Introduction to Pandas
The Python library to do the mathematical operations in a flexible manner is called Pandas library. This is an open-source library used in data analysis and also in data manipulation so that data scientists can retrieve information from the data. It has a BSD license, and the number tables are manipulated easily. It is written in Python, Cython, and C language. It is open-source software and has high performance when compared with other libraries. The library provides tables and not arrays for memory retrieval. The data structures are easy to use.
Why do people consider python?
1) programmer friendliness and easy to understand
2) Extensive support libraries
3) Good flexibility and component Integration (Can be combined easily with applications and tools)
4) platform portability
5) Opensource availability Etc……..
Work areas of python?
1) System programming (Scripting face of python)
2) Build GUI’s (Ex: thinner)
3) Web Design
4) Database programming
5) Scientific programming (Ex: For Analytics)
6) Gaming, Image processing, Robotics Etc.
Role of Pandas in Python
Pandas is an open-source setup for a python programming language and a python library licensed by which offers high-performance data analysis tools and easy-to-use data structures for the Python programming language.
For achieving profound performance in data manipulation functions and analysis, segment Pandas was introduced by developer Mckinney as a part of python. Being an open-source library. here the abbreviation of pandas is as below
Pandas ==> Pan (Panel) + Das (Data)
Preparing the data and munging the same was the initial outcomes of python before the introduction of Panda libraries. after the introduction of panda libraries, python began to flourish a lot in the analytics sector. The major outcomes of the panda are:
1) analysis of data
2) preparation of data
3) data manipulation
4) data modeling
5) data analysis
The major fields in which Python with Pandas is used are as below,
1) Finance
2) economics
3) analytics etc
Pandas package installation
1) Open Installed anaconda prompt
2) Use the below command for package installation
pip install <packagename>
Ex: pip install pandas
3) Now, we can import the installed package into your program
Understanding Pandas
The key data structures in pandas are as below:
1. Series: One-dimensional data structure is immutable by size.
Ex :
| 10 | 23 | 56 | 17 | 52 | 61 | 73 | 90 | 26 | 72 |
Parameters :
| Parameter | Description |
| data | Constants, lists, and ndarrays |
| Index | Unique values which act as index representation |
| dtype | Represents the data type |
| copy | Copy data. false by default |
Sample Code Snippet :
import pandas as PD
import numpy as np
Test_data = np.array(['a','b','c','d'])
Sample = PD.Series(Test_data)
print sample
2. Dataframe: An array that is heterogeneous and two-dimensional in format.
Ex :
| Name | Age | Gender | Rating |
| Steve | 32 | Male | 3.45 |
| Lia | 28 | Female | 4.6 |
| Vin | 45 | Male | 3.9 |
| Katie | 38 | Female | 2.78 |
Parameters :
| Parameter | Description |
| Data | Ndarrays, series, maps, list |
| Index | Unique values which act as index representation |
| Columns | Labels for columns |
| dtype | Data type values |
| copy | Used to copy data |
Sample Code snippet :
import pandas as PD
data = [[ ' Alex ' , 10 ],[ ' Bob ', 12 ],[ ' Clarke ' , 13 ]]
df = PD.DataFrame(data,columns=[' Name ' , ' Age '])
print df
3. Panel: It is a heterogeneous data structure that is three-dimensional in format. Which handles data in panels.
Parameters :
| Parameter | Description |
| data | Data takes various forms like ndarray, series, map, lists, dict, constants, and also another DataFrame |
| items | axis=0 |
| major_axis | axis=1 |
| minor_axis | axis=2 |
| dtype | A data type of each column |
| copy | Copy data. Default, false |
Sample Code snippet :
import pandas as PD
import numpy as np
data = {'Item1' : PD.DataFrame(np.random.randn(4, 3)),
'Item2' : PD.DataFrame(np.random.randn(4, 2))}
p = PD.Panel(data)
print
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Pandas Advantages
1) Customizable indexed data frame objects.
2) Various tools to support data load into data objects irrespective of their file formats.
3) Data alignment in an efficient manner.
4) Pivot dataset.
5) Reshape datasets.
6) Label-oriented slicing.
7) Data Indexing and subsetting higher volume dataset.
8) Merging high-performance datasets in an efficient manner
9) Time series-functionality
Required Python Pandas Skills
1. Knowledge of python web
2. ORM and related libraries familiarity
3. Database integration
4. Problem-solving ability
5. Capability to effectively organize code
The audience for Python pandas
- Audience with interest to learn Python.
- Individual who aspire to become Python Architect, developer, Analyst, Tester also relative professional roles.
- It helps to move forward the professional aspects and technical skillset of professionals who are intended to do the same.
- Python application development interested candidates.
- People who are interested to learn analytics and get expertise in this field.
Conclusion
Definitely, Python is one of the most versatile and stable languages in over a decade. In this extremely stable programmatic setup, panda library programs hold a great role in boosting the data-related aspects of this widely spread language. All the major data handling-related needs of this flexible language is nicely addressed in pandas setup.
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