variable column name in dask assign() or apply()

问题

I have code that works in pandas, but I'm having trouble converting it to use dask. There is a partial solution here, but it does not allow me to use a variable as the name of the column I am creating/assigning to.

Here's the working pandas code:

percent_cols = ['num_unique_words', 'num_words_over_6']

def find_fraction(row, col):
    return row[col] / row['num_words']

for c in percent_cols:
    df[c] = df.apply(find_fraction, col=c, axis=1)

Here's the dask code that doesn't do what I want:

data = dd.from_pandas(df, npartitions=8)

for c in percent_cols:
    data = data.assign(c = data[c] / data.num_words)

This assigns the result to a new column called c rather than modifying the value of data[c] (what I want). Creating a new column would be fine if I could have the column name be a variable. E.g., if this worked:

for c in percent_cols:
    name = c + "new"
    data = data.assign(name = data[c] / data.num_words)

For obvious reasons, python doesn't allow an expression left of an = and ignores the previous value of name.

How can I use a variable for the name of the column I am assigning to? The loop iterates far more times than I'm willing to copy/paste.

回答1:

This can be interpreted as a Python language question:

Question: How do I use a variable's value as the name in a keyword argument?

Answer: Use a dictionary and ** unpacking

c = 'name'
f(c=5)       # 'c' is used as the keyword argument name, not what we want
f(**{c: 5})  # 'name' is used as the keyword argument name, this is great

Dask.dataframe solution

For your particular question I recommend the following:

d = {col: df[col] / df['num_words'] for col in percent_cols}
df = df.assign(**d)

Consider doing this with Pandas as well

The .assign method is available in Pandas as well and may be faster than using .apply.

来源：https://stackoverflow.com/questions/33557022/variable-column-name-in-dask-assign-or-apply

标签

python

pandas

dask