Set value for particular cell in pandas DataFrame using index Set value for particular cell in pandas DataFrame using index python python

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.


matomo