If True, then a new Series would be returned. Modifying this Series would not mutate the original data and vice versa.
If False, then the returned Series uses the same block of memory occupied by data. This means that modifying the Series would mutate the original data and vice versa.

copy=False only takes effect if data is a Numpy array or another Series. By default, copy=True.

Return Value

A Series object.

Examples

Using an array

To initialise a Series using an array:


        
        
            
                
                
                    s = pd.Series([3,4,5])
s
                
            
            0   3
1   4
2   5
dtype: int64

Since we did not specify index, the default integer index was used ([0,1,2]).

Using a dictionary

To initialise a Series using a map:


        
        
            
                
                
                    my_data = {"A":3, "B":4, "C":5}
s = pd.Series(my_data)
s
                
            
            A   3
B   4
C   5
dtype: int64

Here, notice how the key of the dictionary became the index of the Series.

Specifying index

To set the index of the Series, pass in the index parameter:


        
        
            
                
                
                    s = pd.Series([3,4,5], index=["A","B","C"])
s
                
            
            A    3
B    4
C    5
dtype: int64

Specifying dtype

To enforce a data type on the Series:


        
        
            
                
                
                    s = pd.Series(["3","4","5"], dtype="int")
s
                
            
            0   3
1   4
2   5
dtype: int64

Specifying name

To assign a name to the Series, pass in the name parameter:


        
        
            
                
                
                    s = pd.Series([3,4,5], name="my_series")
s
                
            
            0   3
1   4
2   5
Name: my_series, dtype: int64

Specifying copy

By default, copy=True, which means that a new block of memory is allocated for the constructed Series. Modifying this Series will therefore not mutate the original data and vice versa.

Instead of creating a new copy of data, we can initialise a Series that uses the same memory block occupied by data:


        
        
            
                
                
                    s1 = pd.Series([3,4])
s2 = pd.Series(s1, copy=False)
s2
                
            
            0    3
1    4
dtype: int64

Here, we are initialising a Series s2 from Series s1. The key is that these Series share the same block of memory for their data, which means that modifying one would mutate the other:


        
        
            
                
                
                    s1[0] = 9
s2
                
            
            0    9
1    4
dtype: int64