update() method
In Pandas.update()method is used to update the value in one DataFrame or Series object to the corresponding value in another DataFrame or Series object.
This method can be used to update data in place without creating a new object.
update()method has several parameters, the most important of which is theotherparameter, which specifies another DataFrame or Series object to use to update the current object.
When calling theupdate()method, it will set theotherobject replaces the value at the corresponding position in the current object.
Here is the basic syntax of the update() method:
(other, overwrite=True, filter_func=None, errors='raise')
- other: another DataFrame or Series object to be used to update the current object.
- overwrite: a boolean value specifying whether to overwrite the value in the current object. The default is True, which means to completely replace the value in the current object with the value in the other object; if it is set to False, only the NaN value will be replaced.
- filter_func: a callable object that filters the values to be updated. Only values that return True will be updated.
- errors: specify how to handle errors. The default is 'raise', which means that if an error occurs during the update process, an exception will be thrown; if it is set to 'ignore', the error will be ignored and execution will continue.
Note that the update() method modifies the current object in-place without returning a new object. This is different from the behavior of many Pandas methods, which typically return a new object. So before using the update() method, make sure that you have made a proper backup of your data or that there is no need to destroy the original data.
Let's start with the need to update, our data is as follows:
We want to match the following data to the original data:
If you use it directly, see what the result is:
(df1) df
All cells will be replaced unless our new DF is empty, and the update() method inline changes the original data instead of creating a copy.
overwrite parameter
All cells are replaced except for null values, at this point because .update() just assumes that the new data is more relevant. If you only want to replace missing values, you can set the parameter ' overwrite = False '
(df1,overwrite=False) df
filter_func parameters
It is also possible to update cells other than null by using the 'filter_func ' parameter. For example replace only even values.
(df1,filter_func=lambda x : x%2==0) df
You can see that only the values that meet the judgment conditions are updated. update() method can be convenient to update the value of a DataFrame or Series object to the corresponding value in another DataFrame or Series object, but we rarely use it. So the update method in pandas is a very useful tool when dealing with missing or expired data updates. However, it should be noted that before using the update() method, you need to have a proper backup of the data or make sure that there is no need to destroy the original data, because he will directly modify our DF.
to this article on the pandas.update() method analysis of the article is introduced to this, more related pandas.update() method content please search my previous posts or continue to browse the following related articles I hope you will support me in the future!