x=[]
y1=[]
r1=len(df)
L1=len(df.columns)
for i in range(r1):
ll=(df.loc[i,'LL'])
ul=(df.loc[i,'UL'])
count1 =0
for j in range(5,L1):
if isinstance(df.iloc[i,j],str):
df.loc[i,j]=0
if ll<=df.iloc[i,j]<=ul:
count1=count1+1
if count1==(L1-5):
x.append('Pass')
else:
x.append('Fail')
y1.append(count1)
se = pd.Series(x)
se1=pd.Series(y1)
df['Min']=min1.values
df['Mean']=mean1.values
df['Median']=median1.values
df['Max']=max1.values
df['Pass Count']=se1.values
df['Result']=se.values
min1 = df.iloc[:,5:].min(axis=1)
mean1=df.iloc[:,5:].astype(float).mean(axis=1,skipna = True)
median1=df.iloc[:,5:].astype(float).median(axis=1,skipna = True)
max1=df.iloc[:,5:].max(axis=1)
count1=df.iloc[:,5:].count(axis=1)
yield1=[]
for i in range(len(se1)):
yd1=(se1[i]/(L1-3))*100
yield1.append(yd1)
se2=pd.Series(yield1)
df['Yield']=se2.values
df1=df.loc[:,['PARAMETER','Min','Mean','Median','Max','Result','Pass Count','Yield']]
df1
Below is my data set, it is sensor data on daily basis. Daily data should be within the Lower Limit (LL) and Upper Limit(UL). I want to count how many days sensors data is within the LL and UL.
I am not able to calculate the number of days for sensor data within LL and UL using Pandas. How can I calculate the number of days for sensor data within LL and UL?
Take a few key ideas
need a list of the columns that go into calc daycols
transpose these columns into an array then to test, gives a boolean array
sum this boolean array and you have your desired calc
df = pd.read_csv(io.StringIO("""sensor location,LL,UL,day1,day2,day3,day4,day5,day6,day7,number of days sensor data within LL and UL
A,1,10,12,6,9,4,9,7,15,5
B,1,12,4,15,7,1,11,1,7,6
C,1,15,13,13,13,10,7,13,13,7
D,1,10,12,1,14,12,15,4,4,3
E,1,20,11,15,8,14,1,14,14,7"""))
daycols = [d for i,d in enumerate(df.columns) if "day" in d and "number" not in d]
df = df.assign(
# use fact true is 1 so sum a truth array gives the answer
daysBetween=lambda dfa: ((dfa.loc[:,daycols].T>=dfa["LL"]) &
(dfa.loc[:,daycols].T<=dfa["UL"])).sum()
)
print(df.to_string(index=False))
output
sensor location LL UL day1 day2 day3 day4 day5 day6 day7 number of days sensor data within LL and UL daysBetween
A 1 10 12 6 9 4 9 7 15 5 5
B 1 12 4 15 7 1 11 1 7 6 6
C 1 15 13 13 13 10 7 13 13 7 7
D 1 10 12 1 14 12 15 4 4 3 3
E 1 20 11 15 8 14 1 14 14 7 7
speed up
It you have many columns then you can use slice capability to identify them and turn into indexes so iloc can be used. Additionally the transpose is not necessary.
dayi = [df.columns.get_loc(c) for c in df.columns[3:-1]]
df = df.assign(
# use fact true is 1 so sum a truth array gives the answer
daysBetween=lambda dfa: ((dfa.iloc[:,dayi]>=dfa["LL"]) &
(dfa.iloc[:,dayi]<=dfa["UL"])).sum()
)
Related
I am running a simulation test where I want to dynamically change some values present in rows for each column based on certain set of conditions
The Problem Statement
My dataset has 400 rows and my first test case is to update 5% of the rows in each column, so 5% of 400 = 20 rows which needs to be updated
These 20 rows should be only updated for the top 5 categories that are present in my dataset. So 4 rows each which needs to be updated
My dataframe looks like this:
A B C D Category
1 10 3 4 X
4 9 6 9 Y
9 3 7 10 XX
10 1 9 7 YY
10 1 9 7 ZZ
10 1 9 7 YZZ
10 1 9 7 YZZ
10 1 9 7 YYYY
......400 rows
The conditions are:
While updating the rows I would want to make sure that 20 rows (5% of the overall dataset) should be updated only where the top 5 categories are encountered. In my case the top 5 categories are X, Y , XX, YY and ZZ. These rows should be updated to value 7 where the previous value was 1,2,3,4,5,6
The resultant datframe should look like this:
A B C D Category
7 10 7 7 X
7 9 7 9 Y
9 7 7 10 XX
10 7 9 7 YY
10 7 9 7 ZZ
10 1 9 7 YZZ
10 1 9 7 YZZ
10 1 9 7 YYYY
......400 rows
In the resultant dataframe, there is no impact on the categories which are not the top 5 categories, this case YZZ or YYYY and to demonstrate an example I can't show all the updated rows but for example in the above dataframe, 2 rows have been updated for column A where previous value was <=6 to a new value 7 and similarly the other two rows will get updated to 7 wherever the condition is met.
How can I achieve this?
You can try the following logic:
# get only desired Categories
m = df['Category'].isin(['X', 'Y', 'XX', 'YY', 'ZZ'])
# select 20 random rows from the above
idx = df[m].sample(n=20).index
# replace the 1 ≤ values ≤ 6 by 7
df.loc[idx] = df.loc[idx].mask(df.loc[idx].ge(1)&df.loc[idx].le(6), 7)
If you rather want 4 rows per Category, use this variant for the random sampling:
idx = df[m].groupby('Category').sample(n=4).index
I have a python data frame of weekly data like this :
Week Val
1 11
2 11
3 11
4 11
5 9
6 9
7 9
8 9
I would like create an output table like this:
Week 1 Week 2 Val
1 4 11
5 8 9
Apologies, I am quite new to python and its iterative tools. I am not sure how to solve this problem.
