I have a dataset where I would like to sum two columns and then perform a subtraction while displaying a cumulative sum.
Data
id date t1 t2 total start cur_t1 cur_t2 final_o finaldb de_t1 de_t2
a q122 4 1 5 50 25 20 55 21 1 1
a q222 1 1 2 50 25 20 57 22 0 0
a q322 0 0 0 50 25 20 57 22 5 5
b q122 5 5 10 100 30 40 110 27 4 4
b q222 2 2 4 100 30 70 114 29 5 1
b q322 3 4 7 100 30 70 121 33 0 1
Desired
id date t1 t2 total start cur_t1 cur_t2 final_o finaldb de_t1 de_t2 finalt1
a q122 4 1 5 50 25 20 55 21 1 1 28
a q222 1 1 2 50 25 20 57 22 0 0 29
a q322 0 0 0 50 25 20 57 22 5 5 24
b q122 5 5 10 100 30 40 110 27 4 4 31
b q222 2 2 4 100 30 70 114 29 5 1 28
b q322 3 4 7 100 30 70 121 33 0 1 31
Logic
Create 'finalt1' column by summing 't1' and 'cur_t1'
initially and then subtracting 'de_t1' cumulatively and grouping by 'id' and 'date'
Doing
df['finalt1'] = df['cur_t1'].add(df.groupby('id')['t1'].cumsum())
I am still researching on how to subtract the 'de_t1' column cumulatively.
I can't test right now, but logically:
(df['cur_t1'].add(df.groupby('id')['t1'].cumsum())
.sub(df.groupby('id')['de_t1'].cumsum())
)
Of note, there was also this possibility to avoid grouping twice (it is calculating both cumsums at once and computing the difference), but it is actually slower:
df['cur_t1'].add(df.groupby('id')[['de_t1', 't1']].cumsum().diff(axis=1)['t1'])
Related
my dataset
name day value
A 7 88
A 15 101
A 21 121
A 29 56
B 21 131
B 30 78
B 35 102
C 8 80
C 16 101
...
I am trying to plot with values for these days, but I want to label because there are too many unique numbers of days.
I try to label it consistently,
Is there a way to speed up labeling by cutting it every 7 days(week)?
For example, ~ 7day = 1week, 8 ~ 14day = 2week, and so on.
output what I want
name day value week
A 7 88 1
A 15 101 3
A 21 121 3
A 29 56 5
B 21 131 3
B 30 78 5
B 35 102 5
C 8 80 2
C 16 101 3
thank you for reading
Subtract 1, then use integer division by 7 and last add 1:
df['week'] = (df['day'] - 1) // 7 + 1
print (df)
name day value week
0 A 7 88 1
1 A 15 101 3
2 A 21 121 3
3 A 29 56 5
4 B 21 131 3
5 B 30 78 5
6 B 35 102 5
7 C 8 80 2
8 C 16 101 3
I am new to python and I was facing some issue solving the following problem.
I have the following dataframe:
SoldDate CountSoldperMonth
2019-06-01 20
5
10
12
33
16
50
27
2019-05-01 2
5
11
13
2019-04-01 32
35
39
42
47
55
61
80
I need to add a Target column such that for the top 5 values in 'CountSoldperMonth' for a particular SoldDate, target should be 1 else 0. If the number of rows in 'CountSoldperMonth' for a particular 'SoldDate' is less than 5 then only the row with highest count will be marked as 1 in the Target and rest as 0. The resulting dataframe should look as below.
SoldDate CountSoldperMonth Target
2019-06-01 20 1
5 0
10 0
12 0
33 1
16 1
50 1
27 1
2019-05-01 2 0
5 0
11 0
13 1
2019-04-01 32 0
35 0
39 0
42 1
47 1
55 1
61 1
80 1
How do I do this?
