Today in this article we are going to learn about the Pandas Series Attributes. The series object is similar to the data frame object that we have in the Pandas library. The series object has many attributes that help us to determine the different data associated with the series. In this, we have the size, dimensions, values, etc.

So we will learn about all these series attributes and we will see examples of each attribute to understand how they work. Let us begin with our tutorial:

### Example : Create a series object

We will start by creating a Pandas series object and then we will see the different attributes that this object has built-in.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series object is : ",ser)
```

**Output**

```
The Series object is : 0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 NaN
dtype: float64
```

### Series.index

The series. index (axis labels) attribute of the Series gives us the information about the start index and end index of the series object. It also tells us about the step size of this series.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series index is : ",ser.index)
```

Output

```
The Series index is : RangeIndex(start=0, stop=6, step=1)
```

### Series.array

The Series. array attribute gives us the Extension Array of the data backing this Series or Index. Along with the array it also gives us the details about the length and data type of the elements it holds.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series array is : ",ser.array)
```

Output

```
The Series array is : <PandasArray>
[1.0, 2.0, 3.0, 4.0, 5.0, nan]
Length: 6, dtype: float64
```

### Series.values

Return Series as ndarray or ndarray-like depending on the dtype.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series values are : ",ser.values)
```

Output

```
The Series values are : [ 1. 2. 3. 4. 5. nan]
```

### Series.dtype

Return the dtype object of the underlying data.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series dtype is : ",ser.dtype)
```

Output

```
The Series dtype is : float64
```

### Series.shape

Return a tuple of the shape of the underlying data.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series shape is : ",ser.shape)
```

Output

```
The Series shape is : (6,)
```

### Series.nbytes

Return the number of bytes in the underlying data.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series nbytes is : ",ser.nbytes)
```

Output

```
The Series nbytes is : 48
```

### Series.ndim

The number of dimensions of the underlying data, by definition 1.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series ndim is : ",ser.ndim)
```

Output

```
The Series ndim is : 1
```

### Series.size

Return the number of elements in the underlying data.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series size is : ",ser.size)
```

Output

```
The Series size is : 6
```

### Series.T

Return the transpose, which is by definition self.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series Transpose is : ",ser.T)
```

Output

```
The Series Transpose is : 0 1.0
1 2.0
2 3.0
3 4.0
4 5.0
5 NaN
dtype: float64
```

### Series.hasnans

Return the memory usage of the Series.**Series.memory_usage([index, deep])**.Return if I have any nans; enables various perf speedups.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series has nans : ",ser.hasnans)
```

Output

```
The Series has nans : True
```

### Series.empty

Indicator whether DataFrame is empty.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
print("The Series is empty: ",ser.empty)
```

Output

```
The Series is empty: False
```

### Series.dtypes

Return the dtype object of the underlying data.

```
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3, 4, 5, np.nan])
#Indicator whether DataFrame is empty.
print("The Series dtypes are : ",ser.dtypes)
```

Output

```
The Series dtypes are : float64
```

### Series.name

Return the name of the Series.

```
import pandas as pd
import numpy as np
s = pd.Series([1, 2, 3], name='MySeries')
print("The Series name is : ",s.name)
```

Output

```
The Series name is : MySeries
```