Pandas DataFrame to_dict() Method – Explained with examples

Pandas is a powerful and versatile library in Python that is widely used for data manipulation and analysis. One of the core data structures in Pandas is the DataFrame, which can be thought of as a table or a 2-dimensional array with labeled axes (rows and columns). Among the many methods available for DataFrames, the to_dict() method is particularly useful for converting a DataFrame into a dictionary. This blog post will provide a detailed explanation of the to_dict() method, its various orientations, and practical examples to illustrate its usage.

What is the to_dict() Method?

The to_dict() method in Pandas is used to convert a DataFrame into a dictionary. The structure of the resulting dictionary can be controlled by the orient parameter, which determines the format of the dictionary. The available orientations are:

1. dict (default)
2. list
3. series
4. split
5. records
6. index

Let’s explore each of these orientations with examples.

Syntax

DataFrame.to_dict(orient='dict', into=<class 'dict'>)

• orient: Specifies the format of the resulting dictionary. The default is dict.
• into: The class to use for the resulting dictionary. The default is dict, but you can specify any dictionary-like class.

Orientations

1. dict (default)

In this orientation, the DataFrame is converted into a dictionary where the keys are column labels, and the values are dictionaries with index labels as keys and the corresponding column values as values.

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

result = df.to_dict(orient='dict')
print(result)

Output:

{
    'A': {0: 1, 1: 2, 2: 3},
    'B': {0: 4, 1: 5, 2: 6},
    'C': {0: 7, 1: 8, 2: 9}
}

2. list

In the list orientation, the DataFrame is converted into a dictionary where the keys are column labels, and the values are lists containing the values of each column.

result = df.to_dict(orient='list')
print(result)

Output:

{
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
}

3. ‘series’

In this orientation, the DataFrame is converted into a dictionary where the keys are column labels, and the values are Pandas Series objects.

result = df.to_dict(orient='series')
print(result)
print("\nGetting column 'A' \n")
print(result['A'])

Output:

{'A': 0    1
1    2
2    3
Name: A, dtype: int64,
'B': 0    4
1    5
2    6
Name: B, dtype: int64, 
'C': 0    7
1    8
2    9
Name: C, dtype: int64}

Getting column 'A' 

0    1
1    2
2    3
Name: A, dtype: int64

4. ‘split’

In the split orientation, the DataFrame is converted into a dictionary containing three keys: 'index', 'columns', and 'data'. The values are lists representing the DataFrame’s index, columns, and data, respectively.

result = df.to_dict(orient='split')
print(result)

Output:

{
    'index': [0, 1, 2],
    'columns': ['A', 'B', 'C'],
    'data': [[1, 4, 7], [2, 5, 8], [3, 6, 9]]
}

5. ‘records’

In the records orientation, the DataFrame is converted into a list of dictionaries where each dictionary represents a row in the DataFrame.

result = df.to_dict(orient='records')
print(result)

Output:

[
    {'A': 1, 'B': 4, 'C': 7},
    {'A': 2, 'B': 5, 'C': 8},
    {'A': 3, 'B': 6, 'C': 9}
]

6. ‘index’

In the index orientation, the DataFrame is converted into a dictionary where the keys are index labels, and the values are dictionaries containing column labels and corresponding values.

result = df.to_dict(orient='index')
print(result)

Output:

{
    0: {'A': 1, 'B': 4, 'C': 7},
    1: {'A': 2, 'B': 5, 'C': 8},
    2: {'A': 3, 'B': 6, 'C': 9}
}

Practical Examples

Example 1: Using the ‘list’ Orientation

Suppose you have a DataFrame representing sales data and you want to convert it into a dictionary where each key is a product and the values are lists of sales figures.

sales_data = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'January': [150, 200, 300],
    'February': [220, 230, 340],
    'March': [180, 210, 320]
})

result = sales_data.set_index('Product').to_dict(orient='list')
print(result)

Output:

{
    'January': [150, 200, 300],
    'February': [220, 230, 340],
    'March': [180, 210, 320]
}

In this example, we have a DataFrame representing sales data for three products (A, B, and C) over three months (January, February, and March). By converting this DataFrame into a dictionary using the list orientation, we create a dictionary where each key is a month and the corresponding value is a list of sales figures for that month across all products. This structure allows for easy access to sales data on a per-month basis, making it simple to analyze sales trends over time for each product.

Example 2: Using the ‘records’ Orientation

If you want to convert a DataFrame representing student grades into a list of dictionaries where each dictionary represents a student’s record, you can use the records orientation.

grades = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Math': [85, 90, 78],
    'Science': [92, 88, 84],
    'English': [87, 91, 80]
})

result = grades.to_dict(orient='records')
print(result)

Output:

[
    {'Name': 'Alice', 'Math': 85, 'Science': 92, 'English': 87},
    {'Name': 'Bob', 'Math': 90, 'Science': 88, 'English': 91},
    {'Name': 'Charlie', 'Math': 78, 'Science': 84, 'English': 80}
]

In this example, we have a DataFrame containing student grades for three subjects (Math, Science, and English) for three students (Alice, Bob, and Charlie). By converting this DataFrame into a dictionary using the records orientation, we transform it into a list of dictionaries, where each dictionary represents an individual student’s record. Each dictionary contains the student’s name and their grades for all subjects. This format is particularly useful for iterating over student records and performing operations on each student’s data individually, such as calculating average grades or identifying top performers.

Conclusion

The to_dict() method in Pandas is a versatile tool for converting DataFrames into various dictionary formats, making it easier to work with data in different contexts. Understanding the different orientations and their use cases allows you to leverage this method effectively in your data manipulation and analysis tasks. Whether you need a dictionary of lists, a list of dictionaries, or any other structure, the to_dict() method provides the flexibility to meet your needs.

Also Explore:

• Create a Pandas DataFrame from a List of Dicts
• Pandas DataFrame.columns – Explained with examples