Pandas is a library in python. It is an open source package in python. Pandas in python are used for data cleaning and data analysis. Data frame mainly consists of data, rows and columns.
Pandas package is built on the top of numpy. Pandas are used to handle messy data in an easy manner and it is one of the best tools to handle messy data.
The word pandas is derived from “panel data “.
We can install the python package using the following command that is “pip installs pandas “.
Before the installation of pandas we have to check the version of python.
We will store all the data in many formats like CSV, Excel, SQL etc. We will access the given data using the above formats.
Apply ( ):
Apply ( ) is a function that is used in pandas. It is used to allow the user to pass the function and we apply the function on each and every single value of the panda series.
apply ( ) is used to apply a function along the axis of a data frame. Axis here indicates the row or column.
It is an improvement for pandas library because this function helps us to separate the given data based upon the conditions required to which it is used in data science and machine learning.
Objects that are passed to the applied function are the series objects where the index is considered as either data frame’s index or it may consider as data frame’s columns.
Here the axis of data frame’s index is 0 and the axis of data frame’s columns is 1.
We can use apply ( ) function to each row or columns.
It takes the series of elements and after applying the function it also returns the series of elements. In apply ( ) method there is no inplace parameter.
DataFrame .apply ( self , func , axis = 0 , broadcast = None , raw = False , reduce = None , result_type = None , args = ( ) , **kwds )
Function: The parameter func is the function that is to be applied to each column or row or a data frame.
Default value in the func is function. It is the required field in the parameters of apply.
Axis: Axis is one of the parameter in the apply( ).
In axis we may consider 0 or 1.
Where 0 indicates index and 1 indicates the columns. Axis along which the function is applied is as shown below:
- 0 or ‘index’ is used to apply the function to each and every column.
- 1 or ‘columns’ is used to apply the function to each and every row.
Default value for the parameter axis is 0. It is also a required field in the parameters of apply.
Raw: raw is a parameter in the apply ( ) that is used to determine whether the column or row is passed as a series or as a ND array object.
It may consist of False and True as shown below:
- False: It passes each row and column as series to the applied function.
- True: The passed function is going to receive ND array objects instead of series objects.
- If we use numpy this function may give better performance.
Default value for the parameter raw is false. It is also one of the required field in the parameters of apply.
Result_type : result_type parameter in the apply ( ) is used to act only when the axis = 1 or columns expand ,reduce, broadcast as shown below :
- Expand: The list-like results are turned into columns.
- Reduce: It returns the series if possible rather than expanding list-like results. This is complete contrast of expand.
- Broadcast: It is used to broadcast to the actual shape of data frame so that the actual index and columns will be returned.
Default behavior of this parameter is none it depends on the applied function’s return value and the list-like results will be retained as series. It is also a required parameter in apply ( ).
Args ( ):args () is a parameter in the apply ( ) where we will use the tuple data type. These are the positional arguments that are passed to func in addition to series.
**kwds: It is an additional keyword arguments that are passed as keyword arguments to the func.
Returns: It returns the result of applying func along the given axis of the data frame or series.