Pandas – Convert DataFrame to Dictionary (Dict)

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pandas.DataFrame.to_dict() method is used to convert DataFrame to a Dictionary (dict) object. Use this method If you have a DataFrame and want to convert it to a Python dictionary (dict) object by converting column names as keys and the data for each row as values.

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This method takes the param orient that is used the specify the output format. It takes values 'dict', 'list', 'series', 'split', 'records', and 'index'. In this article, I will explain each of these with examples.

Key Points –

  • Use the to_dict() method for conversion, offering various formats (e.g., ‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’).
  • The default conversion format is ‘dict’, which creates a dictionary where column names are keys.
  • You can specify different formats by passing the orient parameter to the to_dict() method.
  • The DataFrame index can be used as keys in certain dictionary formats, such as ‘index’ and ‘records’.
  • Some formats produce nested dictionaries, which can represent hierarchical data structures.

Related: You can create DataFrame using a Python dictionary.

Syntax of pandas.DataFrame.to_dict() method –


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

Quick Examples of Converting DataFrame to Dictionary

If you are in a hurry, below are some quick examples of how to convert Pandas DataFrame to the dictionary (dict).


# Quick examples of converting dataframe to dictionary

# Example 1: Use DataFrame.to_dict() 
# To convert DataFrame to dictionary
dict = df.to_dict()

# Example 2: Use dict as orient
dict = df.to_dict('dict')

# Example 3: Convert DataFrame to dictionary 
# Using dict() and zip() methods
dict = dict([(i,[x,y,z ]) for i, x,y,z in zip(df.Courses, df.Fee,df.Duration,df.Discount)])

# Example 4: Using dict() and zip() methods
dict = dict([(i,[x,y,z]) for i,x,y,z in zip(df['Courses'], df['Fee'],df['Duration'],df['Discount'])])

To run some examples of converting Pandas DataFrame to a dictionary, let’s create Pandas DataFrame using data from a dictionary.


# Create DataFrame
import pandas as pd
technologies = [
            ("Spark", 22000,'40days',1500.0),
            ("PySpark",25000,'50days',3000.0),
            ("Hadoop",23000,'30days',2500.0)
            ]
df = pd.DataFrame(technologies,columns = ['Courses','Fee','Duration','Discount'])
print("Create DataFrame:\n", df)

Yields below output.

pandas DataFrame convert dictionary

Use DataFrame.to_dict() to Convert DataFrame to Dictionary

To convert Pandas DataFrame to a Dictionary object, use the to_dict() method, which takes orient as dict by default which returns the DataFrame in the format {column -> {index -> value}}. When no orient is specified, to_dict() returns in this format.


# Use DataFrame.to_dict() to convert DataFrame to dictionary
dict = df.to_dict()
print("After converting a DataFrame to dictionary:\n", dict)

# Using orient as dict
dict = df.to_dict('dict')
print("After converting a DataFrame to dictionary:\n", dict)

Yields below output.

pandas DataFrame convert dictionary

Convert DataFrame to Dictionary With Column as Key

list orient – Each column is converted to a list and the lists are added to a dictionary as values to column labels. To get the dict in format {column -> [values]}, specify with the string literal “list” for the parameter orient.

Related: You can convert a list of dictionaries to a DataFrame.


# Using orient as list
dict = df.to_dict('list')
print("After converting a DataFrame to dictionary:\n", dict)

Yields below output.


# Output:
# After converting a DataFrame to dictionary:
{'Courses': ['Spark', 'PySpark', 'Hadoop'], 
 'Fee': [22000, 25000, 23000], 
 'Duration': ['40days', '50days', '30days'], 
 'Discount': [1500.0, 3000.0, 2500.0]}

Convert DataFrame to Dictionary of Series

series orient – Each column is converted to a Pandas Series, and the series is represented as values.

To get the dict in format {column -> Series(values)}, specify with the string literal “series” for the parameter orient.


# Using orient as series
df2 = df.to_dict('series')
print("After converting a DataFrame to dictionary:\n", df2)

Yields below output. 


# Output:
# After converting a DataFrame to dictionary:
{'Courses': 0      Spark
1    PySpark
2     Hadoop
Name: Courses, dtype: object, 'Fee': 0    22000
1    25000
2    23000
Name: Fee, dtype: int64, 'Duration': 0    40days
1    50days
2    30days
Name: Duration, dtype: object, 'Discount': 0    1500.0
1    3000.0
2    2500.0
Name: Discount, dtype: float64}

Convert DataFrame to Dictionary of Split

split orient – Each row is converted to a list and they are wrapped in another list and indexed with the key “data”.

To get the dict in format {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}, specify with the string literal “split” for the parameter orient.


# Using orient as split
df2 = df.to_dict('split')
print("After converting a DataFrame to dictionary:\n", df2)

Yields below output.


# Output:
# After converting a DataFrame to dictionary:
{'index': [0, 1, 2], 'columns': ['Courses', 'Fee', 'Duration', 'Discount'], 'data': [['Spark', 22000, '40days', 1500.0], ['PySpark', 25000, '50days', 3000.0], ['Hadoop', 23000, '30days', 2500.0]]}

Convert DataFrame to Dictionary of Records

records orient – Each column is converted to a dictionary where the column name is key and the column value for each row is a value.

