How to convert dataframe to dictionary in pandas WITHOUT index

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}

you can do something like this:

data.to_dict('list')

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

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.

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