How to Clean Chart Review Data for Data Analysis

The Ultimate Guide to Data Cleaning

When the data is spewing garbage

source

I spent the terminal couple of months analyzing information from sensors, surveys, and logs. No matter how many charts I created, how well sophisticated the algorithms are, the results are always misleading.

Throwing a random forest at the data is the same every bit injecting it with a virus. A virus that has no intention other than hurting your insights as if your data is spewing garbage.

Even worse, when y'all show your new findings to the CEO, and Oops judge what? He/she institute a flaw, something that doesn't smell right, your discoveries don't match their agreement well-nigh the domain — After all, they are domain experts who know amend than y'all, you every bit an analyst or a developer.

Right away, the blood rushed into your face, your easily are shaken, a moment of silence, followed by, probably, an apology.

That's cracking at all. What if your findings were taken as a guarantee, and your visitor ended upwardly making a conclusion based on them?.

You ingested a bunch of dingy data, didn't make clean it up, and you told your company to exercise something with these results that turn out to be wrong. Yous're going to be in a lot of trouble!.

Incorrect or inconsistent information leads to false conclusions. And so, how well you make clean and empathize the data has a loftier touch on on the quality of the results.

Two existent examples were given on Wikipedia.

For instance, the government may desire to analyze population demography figures to make up one's mind which regions crave further spending and investment on infrastructure and services. In this case, it will exist important to accept access to reliable data to avoid erroneous fiscal decisions.

In the business organization world, wrong data tin can be costly. Many companies use client data databases that record data like contact information, addresses, and preferences. For instance, if the addresses are inconsistent, the visitor will suffer the toll of resending mail or even losing customers.

Garbage in, garbage out.

In fact, a elementary algorithm can outweigh a complex 1 just because information technology was given plenty and high-quality data.

Quality data beats fancy algorithms.

For these reasons, information technology was of import to take a footstep-by-step guideline, a cheat sheet, that walks through the quality checks to exist applied.

Merely first, what'southward the thing we are trying to achieve?. What does it mean quality information?. What are the measures of quality data?. Understanding what are you lot trying to accomplish, your ultimate goal is critical prior to taking whatsoever actions.

Index:

• Data Quality (validity, accuracy, completeness, consistency, uniformity)
• The workflow (inspection, cleaning, verifying, reporting)
• Inspection (data profiling, visualizations, software packages)
• Cleaning (irrelevant data, duplicates, blazon conver., syntax errors, half-dozen more)
• Verifying
• Reporting
• Final words

Information quality

Bluntly speaking, I couldn't find a ameliorate explanation for the quality criteria other than the one on Wikipedia. And so, I am going to summarize it here.

Validity

The degree to which the data arrange to divers business rules or constraints.

• Data-Type Constraints: values in a particular cavalcade must be of a particular datatype, eastward.k., boolean, numeric, date, etc.
• Range Constraints: typically, numbers or dates should fall inside a certain range.
• Mandatory Constraints : certain columns cannot be empty.
• Unique Constraints: a field, or a combination of fields, must be unique across a dataset.
• Set up-Membership constraints: values of a column come up from a set of discrete values, eastward.g. enum values. For example, a person's gender may be male or female.
• Foreign-primal constraints: as in relational databases, a foreign key column can't have a value that does not exist in the referenced primary key.
• Regular expression patterns: text fields that take to exist in a certain pattern. For instance, phone numbers may exist required to accept the pattern (999) 999–9999.
• Cross-field validation: certain conditions that bridge across multiple fields must concur. For example, a patient'due south appointment of belch from the hospital cannot exist earlier than the date of admission.

Accuracy

The caste to which the data is shut to the true values.

While defining all possible valid values allows invalid values to be easily spotted, information technology does non mean that they are accurate.

A valid street address mightn't actually exist. A valid person'due south eye color, say blue, might be valid, simply not truthful (doesn't stand for the reality).

Some other affair to note is the departure between accuracy and precision. Saying that y'all live on the earth is, actually true. But, non precise. Where on the earth?. Saying that you live at a detail street address is more precise.

Abyss

The caste to which all required information is known.

Missing information is going to happen for various reasons. Ane can mitigate this problem by questioning the original source if possible, say re-interviewing the field of study.

Chances are, the subject field is either going to give a dissimilar reply or will exist hard to reach again.

Consistency

The degree to which the information is consistent, inside the same information set or across multiple information sets.

Inconsistency occurs when 2 values in the data ready contradict each other.

A valid age, say 10, mightn't match with the marital status, say divorced. A customer is recorded in ii unlike tables with two different addresses.

Which one is true?.

