Data wrangling is the process that takes care of cleaning, restructuring, and enriching raw data extracted from the internet. The process is capable of turning or mapping out large amounts of data extracted from the internet into a more useful format, suitable for consumption and for analysis as the case may be. Data munging can also combine different types of data into indexed data sets that are searchable.

After data extraction is complete from the internet, data wrangling should be the next step to take. This is because the raw data you collect are complex and untidy. So what the process does is to sort and unify the data so that it can be accessed easily and translated.  Bad data among the data sets can be corrected or removed and the data is transformed into something usable and functional.

Doing this makes it easier for the non-technical personnel of the company, or those in charge of data collection to easily and quickly understand the data, and make informed decisions.

Every company that extracts data online should make it a point of duty to always carry out data-wrangling after completing the data extraction. E-commerce and travel industries for example collect price comparison data regularly. Doing this gives them proper understanding and vision to make the best decisions on the price to put on their products and services.

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Large amounts of raw data will contain in addition to the important data, other data that has no structure, and contains objects of no interest to the companies. This makes it unsuitable for use in analysis and planning. So data-wrangling helps companies and businesses act on data quickly. The ease that comes with the actionable insights is especially important is companies want to implement surge pricing or flexible pricing strategies. This allows for real-time adaptation to changing market conditions, and to follow suit with the actions of their competitors.

Data is being used in every organization and helps in the decision making process. Data that is understandable is the only type that is useful and that is why data wrangling comes in. it prepares the extracted data in a form that is useable and easy to analyze.

Data, as it exists on the internet, is unstructured and when extracted, could contain a lot of information that may not be important to the company. So without proper data preparation, projects that are dependent on the collected data could fail. Proper analysis and the making of decisions may take too long to be useful, the data may even be biased and you could read it wrongly leading to poor understanding and wrong decisions.

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Good time needs to be spent in cleaning and preparing data into a format where it can be effectively scrutinized and consumed. This is sometimes an issue because data is very important for every decision to be made and so business users have little time to spare while waiting for prepared data.

Visual aids and statistics used in reports and meetings all need organized and structured data to do the required analysis. Changing the form of data at your disposal into something indexed and searchable allows you to effectively learn from it, gather intelligence, and make the best decisions from accurate information gathered.

1 . HELPS WITH EASY ANALYSIS

Once data has been wrangled, data analysts and other stakeholders in your company will be able to easily analyze the complex data, quickly, and more efficiently.

2. MORE TIME FOR PRODUCTIVE PURPOSES

When you wrangle data, you organize it and this takes away the time that would have otherwise been used to sort out raw and scattered data for use. With more time on their hands, data scientists and other professionals will focus on other productive activities like data extraction and administrative functions. The analysts, stakeholders, and those whose jobs are not technical will also get quicker insights and make faster decisions based on their ease to understand and digest the data before them.

3. SIMPLICITY IN DATA MANAGEMENT

Raw and unstructured data from extraction are cleaned and transformed into useful and properly arranged data using rows and columns. This form of data is easier to manage, is more useful, and makes more meaning. Also due to the better format, data from all sources that are necessary can be put side by side to provide better information for better judgment.

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4. BETTER DATA VISUALIZATION

After cleaning up and wrangling extracted data, you can then export it to any platform you wish to view it with. It could be Microsoft Excel or any data visualization tool of your choice. This flexibility allows you better summarize your data, sort it, analyze it, and visualize it in any form you want.

5. INFORMED DECISION MAKING

With a good amount of data at your company’s disposal, they can make better decisions based on facts. This data first has to be in a format that is easily understandable and digestible and that is only possible with data wrangling.

The steps below will take you through the process of preparing extracted data for use. You may not get the results you want at first so keep trying until you get there.

1 . JOINING

This step involves merging all your collected data sets and having them in one place. You can do this using Python Pandas Library.

2. DISCOVERY

Go through the data you have and with that knowledge, make up your mind on the best way to organize them that would make analysis easy.

3. STRUCTURING

Raw extracted data lacks structure, and so you need to give it a structure that would permit proper analysis in the future.

4. CLEANING

Cleaning involves the removal of any outliers within the collected data that could distort your result after analysis. Change any values that are null and use a standard data format so that you can improve the quality of results you get and ensure consistency.

