更新变量中的熊猫数据框行

Question

I'm doing the following to update a dataframe row:

val = "123"
row = df.loc[id, :]
t = type(row['col1'])
val = t(val)
df.loc[id, 'col1'] = val

If I do row['col1'] = val, it doesn't update the original dataframe. Is there a way to update the original dataframe without using .loc twice?

Answer 1

Updated for comment below
I believe the short answer is no to

Is there someway of saying "change the row you've already fetched"

This is because operations in Pandas return a copy not a view to the original DataFrame. There is a StackOverflow post from JeffR who writes much of Pandas here

If you're trying to assign the val to one cell in the DataFrame you can use the set_value function. The documentation for that method is here .

val = "123"
row = df.loc[id, :]
t = type(row['col1'])
val = t(val)
df.set_value(id, 'col1', val)

If you're trying to assign the val with particular data type to all cells in column, can use the apply method

def changeType(cell, val):
    # You could pass the id or list of ids as well if you need to perform some logic with it
    t = type(cell)
    cell = t(val)
    return cell

val = "123"
df.loc[:, 'col1'] = df.loc[:, 'col1'].apply(changeType, args=(val,))