python pandas operations on columns

pandas, python

Solution

You can just use a boolean mask with either the `.loc` or `.ix` attributes of the DataFrame.

mask = df['A'] > 2
df.ix[mask, 'A'] = df.ix[mask, 'C'] - df.ix[mask, 'D']

If you have a lot of branching things then you can do:

def func(row):
    if row['A'] > 0:
        return row['B'] + row['C']
    elif row['B'] < 0:
        return row['D'] + row['A']
    else:
        return row['A']

df['A'] = df.apply(func, axis=1)

`apply` should generally be much faster than a for loop.

Problem

Hi I would like to know the best way to do operations on columns in python using pandas. I have a classical database which I have loaded as a dataframe, and I often have to do operations such as for each row, if value in column labeled 'A' is greater than x then replace this value by column'C' minus column 'D' for now I do something like ``` for i in len(df.index): if df.ix[i,'A'] > x : df.ix[i,'A'] = df.ix[i,'C'] - df.ix[i, 'D'] ``` I would like to know if there is a simpler way of doing these kind of operations and more importantly the most effective one as I have large databases I had tried without the for i loop, like in R or Stata, I was advised to use "a.any" or "a.all" but I did non find anything either here or in the pandas docs. Thanks by advance.

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