I need to determine the skewness of each column in a dataframe.
Dataframe- df
Columns to determine skewness - col1,col2
Write a function to determine the skewness of each column
Related
I'm trying to loop through a dataframe. All the columns are numeric. I want to get the sum of each column and add it to a dictionary that has the name of the column as the key and the sum of the column as the value.
I have a dataset in which all the columns are numbers and there are some nans I want to fill. The rows must be seen as a temporal serie, so I want to fill those nans with the average of next and previous values. Is there any way to do it in Pyspark?
Thanks!!
I have a dataframe called df(please refer the figure given)
I want the correlation between A1&A2 in separate column A3.
Code I have written:
I have created new column A3 to my dataframe
df['A3']=df['A1'].corr(df['A2'])
with the above I am getting incorrect correlation value
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I have two datasets: both have an id column and a date column.
Dataset A can have multiple date entries (rows) per id - i.e., it is a long dataset
Dataset B only has one date entry per id
The two datasets are in a single spreadsheet:
Columns A and B are the id and date for dataset A
Columns E and F are the id and date for dataset B
I am trying to use the =AND formula in Excel to determine which rows in Dataset A match exactly to their respective row in Dataset B.
Example
Here is a toy example with the desired results in Column C.
How should this be coded?
I assumed that the following formula in column C (e.g., C2=AND(A2=E:E,B2=F:F) would return TRUE when the exact match occurs; however, the formula returns FALSE in all cells.
So my method is pretty lengthy but here's the code
I put this under C2
=IF(ISNA(VLOOKUP(B2,F:F,1,FALSE)), "FALSE", "TRUE")
So basically VLookup looks at B2 and checks if its in the F column.
If it isn't it returns N/A, if it is, it returns the date value.
So if the value is N/A, it will return "FALSE", which in C2 it does return.
It should return "TRUE" for the corresponding true values.
The third parameter is 1 by default since F:F has only 1 column.
There's probably more elegant solutions but I hope that helps!
I created a measure in PowerPivot that has the following formula:
Calculated Percentage:=[PercentageA]+[PercentageB]*AVERAGE([Multiplier])
Here is the result:
What I would like from this measure is in the Desired values column. The point would be to see the grand total as the SUM of the values of the measure instead of multiplying the grand total PercentageB with the grand total average of Multiplier.
One way to solve this would be to use SUMX function. Which based on a provided table or column does an iterative calculation based on a formula provided, and then sums the result.
So in the below example, the VALUES( table_Name[Row Labels]) is used to create a table of unique values from the Row Lables column what it will iterate on. Then within each row grouping it will apply the defined formula.
Measure:= SUMX( VALUES( table_Name[Row Labels]),
Calculate([PercentageA]+[PercentageB]*AVERAGE([Multiplier])))
Note: Depending on how your average([Multiplier]) is defined, you may need to use calculate to override the context. E.g. if it is suppose to be the average over the entire set instead of the row.