Click here for the imageI m trying to create a list from 3 different series which will be of the shape "({A} {B} {C})" where A denotes the 1st element from series 1, B is for 1st element from series 2, C is for 1st element from series 3 and this way it should create a list containing 600 element.
List 1 List 2 List 3
u_p0 1 v_p0 2 w_p0 7
u_p1 21 v_p1 11 w_p1 45
u_p2 32 v_p2 25 w_p2 32
u_p3 45 v_p3 76 w_p3 49
... .... ....
u_p599 56 v_p599 78 w_599 98
Now I want the output list as follows
(1 2 7)
(21 11 45)
(32 25 32)
(45 76 49)
.....
These are the 3 series I created from a dataframe
r1=turb_1.iloc[qw1] #List1
r2=turb_1.iloc[qw2] #List2
r3=turb_1.iloc[qw3] #List3
Pic of the seriesFor the output I think formatted string python method will be useful but I m quite not sure how to proceed.
turb_3= ["({A} {B} {C})".format(A=i,B=j,C=k) for i in r1 for j in r2 for k in r3]
Any kind of help will be useful.
Use pandas.DataFrame.itertuples with str.format:
# Sample data
print(df)
col1 col2 col3
0 1 2 7
1 21 11 45
2 32 25 32
3 45 76 49
fmt = "({} {} {})"
[fmt.format(*tup) for tup in df[["col1", "col2", "col3"]].itertuples(False, None)]
Output:
['(1 2 7)', '(21 11 45)', '(32 25 32)', '(45 76 49)']
I have a pandas dataframe as below:
import pandas as pd
import numpy as np
df = pd.DataFrame({'ORDER':["A", "A", "B", "B"], 'var1':[2, 3, 1, 5],'a1_bal':[1,2,3,4], 'a1c_bal':[10,22,36,41], 'b1_bal':[1,2,33,4], 'b1c_bal':[11,22,3,4], 'm1_bal':[15,2,35,4]})
df
ORDER var1 a1_bal a1c_bal b1_bal b1c_bal m1_bal
0 A 2 1 10 1 11 15
1 A 3 2 22 2 22 2
2 B 1 3 36 33 3 35
3 B 5 4 41 4 4 4
I want to create new columns as below:
a1_final_bal = sum(a1_bal, a1c_bal)
b1_final_bal = sum(b1_bal, b1c_bal)
m1_final_bal = m1_bal (since we only have m1_bal field not m1c_bal, so it will renain as it is)
I don't want to hardcode this step because there might be more such columns as "c_bal", "m2_bal", "m2c_bal" etc..
My final data should look something like below
ORDER var1 a1_bal a1c_bal b1_bal b1c_bal m1_bal a1_final_bal b1_final_bal m1_final_bal
0 A 2 1 10 1 11 15 11 12 15
1 A 3 2 22 2 22 2 24 24 2
2 B 1 3 36 33 3 35 38 36 35
3 B 5 4 41 4 4 4 45 8 4
You could try something like this. I am not sure if its exactly what you are looking for, but I think it should work.
dfforgroup = df.set_index(['ORDER','var1']) #Creates MultiIndex
dfforgroup.columns = dfforgroup.columns.str[:2] #Takes first two letters of remaining columns
df2 = dfforgroup.groupby(dfforgroup.columns,axis=1).sum().reset_index().drop(columns =
['ORDER','var1']).add_suffix('_final_bal') #groups columns by their first two letters and sums the columns up
df = pd.concat([df,df2],axis=1) #concatenates new columns to original df
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
My current DataFrame is:
Term value
Name
A 1 35
A 2 40
A 3 50
B 1 20
B 2 45
B 3 50
I want to get a dataframe as:
Term 1 2 3
Name
A 35 40 50
B 20 45 50
How can i get it?I've tried using pivot_table but i didn't get my expected output.Is there any way to get my expected output?
Use:
df = df.set_index('Term', append=True)['value'].unstack()
Or:
df = pd.pivot(df.index, df['Term'], df['value'])
print (df)
Term 1 2 3
Name
A 35 40 50
B 20 45 50
EDIT: If duplicates in pairs Name with Term is necessary aggretion, e.g. sum or mean:
df = df.groupby(['Name','Term'])['value'].sum().unstack(fill_value=0)
Say I have the following txt file
Distances Counts
1 5
2 5
3 9
4 10
9 10
10 10
11 5
14 5
20 1
21 1
23 2
I would like a way to bin according to the first column and sum the second column.
The correct output if you use a bin of 5 would be
0-5 29
5-10 20
10-15 10
15-20 20
20-25 3
or just
5 29
10 20
15 10
20 20
25 3
i tried
binfile = open('distances.txt', 'r')
binsize = 5
summar = 0
binsize2 = binsize
for line in binfile:
line = line.strip().split('\t')
distance = int(line[0])
counts = int(line[1])
if distance <= binsize2:
summar += counts
else:
print(str(binsize2)+'\t'+str(summar))
binsize2 = binsize2 + binsize
summar = counts
but it doesn't print the last bin. Any suggestions?