I have created column as mentioned below:
0 A 00
1 B 01
2 C 02
3 D 03
4 E 04
5 F 05
6 G 06
7 H 07
8 I 08
9 J 09
10 K 010
I have two columns , first column has 0-10 and second column has A-K and I have created a new column with combinations of AA , AB till ZZ in numbers as in 00 , 01 till 010. I need to substitute the number with the alphabets using vlookup and if condition . Kindly help me with it
Can I do it with help of =Vlookup() and/or =If() condition alone?
Sorry, not really sure what you're after, I can't read your initial starting data very clearly ?
To replace a value, you can:
=SUBSTITUTE(A1,"0","A")
not sure if that does what you want, though?
I'll start a new answer, it's different enough from the first post.
So, taking your initial data:
I add 2 more columns: It's simply the mapping of Column C, split out into column D and E. At time of writing this, I have no idea how you translate that programatically, so until we have the rules, I'll just do this . and we can plug that other part in later.
0 A 00 0 0
1 B 01 0 1
2 C 02 0 2
3 D 03 0 3
4 E 04 0 4
5 F 05 0 5
6 G 06 0 6
7 H 07 0 7
8 I 08 0 8
9 J 09 0 9
10 K 010 0 10
And then use this formula:
=CONCATENATE(VLOOKUP(D1,$A$1:$B$11,2,FALSE),VLOOKUP(E1,$A$1:$B$11,2,FALSE))
and you'll end up with this:
AA
AB
AC
AD
AE
AF
AG
AH
AI
AJ
AK
If your ultimate goal is to generate AA thru ZZ, first enter the following UDF in a standard module:
Public Function BumpString(s As String) As String
Dim aryIN(1 To 1000) As Integer
Dim L As Long, i As Long, carry As Boolean
L = Len(s)
j = 1
For i = L To 1 Step -1
aryIN(j) = Asc(Mid(s, i, 1))
j = j + 1
Next i
For i = 1 To L
carry = False
aryIN(i) = aryIN(i) + 1
If aryIN(i) = 91 Then
aryIN(i) = 65
carry = True
Else
Exit For
End If
Next
If carry Then
L = L + 1
aryIN(L) = 65
End If
For i = 1 To L
BumpString = Chr(aryIN(i)) & BumpString
Next i
End Function
Then pick a cell, say D1 and enter AABelow it enter:
=bumpstring(D1)
Then copy D2 down thru D676
You don't need VLOOKUP for this.
You can use the CODE function with the first and last character:
=(CODE(LEFT(A1))-65) & (CODE(RIGHT(A1))-65)
Note that many combinations will map to the same number, such as "BU" and "MA":
Related
I have a data frame that looks like:
tdelta A B label
1 11 21 Lab1
2 24 45 Lab2
3 44 65 Lab3
4 77 22 Lab4
5 12 64 Lab5
6 39 09 Lab6
7 85 11 Lab7
8 01 45 Lab8
And I need to transform this dataset into:
For selected window: 4
A1 A2 A3 A4 B1 B2 B3 B4 L1 label
11 24 44 77 21 45 65 22 Lab1 Lab4
12 39 85 01 64 09 11 45 Lab5 Lab8
So based on the selected window - 'w', I need to transpose w rows with the first corresponding label as my X values and the corresponding last label as my Y value. here is what I have developed till now:
def data_process(data,window):
n=len(data)
A = pd.DataFrame(data['A'])
B = pd.DataFrame(data['B'])
lb = pd.DataFrame(data['lab'])
df_A = pd.concat([gsr.loc[i] for i in range(0,window)],axis=1).reset_index()
df_B = pd.concat([st.loc[i] for i in range(0,window)],axis=1).reset_index()
df_lb = pd.concat([lb.loc[0],axis=1).reset_index()
X = pd.concat([df_A,df_B,df_lab],axis=1)
Y = pd.DataFrame(data['lab']).shift(-window)
return X, Y
I think this works for only the first 'window' rows. I need it to work for my entire dataframe.
This is essentially a pivot, with a lot of cleaning up after the pivot. For the pivot to work we need to use integer and modulus division so that we can group the rows into windows of length w and figure out which column they then belong to.
# Number of rows to group together
w = 4
df['col'] = np.arange(len(df))%w + 1
df['i'] = np.arange(len(df))//w
# Reshape and flatten the MultiIndex
df = (df.drop(columns='tdelta')
.pivot(index='i', columns='col')
.rename_axis(index=None))
df.columns = [f'{x}{y}'for x,y in df.columns]
# Define these columns and remove the intermediate label columns.
df['L1'] = df['label1']
df['label'] = df[f'label{w}']
df = df.drop(columns=[f'label{i}' for i in range(1, w+1)])
print(df)
A1 A2 A3 A4 B1 B2 B3 B4 L1 label
0 11 24 44 77 21 45 65 22 Lab1 Lab4
1 12 39 85 1 64 9 11 45 Lab5 Lab8
I've loaded data from a tab deliminated file into a DF. The Tab data is a form filled out with a template.
A critical concept is that a variable number of rows makes up one entry in the form. In DF1 below, every time the index is "A", a new record is starting. So the code will need to iterate through the rows to rebuild each record in DF2. Each record will be represented as one row in DF2.
