# Trending February 2024 # Index Match Functions In Excel # Suggested March 2024 # Top 9 Popular

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In this tutorial, we’ll dive into the powerful Excel INDEX and MATCH functions, which are essential for manipulating and analyzing large sets of data.

We’ll start by exploring what these functions do and how they retrieve specific information from a table, and then we’ll write INDEX and MATCH formulas together as an alternative to the VLOOKUP formula.

We’ll also cover some practical use cases for INDEX and MATCH formulas.

Note: if you have Excel 2023 or later, or Microsoft 365 you should use the XLOOKUP function as this is easier and potentially more efficient.

Watch the INDEX and MATCH Video

How the INDEX function works:

The INDEX function returns the value at the intersection of a column and a row.

The syntax for the INDEX function is:

=INDEX(

reference

,

row_num

,

[column_num]

, [area_num])

In English:

=INDEX( the range of your table, the row number of the table that your data is in, the column number of the table that your data is in, and if your reference specifies two or more ranges (areas) then specify which area*)

*Typically only one area is specified so the area_num argument can be omitted. The examples below don’t require area_num.

INDEX will return the value that is in the cell at the intersection of the row and column you specify.

For example, looking at the table below in the range B17:F24 we can use INDEX to return the number of program views for Bat Man in the North region with a formula as follows:

=INDEX(

B17:F24

,

2

,

3

)

The result returned is 91.

On its own the INDEX function is pretty inflexible because you have to hard key the row and column number, and that’s why it works better with the MATCH function.

Note: You may have noticed that the INDEX function works in a similar way to the OFFSET function, in fact you can often interchange them and achieve the same results.

How the MATCH function works:

The MATCH function finds the position of a value in a list.  The list can either be in a row or a column.

The syntax for the MATCH function is:

=MATCH(

lookup_value

,

lookup_array

,

[match_type]

)

Now I don’t want to go all syntaxy (real word 🙂 ) on you, but I’d like to point out some important features of the [match_type] argument:

The match_type argument specifies how Excel matches the lookup_value with values in lookup_array. You can choose from -1, 0 or 1 (1 is the default)

[match_type] is an optional argument, hence the square brackets. If you leave it out Excel will use the default of 1, which means it will find the largest value that is

0 will find the first value that is exactly equal to the lookup_value. The values in the lookup_array can be in any order.

Ok, that’s enough of the syntax.

In English and using the previous example:

=MATCH(

find what row Bat Man is on

,

in the column range B17:B24

,

match it exactly (for this we'll use 0 as our argument)

)

The result is row 2.

We can also use MATCH to find the column number like this:

=MATCH(

find what column North is in

,

in the row range B17:F17

,

match it exactly (again we'll use 0 as our argument)

)

The result is column 3.

So in summary, the INDEX function returns the value in the cell you specify, and the MATCH function tells you the column or row number for the value you are looking up.

INDEX MATCH Together:

The INDEX and MATCH functions are a popular alternative to the VLOOKUP. Even though I still prefer VLOOKUP as it’s more straight forward to use, there are certain things the INDEX + MATCH functions can do that VLOOKUP can’t.  More on that later.

Using the above example data we’ll use the INDEX and MATCH functions to find the program views for Bat Man in the East region.

=INDEX(

,

replace this with a MATCH function to find the row number for Bat Man

,

replace this with a MATCH function to find the column number for East

)

The formula will read like this:

=INDEX(

return the value in the table range B17:F24 in the cell that is at the intersection of

, MATCH(

the row Bat Man is on

) and, MATCH(

the column East is in

)

The formula looks like this:

=INDEX(

\$B\$18:\$F\$24

,MATCH(

"Bat Man",\$B\$18:\$B\$24,0

), MATCH(

“East”,\$B\$17:\$F\$17,0

))

So why would you put yourself through all that rigmarole when VLOOKUP can do the same job.

Reasons to use INDEX and MATCH rather than VLOOKUP

1) VLOOKUP can’t go left

Taking the table below, let’s say you wanted to find out what program was on the Krafty Kids channel.

VLOOKUP can’t do this because you’d be asking it to find Krafty Kids and then return the value in column B to the left, and VLOOKUP can only look to the right.

In comes INDEX and MATCH with a formula like this:

=INDEX(\$B\$33:\$B\$40,MATCH("Krafty Kids",\$C\$33:\$C\$40,0))

And you get the answer; ‘Mr Maker’.