I tried to match using the previous row columns but I do not think how to go further:
df['Match'] = df['Val'].eq(df['Val'].shift(-1))
You want to groupby the consecutive blocks of Val. So you can use cumsum on the non-zero differences to get the block:
blocks = df['Val'].ne(df['Val'].shift(1)).cumsum()
(df.groupby(blocks, as_index=False)
.agg(Week1=('Week','min'), Week2=('Week','max'), Val=('Val', 'first'))
)
Or you can chain:
(df.groupby(df['Val'].ne(df['Val'].shift(1)).cumsum(), as_index=False)
.agg(Week1=('Week','min'), Week2=('Week','max'),Val=('Val', 'first'))
)
Output:
Week1 Week2 Val
0 1 4 11
1 5 8 9
I am working on graph and in need data in below format. I have data in COL A. I need to calculate COL B values as in below picture.
What is the formula for obtaining this in excel?
You can do with cumsum and shift:
# sample data
df = pd.DataFrame({'COL A': np.arange(11)})
df['COL B'] = df['COL A'].shift(fill_value=0).cumsum()
Output:
COL A COL B
0 0 0
1 1 0
2 2 1
3 3 3
4 4 6
5 5 10
6 6 15
7 7 21
8 8 28
9 9 36
10 10 45
Use simple MS technique.
You can use the formula (A3*A2)/2 for COL2
This question already has answers here:
How to groupby consecutive values in pandas DataFrame
(4 answers)
Closed 3 years ago.
So I have a DataFrame with two columns, one with label names (df['Labels']) and the other with int values (df['Volume']).
df = pd.DataFrame({'Labels':
['A','A','A','A','B','B','B','B','B','B','A','A','A','A','A','A','A','A','C','C','C','C','C'],
'Volume':[10,40,20,20,50,60,40,50,50,60,10,10,10,10,20,20,10,20,80,90,90,80,100]})
I would like to identify intervals where my labels change and then calculate the median on the column 'Volume' for each of these intervals. Later I should replace every value of column 'Volume' by the respective median of each interval.
In case of label A, I would like to have the median for both intervals.
Here is how my DataFrame should looks like:
df2 = pd.DataFrame({'Labels':['A','A','A','A','B','B','B','B','B','B','A','A','A','A','A','A','A','A','C','C','C','C','C'],
'Volume':[20,20,20,20,50,50,50,50,50,50,10,10,10,10,10,10,10,10,90,90,90,90,90]})
You want to groupby the blocks and transform median:
blocks = df['Labels'].ne(df['Labels'].shift()).cumsum()
df['group_median'] = df['Volume'].groupby(blocks).transform('median')
Use Series.cumsum + Series.shift() to create groups using groupby and then use transform
df['Volume']=df.groupby(df['Labels'].ne(df['Labels'].shift()).cumsum())['Volume'].transform('median')
print(df)
Labels Volume
0 A 20
1 A 20
2 A 20
3 A 20
4 B 50
5 B 50
6 B 50
7 B 50
8 B 50
9 B 50
10 A 10
11 A 10
12 A 10
13 A 10
14 A 10
15 A 10
16 A 10
17 A 10
18 C 90
19 C 90
20 C 90
21 C 90
22 C 90
I have a dataframe that I need to randomise in a very specific way with a particular rule, and I'm a bit lost. A simplified version is here:
idx type time
1 a 1
2 a 1
3 a 1
4 b 2
5 b 2
6 b 2
7 a 3
8 a 3
9 a 3
10 b 4
11 b 4
12 b 4
13 a 5
14 a 5
15 a 5
16 b 6
17 b 6
18 b 6
19 a 7
20 a 7
21 a 7
If we consider this as containing seven "bunches", I'd like to randomly shuffle by those bunches, i.e. retaining the time column. However, the constraint is that after shuffling, a particular bunch type (a or b in this case) cannot appear more than n (e.g. 2) times in a row. So an example correct result looks like this:
idx type time
21 a 7
20 a 7
19 a 7
7 a 3
8 a 3
9 a 3
17 b 6
16 b 6
18 b 6
6 b 2
5 b 2
4 b 2
2 a 1
3 a 1
1 a 1
14 a 5
13 a 5
15 a 5
12 b 4
11 b 4
10 b 4
I was thinking I could create a separate "order" array from 1 to 7 and np.random.shuffle() it, then sort the dataframe by time in that order, which will probably work - I can think of ways to do that part, but I'm especially struggling with the rule of restricting the number of repeats.
I know roughly that I should use a while loop, shuffle it in that way, loop over the frame and track the number of consecutive types, if it exceeds my n then break out and start the while loop again until it completes without breaking out, in which case set a value to end the while loop. But this got so messy and didn't work.
Any ideas?
See if this works.
import pandas as pd
import numpy as np
n = [['a',1],['a',1],['a',1],
['b',2],['b',2],['b',2],
['a',3],['a',3],['a',3]]
df = pd.DataFrame(n)
df.columns = ['type','time']
print(df)
order = np.unique(np.array(df['time']))
print("Before Shuffling",order)
np.random.shuffle(order)
print("Shuffled",order)
n =2
for i in order:
print(df[df['time']==i].iloc[0:n])