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5. ENRICHING

After cleaning your data, you will see that its size will be reduced. The next thing to do is to skim through to see if you need any more data for your analysis.

6. VALIDATING

In this step of performing data wrangling, you will check that the data you have is of good quality, is consistent and secure. This can be done by checking for the accuracy of data sets in their respective fields, or example if the attributes are distributed normally.

7 . PUBLISHING

After all, have been done, publish the data somewhere that can be easily accessed by you and other stakeholders of the company whenever it’s needed.

Python is one programming language that can be used to perform data wrangling using the Python Pandas Library. This library has built-in features that you can utilize to apply data transformation methods like merging, grouping, and concatenating data for you to achieve the intended analytical results.

Merging multiple data sets brings them together for easy analysis. By grouping data, you can easily organize it using certain parameters such as the year, and by concatenating data, you combine data and pace them side by side.

1 . MERGING DATA

In the Python Pandas Library, the ‘merge’ function is provided as the entry point for all standard database operations involving DataFrame objects.

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,

left_index=False, right_index=False, sort=True)

for this exercise, we will create two different DataFrames and use the merge function on it

# import the pandas library

import pandas as pd

left = pd.DataFrame({

'id':[1,2,3,4,5],

'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],

'subject_id':['sub1','sub2','sub4','sub6','sub5']})

right = pd.DataFrame(

{'id':[1,2,3,4,5],

'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],

'subject_id':['sub2','sub4','sub3','sub6','sub5']})

print left

print right

the output would therefore read;

Name id subject_id

0 Alex 1 sub1

1 Amy 2 sub2

2 Allen 3 sub4

3 Alice 4 sub6

4 Ayoung 5 sub5

Name id subject_id

0 Billy 1 sub2

1 Brian 2 sub4

2 Bran 3 sub3

3 Bryce 4 sub6

4 Betty 5 sub5

2. GROUPING DATA

Grouping the sets of data you have collected is a very important and frequently required step in data analysis as the results would be needed as different groups, as they are in the data sets. Python Pandas Library also has built-in features that can put the various data into their respective groups.

For this exercise, we will group the data using the year and get the result for one year.

# import the pandas library

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',

'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],

'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],

'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],

'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}

df = pd.DataFrame(ipl_data)

grouped = df.groupby('Year')

print grouped.get_group(2014)

the output would therefore read;

Points Rank Team Year

0 876 1 Riders 2014

2 863 2 Devils 2014

4 741 3 Kings 2014

9 701 4 Royals 2014

3. CONCATENATING DATA

There are a number of features that can be used to easily combine series, DataFrame, and Panel objects together. In the exercise below, the concatenate function would be used to concatenate along an axis.

import pandas as pd

one = pd.DataFrame({

'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],

'subject_id':['sub1','sub2','sub4','sub6','sub5'],

'Marks_scored':[98,90,87,69,78]},

index=[1,2,3,4,5])

two = pd.DataFrame({

'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],

'subject_id':['sub2','sub4','sub3','sub6','sub5'],

'Marks_scored':[89,80,79,97,88]},

index=[1,2,3,4,5])

print pd.concat([one,two])

the output would therefore read;

Marks_scored Name subject_id

1 98 Alex sub1

2 90 Amy sub2

3 87 Allen sub4

4 69 Alice sub6

5 78 Ayoung sub5

1 89 Billy sub2

2 80 Brian sub4

3 79 Bran sub3

4 97 Bryce sub6

5 88 Betty sub5

Data wrangling is done to ensure that a company gets accurate analytical and predictive results using data that have been cleaned and fixed of errors. It sounds pretty easy and theoretically it is, but data cleaning can’t be done using manual processes at this age. Available data is large and continuous, and so it calls for the most intelligent approach to wrangling.

Data wrangling affords companies the information needed to make important decisions quickly to meet up with the client’s needs as well as those of the company. And so skipping data-wrangling after extraction will lead to many problems in the nearest future. Data preparation doesn’t only help with solving immediate problems but also helps companies efficiently deal with future problems.

In the past, cleaning data was efficient but as the world has become more advanced and more digital, the size of available data is more and this makes it easier to make multiple mistakes if cleaning is done manually.

