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How can you use data cleaning software to create more accurate histograms?

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Histograms are useful tools for data visualization, as they show the distribution of a numerical variable in a graphical way. However, histograms can be misleading or inaccurate if the data is not cleaned properly before plotting. Data cleaning software can help you avoid common pitfalls and create more accurate histograms. In this article, you will learn how to use data cleaning software to:

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Eliminate outliers:

Use data cleaning software to detect and handle outliers. This ensures your histogram accurately reflects the true distribution by removing or adjusting extreme values that can distort the visualization.### *Address missing values:Employ functions like dropna in Pandas to manage missing data. Clean data without gaps helps create histograms that more accurately represent your dataset's overall pattern.

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Aditya Prasad Satapathy

Data Scientist @Kenvue || Procurement…
Tianyu Xu

I help people acquire future-proof…

1 Check for outliers

Outliers are extreme values that deviate significantly from the rest of the data. They can skew the shape and scale of the histogram, making it harder to see the true distribution. Data cleaning software can help you identify and remove outliers , or adjust them to a more reasonable range. For example, you can use the boxplot function in R or Python to detect outliers based on the interquartile range, and then filter them out or replace them with the median value.

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | Trainer | Consultant | Explorer
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Try this prompt on ChatGPT’s Advanced Data Analysis (for non-sensitive data): You are Data Cleaner GPT. For the file(s) I upload, 1. Describe the structure and data type. 2. Convert the columns to the correct data type for [my purpose]. 3. Handle missing values and outliers. 4. Double check and return me a download link of the cleaned data file. The output is a clean data file ready for the next steps. Data cleaning is most critical but most boring, fortunately, AI is changing every aspect of this task. There are alternatives to ChatGPT. I’ll find another chance to explain.

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Daniel Roberto de Ponte

IT Data Analyst, Business Analyst/ Lean Six Sigma Black Belt
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In my experience, when working with outliers, it's important to evaluate them in order to find out if the data comes from an error, a seasonality characteristic or if it's a true outlier provided by a slightly different process outcome. After the assessment, you can decide whether to clean up or keep it in your analysis.

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2 Handle missing values

Missing values are another common issue that can affect the accuracy of histograms. Depending on how the data is collected and stored, missing values can be represented by different symbols, such as NA, NULL, or blank cells. Data cleaning software can help you find and deal with missing values, either by deleting them, imputing them, or ignoring them. For example, you can use the dropna function in Pandas or the na.omit function in R to remove rows or columns with missing values.

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Aditya Prasad Satapathy

Data Scientist @Kenvue || Procurement Analytics Intern @ Red Bull || VGSoM, IIT Kharagpur (Business Analytics Specialization) || VSSUT Burla
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One of the best ways of handling the missing values is finding out initially the data type as object or number subsequently we go for the mean or mode impudation. One of the other ways is to delete the respective column or row. I prefer the later as I consider having more data is good , however if the variable has a lot of data missing the best way is to drop that variable because it doesn't add any value to the data.

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Daniel Roberto de Ponte

IT Data Analyst, Business Analyst/ Lean Six Sigma Black Belt
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Some missing values can be generated in the load process if the datatype is not correct. One common mistake is to set a column as an integer datatype and you have, at some point, some letters included in the column. Another thing to keep in mind is the difference between zero and blank. Zero is a piece of information that can be very useful depending on the data and your objectives whereas blank is the absence of value.

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3 Choose the right bin size

The bin size is the width of each interval on the x-axis of the histogram. It determines how many bins or bars the histogram will have, and how smooth or jagged it will look. The bin size can have a big impact on the interpretation of the histogram, as it can reveal or hide patterns and trends in the data. Data cleaning software can help you choose the right bin size for your data, based on various criteria, such as the range, the standard deviation, or the number of observations. For example, you can use the hist function in R or the plt.hist function in Matplotlib to automatically calculate the optimal bin size, or specify your own.

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4 Normalize the data

Normalization is a process of scaling the data to a common range, usually between 0 and 1. It can be useful for comparing histograms of different variables or groups, as it eliminates the effect of different units and scales. Normalization can also help reduce the influence of outliers and skewness on the histogram. Data cleaning software can help you normalize the data, using different methods, such as min-max scaling, z-score scaling, or log transformation. For example, you can use the scale function in R or the MinMaxScaler class in Scikit-learn to normalize the data.

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5 Add labels and titles

Labels and titles are essential elements of any data visualization, as they provide context and clarity for the audience. They should be informative, concise, and consistent. Data cleaning software can help you add labels and titles to your histograms, using various functions and parameters. For example, you can use the xlab, ylab, and main arguments in R or the xlabel, ylabel, and title methods in Matplotlib to add labels and titles to your histograms.

Data cleaning software can make your histograms more accurate and effective, by helping you check for outliers, handle missing values, choose the right bin size, normalize the data, and add labels and titles. By following these steps, you can create histograms that reflect the true distribution of your data and communicate your insights clearly.

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Daniel Roberto de Ponte

IT Data Analyst, Business Analyst/ Lean Six Sigma Black Belt
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Labels and titles are truly important to share your message. Any good data analysis work could be spoiled if you don't make it clear what the data is telling to the audience. So, as important as the methods you use to load, cleanse and analyze data, is the way you communicate with the clients and, using labels and titles, can help you to deliver the right message.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | Trainer | Consultant | Explorer
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Before everyone’s eyes on generative AI tools, there were already a number of excellent data preparation tools available. Tableau Prep is among the best in this category. If you can’t use ChatGPT Advanced Data Analysis, consider Tableau Prep. Why? Easy to use, no code, seamless transition to data visualization works.

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How can you use data cleaning software to create more accurate histograms?

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2

3

4

5

6

1 Check for outliers

2 Handle missing values

3 Choose the right bin size

4 Normalize the data

5 Add labels and titles

6 Here’s what else to consider

Data Visualization

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How can you use data cleaning software to create more accurate histograms?

1

2

3

4

5

6

1 Check for outliers

2 Handle missing values

3 Choose the right bin size

4 Normalize the data

5 Add labels and titles

6 Here’s what else to consider

Data Visualization

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