Uniformity

The degree to which the data is specified using the aforementioned unit of measurement of measure out.

The weight may be recorded either in pounds or kilos. The date might follow the USA format or European format. The currency is sometimes in USD and sometimes in YEN.

And and then data must exist converted to a unmarried measure unit.

The workflow

The workflow is a sequence of three steps aiming at producing high-quality data and taking into account all the criteria we've talked about.

1. Inspection: Find unexpected, incorrect, and inconsistent data.
2. Cleaning: Fix or remove the anomalies discovered.
3. Verifying: After cleaning, the results are inspected to verify correctness.
4. Reporting: A written report about the changes made and the quality of the currently stored information is recorded.

What you lot see as a sequential process is, in fact, an iterative, countless process. One tin get from verifying to inspection when new flaws are detected.

Inspection

Inspecting the information is time-consuming and requires using many methods for exploring the underlying data for error detection. Hither are some of them:

Information profiling

A summary statistics well-nigh the information, chosen information profiling, is really helpful to requite a general idea almost the quality of the data.

For case, check whether a item cavalcade conforms to particular standards or blueprint. Is the information column recorded as a string or number?.

How many values are missing?. How many unique values in a column, and their distribution?. Is this data set up is linked to or have a human relationship with another?.

Visualizations

By analyzing and visualizing the data using statistical methods such as mean, standard deviation, range, or quantiles, one tin can notice values that are unexpected and thus erroneous.

For case, by visualizing the average income beyond the countries, one might come across in that location are some outliers (link has an image). Some countries have people who earn much more than anyone else. Those outliers are worth investigating and are non necessarily wrong data.

Software packages

Several software packages or libraries available at your language will let you specify constraints and check the data for violation of these constraints.

Moreover, they can not only generate a report of which rules were violated and how many times but too create a graph of which columns are associated with which rules.

The age, for instance, can't exist negative, and then the height. Other rules may involve multiple columns in the aforementioned row, or across datasets.

Cleaning

Data cleaning involve dissimilar techniques based on the problem and the data blazon. Different methods tin can exist applied with each has its ain trade-offs.

Overall, incorrect data is either removed, corrected, or imputed.

Irrelevant data

Irrelevant information are those that are not really needed, and don't fit nether the context of the problem we're trying to solve.

For example, if we were analyzing information most the general health of the population, the phone number wouldn't exist necessary — column-wise.

Similarly, if you were interested in but one particular state, you wouldn't want to include all other countries. Or, study but those patients who went to the surgery, we wouldn't include anybody — row-wise.

But if you are sure that a slice of data is unimportant, you may drop it. Otherwise, explore the correlation matrix betwixt feature variables.

And even though y'all noticed no correlation, you should ask someone who is domain expert. You never know, a feature that seems irrelevant, could be very relevant from a domain perspective such as a clinical perspective.

Duplicates

Duplicates are data points that are repeated in your dataset.

It ofttimes happens when for example

• Information are combined from different sources
• The user may hit submit button twice thinking the form wasn't really submitted.
• A asking to online booking was submitted twice correcting wrong information that was entered accidentally in the beginning time.

A common symptom is when two users have the same identity number. Or, the same article was scrapped twice.

And therefore, they only should exist removed.

Blazon conversion

Brand sure numbers are stored equally numerical data types. A date should exist stored equally a appointment object, or a Unix timestamp (number of seconds), and so on.

Categorical values can be converted into and from numbers if needed.

This is can be spotted quickly by taking a peek over the data types of each cavalcade in the summary (we've discussed above).

A word of caution is that the values that tin't be converted to the specified blazon should be converted to NA value (or whatsoever), with a warning being displayed. This indicates the value is incorrect and must be stock-still.

Syntax errors

Remove white spaces: Extra white spaces at the commencement or the finish of a cord should be removed.

          "   hello earth  " => "hi world        

Pad strings: Strings can be padded with spaces or other characters to a sure width. For example, some numerical codes are often represented with prepending zeros to ensure they always have the same number of digits.

          313 => 000313 (6 digits)        

Prepare typos: Strings can exist entered in many different ways, and no wonder, can have mistakes.

                      Gender            
m
Male
fem.
FemalE
Femle        

This categorical variable is considered to have 5 different classes, and not ii as expected: male person and female since each value is unlike.

A bar plot is useful to visualize all the unique values. One can observe some values are unlike merely do mean the same matter i.e. "information_technology" and "IT". Or, possibly, the difference is just in the capitalization i.e. "other" and "Other".

Therefore, our duty is to recognize from the above data whether each value is male or female. How can we practise that?.

