Excel has many tools for data analysis but the data that you work with needs to be in the right form. If the variations are large, it can be difficult to establish relations between the different number sets. For that reason, mean as well as standard deviation are perimeters that help to normalize any set of data in excel frequently.

If you wish to normalize a data set before you use other analytical tools on them, it is easy to do so. You can achieve normalized or standardized datasets in Excel in certain ways.

Understanding Normalized Data

Standardized data is usually the outcome of normalization. Data or a whole set of numbers transforms with the help of average or mean as well as standard deviation calculated for an entire set. Standard distribution for normalized data usually represents a mean of 0 along with 1 as a variance.

When a data set is normalized, you will find positive values to live above meanwhile negative values are below mean. +1 represents a value which deviates by 1 above meanwhile -1 is what is represented when the standard deviation is one below mean.

Functions Used for Normalization

  • Two main analyses are necessary when someone wishes to normalize a data set. For instance, data that runs from A2 to A51, when you want to normalize it, you need to find the average and standard deviation value of this set.
  • To choose the average of such data, select a cell that is next to the data and is empty; here you can label as mean and enter the formula ‘=AVERAGE(A2: A51)’.

You can change the range of numbers as per the dataset you wish to consider. For instance, if data is between B4 and B55, type in the formula ‘=AVERAGE(B4: B55)’.

  • To find a standard deviation, choose another cell that is empty. Label it as standard deviation and type in the formula ‘=STDEV(A2: A51)’. Here, you can adjust cell coordinates as per the dataset range.
  • The final stage is to use Standardize function, a useful tool present in Excel. It contains three arguments or information bits inside a format given as STANDARDIZE(value, mean, standard deviation).
  • You can type in normalized data as a label inside a cell beside the data or after the cells where average and standard deviation are shown.
  • In the cell chosen, you need to type in the formula ‘=STANDARDIZE(A2, $C$2, $D$2)’; this indicates the need to normalize data between cell A2, mean found in C2 and standard deviation in the cell D2. The $ sign makes the formula easy to replicate in the next steps.
  • Hover the mouse pointer on the bottom corner of a cell and when the black cross shows up you need to click on it and drag it down so that it lines up the cells of the data; it would duplicate the formula and allow input data to change location, match row cells and instill mean and standard deviation.

Steps to Normalize Data in Excel

Excel helps the handling of large data sets easy. With the normalization parameters, here you can use large number sets to reduce the same to smaller scales. The normalization equation helps to compare different data sets as well.

  1. Open a New or Existing in Excel

This is the first step to take when you wish to normalize data in Excel. You might have a spreadsheet of data existing that needs to be normalized. Launch Microsoft Excel. This opens up a spreadsheet as a new document; you could start by entering data here or choose to open a saved document by clicking the ‘Open’ option.

  1. Start with Arithmetic Mean

Start at cell C1 and type in the formula “=AVERAGE(A1: AX)”. Instead of AX enter the last cell of data in column A. This will complete inputs for the average function. It will also return arithmetic mean used for normalization.

  1. Calculate the Standard Deviation

As seen in the image above, select cell C2 where you can type in the function “STDEV.S(A1: AX)”. Quotation marks are not necessary and you simply need to change the value of AX as per the last data cell in column A as mentioned for the average calculation function. This starts the standard deviation calculation and comes of use for normalizing data.

Enter STANDARDISE formula by clicking on cell B1 where you type in “STANDARDISE(A1, C$1, C$2)”. However, at the time of input of the symbols, you don’t need to put quotation marks. The use of a dollar sign helps to make it versatile. You can copy it and past it in any other cell and relative references for cells in rows and columns are picked up automatically.

This helps the user to use the formula anywhere and cell references C1, C2 do not need not be changed. With this function completed normalized form cell A1 comes on B1.

  1. Normalize Data Remaining

Once you normalize data in column A’s first cell, you need to do the same for the rest of column A. Select B1, click on the same and hold, dragging the mouse down the column and rest of cells. Keep up the same till all cells in the B column are covered. Release the mouse to see the standardized formula applied to column B as well.

Excel Functions that Help Normalize Data

When you are normalizing data in spreadsheets, the formulas IF, ‘AND’ and ‘DATEDIF’ are useful if function helps create flags that filter out data. The ‘DATEDIF’ is a function that helps determine time periods that pass between two given dates. ‘AND’ function showcases linkage between two or more columns.

