Identifying patterns in DataFrames using Data-Pattern Module

Prerequisites: Pandas Module, Pandas Data frame

Pandas is an open-source library that is built on top of NumPy library. It is a Python package that offers various data structures and operations for manipulating numerical data and time series. It is mainly popular for importing and analyzing data much easier. Pandas are fast and it has high-performance & productivity for users.

Data Frame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns) in Pandas. A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas data frame consists of three principal components, the data, rows, and columns.

Data Pattern module, In order to find the simple data patterns in the data frame we will use the data-patterns module in python, this module is used for generating and evaluating patterns in structured datasets and exporting to Excel and JSON and transforming generated patterns into Pandas code. 

Installation:

pip install data-patterns

Step-by-step Approach:

Import required modules.

Assign data frame.

Create pattern-mixer object with the data frame as a constructor argument.

Call find() method of the pattern-mixer object to identify various patterns in the data frame.

Implementation:

Below are some programs based on the above approach:

a

Output:

The data items value4 and value5 are having equal patterns with support of 9 and 1 exceptions.
Also, this data can be analyzed in proper format with the help of analyze() method, below is the improved program: 

Python3

# importing the data_patterns module
import data_patterns


# importing the pandas module
import pandas as pd


# creating a pandas dataframe
df = pd.DataFrame(columns=['Name', 'Grade', 'value1',
                           'Value2', 'Value3', 'Value4', 'value5'],
                  data=[['Alpha', 'A', 1000, 800, 0, 200, 200],
                        ['Beta', 'B', 4000, 0, 3200, 800, 800],
                        ['Gamma', 'A', 800, 0, 700, 100, 100],
                        ['Theta', 'B', 2500, 1800, 0, 700, 700],
                        ['Ceta', 'C', 2100, 0, 2200, 200, 200],
                        ['Sayian', 'C', 9000, 8800, 0, 200, 200],
                        ['SSai', 'A', 9000, 0, 8800, 200, 200],
                        ['SSay', 'A', 9000, 8800, 0, 200, 200],
                        ['Geeks', 'A', 9000, 0, 8800, 200, 200],
                        ['SsBlue', 'B', 9000, 0, 8800, 200, 19]])

# setting datag=frame index
df.set_index('Name', inplace=True)


# creating a pattern mixer object
miner = data_patterns.PatternMiner(df)


# finding the pattern in the dataframe
# name is optional
# other patterns which can be used  ‘>’, ‘<’, ‘<=’, ‘>=’, ‘!=’, ‘sum’
df_patterns = miner.find({'name': 'equal values',
                          'pattern': '=',
                          'parameters': {"min_confidence": 0.5,
                                         "min_support": 2,
                                         "decimal": 8}})


# getting the analyzed dataframe
df_results = miner.analyze(df)


# printing the analyzed results
print(df_results)

Output:

As we can see here, various patterns are identified between different data items present in the data frame.