Replace value for a selected cell in pandas DataFrame without using index

Question

this is a rather similar question to this question but with one key difference: I\'m selecting the data I want to change not by its index but by some criteria.

If th

Answer 1

Old question, but I'm surprised nobody mentioned numpy's .where() functionality (which can be called directly from the pandas module).

In this case the code would be:

d.sales = pd.np.where(d.sales == 24, 100, d.sales)

To my knowledge, this is one of the fastest ways to conditionally change data across a series.

Answer 2

Many ways to do that

1

In [7]: d.sales[d.sales==24] = 100

In [8]: d
Out[8]: 
   day     flavour  sales  year
0  sat  strawberry     10  2008
1  sun  strawberry     12  2008
2  sat      banana     22  2008
3  sun      banana     23  2008
4  sat  strawberry     11  2009
5  sun  strawberry     13  2009
6  sat      banana     23  2009
7  sun      banana    100  2009

2

In [26]: d.loc[d.sales == 12, 'sales'] = 99

In [27]: d
Out[27]: 
   day     flavour  sales  year
0  sat  strawberry     10  2008
1  sun  strawberry     99  2008
2  sat      banana     22  2008
3  sun      banana     23  2008
4  sat  strawberry     11  2009
5  sun  strawberry     13  2009
6  sat      banana     23  2009
7  sun      banana    100  2009

3

In [28]: d.sales = d.sales.replace(23, 24)

In [29]: d
Out[29]: 
   day     flavour  sales  year
0  sat  strawberry     10  2008
1  sun  strawberry     99  2008
2  sat      banana     22  2008
3  sun      banana     24  2008
4  sat  strawberry     11  2009
5  sun  strawberry     13  2009
6  sat      banana     24  2009
7  sun      banana    100  2009

Answer 3

Not sure about older version of pandas, but in 0.16 the value of a particular cell can be set based on multiple column values.

Extending the answer provided by @waitingkuo, the same operation can also be done based on values of multiple columns.

d.loc[(d.day== 'sun') & (d.flavour== 'banana') & (d.year== 2009),'sales'] = 100