1

I defined a function

def test_function(id):
.....
.....
.....

I was calling that function via

df = test_function('123')

but what I want to do now is, I have another Dataframe df2 =

values
123
354
645
456
176

I want to write an apply function which would take 1 value at a time from df2 and run my test_function instead of manually adding values.

and also my df should be appended for different values, such that df contain a column name 'value' which would be the value selected one at a time

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2 Answers 2

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1

I think you need apply with DataFrame.values and last concat:

Notice: [] are necessary, because pandas function Series.values - so df2.values return numpy array and df2['values'] select column values. Same problem is with another column names with same names as some functions in pandas like count, size, sum...

a = f2['values'].apply(test_function).values
print (pd.concat(a, ignore_index=True))
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10 Comments

` df['values']` or ` df2['values'] `
Sorry df2['values']
And I am not sure about df, but it seems need same like df['values'].
i'm getting result as a pandas.core.series.Series because i stored it in d3 = df2['values'].apply(test_function) df3 has 3 tables in it can i concatenate this 3 tables into one and make type(df3) as dataframe instead of series.
So need concat ? Like pd.concat([df31, df32, df33]) or pd.concat([df31, df32, df33], axis=1) ?
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0

No need to call this function everytime, instead you do this:

df = pd.DataFrame(columns=['Val'])
df['Val']=list(df2['values'])
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