Simultaneous Replacement
The method described in the previous chapter allows you to replace specific values in one column 'manually'. But we need to perform replacements in 4 columns, which means we need to repeat the actions at least 3 more times.
However, pandas
predicted that task, too. Let's consider the method that allows to perform replacement for all dataframe columns.
Explanation: condition
is the first parameter, if True
, then keeps original values, if False
, then replaces them by values specified in the other
parameter. inplace
- if True
, then rewrites the data. If you want to 'revert' the condition to opposite, place the ~
symbol in front of it. For instance, let's replace all the zeros with the word null
.
As you can see, there are many 'null'
s appeared in the dataframe. If you remove the ~
symbol within the .where()
method, then all values but 0
will be replaced to 'null'
.
Everything was clear?
Course Content
Data Manipulation using pandas
Data Manipulation using pandas
2. Preprocessing Data: Part II
Simultaneous Replacement
The method described in the previous chapter allows you to replace specific values in one column 'manually'. But we need to perform replacements in 4 columns, which means we need to repeat the actions at least 3 more times.
However, pandas
predicted that task, too. Let's consider the method that allows to perform replacement for all dataframe columns.
Explanation: condition
is the first parameter, if True
, then keeps original values, if False
, then replaces them by values specified in the other
parameter. inplace
- if True
, then rewrites the data. If you want to 'revert' the condition to opposite, place the ~
symbol in front of it. For instance, let's replace all the zeros with the word null
.
As you can see, there are many 'null'
s appeared in the dataframe. If you remove the ~
symbol within the .where()
method, then all values but 0
will be replaced to 'null'
.
Everything was clear?