Obviously d can be found from e and φ(n) but can e be right if d is given and φ(n) is given by putting in the values in the algorithm
Related
If I had data in rows A to E as seen below in the table. Some of the values can be NA. IN column F if i wanted to input data from columns A to E in a way that if data in A exists use that otherwise if data in B exists use that otherwise until column E. If none of them have any values return NA. I would like to automate this where somewhere I just specify the order for example A, B, C, D and E OR A, C, E, D, B and the values in F update according to the reference table
Reference : C - B - A - E - D
a
b
c
d
e
f
3
4
3
2
2
7
1
7
NA
1
4
2
4
2
2
4
2
2
Use FILTER() with # operator.
=#FILTER(A2:E2,A2:E2<>"","NA")
For dynamic array approach (spill results automatically), try-
=BYROW(A2:E7,LAMBDA(x,INDEX(FILTER(x,x<>"","NA"),1,1)))
I have two txt files in Linux.
fileone.txt has 40 rows. col names are: A B C D
filetwo.txt has 50,000 rows. col names are: D E F G
I would like to merge the two files (using column D) but only keeping the 40 rows from fileone.txt
So I'd like filethree.txt to have 40 rows and col names: A B C D E F G
What command do I need to do this?
I made a pandas df from parts of 2 others:
Here is the pseudocode for what I want to do.
4-column pandas dataframe, values in all columns are single words.
cols A B C D and I want this: cols A B C D E F
in pseudcode:
(for every s in A;
if s equals any string (not substring) in D;
write Yes to E (new column) else write No to E;
if str in B (same row as s) equals str in C (same row as string found in D) write yes to F (new column)
else write No to F)
The following code works but now I need a function to do what is described above:
cols = [1,2,3,5]
df3.drop(df3.columns[cols],axis=1, inplace=True)
df4.drop(df4.columns[1],axis=1, inplace=True)
listi = [df4]
listi.append(df3)
df5 = pd.concat(listi, axis = 1)
It should be if i)if x['A'] == x['D'] and ii) if x['B'] == x['C'] and also I need to add column G which is the string found in C or if string not found.
Here is a small sample data set and expected outcome:
A B C D
cats cat cat cats
went be have had
tried try enter entering
entering enter try tried
Expected outcome
A B C D E F G
cats cat cat cats yes yes cat
went be have had no no tried
try entering entering yes no try
entering enter try tried yes no entering
Column G is the word found in C if the word is found else
For what I understood you can apply a lamda to your DataFrame
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html
Anyway I built a small example
import pandas as pd
df = pd.DataFrame([['a','a','a','b'],['b','c','b','b'],['d','d','d','g'],['j','j','c','d']],[1,2,3,4], columns=['A','B','C','D'])
df
# A B C D
#1 a a a b
#2 b c b b
#3 d d d g
#4 j j c d
df['E']=df.apply(lambda x: 'yes' if x['A'] == x['B'] else 'no', axis=1)
df['F']=df.apply(lambda x: 'yes' if x['C'] == x['D'] else 'no', axis=1)
df
# A B C D E F
#1 a a a b yes no
#2 b c b b no yes
#3 d d d g yes no
#4 j j c d yes no
I am trying to reverse predict an initial required value from the predicted result % and cannot find the correct formula to do so (assuming the drop in value from input to result is identical).
Here is an example of the data:
A B C D E F G
Input Result % New Input New Result New % Required %
2000 700 35 1857.05 557.05 30 30
A = Raw data
B = Raw data
C = B / A
D = (A - B) * 1.4285 'This is the formula i need help with, i want this formula to calculate the required new input to receive result G but can only get result G currently by manually tweaking the number in D formula or causing a circular reference
E = D - (A - B)
F = E / D
G = User input
I would like the user to type in a required % in G and see the New Input change to the number required to result in that % in F.
Hope this makes sense, thanks in advance.
Is it a one-time thing? In that case, use Goal Seek
There have been a lot of posts concerning splitting a single column into multiples, but I couldn't find an answer to a slight modification to the idea of splitting.
When you use str.split, it splits the string independent of order. You can modify it to be slightly more complex, such as ordering it by sorting alphabetically
e.x. dataframe (df)
row
0 a, e, c, b
1 b, d, a
2 a, b, c, d, e
3 d, f
foo = df['row'].str.split(',')
will split based on the comma and return:
0 1 2 3
0 a e c b
....
However that doesn't align the results by their unique value. Even if you use a sort on the split string, it will still only result in this:
0 1 2 3 4 5
0 a b c e
1 a b d
...
whereas I want it to look like this:
0 1 2 3 4 5
0 a b c e
1 a b d
2 a b c d e
...
I know I'm missing something. Do I need to add the columns first and then map the split values to the correct column? What if you don't know all of the unique values? Still learning pandas syntax so any pointers in the right direction would be appreciated.
Using get_dummies
s=df.row.str.get_dummies(sep=' ,')
s.mul(s.columns)
Out[239]:
a b c d e f
0 a b c e
1 a b d
2 a b c d e
3 d f