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Python Pandas Tutorial Example | Python Data Analysis Library

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Python Pandas Tutorial Example | Python Data Analysis Library is today’s topic. Python has been great for data manipulation and preparation, but less so for data analysis and modeling. Pandas help fill this gap by enabling you to carry out your entire data analysis workflow in Python without having to switch to the more domain-specific language like R for data analysis. Pandas do not implement significant modeling functionality outside of linear and panel regression.

Key Features of Pandas

The key features of Pandas are following.

  1. Pandas library is fast and efficient DataFrame object with default and customized indexing.
  2. Pandas library helps for loading the data into in-memory data objects from different file formats.
  3. It has functions that deal with Data alignment and integrated handling of missing data.
  4. Using Pandas, we can reshape and pivot the data sets.
  5. It has Label-based slicing, indexing, and subsetting of more massive datasets.
  6. Pandas can insert or delete the Columns from the data structure.
  7. We can use Pandas for data aggregation and transformations.
  8. It gives the High-performance merging and joining of data.
  9. Time Series functionality.

Python Pandas Tutorial Example

Pandas is the Python package providing fast, reliable, flexible, and expressive data structures designed to make working with ‘relational’ or ‘labeled’ data both easy and intuitive way. 

Pandas aim to be the fundamental high-level building block for doing practical, real-world data modeling and analysis in Python Programming Language.

Install Pandas on Mac

Install Pandas, if you have not installed previously on your machine.

You can install via PyPI using the following command.

python3 -m pip install --upgrade pandas

If you want to upgrade version, then you can go for the following command.

python3 -m pip install --upgrade pandas==0.23.0

Make sure; you will install it with proper permission such as use sudo if you are on Linux or Mac.

Standard Python distribution does not come with the Pandas module. An alternative way is to install NumPy using popular Python package installer, pip.

If you have installed a software pack something like Anaconda then pandas already been installed.

Now, let’s test by the following example.

# app.py

import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
seri = pd.Series(data)
print(seri)

Go to the terminal and type the following command to run the file.

 

Python Pandas Tutorial Example | Python Data Analysis Library

If you will get the above output then congrats!!. You have installed the Pandas successfully in your machine.

Pandas Data Structure

Pandas deals with the following two data structures.

  1. DataFrames
  2. Series
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data is with a MultiIndex on a DataFrame, via the Panel.to_frame() method.

DataFrames in Pandas

DataFrames allow you to store and manipulate the tabular data in rows of observations and columns of variables. 

DataFrames in Python are very similar as they come with the Pandas library, and they are defined as two-dimensional labeled data structures with columns of potentially different types.

Features of DataFrame

  1. Potentially columns are of different types
  2. Size – Mutable
  3. Labeled axes (rows and columns)
  4. Can Perform Arithmetic operations on rows and columns

A pandas DataFrame can be created using the following constructor.

pandas.DataFrame( data, index, columns, dtype, copy)

Let’s see the DataFrame example.

# app.py

import pandas as pd
import numpy as np

data = [['Krunal', 21],['Rushikesh', 22],['Hardik',30]]
df = pd.DataFrame(data, columns=['Name', 'Enrollment Number'])
print(df)

Now, run the above file and see the output.

 

DataFrames Data Structure in Pandas

In the above example, we have taken the data which is Name and Enrollment Number. For that data, we have used the NumPy library.

Then, we have passed that data to the DataFrame and create a tabular data structure.

Series in Pandas

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

Labels need not be unique but must be a hashable type. The object supports both integer and label-based indexing and provides a host of methods for performing operations involving the index.

The syntax of Series in Pandas is following.

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

Let’s create a primary series.

# app.py

import pandas as pd

data = [1, 2, 3, 4, 5, 6, 7]
df = pd.Series(data)
print(df)

Run the file and see the output.

 

Series in Pandas

So, the basics of Pandas is over. Finally, Python Pandas Tutorial Example | Python Data Analysis Library article is over. Thanks for taking.

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