Solved: sumif in python on a column and create new column

The main problem with sumif in Python is that it can only sum values up to a certain limit. If you need to sum values over a larger range, you’ll need to use another function like max or min.

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I have a dataframe that looks like this:
<code>df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 4, 5], 'C': [3, 4, 5, 6]})

A  B  C
0  1  2  3
1  2  3  4
2  3  4  5
3  4  5  6
</code>
I want to create a new column D that sums the values in column A if the value in column B is greater than the value in column C. So for row 0 it would be <code>1+2+3=6</code>, for row 1 it would be <code>2+3=5</code>, and so on. The expected output is:
<code>   A  B   C    D
0   1   2   3    6     # (1+2+3) since B &gt; C for row 0 only
1   2   3   4    5     # (2+3) since B &gt; C for row 1 only
2   3   4   5    0     # no values added since B &lt;= C
3   4   5   6    0     # no values added since B &lt;= C

liedToListOfAllModeValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[len([])]]]]]]]]]]])],[len([])]],[len([])]],[len([])]],[len ([])]],[…],…,…,…,…,…,…)==numberOfModeValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneModeValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==one weighted average calculation method whereby you sort your data points either ascendingly or descendingly according to their numerical values then you multiply each unique numerical value by the number of times it occurs using collections library’s Counter class then you return either one most common element MCE if your dataset’s length LEN modulo division remainder RMD after division through two == zero OR you return two most common elements MCEs=[MCE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-(-(-(-(-(-(-(–(–(—))))))))AND MCE_HIGH=(LEN/2)+((RMD)/4)*(-((RMD)/4)))+(–)]then you calculate their arithmetic mean AMEAN=(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToListOfAllWeightedValuesInDataset), where len([MCE_LOW,[MCE_HIGH]])=len([[len([[len([[[[[[[[[[[[len([])]]]]]]]]]]])],[len ([])]],[…],…,…,…,…)==numberOfWeightedValuesInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==zeroORoneWeightedValueInDatasetModuloDivisionRemainderAfterDivisionThroughTwo==one geometric mean average calculation method whereby you sort your data points either ascendingly or descendingly according to their numerical values then you multiply all unique numerical values together using collections library’s Counter class then you return either one most common element MGE if your dataset’s length LEN modulo division remainder RMD after division through two == zero OR you return two most common elements MGES=[MGE_LOW=(LEN/2)-((RMD)/4)*(-((RMD)/4))-1AND MGE_HIGH=(LEN/2)+((RMD)/4)*(-((RMD)/4)))+1]then you calculate their arithmetic mean AMEAN=10**(AMEAN_(forEachElementInList=[AMEAN_(forEachElementInList=[ameanOfAllElementsExceptForTheFirstAndLastOne)]),ameanOfAllElementsExceptForTheFirstAndLastOne]=meanAverageCalculationMethodAppliedToLogarithmicallyTransformedListOfGeometricMeans)), where len(MGES)=number of geometric means in dataset

This is a Python code that creates a new column D in a pandas DataFrame. The new column D contains the sum of the values in column A, but only if the value in column B is greater than the value in column C.

Sumif

Sumif is a Python library for calculating summaries of data. It can be used to calculate the sum, average, minimum, maximum, or percentile of a list of values.

Create columns

In Python, you can create columns in a dataframe by using the column() function. The syntax for column() is as follows:

column(name, data)

where name is the name of the column and data is the data you want to put in that column.

Work with data and columns

In Python, you can work with data in columns by using the dict() function. This function takes as its argument a list of column names, and returns a dictionary object. Each key in this dictionary is a column name, and each value is a corresponding value from the data set.

For example, to create a dictionary object that contains the values from the data set “data” in columns “name” and “age”, you could use the following code:

data = [ ‘name’ , ‘age’ ] dict ( data )

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