Pandas: Convert GroupBy results to Dictionary of Lists

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# Pandas: Convert GroupBy results to Dictionary of Lists

To convert the results of a groupby() call in a Pandas DataFrame to a dictionary of lists:

Call the groupby() method on the DataFrame, passing it the specific column as a parameter.
Use square brackets to select the column you want to have as dictionary values.
Convert the result to a dictionary using to_dict().

main.py

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import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})

print(df)

a_dict = df.groupby('Animal')['Max Speed'].apply(list).to_dict()

print('-' * 50)

# 👇️ {'Cat': [25, 35, 45], 'Dog': [55, 65, 75]}
print(a_dict)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
  Animal  Max Speed
0    Cat         25
1    Cat         35
2    Cat         45
3    Dog         55
4    Dog         65
5    Dog         75
--------------------------------------------------
{'Cat': [25, 35, 45], 'Dog': [55, 65, 75]}

groupby results to dictionary of lists in pandas dataframe

We used the DataFrame.groupby() method to group the DataFrame by the Animal column.

main.py

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a_dict = df.groupby('Animal')['Max Speed'].apply(list).to_dict()

# 👇️ {'Cat': [25, 35, 45], 'Dog': [55, 65, 75]}
print(a_dict)

The next step is to select the column you want to have as values in the dictionary using bracket [] notation.

The DataFrame.apply() method applies a function along an axis of the DataFrame.

We used the list class to convert each row to a list.

The last step is to call the DataFrame.to_dict() method to convert the resulting DataFrame to a dictionary.

# GroupBy results to Dictionary of Lists using a dict comprehension

You can also use a dict comprehension to convert the results of a GroupBy to a dictionary of lists.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})

print(df)

a_dict = {
    key: list(value)
    for key, value in df.groupby('Animal')['Max Speed']
}

print('-' * 50)

print(a_dict)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
  Animal  Max Speed
0    Cat         25
1    Cat         35
2    Cat         45
3    Dog         55
4    Dog         65
5    Dog         75
--------------------------------------------------
{'Cat': [25, 35, 45], 'Dog': [55, 65, 75]}

convert groupby results to dictionary of lists using dict comprehension

Dict comprehensions are very similar to list comprehensions.

main.py

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a_dict = {
    key: list(value)
    for key, value in df.groupby('Animal')['Max Speed']
}

They perform some operation for every key-value pair in the dictionary or select a subset of key-value pairs that meet a condition.

On each iteration, we return the key and convert the value to a list to construct the dictionary.

# GroupBy results to Dictionary of Lists using a `for` loop

You can also use a simple for loop to convert the results of a groupby() call to a dictionary of lists.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})

print(df)

a_dict = {}

for key, value in df.groupby('Animal')['Max Speed']:
    a_dict[key] = list(value)

print('-' * 50)

print(a_dict)

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
  Animal  Max Speed
0    Cat         25
1    Cat         35
2    Cat         45
3    Dog         55
4    Dog         65
5    Dog         75
--------------------------------------------------
{'Cat': [25, 35, 45], 'Dog': [55, 65, 75]}

convert groupby results to dictionary using for loop

We used a for loop to iterate over the GroupBy object and added the current key and value to the new dictionary.

# Converting the `groupby()` result to a dictionary of DataFrames

If you want to convert the groupby() result to a dictionary of DataFrames, use the dict() class.

main.py

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import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})

print(df)

a_dict = dict(tuple(df.groupby('Animal')))

print('-' * 50)

print(a_dict)

print('-' * 50)

print(type(a_dict['Cat']))

The code for this article is available on GitHub

Running the code sample produces the following output.

shell

Copied!
  Animal  Max Speed
0    Cat         25
1    Cat         35
2    Cat         45
3    Dog         55
4    Dog         65
5    Dog         75
--------------------------------------------------
{'Cat':   Animal  Max Speed
0    Cat         25
1    Cat         35
2    Cat         45, 'Dog':   Animal  Max Speed
3    Dog         55
4    Dog         65
5    Dog         75}
--------------------------------------------------
<class 'pandas.core.frame.DataFrame'>

convert groupby result to dictionary of dataframes

We first used the tuple class to convert the groupby() result to a tuple.

main.py

Copied!
import pandas as pd

df = pd.DataFrame({
    'Animal': ['Cat', 'Cat', 'Cat', 'Dog', 'Dog', 'Dog'],
    'Max Speed': [25, 35, 45, 55, 65, 75]
})

# (('Cat',   Animal  Max Speed
# 0    Cat         25
# 1    Cat         35
# 2    Cat         45), ('Dog',   Animal  Max Speed
# 3    Dog         55
# 4    Dog         65
# 5    Dog         75))
print(tuple(df.groupby('Animal')))

The code for this article is available on GitHub

The last step is to convert the tuple of tuples to a dictionary using the dict() class.

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Pandas: Convert GroupBy results to Dictionary of Lists

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