The 'Series' object¶

A Series object is a one-dimensional array (similar to that of numpy ndarray) but with a significant addition: it includes a data label index (even if one is not explicitly specified).

Example:¶

Suppose that your data are country popoulation and you want to index it by the country two-letter code. The Series object for this data representation is the following:
- Population data in one column
- Label indexes (country code) in another

Series

Constructing a Series object¶

Constructing a Series from a list¶

Most of the times you will populate Series objects by reading data from external files. However, it is useful to understand that a Series object can be constructed by calling the Series() constructor and passing an array-like object as argument. The simplest case is to pass a list argument.

import pandas as pd

s = pd.Series([10,20,30,40,50], dtype='int')
print(s)
print(s.index, s.values)

0    10
1    20
2    30
3    40
4    50
dtype: int32
Int64Index([0, 1, 2, 3, 4], dtype='int64') [10 20 30 40 50]

In this example, 's' is constructed as a Series object by passing a list to the Series constructor (the syntax is similar to the construction of a numpy array).
As we did not define any specific index, sequential integers starting from zero (the integers 0..4 in the example) are automatically inserted as indexes in the constructed Series object.
See also that we can refer to indexes and values by calling s.index and s.values respectively

Constructing a Series from an array¶

Similar to the above example but with a numpy array instead of a list.

import numpy as np
import pandas as pd

s = pd.Series(np.array([chr(i) for i in range(65,70)]))
print(s)
print(s.index, s.values)

0    A
1    B
2    C
3    D
4    E
dtype: object
Int64Index([0, 1, 2, 3, 4], dtype='int64') ['A' 'B' 'C' 'D' 'E']

Constructing a Series from a dictionary¶

You can think of a Series as a dictionary, that is, an object mapping the indexes on data values.
Furthermore, you can construct a Series object by passing a dictionary as argument to the Series() constructor.
When a dictionary is passed to the Series() constructor then:
- dict keys become Series indexes
- dict values become Series values

import numpy as np
import pandas as pd

data = {'a':1,
        'b':2,
        'c':3}
s = pd.Series(data)
print(s)

# and vice-versa: you get back your dictionary by passing a Series to the dict() constructor
d = dict(s)
d

a    1
b    2
c    3
dtype: int64

{'a': 1, 'b': 2, 'c': 3}

Another example of constructing a Series from a dictionary

import numpy as np
import pandas as pd

# Data: Unemployment percentages in various countries
un_data = {'Greece':27,
           'Spain':21,
           'Italy':20}
uns = pd.Series(un_data)     # uns is constructed as a Series object based on un_data dictionary
print(uns, '\n')

Greece    27
Italy     20
Spain     21
dtype: int64

Accessing Series values¶

What you already know about accessing values using integer indexes (as in lists) or string indexes (as in dictionaries) holds true for accessing Series data, as the examples below demonstrate.
However, accessing data in a Series or DataFrame object is a bit more complex and we thoroughly analyze it in next sections.

import numpy as np
import pandas as pd

un_data = {'Greece':27,
           'Spain':21,
           'Italy':20}
uns = pd.Series(un_data)     

print(uns['Greece'])
print(uns['Greece']+uns['Spain'])

uns['Greece'] = 30
print(uns['Greece'])

27
48
30

import numpy as np
import pandas as pd

s = pd.Series(np.array([chr(i) for i in range(65,70)]))
print(s)

print(s[0], s[3]+s[4])
s[0] = 'NEW'
s[1] = 'OLD'
print(s)

0    A
1    B
2    C
3    D
4    E
dtype: object
A DE
0    NEW
1    OLD
2      C
3      D
4      E
dtype: object

Series supports vectorized operations¶

A Series (just like ndarrays) is an object supporting vectorized operations

import numpy as np
import pandas as pd

s = pd.Series([10,20,30,40,50])
print(s)

print(s[s>30])       # printing only Series values > 30 

print(s*2)           # multiplies all Series data by 2

print(np.sqrt(s))    # computes the square root of all Series data

0    10
1    20
2    30
3    40
4    50
dtype: int64
3    40
4    50
dtype: int64
0     20
1     40
2     60
3     80
4    100
dtype: int64
0    3.162278
1    4.472136
2    5.477226
3    6.324555
4    7.071068
dtype: float64

Copyright¶

. Free learning material
. See full copyright and disclaimer notice