Notice only the Programs column (B) was referenced in INDEX’s array argument? This means we can omit INDEX’s column number argument as there’s only one column in the INDEX array.

2) Two way lookup

The table below has a drop down list in B1 that enables me to choose the Sales Person from the table, and a drop down list in A2 for the region.  In B2 I’ve got an INDEX + MATCH formula that returns the sales that match my two criteria.

=INDEX(A4:J10,MATCH(A2,A4:A10,0),MATCH(B1,A4:J4,0))

Note: An alternative is to use a VLOOKUP and replace the hard keyed column number with a MATCH formula like this:

Ways to improve these formulas:

1) Use named ranges instead of \$C\$33:\$C\$40 etc. to make formulas more intuitive and quicker to create.

2) An alternative to using a named range is to convert the data to an Excel Table whereby Excel automatically gives the table a named range.

3) If there is nothing else in the columns other than your table you could use column references like this C:C which will search the whole column.

You're reading Index Match Functions In Excel

## The Fifth Adwords Match Type: Phrase Match Modifier

Have you ever wanted to merge Phrase Match and Broad Match Modifier into a single keyword like these people?

It can be frustrating when you want the reach of BMM, but the control of Phrase Match.

Here is what one user had to say:

I am trying to create a keyword pattern that will match the following searches …

london to paris by bus

bus from london to paris

… but not the following searches (opposite travel direction) …

paris to london by bus

bus from paris to london

I imagine using a keyword like the following, but I don’t thinks this is allowed:

“london to paris” +bus

This user was disappointed to hear that:

You cannot use different match types within the same keywords,

The best approach would be to use exact match type with all combinations possible of this query.

The search terms report can be a useful tool.

A few years ago, I was also frustrated that I couldn’t use Phrase Match with a Broad Match Modifier.

I started testing different syntaxes and measuring results in the search terms report.

I tried everything from “the regular” +symbols to #much_more=bizarre &symbols.

After a few months of testing, I noticed something interesting in a search term report.

I saw that when chúng tôi was bid on, it showed up in the search term report as www url com.

I then had a theory that a period would be processed as a space.

After another few rounds of testing, I found a combination that produced the results I was looking for.

How to Use Phrase Match Modifier

For the first time, not protected by an NDA, I am happy to share Phrase Match Modifier for AdWords.

You can now combine Phrase Match with Broad Match, Broad Match Modifier, and other Phrase Match phrases within one keyword. Here are examples of how to use them.

When you add the period between words that start with +, you are binding those words together in that order. They then function like a phrase match within the larger keyword.

PMM is most useful when the specific order of words significantly changes what they mean.

The order of those words completely changes the intent of the search.

You can use PMM to get the semantic control of Phrase Match while still keeping the reach and flexibility of BMM.

A great use case for PMM is when you want to add geographic modifiers to phrase match terms.

For instance, +vacation.home +Florida. A user searching for “home vacation” is not looking for the same thing as a user searching for “vacation home”, however, “Florida” can show up nearly anywhere in the search and hold the same meaning.

Geographic modifiers are a powerful tool for identifying intent for users.

In many industries, users include a location in their search because they are looking for a business where they can take action. They are often further down the funnel, and PMM allows you to easily keep a tight hold on the placements you buy while capturing all the variations of geographically modified searches.

You may be wondering what the PMM syntax does to quality score.

Quality score is calculated for every auction, and the keyword itself only serves to enter an ad into that auction.

The keyword text is not an element of the calculation, just a gatekeeper to entering the auction.

Quality score is then calculated by measuring the relevance of the user’s search phrase to the ad and landing page.

When I use PMM, I see higher quality scores as a result of earning a higher CTR through entering fewer irrelevant auctions.

How Does PMM Impact Stemming

PMM will allow stemming or close variants within your keywords, but I find that the phrase match portion of the PMM tends to be more conservative in its variant matches than the BMM portion.

Check out this real search term report for the PMM +truck.driving.school +NJ. Driving matched to driver, school matched to schools, and NJ matched to New Jersey:

In this example of a PMM search term report, you can see how it works for +cdl +in.NJ.

The phrase match portion, +in.NJ, was not able to match to “in New Jersey”, only “in NJ”.

What I typically see is that the phrase match portions of the PMM will not expand abbreviations like BMM will, but will catch basic stemming like going from singular to plural.

