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Efficient mean and total aggregation over multiple Pandas DataFrame columns

Suppose I have a DataFrame that looks something like this:
id
country
grade
category
amount
0
7
fr
a
mango
52
1
5
fr
b
banana
68
2
7
fr
a
banana
73
3
4
it
c
mango
70
4
5
fr
b
banana
99
5
9
uk
a
apple
29
6
3
uk
a
mango
83
7
0
uk
b
banana
59
8
2
it
c
mango
11
9
9
uk
a
banana
91
10
0
uk
b
mango
95
11
8
uk
a
mango
30
12
3
uk
a
mango
82
13
1
it
b
banana
78
14
3
uk
a
apple
76
15
6
it
c
apple
76
16
2
it
c
mango
10
17
1
it
b
mango
30
18
9
uk
a
banana
17
19
2
it
c
mango
58
Where each id belongs to a grade and lives in a country, and spends a certain amount on various fruits (category). Let's say the data covers a whole year. (Dataframe reproducible using the code below.)
import pandas as pd
df = pd.DataFrame({
"id":[7,5,7,4,5,9,3,0,2,9,0,8,3,1,3,6,2,1,9,2],
"country":["fr","fr","fr","it","fr","uk","uk","uk","it","uk","uk","uk","uk","it","uk","it","it","it","uk","it"],
"grade":["a","b","a","c","b","a","a","b","c","a","b","a","a","b","a","c","c","b","a","c"],
"category":["mango","banana","banana","mango","banana","apple","mango","banana","mango","banana","mango","mango","mango","banana","apple","apple","mango","mango","banana","mango"],
"amount":[52,68,73,70,99,29,83,59,11,91,95,30,82,78,76,76,10,30,17,58]
})
I would like to add two columns to this DF.
First, I'd like a column giving the mean annual (ie total) spent on each category by each combination of country and grade. So, for example, the Italy C-grade people have spent the following on mangos:
id: 4 total: 70
id: 2 total: 11 + 10 + 58 = 79
So the mean annual mango spend for Italy C-grade people is 74.5. I'd like to find this value for all of the country/grade/category combinations.
The second column I want to add is the same but for the mean annual count for each combination.
Desired output and the best I could come up with:
I've managed to populate these two desired columns using the following code:
import math
combos = [[i,j,k] for i in set(df["country"]) for j in set(df["grade"]) for k in set(df["category"])]
for c in combos:
x = df.loc[(df["country"]==c[0])&(df["grade"]==c[1])&(df["category"]==c[2])]
m = x.groupby("id").sum()["amount"].mean()
k = x.groupby("id").count()["amount"].mean()
if math.isnan(m):
m = 0
if math.isnan(k):
k = 0
c.append(m)
c.append(k)
temp_grouping = pd.DataFrame(combos,columns=["country","grade","category","mean_totals","mean_counts"])
df = df.merge(temp_grouping,on=["country","grade","category"],how="left")
Which gives the desired output:
id
country
grade
category
amount
mean_totals
mean_counts
0
7
fr
a
mango
52
52
1
1
5
fr
b
banana
68
167
2
2
7
fr
a
banana
73
73
1
3
4
it
c
mango
70
74.5
2
4
5
fr
b
banana
99
167
2
5
9
uk
a
apple
29
52.5
1
6
3
uk
a
mango
83
97.5
1.5
7
0
uk
b
banana
59
59
1
8
2
it
c
mango
11
74.5
2
9
9
uk
a
banana
91
108
2
10
0
uk
b
mango
95
95
1
11
8
uk
a
mango
30
97.5
1.5
12
3
uk
a
mango
82
97.5
1.5
13
1
it
b
banana
78
78
1
14
3
uk
a
apple
76
52.5
1
15
6
it
c
apple
76
76
1
16
2
it
c
mango
10
74.5
2
17
1
it
b
mango
30
30
1
18
9
uk
a
banana
17
108
2
19
2
it
c
mango
58
74.5
2
The above code works, but it is not usable on my real data because it is pretty slow. I'm searching, therefore, for a faster/more efficient solution to my problem. Thanks very much.

