Saving values updated in a python for loop using Pandas Dataframe

Question

I'm new to Python and Pandas and I'm trying to replace all null values in an array with a specific value.

Everytime I run this, the updated values don't persist.

I've seen that Pandas doesn't save changes when iterating rows...so how CAN I save the changes?

Here is my code

animal_kinds = set(df.AnimalKind) # this gives categories used below in the "ak" like dog, cat, bird
new_color_dog = 'polka dots'
new_color_cat = 'plaid'
new_color_bird = 'stripes'

for ak in animal_kinds:
    ak_colors = ak['colors']


    ak_with_no_color = animals[(df["Kind"] == ak ) & (df["Color"] == "" ) ] 


    result_count = len(ak_with_no_color)
    if result_count:

        ak_with_no_color.at["Color"] = new_color_ak #sets new color based on kind of animal (ak) 
        print(str(ak) 'color is changed to ' + str(new_color_ak))

jpp · Accepted Answer · 2018-12-10 01:44:22Z

Avoid chained indexing

This kind of operation is known as chained indexing and it is explicitly discouraged in the docs:

df[(df['kind'] == 'dog') & (df['colour'] == '')].at['colour'] = 'black'

Instead, calculate and then use a Boolean mask:

mask = (df['kind'] == 'dog') & (df['colour'] == '')
df.loc[mask, 'colour'] = 'black'

Use a dictionary for a variable number of variables

This kind of operation does not work in Python:

new_colour_dog = 'polka dots'
new_colour+'_dog'  # want 'polka dots', but will not work

Use a dictionary instead:

new_colours = {'dog': 'polka dots', 'cat': 'plaid', 'bird': 'stripes'}

You can then iterate your dictionary's key-value pairs:

for animal, new_colour in new_colours.items():
    mask = (df['kind'] == animal) & (df['colour'] == '')
    df.loc[mask, 'colour'] = new_colour

You don't need to test / special-case instances when mask returns a series of False values.

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Saving values updated in a python for loop using Pandas Dataframe

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Avoid chained indexing

Use a dictionary for a variable number of variables

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Collectives™ on Stack Overflow

1 Answer 1

Avoid chained indexing

Use a dictionary for a variable number of variables

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