The less comprehensive matching still saves you from having to build out excessively long keyword lists to account for basic variations.

Summary

PMM can be an effective tool for optimizing your AdWords accounts, but only when used correctly.

PMM shines when the order of specific words has a large impact on intent (e.g., free care vs care free), but you want to add modifiers, like a city name, that do not require a specific order for semantic relevance.

## Optimizing Exploratory Data Analysis Using Functions In Python!

This article was published as a part of the Data Science Blogathon.

“The more, the merrier”.

It is a perfect saying for the amount of analysis done on any dataset.

As more and more opt for a career in Data Science, the more is the need to have a Fastrack way to guide each and everyone through the path. I learned python as the base to start and then gradually added skills that helped me grow in the data science domain.

In this post, I will be adding all the important steps and python functions you can use for Exploratory Data Analysis (EDA) on any dataset.

Okay, today’s plan is to run our fingers through data and figure out as much as we can but all in an optimized way. I am writing this article to share user-defined functions to help and shorten the EDA coding time.

The most important steps to follow in a project are:

Importing the data

Data validation Column datatype

Imputing null/missing values

Data exploration (EDA) Univariate

Bivariate

Multivariate

Feature Engineering

Transformation/Scaling

Model building (applying machine-learning algorithms) and tuning

Score calculation

Index Introduction

Univariate analysis

Bi-variate analysis

Multi-variate analysis

Summary

Introduction

The most important and time-consuming part of any analytics problem is understanding the data. It is better to spend time studying the data rather than coding the same thing again and again.

The functions we are going to build today are pretty general and you can adapt them as per your requirement.

The pseudo-code for a user-defined function in python is:

Function Definition:

def func_name(parameters ): # function name and parameters "function_steps" function_commands return [return_value] Function call:

func_name(parameters) Function for Univariate analysis: Categorical:

Below function plots count plot for the feature being passed to the function.

def plot_cat(var, l=8,b=5): plt.figure(figsize = (l, b)) sns.countplot(df1[var], order = df1[var].value_counts().index) Continuous: For a simple distplot for continuous feature

def plot_cont(var, l=8,b=5): plt.figure(figsize=(l, b)) sns.distplot(df1[var]) plt.xlabel(var)

2. To view a detailed kde plot with all details:

# plot kde plot with median and Std values def plot_cont_kde(var, l=8,b=5): mini = df1[var].min() maxi = df1[var].max() ran = df1[var].max()-df1[var].min() mean = df1[var].mean() skew = df1[var].skew() kurt = df1[var].kurtosis() median = df1[var].median() st_dev = df1[var].std() points = mean-st_dev, mean+st_dev fig, axes=plt.subplots(1,2) sns.boxplot(data=df1,x=var, ax=axes[0]) sns.distplot(a=df1[var], ax=axes[1], color='#ff4125') sns.lineplot(points, [0,0], color = 'black', label = "std_dev") sns.scatterplot([mini, maxi], [0,0], color = 'orange', label = "min/max") sns.scatterplot([mean], [0], color = 'red', label = "mean") sns.scatterplot([median], [0], color = 'blue', label = "median") fig.set_size_inches(l,b) plt.title('std_dev = {}; kurtosis = {};nskew = {}; range = {}nmean = {}; median = {}'.format((round(points[0],2),round(points[1],2)), round(kurt,2),round(skew,2),(round(mini,2),round(maxi,2), round(ran,2)),round(mean,2), round(median,2))) Functions for Bi-variate analysis:

The bi-variate analysis is very helpful in finding out correlation patterns and to test our hypothesis. This will help us infer and build different features to feed into our model.

Categorical-Categorical:

def BVA_categorical_plot(data, tar, cat): '''take data and two categorical variables, calculates the chi2 significance between the two variables and prints the result with countplot & CrossTab ''' #isolating the variables data = data[[cat,tar]][:] #forming a crosstab table = pd.crosstab(data[tar],data[cat],) f_obs = np.array([table.iloc[0][:].values, table.iloc[1][:].values]) #performing chi2 test from scipy.stats import chi2_contingency chi, p, dof, expected = chi2_contingency(f_obs) #checking whether results are significant if p<0.05: sig = True else: sig = False #plotting grouped plot sns.countplot(x=cat, hue=tar, data=data) plt.title("p-value = {}n difference significant? = {}n".format(round(p,8),sig)) #plotting percent stacked bar plot #sns.catplot(ax, kind='stacked') ax1 = data.groupby(cat)[tar].value_counts(normalize=True).unstack() ax1.plot(kind='bar', stacked='True',title=str(ax1)) int_level = data[cat].value_counts()