You can create mean_totals column as follows:
mean_total_df = df.groupby(['country', 'category', 'grade']).apply(lambda x: x.amount.sum()/ x.id.nunique())
df['mean_totals'] = df.apply(lambda x: mean_total_df.loc[x.country, x.category, x.grade], axis=1)
which gives
0 7 fr a mango 52 52.0
1 5 fr b banana 68 167.0
2 7 fr a banana 73 73.0
3 4 it c mango 70 74.5
4 5 fr b banana 99 167.0
5 9 uk a apple 29 52.5
6 3 uk a mango 83 97.5
7 0 uk b banana 59 59.0
8 2 it c mango 11 74.5
9 9 uk a banana 91 108.0
10 0 uk b mango 95 95.0
11 8 uk a mango 30 97.5
12 3 uk a mango 82 97.5
13 1 it b banana 78 78.0
14 3 uk a apple 76 52.5
15 6 it c apple 76 76.0
16 2 it c mango 10 74.5
17 1 it b mango 30 30.0
18 9 uk a banana 17 108.0
19 2 it c mango 58 74.5

It looks like you need a double groupby. Once for the sum, once for the mean:
out = (df
.groupby(['country', 'grade', 'category', 'id']).sum()
.groupby(['country', 'grade', 'category']).mean()
)
output:
amount
country grade category
fr a banana 73.0
mango 52.0
b banana 167.0
it b banana 78.0
mango 30.0
c apple 76.0
mango 74.5
uk a apple 52.5
banana 108.0
mango 97.5
b banana 59.0
mango 95.0

I hope this will work fast.. First group and compute the required details and merge with existing df.
import pandas as pd
df = pd.DataFrame({
"id":[7,5,7,4,5,9,3,0,2,9,0,8,3,1,3,6,2,1,9,2],
"country":["fr","fr","fr","it","fr","uk","uk","uk","it","uk","uk","uk","uk","it","uk","it","it","it","uk","it"],
"grade":["a","b","a","c","b","a","a","b","c","a","b","a","a","b","a","c","c","b","a","c"],
"category":["mango","banana","banana","mango","banana","apple","mango","banana","mango","banana","mango","mango","mango","banana","apple","apple","mango","mango","banana","mango"],
"amount":[52,68,73,70,99,29,83,59,11,91,95,30,82,78,76,76,10,30,17,58]
})
intermediate_df = df.groupby(by=['country','grade','category','id'], as_index=False).agg(int_totals=pd.NamedAgg(column='amount',aggfunc='sum'),int_counts=pd.NamedAgg(column='id',aggfunc='count')).groupby(by=['country','grade','category'], as_index=False).agg(mean_totals=pd.NamedAgg(column='int_totals',aggfunc='mean'),mean_counts=pd.NamedAgg(column='int_counts',aggfunc='mean'))
output_df = pd.merge(df,intermediate_df, left_on = ['country','grade','category'],right_on = ['country','grade','category'], how='left')
print(output_df)
Output_dataframe

An unwanted level and wrong calculation on the graphic on pandas

I have the following dataset:
df = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10,11,12],
'city':['Pau','Pau','Pau','Pau','Pau','Pau','Lyon','Dax','Dax','Lyon','Lyon','Lyon'],
'type':['A','A','A','A','B','B','B','A','B','A','B','B'],
'val':[100,90,95,95,90,75,100,70,75,90,95,85]})
id city type val
0 1 Pau A 100
1 2 Pau A 90
2 3 Pau A 95
3 4 Pau A 95
4 5 Pau B 90
5 6 Pau B 75
6 7 Lyon B 100
7 8 Dax A 70
8 9 Dax B 75
9 10 Lyon A 90
10 11 Lyon B 95
11 12 Lyon B 85
And I want to create a plot grouped by variable city, and get the frequency percentage per type. I have tried this:
df.groupby(['city','type']).agg({'type':'count'}).transform(lambda x: x/x.sum()).unstack().plot()
But I get wrong values per group and an unwanted 'None'. The expected values should be:
type A B
city
Dax .50 .50
Lyon .33 .66
Pau .66 .33

Looking at your requirement, you may want crosstab with normalize:
pd.crosstab(df['city'],df['type'],normalize='index').plot()
Where:
print(pd.crosstab(df['city'],df['type'],normalize='index'))
type A B
city
Dax 0.500000 0.500000
Lyon 0.250000 0.750000
Pau 0.666667 0.333333

Conditional filling of column based on string

I have a dataset which I have to fill conditional or dropping the conditional rows. But, I am still unsuccessful.
Idx Fruits Days Name
0 60 20
1 15 85.5
2 10 62 Peter
3 40 90 Maria
4 5 10.2
5 92 66
6 65 87 John
7 50 1 Eric
8 50 0 Maria
9 80 87 John
Now, I have some empty cells. I can fill with fillna or regex or can drop empty cells.
I want only first starting cells until the string starts, either dropping or filling with "."
Like below
Idx Fruits Days Name
0 60 20 .
1 15 85.5 .
2 10 62 Peter
3 40 90 Maria
4 5 10.2
5 92 66
6 65 87 John
7 50 1 Eric
8 50 0 Maria
9 80 87 John
and
Idx Fruits Days Name
2 10 62 Peter
3 40 90 Maria
4 5 10.2
5 92 66
6 65 87 John
7 50 1 Eric
8 50 0 Maria
9 80 87 John
Is there any possibility using pandas? or any looping?

