大熊猫遍历每一行

Question

There have been no questions which addressed this specific point:

I want to iterate through the rows of a dataframe. Specifically, within each row, I would like to call by column name. Is there a way to do this? If so, please demonstrate.

I am familiar with the df[<column_name>][<index_name>] , but I don't think this addresses things. Perhaps I can mix this with the transpose function, but then I lose track of datatypes, right?

For example, if we have

    a b c d
 i1 1 1 2 1
 i2 2 2 1 1

I want to be able to say:

for f in some_iterator():
    print 'a is ' str(f['a'])
    print f['b'] + f['c']
    #skip f['d']

But as it stands, I can't depend on the column names, in this case, "a,b,c,d" to do this.

Answer 1

Let df be a pandas.DataFrame object.

We can iterate through the rows by:

for row in df.iterrows():
    print str(row[0]) + " is the index of the row"
    print str(row[1]) + " is a series with rows having \
    labels the same as the columns of the dataframe"