Categorical-Continuous:

Here, I have used two functions, one to calculate z-value and the others to plot the relation between our features.

def TwoSampleZ(X1, X2, sigma1, sigma2, N1, N2): ''' function takes mean, standard dev., and no. of observations and returns: p-value calculated for 2-sampled Z-Test ''' from numpy import sqrt, abs, round from scipy.stats import norm ovr_sigma = sqrt(sigma1**2/N1 + sigma2**2/N2) z = (X1 - X2)/ovr_sigma pval = 2*(1 - norm.cdf(abs(z))) return pval def Bivariate_cont_cat(data, cont, cat, category): #creating 2 samples x1 = data[cont][data[cat]==category][:] # all categorical features x2 = data[cont][~(data[cat]==category)][:] # all continuous features #calculating descriptives n1, n2 = x1.shape[0], x2.shape[0] m1, m2 = x1.mean(), x2.mean() # calculates mean std1, std2 = x1.std(), x2.mean() # calculates standard deviation #calculating p-values z_p_val = TwoSampleZ(m1, m2, std1, std2, n1, n2) #table table = pd.pivot_table(data=data, values=cont, columns=cat, aggfunc = np.mean) #plotting plt.figure(figsize = (15,6), dpi=140) #barplot plt.subplot(1,2,1) sns.barplot([str(category),'not {}'.format(category)], [m1, m2]) plt.ylabel('mean {}'.format(cont)) plt.xlabel(cat) plt.title(' n z-test p-value = {}n {}'.format(z_p_val,table)) # boxplot plt.subplot(1,2,2) sns.boxplot(x=cat, y=cont, data=data) plt.title('categorical boxplot') Continuous-Continuous: #Defining a function to calculate correlation among columns: def corr_2_cols(Col1, Col2): res = pd.crosstab(df1[Col1],df1[Col2]) # res = df1.groupby([Col1, Col2]).size().unstack() res['perc'] = (res[res.columns[1]]/(res[res.columns[0]] + res[res.columns[1]])) return res Functions for Multi-variate analysis: def Grouped_Box_Plot(data, cont, cat1, cat2): #boxplot sns.boxplot(x=cat1, y=cont, hue=cat2, data=data, orient='v') plt.title('Boxplot') Summary

All the above functions help us cut the time and reduce redundancy in our code.

There are times when you will be in need to change the type of plot or add more details in the same. You can alter any function as per your requirement. Do note “Always follow a structure to complete your EDA”. I have shared the steps above you should follow while working with the dataset.

-Rohit

Related

## Accounting Number Format In Excel

Excel functions, formula, charts, formatting creating excel dashboard & others

Accounting Number Format in Excel Difference between Currency and Accounting Number Format in Excel

Accounting Format:

The difference between Currency and Accounting format is shown in the below screenshot.

How to Use Accounting Number Format in Excel?

In Microsoft Excel, we can find the accounting format under the number formatting group shown in the below screenshot.

Also, we can format the number in accounting format by choosing the dollar sign \$ in the number group, which is also one of the shortcuts for the accounting number format shown in the below screenshot.

Example #1

Converting Number to Excel Accounting Format

You can download this Accounting Number Format Excel Template here – Accounting Number Format Excel Template

In this example, we will learn how to convert the normal number to accounting format. Consider the below example, which shows MRP, Selling Price of the individual product with local, national and zonal prices.

As we can notice that all the numbers are in general format by default, Assume that we need to convert the “Selling Price” to Accounting Number format along with Local, Zonal, and National selling prices.

In order to convert the number to Accounting format, follow the below procedure step by step.

First, select the column from E to H, where it contains the product’s selling price, which is shown in the below screenshot.

Once we choose the Accounting number format, we will get the output as ###, which is shown below.

We can notice that once we convert the number to accounting number format, excel will align the dollar sign at the left edge of the cell and display with two decimal points that we are getting the ### hash symbols.

Enlarge all the columns so that we can see the exact accounting format output, which is shown below.

In the below result, we can see that all the numbers are converted where we can see the Dollar sign\$ in each left edge of the cell separated by commas and with two decimal numbers.

Example #2

To apply accounting number formatting, follow the below step by step procedure as follows.

