Index.astype()
Pandas Index.astype()
creates an index with values cast to dtypes ... The class of the new index is determined by the dtype. When conversion is not possible, a ValueError is thrown.
Syntax: Index.astype (dtype, copy = True)
Parameters:
dtype: numpy dtype or pandas type
copy: By default, astype always returns a newly allocated object. If copy is set to False and internal requirements on dtype are satisfied, the original data is used to create a new Index or the original Index is returned.
Example # 1: Use Index.astype ()
to change the data type of an index from floating to integer.
# import pandas as pd import pandas as pd # Create index df = pd.Index ([ 17.3 , 69.221 , 33.1 , 15.5 , co de> 19.3 , 74.8 , 10 , 5.5 ]) print ( "Dtype before applying function:" , df) print ( "After applying astype function:" ) # Convert df data type to int64 df.astype ( ’int64’ ) |
Output:
< p>
Example # 2: Use
Index.astype ()
to change the data type of this index to string form.
# import pandas as pd import pandas as pd # Create index df = pd.Index ([ 17.3 , 69.221 , 33.1 , 15.5 , 19.3 , 74.8 , 10 , 5.5 ]) print ( "Dtype before applying function:" , df) print ( "After applying astype function:" ) # Convert datatype df to int64 df.astype ( ’str’ ) |
Output:
Example # 3: Let’s do something interesting with index.astype ()
.
Observe this DataFrame.
Setting the Number column as index.
# pandas module import import pandas as pd # read CSV file from URL data = pd.read_csv ( " https://media.python.engineering/ wp-content / uploads / nba.csv " ) # remove null columns to avoid errors data.drop na (inplace = True ) # Setting the column number as index data = data.set_index ( ’Number’ ) # Installation index as None data.index.names = [ None ] data. head ( 5 ) |
table>
Output:
Now let’s convert the index to an integer h islo.
# applying astype to index data.index.astype ( ’int64’ ) |
table>
Output:
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