1. DATA PREPARATION METHODS

After data has been extracted, the process of wrangling looks out for any problems that may exist, including missing information, inconsistencies, and skewed data. You must format the data to take care of errors like abbreviation errors or different data formats, for example, parameters like time, date, and name should all be written in the same way. Doing this transforms raw data into useable data. Companies can then separate the relevant information and split the data for use in evaluation and training.

There are different ways to find issues with the data set and it employs the use of both quantitative and qualitative methods. The quantitative approach to cleaning data involves looking out for statistical errors by visualization, using graphs and charts. Qualitative approaches to cleaning data make use of patterns and set logical rules intended to find errors.

When errors are found, fixing them involves the use of different methods in the wrangling process. Based on statistics, you must understand the methods that fix quantitative errors. In quantitative errors, there is missing information implying empty entries. Entries that are much higher or lower than the dataset mean are referred to as outliers. Outliers are defined as three standard deviations. Duplicates are also an error and are detected when different records have the same value. Another common error is mislabeling attributes.

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After errors have been identified, the next thing to do is to fix them. The solutions to errors could be either deletion or imputation. In deletion, the missing value is gotten rid of while imputation uses the mean, median, and mode. When dealing with categorical values, the model is used as a dummy variable in place of the missing value. Even though deletion is a way of fixing errors, removal of information could affect the dataset and this is also applicable to outliers. Instead of removing the errors from the dataset completely, the mean, median, and mode could be used to input the outliers.

Errors in the data can be detected by different methods. As already mentioned, three standard   deviations above or below the mean is an outlier and standard deviation is one form of detecting errors. Another method for the detection of errors is the Interquartile Range Method. What it does is subtract the 25th and 75th percentile of an attribute and if the value is beyond the IQR range, it’s also considered an outlier.

Different datasets are produced by data wrangling and it’s dependent on the situation and use. An Analytic Base Table is the most commonly used structure in data science and it's used for machine learning especially in finding consistency in patterns and outcomes. The ABT uses rows and columns, with rows representing separate entities e.g a person, and the columns containing attributes about an entity.

2. DATA WRANGLING IN DIFFERENT INDUSTRIES

Accurate data is important in various industries and clean data is even most important in the health sector. All hospitals store medical information of patients and this information needs to be accurate, up to date, and in the right format for easy digest. The availability of accurate patient health information assists health professionals in studying diseases so that they can provide better service to their patients.

The transportation industry is another sector that depends heavily on data is the transportation industry. Public transportation makes use of data that includes the number of passengers, payments for every service, times, and also the duration of each service. With data wrangling, every category of data is properly organized so that passengers can have an easy time booking for services, making payments, and making use of the transport service. The information gathered over time informs the need for expansion to serve better in terms of making more flights or train departures available. You will observe that over time, the transportation industry has seen tremendous growth and that’s partly due to their use of data analytics to make informed decisions.

The media is another industry that depends on data. They use this data for self-evaluation of their services, and to decide on the information the public is more interested in. important statistics include the number of article views, trending stories, and the number of users that return to a website after their first visit, and these stats are used to improve the quality of service you get. The required information for analytics needs to be organized properly or else the data may not be properly understood. To make good use of a large amount of available data, data wrangling catches errors that exist therein and allows you to make the best decisions with accurate information.

1. UNDERSTANDING THE RESULTS

One very common challenge of data wrangling is understanding the results because the important information after the analysis is most times difficult to figure out. Knowledge of the industry is applied in most cases to make observations on the result and even though this could be a long process to ensure no mistakes, it’s worth it.

2. DATA VISUALIZATION

Data visualization is the means through which datasets can be presented and it makes use of charts and graphs. With visualization, conclusions can be drawn from obvious patterns without having to go through the process of using spreadsheets. This speed also means faster solutions to problems and faster discovery of trends that would have been otherwise been missed. This way, businesses can do better because they can quickly attend to the needs of their customers and improve the quality of their products to satisfy customer’s needs.

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With the right visualization tool, even an audience that doesn’t know much about tech can understand what the presenter is passing on. Messages get across faster and are understood easier. But the problem with visualization is that you won’t know the state of the data that led to the analysis and results, and once data isn’t clean or accurate, every other thing goes wrong.