First, select the column from E to H, where it contains the product’s selling price, which is shown in the below screenshot.

In the above screenshot, we can see the list of number formatting options.

Select the Accounting option so that it will display the accounting format, which is shown below.

As we can see, on the right-hand side, we can see decimal places where we can increase and decrease the decimal points, and next to that, we can see the symbol drop-down box, which allows us to select which symbol needs to be displayed. (By default, accounting format will select the Dollar Sign \$)

Once we increase the decimal places, the sample column will display the number with selected decimal numbers which are shown below.

Example #3

This example shows how to sum the accounting number format by following the below steps.

Consider the example which shows sales data for the month of OCT-18.

As we can see that there are normal sales figures in the General number format. Now we will convert the above sales figure to accounting format for accounting purposes.

First, copy the same B column sales figure next to the C column, which is shown below.

Now select the C column and go to the number formatting group and choose Accounting, shown below.

As we can see, the difference that C column has been converted to accounting format with a Dollar sign with two decimal places and at the last column for negative numbers accounting format has shown the number inside the parenthesis.

Put the SUM formula in the C13 column, which will show the SUM in accounting format.

In the below result, we can see that the accounting format which automatically uses the dollar sign, decimal places, and comma to separate a thousand figures where we cannot see those in General number format.

Things to Remember

The accounting number format is normally used for financial and accounting purposes.

The accounting number format is the best way to configure the values.

For negative values accounting format will automatically insert the parenthesis.

Recommended Articles

This has been a guide to Accounting Number Format in Excel. Here we discussed how to use Accounting Number Format along with practical examples and a downloadable excel template. You can also go through our other suggested articles –

## Workday In Excel (Formula, Examples)

WORKDAY in Excel

Workday function in excel returns the Date, which is the official working day from the date which we feed into the syntax. This is quite useful for getting what would the working day date after selective day counts. As per syntax, we just need to select the date from which we need to count the number of the working day, then select how many days we need to count, and if there is any week off, we have optional. If we select today’s date with 5 days and 2 weeks off days, we will get the date of the same weekday.

Excel functions, formula, charts, formatting creating excel dashboard & others

WORKDAY Formula in Excel:

Below is the WORKDAY Formula in Excel.

Explanation of WORKDAY Formula in Excel

A WORKDAY Function in Excel includes two mandatory parameters and one optional parameter.

Start_date: “Starting date of the project or any work”.

Days: The total number of days required to complete the work or project. This does not include weekends (Saturday and Sunday).

[Holidays]: This is an optional parameter. This section asks whether the days you have mentioned include any holidays. For this, you need to make a list of holidays separately.

WORKDAY Function in Excel by default excludes Saturday and Sunday as weekend days. If at all you need weekends for any other day, you can use chúng tôi function. For example: In the Middle East region, weekend days are Friday & Saturday. In these cases, we can use chúng tôi function instead of a normal WORKDAY Function in Excel.

How to Use WORKDAY Function in Excel?

You can download this WORKDAY Function Excel Template here – WORKDAY Function Excel Template

Example #1

Using the WORKDAY Function in excel, we can generate a series of dates even though we can generate by using the drag and drop option.

Step 1: Enter the one date on cell A2 as 12/Nov/2024.

Step 2: Now, in cell A3, apply the WORKDAY Function as shown in the below image.

=WORKDAY(A2,1)

The above formula takes the cell A2 as a reference and increases the date by 1.

Step 3: Drag the formula until cell A18.

Look at the formula here; 12/Nov/2024 is on Monday; we are increasing the day by 1. When we drag the formula, it will increment the date by 1 until 16/Nov/2024. If you drag one more time, it will jump to 19/Nov/2024 and excludes 17/Nov/2024 and 18/Nov/2024; those are weekends.

Similarly, in the next week, workdays are from 19/Nov/2024 to 23/Nov/2024, and weekends are 24/Nov/2024 and 25/Nov/2024.

Example #2

The project starting date and project duration date calculate the project ending date by using a WORKDAY Function in Excel.

Note: No holidays apply to these projects.

Step 1: Copy and paste the above data to an excel sheet.

Step 2: Apply the WORKDAY Function in column C starting from cell C2.

=WORKDAY(A2,B2)

Result is :

Example #3

Consider the above example data for this also. But here, the list of holidays is available to estimate the project ending date.

