Python Pandas - Series

Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index.

pandas.Series

A pandas Series can be created using the following constructor −

pandas.Series( data, index, dtype, copy)

The parameters of the constructor are as follows −

A series can be created using various inputs like −

• Array
• Dict
• Scalar value or constant

Create an Empty Series

A basic series, which can be created is an Empty Series.

Example

#import the pandas library and aliasing as pd
import pandas as pd
s = pd.Series()
print s

Its output is as follows −

Series([], dtype: float64)

Create a Series from ndarray

If data is an ndarray, then index passed must be of the same length. If no index is passed, then by default index will be range(n) where n is array length, i.e., [0,1,2,3…. range(len(array))-1].

Example 1

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
print s

Its output is as follows −

0   a
1   b
2   c
3   d
dtype: object

We did not pass any index, so by default, it assigned the indexes ranging from 0 to len(data)-1, i.e., 0 to 3.

Example 2

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[100,101,102,103])
print s

Its output is as follows −

100  a
101  b
102  c
103  d
dtype: object

We passed the index values here. Now we can see the customized indexed values in the output.

Create a Series from dict

A dict can be passed as input and if no index is specified, then the dictionary keys are taken in a sorted order to construct index. If index is passed, the values in data corresponding to the labels in the index will be pulled out.

Example 1

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data)
print s

Its output is as follows −

a 0.0
b 1.0
c 2.0
dtype: float64

Observe − Dictionary keys are used to construct index.

Example 2

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data,index=['b','c','d','a'])
print s

Its output is as follows −

b 1.0
c 2.0
d NaN
a 0.0
dtype: float64

Observe − Index order is persisted and the missing element is filled with NaN (Not a Number).

Create a Series from Scalar

If data is a scalar value, an index must be provided. The value will be repeated to match the length of index

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
s = pd.Series(5, index=[0, 1, 2, 3])
print s

Its output is as follows −

0  5
1  5
2  5
3  5
dtype: int64

Accessing Data from Series with Position

Data in the series can be accessed similar to that in an ndarray.

Example 1

Retrieve the first element. As we already know, the counting starts from zero for the array, which means the first element is stored at zero^th position and so on.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve the first element
print s[0]

Its output is as follows −

1

Example 2

Retrieve the first three elements in the Series. If a : is inserted in front of it, all items from that index onwards will be extracted. If two parameters (with : between them) is used, items between the two indexes (not including the stop index)

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve the first three element
print s[:3]

Its output is as follows −

a  1
b  2
c  3
dtype: int64

Example 3

Retrieve the last three elements.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve the last three element
print s[-3:]

Its output is as follows −

c  3
d  4
e  5
dtype: int64

Retrieve Data Using Label (Index)

A Series is like a fixed-size dict in that you can get and set values by index label.

Example 1

Retrieve a single element using index label value.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve a single element
print s['a']

Its output is as follows −

1

Example 2

Retrieve multiple elements using a list of index label values.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve multiple elements
print s[['a','c','d']]

Its output is as follows −

a  1
c  3
d  4
dtype: int64

Example 3

If a label is not contained, an exception is raised.

import pandas as pd
s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])

#retrieve multiple elements
print s['f']

Its output is as follows −

…
KeyError: 'f'