In this article, Data handling using Pandas-I you will learn about Python Pandas data structure series.
Introduction to Python Libraries
Python libraries are in-built python modules which allows to perform system related operations, IO operations, data analysis and some other standard operations. Pandas library is used for data analysis.
Introduction to Data handling using Pandas-I
Pandas word derived from PANel Data System. It becomes popular for data analysis. It provides highly optimized performance with back end source code is purely written in C or Python. It makes a simple and easy process for data analysis.
Pandas offers two basic data structures:
To work with pandas import pandas library and create one object like this:
import pandas as pd
Data handling using Pandas-I Series
Series is an important data structure of pandas. It represents one dimensional array, containing an array of data. It can any type of NumPy data. Basically series has two main components:
- An Array
- An index associated with array
Task 1 Creating Series
Series() function is used to create a series in Pandas.
import pandas as pd ser1=pd.Series()
An empty panda series has float64 data type.
Creating non-empty series
In non-empty series data and index will be supplied while creating series. Here data can be one of these data types:
- A python sequence
- An ndarray
- A dictionary
- A scalar value
Creating series with a python sequence (Data handling using Pandas-I)
Range function is used to generate a series with python pandas.
In the above screenshot, a series is created with float numbers.
Creating Series with ndarray (Data handling using Pandas-I)
Creating series from ndarray named nda. Array of odd number between 1 to created through range.
Creating series with dictionary
Crating series from Dictionary object and stored first three days of week in series.
Creating series with scalar value
Series created with scalar value 5.
Task 2 Specifying NaN values in the series
Specified NaN at the index 1.
Task 3 creating series and specifying index
In the above example, two lists created for train numbers and train names. Train no list assigned as data and train name assigned as indexes.
Task 4 Creating series using arithmetic operation
In this example, series is created with a * 3 as data.
Data handling using Pandas-I Common Series attributes
|Series.index||Retrieves index of a series|
|Series.values||Return series as ndarray|
|Series.dtype||Return data type of series|
|Series.shape||Return tuples (no.of rows) of the shape|
|Series.nbytes||Return no. of bytes|
|Series.ndim||Return no. of dimension|
|Series.size||Return no. of elements|
|Series.hasnans||Return true is there are any NaN value else false|
|Series.empty||Return true if the series is empty, else false|
Task 5 Common series attribute Example
Task 6 Accessing elements from series
In above screenshot, I have accessed element by using its index value such as ser and ser. For accessing all the values using indexes you can use for loop.
Task 7 Modifying series elements
In above code, I have changed the element value with a scalar value. In python, series objects are value mutable i.e. values can be changed but size immutable i.e. can’t be changed.
Task 8 Slicing in Series (Data handling using Pandas-I)
Task 9 head() and tail() function in series (Data handling using Pandas-I)
The head() function displays n number of elements from the top in the series. In the above example, I have accessed top 3 elements. If no value is passed in the parameter then by default it will display 5 elements from the top. Similarly, the tail function will work and display n number of elements from the bottom.
Task 10 Vector and arithmetic operations on series
Here I have used different vector operations and operators to perform various tasks on series.
Task 11 reindex() and drop() methods – (Data handling using Pandas-I)
reindex() : Create a similar object but with a different order of same indexes.
drop(): Remove any entry from series.
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