In order to get the list-like format [{column -> value}, … , {column -> value}], specify with the string literal “records” for the parameter orient.


# Using orient as records
df2 = df.to_dict('records')
print("After converting a DataFrame to dictionary:\n", df2)

Yields below output.


# Output:
# After converting a DataFrame to dictionary:
[{'Courses': 'Spark', 'Fee': 22000, 'Duration': '40days', 'Discount': 1500.0}, {'Courses': 'PySpark', 'Fee': 25000, 'Duration': '50days', 'Discount': 3000.0}, {'Courses': 'Hadoop', 'Fee': 23000, 'Duration': '30days', 'Discount': 2500.0}]

Convert DataFrame to Dictionary by Index

index orient – Each column is converted to a dictionary where the column elements are stored against the column name.

In order to get the dict in format {index -> {column -> value}}, specify with the string literal “index” for the parameter orient.


# Using orient as index
df2 = df.to_dict('index')
print("After converting a DataFrame to dictionary:\n", df2)

Yields below output.


# Output:
# After converting a DataFrame to dictionary:
{0: {'Courses': 'Spark', 'Fee': 22000, 'Duration': '40days', 'Discount': 1500.0}, 1: {'Courses': 'PySpark', 'Fee': 25000, 'Duration': '50days', 'Discount': 3000.0}, 2: {'Courses': 'Hadoop', 'Fee': 23000, 'Duration': '30days', 'Discount': 2500.0}}

Convert DataFrame to Dictionary Using dict() and zip() Methods


# Convert DataFrame to dictionary using dict() and zip() methods
df2 = dict([(i,[x,y,z ]) for i, x,y,z in zip(df.Courses, df.Fee,df.Duration,df.Discount)])
print("After converting a DataFrame to dictionary:\n", df2)

# Using dict() and zip() methods
df2 = dict([(i,[x,y,z]) for i,x,y,z in zip(df['Courses'], df['Fee'],df['Duration'],df['Discount'])])
print("After converting a DataFrame to dictionary:\n", df2)

Yields below output.


# Output:
# After converting a DataFrame to dictionary:
{'Spark': [22000, '40days', 1500.0], 'PySpark': [25000, '50days', 3000.0], 'Hadoop': [23000, '30days', 2500.0]}

Complete Example For Convert DataFrame to Dictionary


import pandas as pd
technologies = [
            ("Spark", 22000,'40days',1500.0),
            ("PySpark",25000,'50days',3000.0),
            ("Hadoop",23000,'30days',2500.0)
            ]
df = pd.DataFrame(technologies,columns = ['Courses','Fee','Duration','Discount'])
print(df)                 

# Use DataFrame.to_dict() 
# To convert DataFrame to dictionary
dict = df.to_dict()
print(dict )

# Use dict orient
dict = df.to_dict('dict')
print(dict )

# Use list orient
dict = df.set_index('Courses').T.to_dict('list')
print(dict )

# Use series orient
dict = df.to_dict('series')
print(dict )

# Use split orient
dict = df.to_dict('split')
print(dict )

# Use list orient
dict = df.to_dict('list')
print(dict )

# Use records orient
dict = df.to_dict('records')
print(dict )

# Use index orient
dict = df.to_dict('index')
print(dict )

# Convert DataFrame to dictionary using dict() and zip() methods
dict = dict([(i,[x,y,z ]) for i, x,y,z in zip(df.Courses, df.Fee,df.Duration,df.Discount)])
print(dict )

# Using dict() and zip() methods
dict = dict([(i,[x,y,z]) for i,x,y,z in zip(df['Courses'], df['Fee'],df['Duration'],df['Discount'])])
print(dict )

FAQ on Pandas Convert DataFrame to Dictionary (Dict)

How do I convert an entire DataFrame to a dictionary?

To convert an entire DataFrame to a dictionary in Pandas, you can use the .to_dict() method. By default, this method will create a nested dictionary with column names as keys and the column’s data as another dictionary, where the index is the key and the cell value is the value.

How can I convert a DataFrame to a dictionary where each row is a dictionary?

To convert a DataFrame to a dictionary where each row is represented as a separate dictionary, you can use the 'records' format with the .to_dict() method. This format returns a list of dictionaries, with each dictionary representing a single row in the DataFrame.

How can I include the index as part of the dictionary?

To include the DataFrame’s index as part of the dictionary, you can use the 'index' format with the .to_dict() method. This will create a dictionary where the index values are the keys, and each key maps to another dictionary representing the row data.

How do I convert a DataFrame to a dictionary with column names as keys and lists as values?

To convert a DataFrame to a dictionary where the column names are keys and each key’s value is a list of column values, you can use the 'list' format with the .to_dict() method.

How do I convert only a specific column to a dictionary?

To convert only a specific column in a DataFrame to a dictionary, you can select that column and use the .to_dict() method. The resulting dictionary will have the DataFrame’s index as keys and the values from the selected column as the corresponding values.

Conclusion

You have learned pandas.DataFrame.to_dict() method is used to convert DataFrame to a Dictionary (dict) object by converting column names as keys and the data for each row as values. Also learned when we pass the param orient how it can be specified in the output format.

Happy Learning !!

References

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This Post Has One Comment

  1. Anonymous September 7, 2022

    good article thanks a lot!

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