You can try this:
df['Name'] = df['Name'].replace('', np.nan)
df['Name'] = df['Name'].where(df['Name'].ffill().notna(), '.')
print(df)
Idx Fruits Days Name
0 0 60 20.0 .
1 1 15 85.5 .
2 2 10 62.0 Peter
3 3 40 90.0 Maria
4 4 5 10.2
5 5 92 66.0
6 6 65 87.0 John
7 7 50 1.0 Eric
8 8 50 0.0 Maria
9 9 80 87.0 John

sum values in column grouped by another column pandas

My df looks like this:
country id x y
AT 11 50 100
AT 12 NaN 90
AT 13 NaN 104
AT 22 40 50
AT 23 30 23
AT 61 40 88
AT 62 NaN 78
UK 11 40 34
UK 12 NaN 22
UK 13 NaN 70
What I need is the sum of the y column in the first row that is not NaN in x, grouped by the first number on the left of the column id. This separately for each country. At the end I just need to drop the NaN.
The result should be something like this:
country id x y
AT 11 50 294
AT 22 40 50
AT 23 30 23
AT 61 40 166
UK 11 40 126

You can aggregate by GroupBy.agg by first and sum functions with helper Series by compare non missing values by Series.notna and cumulative sum by Series.cumsum:
df1 = (df.groupby(['country', df['x'].notna().cumsum()])
.agg({'id':'first', 'x':'first', 'y':'sum'})
.reset_index(level=1, drop=True)
.reset_index())
print (df1)
country id x y
0 AT 11 50.0 294
1 AT 22 40.0 50
2 AT 23 30.0 23
3 AT 61 40.0 166
4 UK 11 40.0 126
If possible first value(s) of x are misisng values add DataFrame.dropna:
print (df)
country id x y
0 AT 11 NaN 100
1 AT 11 50.0 100
2 AT 12 NaN 90
3 AT 13 NaN 104
4 AT 22 40.0 50
5 AT 23 30.0 23
6 AT 61 40.0 88
7 AT 62 NaN 78
8 UK 11 40.0 34
9 UK 12 NaN 22
10 UK 13 NaN 70
df1 = (df.groupby(['country', df['x'].notna().cumsum()])
.agg({'id':'first', 'x':'first', 'y':'sum'})
.reset_index(level=1, drop=True)
.reset_index()
.dropna(subset=['x']))
print (df1)
country id x y
1 AT 11 50.0 294
2 AT 22 40.0 50
3 AT 23 30.0 23
4 AT 61 40.0 166
5 UK 11 40.0 126

Use groupby, transform and dropna:
print (df.assign(y=df.groupby(df["x"].notnull().cumsum())["y"].transform('sum'))
.dropna(subset=["x"]))
country id x y
0 AT 11 50.0 294
3 AT 22 40.0 50
4 AT 23 30.0 23
5 AT 61 40.0 166
7 UK 11 40.0 126

Subtract/Add existing values if contents of one dataframe is present in another using pandas

Here are 2 dataframes
df1:
Index Number Name Amount
0 123 John 31
1 124 Alle 33
2 312 Amy 33
3 314 Holly 35
df2:
Index Number Name Amount
0 312 Amy 13
1 124 Alle 35
2 317 Jack 53
The resulting dataframe should look like this
result_df:
Index Number Name Amount Curr_amount
0 123 John 31 31
1 124 Alle 33 68
2 312 Amy 33 46
3 314 Holly 35 35
4 317 Jack 53
I have tried using pandas isin but it only says if the Number column was present or no in boolean. Is there any way to do this efficiently?

Use merge with outer join and then add Series.add (or
Series.sub if necessary):
df = df1.merge(df2, on=['Number','Name'], how='outer', suffixes=('','_curr'))
df['Amount_curr'] = df['Amount_curr'].add(df['Amount'], fill_value=0)
print (df)
Number Name Amount Amount_curr
0 123 John 31.0 31.0
1 124 Alle 33.0 68.0
2 312 Amy 33.0 46.0
3 314 Holly 35.0 35.0
4 317 Jack NaN 53.0