The list of holidays are:

Apply the same formula as shown in example 2, but here you need to add one more parameter, i.e. holidays.

=WORKDAY(A2,B2,\$G\$2:\$G\$21)

Result is :

Example 2 vs Example 3:

Now we will see the difference between the two examples.

In the second example for the first project-ending date is 8/8/2024, and there is one holiday, for example, 3, so the ending date increased by 1 day.

For the second project, the ending date is 30/01/2024, for example, 2 and example 3; there 5 holidays, so the ending date increased by 7 days because of the in-between weekend.

Example #4

Assume you are working in the Accounts Receivable team; you have a list of invoices and due dates against those invoices. You need to find the due days for those invoices.

=WORKDAY(A2,B2)

Result is :

Initially, the result looks like serial numbers. We need to change our formatting to make it correct.

Step 1: Select the entire range.

Step 2: Now press ctrl +1. It will open up a formatting dialogue box.

Step 4: Your result looks like the below one.

Things to Remember

If you want to use different weekends other than Saturday and Sunday, use chúng tôi function.

We can use only numeric values for the day’s argument.

The date and days should be accurate otherwise;, we will get the error as #VALUE!

If the date includes time, then the formula considers only the date portion and ignores the time portion.

If you supply decimal numbers, a formula will round down the value. For example: if you supply 10.6 days, then the formula treats this as 10 days only.

Recommended Articles

This has been a guide to WORKDAY ID in Excel. Here we discuss the WORKDAY Formula in Excel and how to use the WORKDAY Function in Excel along with practical examples and downloadable excel templates. You can also go through our other suggested articles –

## Oracle Expected To Match Earnings Projections

Software giant Oracle, now a hardware company as well thanks to its purchase of Sun Microsystems, reports earnings on Thursday after the close of trading, and at least one analyst believes there will be no major surprises.

That’s good or not so good, depending on your perspective. It could be argued that Oracle’s (NASDAQ: ORCL) only significant competitor on a soup-to-nuts, hardware and software basis is IBM (NYSE: IBM). On the other hand, Oracle isn’t in a high-growth industry. Much of its growth has come from acquisitions in recent years, and there aren’t that many big targets left for it any more.

There’s also the added pressure on Oracle’s margins from Sun. As Broadpoint.AmTech analyst Yun Kim noted in a research note on the company earlier this month, Oracle currently enjoys the highest operating margin in the industry and is a relentless cost cutter.

Sun, however, is a hardware company, and hardware is not known for being a high margin business (with Apple a notable exception) and could cause “Oracle’s overall margin profile to decline substantially and it may be weighed down for some time while the company digests the acquisition,” Kim wrote.

Still, Kim expects Oracle to meet estimates with revenue for the third fiscal quarter ended February 26 of \$6.41 billion, a 9 percent improvement over the second fiscal quarter and a 17 percent improvement over the same quarter last year. Oracle should report net income of \$1.9 billion, or \$0.37 per share.

Agreeing with Kim, a consensus survey by Thomson Reuters estimates Oracle will report earnings of \$6.35 billion and EPS of \$0.38.

One potential area of softness might be the benefit for currency. Recent strength in the U.S. dollar versus the Euro could likely lead to much less than the 7 to 8 percent currency benefit Oracle had forecasted for the quarter. But Kim added he does not expect weaker-than-expected currency to have any significant impact on its non-GAAP EPS.

“We believe its core database business remains solid, although certain local regions and certain verticals faced a more challenging sales environment than expected. Within its database business, ORCL’s middleware business put together yet another strong performance. We believe its application business is likely to remain lackluster,” Kim wrote in his note.

All things considered, he does not project any significant changes to projections as a result. Sun, he wrote, will not be a distraction for now. “We believe that investors are likely to focus on Oracle’s core business in the near-term and not put too much emphasis on Sun’s business as long as it continues to reaffirm its FY11 financial targets, which includes contribution from Sun,” he wrote.

Sun is expected to provide around \$635 million, \$1 million off from an earlier projection by UBS, and it will provide around \$1.22 billion in product and services revenue next quarter, according to Kim.

The fourth fiscal quarter ending in May is traditionally Oracle’s busiest for the year. Kim projects Oracle will report revenue of \$9.61 billion and non-GAAP income of \$2.71 billion, or \$0.58 per share.

Andy Patrizio is a senior editor at chúng tôi the news service of chúng tôi the network for technology professionals.

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