['Alice', 'Bob', 'Charlie', 'Diana']
Ages:  [25, 30, 35, 28]
Type:  <class 'list'>
tip

tolist() converts values to native Python types (int, float, str), making the result safe for JSON serialization and other operations that don't support NumPy types.

Converting a DataFrame to a List of Rows​

Each row becomes a sublist containing all column values. Use df.values.tolist():

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})


rows = df.values.tolist()

print(rows)

Output:

[['Alice', 25, 88.5], ['Bob', 30, 92.3], ['Charlie', 35, 76.8], ['Diana', 28, 95.1]]

For better readability:

for row in rows:
    print(row)

Output:

['Alice', 25, 88.5]
['Bob', 30, 92.3]
['Charlie', 35, 76.8]
['Diana', 28, 95.1]

Converting a DataFrame to a List of Columns​

To get each column as a separate list (grouped by column rather than by row):

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})


columns_list = [df[col].tolist() for col in df.columns]

for col_name, col_data in zip(df.columns, columns_list):
    print(f"{col_name}: {col_data}")

Output:

Name: ['Alice', 'Bob', 'Charlie', 'Diana']
Age: [25, 30, 35, 28]
Score: [88.5, 92.3, 76.8, 95.1]

Including Column Names in the List​

To create a list where the first sublist contains column headers:

result = [df.columns.tolist()] + df.values.tolist()

for row in result:
    print(row)

Output:

['Name', 'Age', 'Score']
['Alice', 25, 88.5]
['Bob', 30, 92.3]
['Charlie', 35, 76.8]
['Diana', 28, 95.1]

This format is useful when exporting data to CSV writers or spreadsheet libraries that expect headers as the first row.

Converting to a List of Dictionaries​

When you need each row as a dictionary (common for JSON APIs and database operations):

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})

records = df.to_dict('records')

for record in records:
    print(record)

Output:

{'Name': 'Alice', 'Age': 25, 'Score': 88.5}
{'Name': 'Bob', 'Age': 30, 'Score': 92.3}
{'Name': 'Charlie', 'Age': 35, 'Score': 76.8}
{'Name': 'Diana', 'Age': 28, 'Score': 95.1}
tip

to_dict('records') is the go-to method when converting DataFrame data for JSON serialization or passing to APIs. Each row becomes a self-describing dictionary with column names as keys.

Converting to a List of Tuples​

When you need immutable row data (useful for database insertions or set operations):

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})


tuples = [tuple(row) for row in df.values]

print(tuples)

Output:

[('Alice', 25, 88.5), ('Bob', 30, 92.3), ('Charlie', 35, 76.8), ('Diana', 28, 95.1)]

Alternatively, use itertuples() for a more memory-efficient approach:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})

tuples = [(row.Name, row.Age, row.Score) for row in df.itertuples()]
print(tuples)

Output:

[('Alice', 25, 88.5), ('Bob', 30, 92.3), ('Charlie', 35, 76.8), ('Diana', 28, 95.1)]

Converting the Index to a List​

import pandas as pd

df = pd.DataFrame(
    {'Score': [88, 92, 76]},
    index=['Alice', 'Bob', 'Charlie']
)

index_list = df.index.tolist()
print(f"Index as list: {index_list}")

Output:

Index as list: ['Alice', 'Bob', 'Charlie']

Converting Specific Rows to a List​

Use slicing or boolean filtering to convert only certain rows:

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Score': [88.5, 92.3, 76.8, 95.1]
})

# First 2 rows
first_two = df.head(2).values.tolist()
print(f"First 2 rows: {first_two}")

# Rows where Score > 90
high_scorers = df[df['Score'] > 90].values.tolist()
print(f"High scorers: {high_scorers}")

Output:

First 2 rows: [['Alice', 25, 88.5], ['Bob', 30, 92.3]]
High scorers: [['Bob', 30, 92.3], ['Diana', 28, 95.1]]

Practical Example: Preparing Data for Export​

import pandas as pd
import json

df = pd.DataFrame({
    'Product': ['Laptop', 'Phone', 'Tablet'],
    'Price': [999, 699, 329],
    'Stock': [50, 120, 85]
})

# For CSV writers (list of lists with header)
csv_data = [df.columns.tolist()] + df.values.tolist()
print("CSV format:")
for row in csv_data:
    print(row)

# For JSON APIs (list of dictionaries)
json_data = df.to_dict('records')
print(f"\nJSON format:\n{json.dumps(json_data, indent=2)}")

Output:

CSV format:
['Product', 'Price', 'Stock']
['Laptop', 999, 50]
['Phone', 699, 120]
['Tablet', 329, 85]

JSON format:
[
  {
    "Product": "Laptop",
    "Price": 999,
    "Stock": 50
  },
  {
    "Product": "Phone",
    "Price": 699,
    "Stock": 120
  },
  {
    "Product": "Tablet",
    "Price": 329,
    "Stock": 85
  }
]

Quick Comparison of Methods​

GoalMethodOutput Structure
Single column → flat listdf['col'].tolist()[val1, val2, ...]
All rows → list of listsdf.values.tolist()[[row1], [row2], ...]
Columns → list of lists[df[c].tolist() for c in df.columns][[col1], [col2], ...]
With headers[df.columns.tolist()] + df.values.tolist()[[headers], [row1], ...]
Rows → list of dictsdf.to_dict('records')[{row1}, {row2}, ...]
Rows → list of tuples[tuple(r) for r in df.values][(row1), (row2), ...]
Index → listdf.index.tolist()[idx1, idx2, ...]

Conclusion​

Pandas provides flexible methods to convert DataFrames into various list formats:

  • Use df['column'].tolist() to extract a single column as a flat list.
  • Use df.values.tolist() to convert the entire DataFrame into a list of row-based sublists.
  • Use df.to_dict('records') for a list of dictionaries - ideal for JSON APIs and data interchange.
  • Use [df.columns.tolist()] + df.values.tolist() when you need column headers as the first row.

Choose the format that matches your downstream requirement - whether it's CSV export, JSON serialization, database insertion, or passing data to non-pandas functions.

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