If (temp = 8) Then
med = 0
Else
med = Application.Median(TP.Columns(j))
End If
Instead of using the in built function Application.Median, how do I calculate the median?
Instead of using in built functions you can create an array to store all your values and then get the median value by arranging the values and getting the one in array length/2.
If your array length / 2 is a decimal value you will have to get the values of rounding up and rounding down the array length/2, adding them and the dividing them by 2.
Related
I'm trying to compute a new variable using 3 other variables. If all 3 conditions are positive, the new variable gives 1. My problem: if just 1 or 2 of these conditions are present, I get a value 0 when it needs to be a system missing.
In order to get SPSS to calculate a value only when you have values in all three variables you can use this:
if nmiss(E’sept_preTx, Eope’gem_preTx, TRpieksnelheid_t1)=0
DD_new=(E’sept_preTx < 10) & (Eope’gem_preTx >= 15) & (TRpieksnelheid_t1 > 2.8).
the nmiss counts the missing values, and the original calculation is carried out only if tere are none.
I have certain numbers within a column of my dataframe that have negative numbers in a string format like this: "500.00-" I need to convert every negative number within the column to numeric format. I'm sure there's an easy way to do this, but I have struggled finding one specific to pandas dataframe. Any help would be greatly appreciated.
I have tried the basic to_numeric function as shown below, but it doesn't read it in correctly. Also, only some of the numbers within the column are negative, therefore I can't simply remove all the negative signs and multiply the column by 1.
Q1['Credit'] = pd.to_numeric(Q1['Credit'])
Sample data:
df:
num
0 50.00
1 60.00-
2 70.00+
3 -80.00
Using series str accessor to check last digit. If it is '-' or '+', swap it to front. Use df.mask to apply it only to rows having -/+ as suffix. Finally, astype column to float
df.num.mask(df.num.str[-1].isin(['-','+']), df.num.str[-1].str.cat(df.num.str[:-1])).astype('float')
Out[1941]:
0 50.0
1 -60.0
2 70.0
3 -80.0
Name: num, dtype: float64
Possibly a bit explicit but would work
# build a mask of negative numbers
m_neg = Q1["Credit"].str.endswith("-")
# remove - signs
Q1["Credit"] = Q1["Credit"].str.rstrip("-")
# convert to number
Q1["Credit"] = pd.to_numeric(Q1["Credit"])
# Apply the mask to create the negatives
Q1.loc[m_neg, "Credit"] *= -1
Let us consider the following example dataframe:
Q1 = pd.DataFrame({'Credit':['500.00-', '100.00', '300.00-']})
Credit
0 500.00-
1 100.00
2 300.00-
We can use str.endswith to create a mask which indicates the negative numbers. Then we use np.where to conditionally convert the numbers to negative:
m1 = Q1['Credit'].str.endswith('-')
m2 = Q1['Credit'].str[:-1].astype(float)
Q1['Credit'] = np.where(m1, -m2, m2)
Output
Credit
0 -500.0
1 100.0
2 -300.0
I am trying to divide 2 pandas columns (same column divided by shifting one cell) but getting an error as below...
..This is surprising as I have done such computation many times before on time series data and never encountered this issue.
Can someone suggest what is going on here?...I am computing the daily returns of Adj Close price of a stock so need the answer in decimal.
I think you need convert to float first column, because dtype is object, what is obviously string:
z = x.astype(float) / y.astype(float)
Or:
data['Adj Close'] = data['Adj Close'].astype(float)
z = data['Adj Close'].shift(-1) / data['Adj Close']
I have an output of ranges from 150-0. I want to map those to 0 to 1. Or perhaps 0 to (some value less than 1 such as 0.5) where 150 is 0 and 0 is 1 ( or some values less than..).
Is this considered interpolation? What is the formula to derive these values? But preferably, is there a built-in StdLib function I can call?
Divide your number by the (Max - min). This would make 150 be 1 and 0 will be 0, with everything else a number in between. Now, to make it the opposite just do 1 - result.
If you need to map 0-1 to any custom range, you need to multiply range with MAX-MIN and then add MIN to it to get the exact number in range.
Formula will be MIN + (MAX-MIN)*value
where value is range in between 0-1;
MIN is number mapped to 0;
MAX is number mapped to 1;
I am writing a custom totaling method for a grid view. I am totaling fairly large numbers so I'd like to use a decimal to get the total. The problem is I need to control the maximum length of the total number. To solve this problem I started using float but it doesn't seem to support large enough numbers, I get this in the totals column(1.551538E+07). So is there some formating string I can use in .ToString() to guarentee that I never get more then X characters in the total field? Keep in mind I'm totaling integers and decimals.
If you're fine with all numbers displaying in scientific notation, you could go with "E[numberOfDecimalPlaces]" as your format string.
For example, if you want to cap your strings at, say, 12 characters, then, accounting for the one character for the decimal point and five characters needed to display the exponential part, you could do:
Function FormatDecimal(ByVal value As Decimal) As String
If value >= 0D Then
Return value.ToString("E5")
Else
' negative sign eats up another character '
Return value.ToString("E4")
End If
End Function
Here's a simple demo of this function:
Dim d(5) As Decimal
d(0) = 1.203D
d(1) = 0D
d(2) = 1231234789.432412341239873D
d(3) = 33.3218403820498320498320498234D
d(4) = -0.314453908342094D
d(5) = 000032131231285432940D
For Each value As Decimal in d
Console.WriteLine(FormatDecimal(value))
Next
Output:
1.20300E+000
0.00000E+000
1.23123E+009
3.33218E+001
-3.1445E-001
3.21312E+016
You could use Decimal.Round, but I don't understand the exact question, it sounds like you're saying that if the total adds up to 12345.67, you might only want to show 4 digits and would then show 2345 or do you just mean that you want to remove the decimals?