You can see an example of a data spreadsheet here,

  • Data here shows the number of apples in kilograms harvested by the farmer, and on which farmland and days.
  • To decide the harvest day within 30 days of start, the formula to use is =DATEDIF(E2, A2,”d”).
  • Set a flag to account for the two variables. This is done by using the formula =AND(D2 = 1, F2 <= 30).
  • The final step can comprise running the two fields added and applying the same to the data set in entirety.

The above steps and functions help in normalizing different sets of data effectively in Excel.

What does it mean to normalize data in Excel?

Normalization — Changing the original numerical values to fit within a certain range.
  • For e.g., you want to modify test scores that could be between 0–100 to be within the range 0–1.
  • You might want to normalize when you have multiple variables with differing ranges.

How do you normalize data?

How do I normalize to 100 in Excel?

To normalize the values in a dataset to be between 0 and 100, you can use the following formula:
  1. zi = (xi – min(x)) / (max(x) – min(x)) * 100.
  2. zi = (xi – min(x)) / (max(x) – min(x)) * Q.
  3. Min-Max Normalization.
  4. Mean Normalization.

What is the best normalization method?

The best normalization technique is one that empirically works well, so try new ideas if you think they’ll work well on your feature distribution. When the feature is more-or-less uniformly distributed across a fixed range. When the feature contains some extreme outliers.

How do you normalize age data?

Theory. Suppose the actual range of a feature named “Age” is 5 to 100. We can normalize these values into a range of [0, 1] by subtracting 5 from every value of the “Age” column and then dividing the result by 95 (100–5).

Which is better normalization or standardization?

Normalization is good to use when you know that the distribution of your data does not follow a Gaussian distribution. Standardization, on the other hand, can be helpful in cases where the data follows a Gaussian distribution. However, this does not have to be necessarily true.

What is difference between standardization and normalization?

Normalization typically means rescales the values into a range of [0,1]. Standardization typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance).

Should I normalize age data?

For machine learning, every dataset does not require normalization. It is required only when features have different ranges. For example, consider a data set containing two features, age, and income(x2). Where age ranges from 0–100, while income ranges from 0–100,000 and higher.

When should you not normalize data?

Some Good Reasons Not to Normalize
  1. Joins are expensive. Normalizing your database often involves creating lots of tables.
  2. Normalized design is difficult.
  3. Quick and dirty should be quick and dirty.
  4. If you’re using a NoSQL database, traditional normalization is not desirable.

Is normalization always good?

3 Answers. It depends on the algorithm. For some algorithms normalization has no effect. Generally, algorithms that work with distances tend to work better on normalized data but this doesn’t mean the performance will always be higher after normalization.

Why do we normalize image data?

Normalizing image inputs: Data normalization is an important step which ensures that each input parameter (pixel, in this case) has a similar data distribution. This makes convergence faster while training the network. The distribution of such data would resemble a Gaussian curve centered at zero.

What is the goal of normalization?

Basically, normalization is the process of efficiently organising data in a database. There are two main objectives of the normalization process: eliminate redundant data (storing the same data in more than one table) and ensure data dependencies make sense (only storing related data in a table).

Do we need to normalize images?

Its normal purpose is to convert an input image into a range of pixel values that are more familiar or normal to the senses, hence the term normalization. If we are using a grayscale image, we only need to normalize using one channel.

How do you normalize an image?

There are some variations on how to normalize the images but most seem to use these two methods:
  1. Subtract the mean per channel calculated over all images (e.g. VGG_ILSVRC_16_layers)
  2. Subtract by pixel/channel calculated over all images (e.g. CNN_S, also see Caffe’s reference network)

How do you normalize data in Python?

Python provides the preprocessing library, which contains the normalize function to normalize the data. It takes an array in as an input and normalizes its values between 0 and 1. It then returns an output array with the same dimensions as the input.

How do you normalize RGB values?

When normalizing the RGB values of an image, you divide each pixel’s value by the sum of the pixel’s value over all channels. So if you have a pixel with intensitied R, G, and B in the respective channels its normalized values will be R/S, G/S and B/S (where, S=R+G+B).

Why do we divide the image by 255?

Since 255 is the maximum value, dividing by 255 expresses a 0-1 representation. Each channel (Red, Green, and Blue are each channels) is 8 bits, so they are each limited to 256, in this case 255 since 0 is included. As the reference shows, systems typically use values between 0-1 when using floating point values.