python pandas - how to apply a normalise function to a dataframe column

Question

I have a pandas dataframe, output similar to below:

index    value
0    5.95
1    1.49
2    2.34
3    5.79
4    8.48

I want to get the normalised value of each column['value'] and store it in a new column['normalised'] but not sure how to apply the normalise function to the column...

my normalising function would look like this: (['value'] - min['value'])/(max['value'] - min['value']

I know I should probably be using the apply or transform function to add the new column to the dataframe but not sure how to pass the normalising function to the apply function...

Sorry if I'm getting the terminology wrong but I'm a newbe to python and in particular pandas!

drammock · Accepted Answer · 2017-05-18 21:54:59Z

3

These are pretty standard column operations:

>>> (df.value - df.value.min()) / (df.value.max() - df.value.min())
0    0.638054
1    0.000000
2    0.121602
3    0.615165
4    1.000000
Name: value, dtype: float64

You can simply write

df['normalized'] = (df.value - ....

edited May 18, 2017 at 21:54

drammock

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answered Feb 22, 2016 at 21:18

Ami Tavory

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user3274289 · Accepted Answer · 2016-02-22 21:14:43Z

2

Lets call your DataFrame DF.

DF['normalised'] = (DF['value']-min(DF['value']))/(max(DF['value']-min(DF['value'])

does the trick.

answered Feb 22, 2016 at 21:14

user3274289

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1 Comment

EdChum Over a year ago

This won't work the type you're passing to min and max are pandas Series and this will raise a ValueError as the types are array-like and so won't understand this type

Thomas Kimber · Accepted Answer · 2016-02-23 08:16:46Z

I'd consider user the lambda/apply method, which I'm sure you'll be able to finesse, which requires determining ahead of time the min and max values.

First, write a function that outputs a value, based on some 'global' parameters, and an input value fetched from a data-row.

def norm(vmax, vmin, val):
    return (val-vmin)/(vmax-vmin)

Next, collect your global values from the dataframe:

val_min = df['value'].min()
val_max = df['value'].max()

Finally, you can apply the function, creating a new field to hold the result:

df['new_field'] = df.apply(lambda row : norm(val_min,val_max,row['value']),axis=1)

df
    value   new_field
0   5.95    0.361946
1   1.49    1.000000
2   2.34    0.878398
3   5.79    0.384835
4   8.48    -0.000000

The beauty of using this 'lambda' approach, you can tweak your functions as you like, which (in my opinion anyway) compartmentalise the code better, allowing for reuse - which is always a good thing.

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