Convert Python Dataframe to Dictionary [9 Code Examples]

Introduction

Python’s Pandas library is a powerful tool for data manipulation and analysis. One common task is converting Pandas DataFrame to dictionary, which allows for easy integration with other Python data structures. In this article, we will delve into various techniques for converting DataFrames to dictionaries, providing seven advanced examples along with detailed explanations and output.

9 Examples explaining the conversion of Pandas Dataframe to Dictionary

Example 1: Basic Conversion of Pandas Dataframe to Dictionary

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Convert DataFrame to dictionary
dictionary = df.to_dict()

print(dictionary)

Explanation:

• We start by importing the pandas library and creating a simple DataFrame with two columns: ‘Name’ and ‘Age’.
• The df.to_dict() function is used to convert the DataFrame into a dictionary.
• The resulting dictionary will have column names as keys and lists of values as corresponding values.

Output:

{'Name': {0: 'John', 1: 'Jane', 2: 'Jim'}, 'Age': {0: 30, 1: 25, 2: 35}}

Example 2: Orient=’list’

data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Convert DataFrame to list of dictionaries
list_of_dicts = df.to_dict(orient='list')

print(list_of_dicts)

Explanation:

• In this example, we use the orient='list' argument when calling df.to_dict().
• This results in a dictionary where each column is represented as a list.

Output:

{'Name': ['John', 'Jane', 'Jim'], 'Age': [30, 25, 35]}

Example 3: Index as Keys

data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Convert DataFrame to dictionary with index as keys
dictionary = df.set_index('Name').to_dict()['Age']

print(dictionary)

Explanation:

• Here, we first set the ‘Name’ column as the index using df.set_index('Name').
• Then, we use to_dict()['Age'] to get a dictionary where ‘Name’ is the key and ‘Age’ is the value.

Output:

{'John': 30, 'Jane': 25, 'Jim': 35}

Example 4: Nested Python Dictionary

data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Convert DataFrame to a nested dictionary
dictionary = df.set_index('Name').to_dict(orient='index')

print(dictionary)

Explanation:

• In this example, we use to_dict(orient='index') to create a nested dictionary where the outer keys are the index values.

Output:

{'John': {'Age': 30}, 'Jane': {'Age': 25}, 'Jim': {'Age': 35}}

Example 5: Using ‘split’ Orient

data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Convert DataFrame to dictionary with 'split' orient
dictionary = df.to_dict(orient='split')

print(dictionary)

Explanation:

• By using orient='split', we get a dictionary containing 'columns', 'data', and 'index' keys.

Output:

{'index': [0, 1, 2], 'columns': ['Name', 'Age'], 'data': [['John', 30], ['Jane', 25], ['Jim', 35]]}

Example 6: Applying Custom Functions

data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35]}
df = pd.DataFrame(data)

# Apply a custom function to convert DataFrame to dictionary
def custom_converter(row):
return {'Name': row['Name'], 'Double_Age': row['Age'] * 2}

dictionary = df.apply(custom_converter, axis=1, result_type='expand').to_dict(orient='records')

print(dictionary)

Explanation:

• Here, we define a custom function custom_converter that takes a row of the DataFrame and returns a dictionary with custom keys and values.
• We use df.apply() to apply this function to each row.
• result_type='expand' ensures that the output is a DataFrame.
• Finally, we convert the DataFrame to a list of dictionaries using to_dict(orient='records').

Output:

[{'Name': 'John', 'Double_Age': 60}, {'Name': 'Jane', 'Double_Age': 50}, {'Name': 'Jim', 'Double_Age': 70}]

Example 7: Handling Missing Values

# Create a sample DataFrame with missing values
data = {'Name': ['John', 'Jane', 'Jim', None],
'Age': [30, 25, 35, None]}
df = pd.DataFrame(data)

# Convert DataFrame to dictionary, handling missing values
dictionary = df.fillna('N/A').to_dict()

print(dictionary)

Explanation:

• This example demonstrates handling missing values by using fillna() to replace them with a specified value (‘N/A’ in this case) before converting to a dictionary.

Output:

{'Name': {0: 'John', 1: 'Jane', 2: 'Jim', 3: 'N/A'}, 'Age': {0: 30.0, 1: 25.0, 2: 35.0, 3: 'N/A'}}

Example 8: Hierarchical Indexing

# Create a sample DataFrame with hierarchical index
data = {'Value': [10, 20, 30, 40, 50, 60]}
index = pd.MultiIndex.from_product([['A', 'B'], [1, 2, 3]], names=['Letter', 'Number'])
df = pd.DataFrame(data, index=index)

# Convert DataFrame to dictionary with hierarchical index
dictionary = df.to_dict()['Value']

print(dictionary)

Explanation:

• In this example, we create a DataFrame with a hierarchical index using pd.MultiIndex.from_product().
• The DataFrame has two levels of index: ‘Letter’ and ‘Number’.
• We then use df.to_dict()['Value'] to get a dictionary where the hierarchical index is preserved.

Output:

{('A', 1): 10, ('A', 2): 20, ('A', 3): 30, ('B', 1): 40, ('B', 2): 50, ('B', 3): 60}

Example 9: Customized Dictionary Structure

# Create a sample DataFrame
data = {'Name': ['John', 'Jane', 'Jim'],
'Age': [30, 25, 35],
'City': ['New York', 'San Francisco', 'Seattle']}
df = pd.DataFrame(data)

# Convert DataFrame to a customized dictionary structure
def custom_converter(row):
  return {'Person Info': {'Name': row['Name'], 'Age': row['Age']}, 'Location': {'City': row['City']}}

dictionary = df.apply(custom_converter, axis=1, result_type='expand').to_dict(orient='records')

print(dictionary)

Explanation:

• In this example, we define a custom function custom_converter that takes a row of the DataFrame and returns a dictionary with a customized structure.
• The resulting dictionary has two main keys: 'Person Info' and 'Location', each containing sub-dictionaries.
• We use df.apply() to apply this function to each row, and result_type='expand' to ensure the output is a DataFrame.
• Finally, we convert the DataFrame to a list of dictionaries using to_dict(orient='records').

Output:

[{'Person Info': {'Name': 'John', 'Age': 30}, 'Location': {'City': 'New York'}}, {'Person Info': {'Name': 'Jane', 'Age': 25}, 'Location': {'City': 'San Francisco'}}, {'Person Info': {'Name': 'Jim', 'Age': 35}, 'Location': {'City': 'Seattle'}}]

Conclusion

In this article, we’ve explored various techniques for converting Python Pandas DataFrame to dictionary. These examples showcase different scenarios and provide a solid foundation for handling more complex data conversion tasks. With this valuable information, you can efficiently integrate DataFrames with other Python data structures, enhancing your data analysis capabilities. You can visit my other articles on Pandas Dataframes as well for more in-depth information.

Thank you for reading it.