The first solution is to manually map each value to either "male person" or "female person".

          dataframe['gender'].map({'            m            ': 'male',             fem.            ': 'female', ...})        

The second solution is to apply blueprint match. For example, we tin expect for the occurrence of m or M in the gender at the beginning of the string.

          re.sub(r"\^chiliad\$", 'Male', 'male', flags=re.IGNORECASE)        

The third solution is to utilize fuzzy matching: An algorithm that identifies the distance between the expected string(southward) and each of the given one. Its bones implementation counts how many operations are needed to plough one cord into another.

                      Gender   male person  female            
one thousand         3      v
Male      1      3
fem.      5      3
Female person    3      2
Femle     3      1        

Furthermore, if y'all accept a variable like a urban center proper noun, where you lot suspect typos or similar strings should be treated the aforementioned. For instance, "lisbon" tin can exist entered equally "lisboa", "lisbona", "Lisbon", etc.

                      Urban center     Distance from "lisbon"
            lisbon       0
lisboa       1
Lisbon       1
lisbona      ii
london       3
...        

If and then, and then nosotros should replace all values that hateful the same thing to one unique value. In this case, replace the outset 4 strings with "lisbon".

Scout out for values like "0", "Not Applicable", "NA", "None", "Null", or "INF", they might mean the same thing: The value is missing.

Standardize

Our duty is to not simply recognize the typos only also put each value in the same standardized format.

For strings, make sure all values are either in lower or upper instance.

For numerical values, make sure all values have a certain measurement unit of measurement.

The hight, for case, can exist in meters and centimetres. The divergence of 1 meter is considered the aforementioned as the divergence of i centimetre. So, the task here is to catechumen the heights to ane single unit.

For dates, the USA version is not the same as the European version. Recording the date as a timestamp (a number of milliseconds) is not the same as recording the date as a date object.

Scaling / Transformation

Scaling means to transform your data so that it fits within a specific scale, such as 0–100 or 0–ane.

For example, exam scores of a student can be re-scaled to exist percentages (0–100) instead of GPA (0–5).

Information technology tin can also assist in making certain types of information easier to plot. For instance, we might want to reduce skewness to aid in plotting (when having such many outliers). The about commonly used functions are log, square root, and inverse.

Scaling tin can likewise take place on data that has different measurement units.

Educatee scores on dissimilar exams say, SAT and ACT, can't be compared since these 2 exams are on a unlike calibration. The difference of 1 SAT score is considered the aforementioned equally the difference of 1 Human activity score. In this instance, nosotros demand re-scale Sat and Human activity scores to take numbers, say, between 0–1.

By scaling, we tin plot and compare unlike scores.

Normalization

While normalization as well rescales the values into a range of 0–1, the intention here is to transform the data so that it is normally distributed. Why?

In most cases, nosotros normalize the information if we're going to be using statistical methods that rely on usually distributed data. How?

I can use the log office, or perhaps, use one of these methods.

Depending on the scaling method used, the shape of the data distribution might change. For case, the "Standard Z score" and "Student'due south t-statistic" (given in the link above) preserve the shape, while the log role mighn't.

Missing values

Given the fact the missing values are unavoidable leaves usa with the question of what to practise when we encounter them. Ignoring the missing information is the aforementioned as excavation holes in a boat; It will sink.

There are 3, or possibly more than, ways to deal with them.

— I. Drop.

If the missing values in a column rarely happen and occur at random, and then the easiest and most forward solution is to drop observations (rows) that have missing values.

If most of the cavalcade's values are missing, and occur at random, then a typical determination is to drop the whole column.

This is especially useful when doing statistical analysis, since filling in the missing values may yield unexpected or biased results.

— Two. Impute.

Information technology ways to calculate the missing value based on other observations. There are quite a lot of methods to exercise that.

— First one is using statistical values similar mean, median. However, none of these guarantees unbiased data, especially if at that place are many missing values.

Hateful is about useful when the original data is not skewed, while the median is more robust, non sensitive to outliers, and thus used when data is skewed.

In a ordinarily distributed data, 1 can become all the values that are within 2 standard deviations from the hateful. Next, fill in the missing values by generating random numbers betwixt (mean — 2 * std) & (mean + 2 * std)

          rand = np.random.randint(average_age - 2*std_age, average_age + 2*std_age, size = count_nan_age)          dataframe["age"][np.isnan(dataframe["age"])] = rand        

— 2d. Using a linear regression. Based on the existing data, one tin can calculate the best fit line betwixt 2 variables, say, firm toll vs. size thousand².

It is worth mentioning that linear regression models are sensitive to outliers.

