Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas Index.copy() function make a copy of this object. The function also sets the name and dtype attribute of the new object as that of original object. If we wish to have a different datatype for the new object then we can do that by setting the dtype attribute of the function.
Syntax: Index.copy(name=None, deep=False, dtype=None, **kwargs)
Parameters :
name : string, optional
deep : boolean, default False
dtype : numpy dtype or pandas typeReturns : copy : Index
Note : In most cases, there should be no functional difference from using deep, but if deep is passed it will attempt to deepcopy.
Example #1: Use Index.copy() function to copy the Index value to a new object and change the datatype of new object to ‘int64’
# importing pandas as pd import pandas as pd
# Creating the Index idx = pd.Index([17.3, 69.221, 33.1, 15.5, 19.3, 74.8, 10, 5.5])
# Print the Index idx |
Output :
Let’s create a copy of the object having ‘int64’ data type.
# Change the data type of newly # created object to 'int64' idx.copy(dtype ='int64')
|
Output :
As we can see in the output, the function has returned a copy of the original Index with ‘int64’ dtype.
Example #2: Use Index.copy() function to make a copy of the original object. Also set the name attribute of the new object and convert the string dtype into ‘datetime’ type.
# importing pandas as pd import pandas as pd
# Creating the Index idx = pd.Index(['2015-10-31', '2015-12-02', '2016-01-03',
'2016-02-08', '2017-05-05'])
# Print the Index idx |
Output :
Let’s make a copy of the original object.
# to make copy and set data type in the datetime format. idx_copy = idx.copy(dtype ='datetime64')
# Print the newly created object idx_copy |
Output :
As we can see in the output, the new object has the data in datetime format and its name attribute has also been set.