In your case , using groupby with your rules chain with apply if...else
df.groupby('SoldDate').CountSoldperMonth.\
apply(lambda x : x==max(x) if len(x)<=5 else x.isin(sorted(x)[-5:])).astype(int)
Out[346]:
0 1
1 0
2 0
3 0
4 1
5 1
6 1
7 1
8 0
9 0
10 0
11 1
12 0
13 0
14 0
15 1
16 1
17 1
18 1
19 1
Name: CountSoldperMonth, dtype: int32
Assume the following:
df1:
x y z
1 10 11
2 20 22
3 30 33
4 40 44
1 20 21
1 30 31
1 40 41
2 10 12
2 30 32
2 40 42
3 10 31
3 20 23
3 40 43
4 10 14
4 20 24
4 30 34
df2:
x b
1 100
2 200
df3:
y c
10 1000
20 2000
I want all rows from df1, for which either x or y appears in either df2 or df3 respectively, meaning in this case
out:
x y z
1 10 11
2 20 22
1 20 21
1 30 31
1 40 41
2 10 12
2 30 32
2 40 42
3 10 31
3 20 23
4 10 14
4 20 24
I would like to do this in pure pandas, with no for loops, seems standard enough to me, but I don't really know what to look for
You can use isin on both cases, chain the conditions with a bitwise OR and perform boolean indexation on the dataframe with the result:
df1[df1.x.isin(df2.x) | df1.y.isin(df3.y)]
I have a data frame with two columns
df = ['xPos', 'lineNum']
import pandas as pd
data = '''\
xPos lineNum
40 1
50 1
75 1
90 1
42 2
75 2
110 2
45 3
70 3
95 3
125 3
38 4
56 4
74 4'''
I have created the aggregate data frame for this by using
aggrDF = df.describe(include='all')
command
and I am interested in the minimum of the xPos value. So, i get it by using
minxPos = aggrDF.ix['min']['xPos']
Desired output
data = '''\
xPos lineNum xDiff
40 1 2
50 1 10
75 1 25
90 1 15
42 2 4
75 2 33
110 2 35
45 3 7
70 3 25
95 3 25
125 3 30
38 4 0
56 4 18
74 4 18'''
The logic
I want to compere the two consecutive rows of the data frame and calculate a new column based on this logic:
if( df['LineNum'] != df['LineNum'].shift(1) ):
df['xDiff'] = df['xPos'] - minxPos
else:
df['xDiff'] = df['xPos'].shift(1)
Essentially, I want the new column to have the difference of the two consecutive rows in the df, as long as the line number is the same.
If the line number changes, then, the xDiff column should have the difference with the minimum xPos value that I have from the aggregate data frame.
Can you please help? thanks,
These two lines should do it:
df['xDiff'] = df.groupby('lineNum').diff()['xPos']
df.loc[df['xDiff'].isnull(), 'xDiff'] = df['xPos'] - minxPos
>>> df
xPos lineNum xDiff
0 40 1 2.0
1 50 1 10.0
2 75 1 25.0
3 90 1 15.0
4 42 2 4.0
5 75 2 33.0
6 110 2 35.0
7 45 3 7.0
8 70 3 25.0
9 95 3 25.0
10 125 3 30.0
11 38 4 0.0
12 56 4 18.0
13 74 4 18.0
You just need groupby lineNum and apply the condition you already writing down
df['xDiff']=np.concatenate(df.groupby('lineNum').apply(lambda x : np.where(x['lineNum'] != x['lineNum'].shift(1),x['xPos'] - x['xPos'].min(),x['xPos'].shift(1)).astype(int)).values)
df
Out[76]:
xPos lineNum xDiff
0 40 1 0
1 50 1 40
2 75 1 50
3 90 1 75
4 42 2 0
5 75 2 42
6 110 2 75
7 45 3 0
8 70 3 45
9 95 3 70
10 125 3 95
11 38 4 0
12 56 4 38
13 74 4 56
I have a dataframe that has values of the different column numbers for another dataframe. Is there a way that I can just return the value from the other dataframe instead of just having the column index.
I basically want to match up the index between the Push and df dataframes. The values in the Push dataframe contain what column I want to return from the df dataframe.
Push dataframe:
0 1
0 1 2
1 0 3
2 0 3
3 1 3
4 0 2
df dataframe:
0 1 2 3 4
0 10 11 22 33 44
1 10 11 22 33 44
2 10 11 22 33 44
3 10 11 22 33 44
4 10 11 22 33 44
return:
0 1
0 11 22
1 10 33
2 10 33
3 11 33
4 10 22
You can do it with np.take ; However this function works on the flattened array. push must be shift like that :
In [285]: push1 = push.values+np.arange(0,25,5)[:,None]
In [229]: pd.DataFrame(df.values.take(push1))
EDIT
No, I just reinvent np.choose :
In [24]: df
Out[24]:
0 1 2 3 4
0 0 1 2 3 4
1 10 11 12 13 14
2 20 21 22 23 24
3 30 31 32 33 34
4 40 41 42 43 44
In [25]: push
Out[25]:
0 1
0 1 2
1 0 3
2 0 3
3 1 3
4 0 2
In [27]: np.choose(push.T,df).T
Out[27]:
0 1
0 1 2
1 10 13
2 20 23
3 31 33
4 40 42
We using melt then replace notice (df1 is your push , df2 is your df)
df1.astype(str).replace(df2.melt().drop_duplicates().set_index('variable').value.to_dict())
Out[31]:
0 1
0 11 22
1 10 33
2 10 33
3 11 33
4 10 22