— Third. Hot-deck: Copying values from other similar records. This is only useful if yous have plenty available data. And, it can be applied to numerical and categorical data.

One can take the random arroyo where nosotros fill in the missing value with a random value. Taking this arroyo i step further, one tin can first divide the dataset into two groups (strata), based on some characteristic, say gender, then fill in the missing values for unlike genders separately, at random.

In sequential hot-deck imputation, the column containing missing values is sorted co-ordinate to auxiliary variable(s) so that records that accept like auxiliaries occur sequentially. Side by side, each missing value is filled in with the value of the commencement following available record.

What is more interesting is that 𝑘 nearest neighbor imputation, which classifies similar records and put them together, can also be utilized. A missing value is then filled out past finding first the 𝑘 records closest to the record with missing values. Next, a value is chosen from (or computed out of) the 𝑘 nearest neighbours. In the example of calculating, statistical methods like mean (as discussed before) can be used.

— 3. Flag.

Some argue that filling in the missing values leads to a loss in information, no thing what imputation method we used.

That's because saying that the data is missing is informative in itself, and the algorithm should know nearly it. Otherwise, we're simply reinforcing the design already exist by other features.

This is particularly important when the missing data doesn't happen at random. Take for case a conducted survey where most people from a specific race refuse to respond a sure question.

Missing numeric data can exist filled in with say, 0, simply has these zeros must be ignored when calculating whatever statistical value or plotting the distribution.

While categorical data can exist filled in with say, "Missing": A new category which tells that this piece of data is missing.

— Accept into consideration …

Missing values are non the aforementioned every bit default values. For instance, null tin be interpreted as either missing or default, but not both.

Missing values are not "unknown". A conducted research where some people didn't remember whether they accept been bullied or not at the school, should be treated and labelled equally unknown and not missing.

Every time we drop or impute values we are losing data. So, flagging might come to the rescue.

Outliers

They are values that are significantly different from all other observations. Whatever data value that lies more (one.5 * IQR) abroad from the Q1 and Q3 quartiles is considered an outlier.

Outliers are innocent until proven guilty. With that being said, they should not be removed unless in that location is a good reason for that.

For example, one tin notice some weird, suspicious values that are unlikely to happen, and so decides to remove them. Though, they worth investigating before removing.

It is also worth mentioning that some models, like linear regression, are very sensitive to outliers. In other words, outliers might throw the model off from where about of the data lie.

In-record & cross-datasets errors

These errors event from having two or more values in the same row or across datasets that contradict with each other.

For example, if we take a dataset about the price of living in cities. The total column must be equivalent to the sum of rent, transport, and food.

                      city       rent  transportation food  total            
libson     500        twenty         40    560
paris      750        forty         threescore    850        

Similarly, a child can't exist married. An employee's salary tin can't be less than the calculated taxes.

The aforementioned idea applies to related data across different datasets.

Verifying

When done, ane should verify definiteness by re-inspecting the data and making sure it rules and constraints practice hold.

For example, after filling out the missing data, they might violate whatsoever of the rules and constraints.

It might involve some manual correction if not possible otherwise.

Reporting

Reporting how healthy the information is, is equally important to cleaning.

As mentioned before, software packages or libraries tin generate reports of the changes made, which rules were violated, and how many times.

In improver to logging the violations, the causes of these errors should be considered. Why did they happen in the start identify?.

Final words …

If y'all fabricated it that far, I am happy y'all were able to hold until the end. Merely, None of what mentioned is valuable without embracing the quality civilization.

No thing how robust and stiff the validation and cleaning process is, i volition proceed to endure as new data come in.

Information technology is better to guard yourself against a affliction instead of spending the time and effort to remedy it.

These questions help to evaluate and improve the data quality:

How the data is nerveless, and nether what atmospheric condition?. The environment where the data was collected does matter. The environment includes, only not express to, the location, timing, weather weather condition, etc.

Questioning subjects about their opinion regarding whatever while they are on their way to work is not the same as while they are at home. Patients under a written report who accept difficulties using the tablets to answer a questionnaire might throw off the results.

What does the data stand for?. Does it include anybody? Just the people in the urban center?. Or, peradventure, only those who opted to answer because they had a potent opinion about the topic.

What are the methods used to clean the data and why?. Different methods can be ameliorate in unlike situations or with different information types.

Practice you invest the time and money in improving the process?. Investing in people and the process is equally critical as investing in the engineering.

And finally, … information technology doesn't go without proverb,

Cheers for reading!

Experience free to reach on LinkedIn or Medium.

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Source: https://towardsdatascience.com/the-ultimate-guide-to-data-cleaning-3969843991d4