Pandas: Exploring Data using Series

Data Structures

• In Pandas, there are two important data structures that are used –

1) Series and

2) Data frames.

Let us discuss them in detail

Exploring Data using Series

• The Pandas series is like a column in a table. It is basically a one dimensional array holding data of any type.

Using the pd.Series function the series can be created. For example ‒

In [3]: import pandas as pd

a = [2,4,6,8,10]

x = pd.series(a)

print(x)

0     2

1     4

2      6

3      8

4      10

dtype: int64

Note that the values of the series are displayed along with the index number. The index number starts with 0.

Syntax of series

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

Parameters:

• data(required): This is the input data, which can be any list, dictionary, etc.

• index(Optional) : The index for the value you use for the series is represented by this number.

• dtype(Optional): This describes the values contained in the series.

• copy(Optional): This makes a copy of the input data.

Creating labels

We can create labels to the elements of the series instead of index numbers. For example

In [4]: import pandas as pd

a = [2,4,6,8,10]

x = pd.series(a,index = ["a","b","c","d","e"])

print(x)

a    2

b    4

c     6

d     8

e    10

dtype: int64

Accessing specific element of series

We can access particular element of the series as follows ‒

In [7]: print(x[3])

8

or

In [6]: print(x["d"])

8

Change in data type of the elements

We can change the data type of the series elements. It can be done as follows ‒

In [8]: import pandas as pd

a = [2,4,6,8,10]

x = pd.Series(a,index = ["a","b","c","d","e"], dtype=float)

print(x)

a     2.0

b     4.0

c     6.0

d     8.0

e     10.0

dtype: float64