Created dictionary called items. Combined values for different keys and put into variable food_list.
items={'Food':['Ice Cream','Salad'],'Computer':['Laptop','Notebook']
food_list= '|'.join(items['Food'])
Description Amount
Lenovo Laptop 300
Chicken Salad 40
Dell Notebook 250
Chocolate Ice Cream 3
I tried to find a string based on dictionary values. If the string is in the dictionary, then the row in the dataframe contains the string. I take the amount it is associated with and add up the total row amounts that fit the condition.
total_amount=df.loc[df['Description'].str.contains(food_list,na=False)
==df['Amount'].sum()]
I usually run the code and get
Empty DataFrame
Columns: [Date, Description, Amount]
Index: []
Related
So I have a dataframe with different data:
Ordernumber
Name
customerID
Abel
1939184849
Rose
1029480129
Rob
1283949203
As you can see the name and customerID are already filled in.
Now I need to generate unique ordernumbers, please keep in mind that the actual database has around 20,000 rows, so the ordernumber has to be unique.
Can it be only numbers? i.e. ranging from 0 to the total length of your dataframe?
df['Ordernumber'] = [n for n in range(0,len(df),1)]
I am currently trying to find optimal portfolio weights by optimizing a utility function that depends on those weights. I have a dataframe of containing the time series of returns, named rets_optns. rets_optns has 100 groups of 8 assets (800 columns - 1st group column 1 to 8, 2nd group column 9 to 16). I also have a dataframe named rf_options with 100 columns that present the corresponding risk free rate for each group of returns. I want to create a new dataframe composed by the portfolio's returns, using this formula: p. returns= rf_optns+sum(weights*rets_optns). It should have 100 columns and each columns should represent the returns of a portfolio composed by 8 assets belonging to the same group. I currently have:
def pret(rf,weights,rets):
return rf+np.sum(weights*(rets-rf))
It does not work
I have a dataset. I am using pandas dataframe and named it df.
The dataset has 50,000 rows - here are the first 5:.
Name_Restaurant cuisines_available Average cost
Food Heart Japnese, chinese 60$
Spice n Hungary Indian, American, mexican 42$
kfc, Lukestreet Thai, Japnese 29$
Brown bread shop American 11$
kfc, Hypert mall Thai, Japnese 40$
I want to create column which contains the no. of cuisines available
I am trying code
df['no._of_cuisines_available']=df['cuisines_available'].str.len()
Then instead of showing the no. of cuisines, it is showing the sum of charecters.
For example - for first row the o/p should be 2 , but its showing 17.
I need a new column that contain number of stores for each restaurant. example -
here kfc has 2 stores kfc, lukestreet and kfc, hypert mall. I have completely
no idea how to code this.
i)
df['cuisines_available'].str.split(',').apply(len)
ii)
df['Name_Restaurant'].str.split(',', expand=True).melt().['value'].str.strip().value_counts()
What ii) does: split columns at ',' and store all strings thus generated in an individual column. Then use melt to make one big column, strip away spaces etc. and count individual entries.
I'm currently working on a report where I'm given 3 different datasets. The report essentially calculates the input, output and losses of a given food production process.
In dataset "Spices", contains the quantity of spices used under a field named "Qty_Spice". In dataset "Meat", contains the quantity of meat used under a field named "Qty_Meat". In dataset "Finished", contains the quantity of finished product used under a field "Qty_Finished".
I'm currently trying to create a table where the amount of input (spice+meat) is compared against output (finished product), such that the table looks like this:
Sum of Inputs (kg) | Finished Product (kg) | Losses (kg)
10 8 2
8 5 3
Total:
18 13 5
What I'm currently doing is using lookupset to get all inputs of both spices and meats (using lookupset instead of lookup because there are many different types of meats and spices used), then using a custom code named "Sumlookup" to sum the quantities lookupset returned.
The problem I'm having is that when I want to get the total sum of all inputs and all finished products (bottom of the table) using "Sumlookup" the table is only returning the first weight it finds. In the example above, it would return, 10, 8 and 2 as inputs, finished products and losses respectively.
Does anyone know how I should approach solving this?
Really appreciate any help
Here is the custom code I used for SumLookUp:
Public Function SumLookup(ByVal items As Object()) As Decimal
Dim suma As Decimal = 0
For Each item As Decimal In items
suma += item
Next
Return suma
End Function
First up, my environment: SQL 2005 + MS DAX 2009.
We have made a table that gets used in a matrix-like fashion for entering in purchase orders via an AX form. So each row will have:
a column for item#
a column for color
columns 1-7 for size (size1, size2,...), quantity (qty1, qty2,...), and cost (cost1, cost2,...).
I am trying to create a report in SSRS that basically uses this data in a more list-like fashion for printing out a PO order form.
I have got it to show the sizing right, but the cost situation complicates it as the unit cost can, and does, differ depending on size (for instance 2XL is more than S-M-L).
For example in our table, item 10000 black has 3 for Small (this data would be qty1), 3 for Medium (qty2) and 4 2XL (qty5). The cost for qty1 and qty2 are the same at $2.50 (cost1 and cost2). The cost for qty5 (cost5) would be $4. I would like to have this broken out into 2 rows by the cost and associated size on the form. So one line would have 10000 black Small and medium info and the second row would have the same item and color, but only have 2XL and its cost data.
Is there a way to "match" fields or somehow cycle through them to get the correct cost without having to have an additional 7 cost columns? Or perhaps there is a more elegant solution that is escaping me?