Set value for particular cell in pandas DataFrame using index

RukTech's answer, df.set_value('C', 'x', 10), is far and away faster than the options I've suggested below. However, it has been slated for deprecation.

Going forward, the recommended method is .iat/.at.

Why df.xs('C')['x']=10 does not work:

df.xs('C') by default, returns a new dataframe with a copy of the data, so

df.xs('C')['x']=10

modifies this new dataframe only.

df['x'] returns a view of the df dataframe, so

df['x']['C'] = 10

modifies df itself.

Warning: It is sometimes difficult to predict if an operation returns a copy or a view. For this reason the docs recommend avoiding assignments with "chained indexing".

So the recommended alternative is

df.at['C', 'x'] = 10

which does modify df.

In [18]: %timeit df.set_value('C', 'x', 10)100000 loops, best of 3: 2.9 µs per loopIn [20]: %timeit df['x']['C'] = 10100000 loops, best of 3: 6.31 µs per loopIn [81]: %timeit df.at['C', 'x'] = 10100000 loops, best of 3: 9.2 µs per loop

Update: The .set_value method is going to be deprecated. .iat/.at are good replacements, unfortunately pandas provides little documentation

The fastest way to do this is using set_value. This method is ~100 times faster than .ix method. For example:

df.set_value('C', 'x', 10)

You can also use a conditional lookup using .loc as seen here:

df.loc[df[<some_column_name>] == <condition>, [<another_column_name>]] = <value_to_add>

where <some_column_name is the column you want to check the <condition> variable against and <another_column_name> is the column you want to add to (can be a new column or one that already exists). <value_to_add> is the value you want to add to that column/row.

This example doesn't work precisely with the question at hand, but it might be useful for someone wants to add a specific value based on a condition.