How to convert dataframe to dictionary in pandas WITHOUT index

Question

I have a dataframe df as follows:

| name  | coverage |
|-------|----------|
| Jason | 25.1     |

I want to convert it to a dictionary. I used the following command in pandas :

dict=df.to_dict()

The output of dict gave me the following:

{'coverage': {0: 25.1}, 'name': {0: 'Jason'}} 

I do not want the 0 in my output. I believe this is captured because of the column index in my dataframe df. What can I do to eliminate 0 in my output ( I do not want index to be captured.) expected output :

{'coverage': 25.1, 'name': 'Jason'} 

Solution 1

When I see your dataset with 2 columns I see a series and not a dataframe.

Try this: d = df.set_index('name')['coverage'].to_dict() which will convert your dataframe to a series and output that.

However, if your intent is to have more columns and not a common key you could store them in an array instead using 'records'. d = df.to_dict('r'). `

Runnable code:

import pandas as pd

df = pd.DataFrame({
    'name': ['Jason'],
    'coverage': [25.1]
})

print(df.to_dict())
print(df.set_index('name')['coverage'].to_dict())
print(df.to_dict('r'))

Returns:

{'name': {0: 'Jason'}, 'coverage': {0: 25.1}}
{'Jason': 25.1}
[{'name': 'Jason', 'coverage': 25.1}]

And one more thing, try to avoid to use variable name dict as it is reserved.

Solution 2

dict1 = df.to_dict('records')

or

dict2 = df.to_dict('list')

list: keys are column names, values are lists of column data

records: each row becomes a dictionary where key is column name and value is the data in the cell

Solution 3

you can do something like this:

data.to_dict('list')

#output:
#{'Feeling low in energy-slowed down': [2, 4, 2, 4]}

Solution 4

if its just 1 column, slice the 1 column (it gets converted to Series) wrapping in a dict function

dict( myDF.iloc[:, -1] )
# [: , -1] means: return all rows, return last column)

{Jason: 25.1}

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Comments

[user528025]

great, magic variables.Thank for your answer, it was exactly what I was looking for :)

[Deepak]

Awesome..this is the answer I was looking for

[Julek]

Thanks! Pandas is great but sometimes so weird in its choices.

[artemis]

This just fixed an issue I had been having for hours. Thank you.