Based on the fact that each "A" row in DF1 starts a new form entry (and corresponding row in DF2), we can see in DF1 below there are just two entries in my example, and will be just two rows in DF2. Also imortant: there are a different number of pieces of data (columns) in each row. Z has 2 (then NAs), A has 3, B has 4.
All of this needs to be mapped to DF2 depending on the index letters Z, A, B (note there are more index letters but this is simplified for this example).
DF 1
- A B C D
Z xyz 5 NA NA
A COA aa bb NA
B RE 01 02 03
B DE 04 05 06
A COB dd ee NA
B RE 01 02 03
B DE 04 05 06
In the past i've done this type of thing in VBA and would have used a CASE statement to transform the data. I've found a good start using dictionaries in this thread:
Replacements for switch statement in Python?
One code example at the above thread suggests using a dictionary type case statement:
return{
'a': 1,
'b': 2,
}[x]
This seems like it would work although i'm not certain how to execute in practice. In addition for each A, B, etc above, I need to output multiple instructions, depending on the index letter. For the most part, the instructions are where to map in DF2. For example, in my:
Index A:
Map column A to DF2.iloc[1]['B']
Map column B to DF2.iloc[1]['C']
Map column C to DF2.iloc[1]['D']
Index B:
Would have four instructions, similar to above.
DF2 would end up looking like so
- A B C D E F G H I J K L
1 xyz COA aa bb RE 01 02 03 DE 04 05 06
2 xyz COB dd ee RE 01 02 03 DE 04 05 06
So for each row in DF1, a different number of instructions is being performed depending on the "index letter." All instructions are telling the code where to put the data in DF2. The mapping instruction for each different index letter will always be the same for the columns, only the row will be changing (some type of counter as you move from one record group to the next in DF2).
How can I handle the different number of instructions for each type of index letter in a switch/case type format?
Thank you
I think you can use:
#filter only 2,3 index rows
df1 = df[df.index.isin([2,3])].copy()
#create new column for same value if 2 in index
df1['new'] = np.where(df1.index == 2, 'Z', df1.A)
#create groups by compare 2
df1['g'] = (df1.index == 2).cumsum()
#convert columns to index and reshape, then change order
df1 = (df1.set_index(['g','new']).unstack()
.swaplevel(0,1, axis=1)
.sort_index(axis=1, ascending=[False, True]))
#default columns names
df1.columns = range(len(df1.columns))
print (df1)
0 1 2 3 4 5 6 7 8 9 10 11
g
1 ABC aa bb cc R 01 02 NaN D NaN 03 04
2 DEF dd ee ff R 01 02 NaN D NaN 03 04
Im trying to have a formula, that gets the first result of entry, for every line.
An Example Table would be like this:
Column A Column B Column C Excepted Output from Formula
3 99 P 18 P 4
4 88 P 144 P 1
2 77 P 2
2 77 P 2
1 88 P 1 P 1
1 99 P 4 P 4
2 44 P 5
3 22 P 7
1 88 P 99 P 1
Now, on Column D it should always show the first time it finds Coulmn A = 1, and Column B the same value as the own row (99 for the first row, 88 for the second, 77 for the 3rd...), and Display the Column C of it.
I tried it with the following Formula:
=CALCULATE(
FIRSTNONBLANK('Table'[Column C]; TRUE());
FILTER('Table';'Table'[Column A]=1);
FILTER('Table';'Table'[Column B]='Table'[Column B])
)
Which doesnt work. No errors, but it ignores the second filter.
If i now replace the "='Table'[Column B]" with a number that it should take (99,88,77...) it shows the correct result. But since its now a static number, it shows the same Result in every line, instead of calc it always new.
Can someone help?
Try this:
= CALCULATE(FIRSTNONBLANK('Table'[Column C], TRUE()),
FILTER(FILTER('Table','Table'[Column A]=1),'Table'[Column B] = earlier('Table'[Column B])))
My current dataframe data is as follows:
df=pd.DataFrame([[1.4,3.5,4.6],[2.8,5.4,6.4],[7.8,6.5,5.8]],columns=['t','i','m'])
t i m
0 14 35 46
1 28 54 64
2 28 34 64
3 78 65 58
My goal is to apply a vectorized operations on a df with a conditions as follows (pseudo code):
New column of answer starts with value of 1.
For row in df.itertuples():
if (m > i) & (answer in row-1 is an odd number):
answer in row = answer in row-1 + m
elif (m > i):
answer in row = answer in row-1 - m
else:
answer in row = answer in row-1
The desired output is as follows:
t i m answer
0 14 35 46 1
1 28 54 59 60
2 78 12 58 2
3 78 91 48 2
Any elegant solution would be appreciated.
I have a excel document where I have something like this
A B C
1 1 45 a
2 2 32 b
3 3 34 H
4 3 37
5 4 49 i
5 5 76 H
Note that 3c is combined but 3b isn't.
I need to do a macro to change the content of B with the content of C but only those that don't have a letter H. so the document would look like this
A B C
1 1 a a
2 2 b b
3 3 34 H
4 3 37
5 4 i i
5 5 76 H
Here a image
Just use this simple formula in column C,
=IF(B1="H",A1,B1)
and drag it throughout the range. Once done, copy paste all of the data in column C to A (paste special)