CODEWITHSUNDEEP
pythonpandasdataframe
ubant
ubant

Reputation: 39

How to reverse all values in a dataframe column?

I wasn't sure how to form this question, so the problem isn't really what it sounds for. Let's say I have a column with floats, ranging from 0.000000 to 1.000000 I want to reverse those values so for example:

1.000000 == 0.000000
0.122000 == 0.888000
0.950000 == 0.050000
0.324546 == 0.675454

How can I do it? I tried

normalized_df = normalized_df[headers[-1]].apply(lambda n: (1.000000 - n))

But I got an error with this block of code:

vals = normalized_df.values.tolist()
for e in vals:
    del e[:3]
results = dict(zip(countries, vals))

An error:

Traceback (most recent call last):
  File "...", line ..., in <module>
    del e[:3]
TypeError: 'float' object does not support item deletion

This error normally doesn't happen without this code at the beginning of my question

Upvotes: 0

Views: 127

Answers (1)

Anurag Dabas
Anurag Dabas

Reputation: 24322

Just simply substract 1 from your series:

s=pd.Series([1.0, 0.122, 0.95, 0.324546])
s=(1-s)
#here s is your Series
#If needed the difference as positive number use abs() method
s=(s-1).abs()

output:

0    0.000000
1    0.878000
2    0.050000
3    0.675454

Or

If you have df with int/float values:

df=pd.DataFrame({0: {0: 1.0, 1: 0.122, 2: 0.95, 3: 0.324546},
 1: {0: 1.0, 1: 0.122, 2: 0.95, 3: 0.324546},
 2: {0: 1.0, 1: 0.122, 2: 0.95, 3: 0.324546}})

df=1-df
#If needed the difference as positive number use abs() method
df.sub(1).abs()

output of df:

      0           1         2
0   0.000000    0.000000    0.000000
1   0.878000    0.878000    0.878000
2   0.050000    0.050000    0.050000
3   0.675454    0.675454    0.675454

Upvotes: 2

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