Getting groups by group index - pandas

I want to access the group by group index. My dataframe is as given below
import pandas as pd
from io import StringIO
import numpy as np
data = """
id,name
100,A
100,B
100,C
100,D
100,pp;
212,E
212,F
212,ds
212,G
212, dsds
212, sas
300,Endüstrisi`
"""
df = pd.read_csv(StringIO(data))
I want to groupby 'id' and access the groups by its group index.
dfg=df.groupby('id',sort=False,as_index=False)
dfg.get_group(0)
I was expecting this to return the first group which is the group for id =1 (which is the first group)

You need pass value of id:
dfg=df.groupby('id',sort=False)
a = dfg.get_group(100)
print (a)
id name
0 100 A
1 100 B
2 100 C
3 100 D
4 100 pp;
dfg=df.groupby('id',sort=False)
a = dfg.get_group(df.loc[0, 'id'])
print (a)
id name
0 100 A
1 100 B
2 100 C
3 100 D
4 100 pp;
If need enumerate groups is possible use GroupBy.ngroup:
dfg=df.groupby('id',sort=False)
a = df[dfg.ngroup() == 0]
print (a)
id name
0 100 A
1 100 B
2 100 C
3 100 D
4 100 pp;
Detail:
print (dfg.ngroup())
0 0
1 0
2 0
3 0
4 0
5 1
6 1
7 1
8 1
9 1
10 1
11 2
dtype: int64
EDIT: Another idea is if need select groups by positions (all id are consecutive groups) with compare by unique values of id selected by positions:
ids = df['id'].unique()
print (ids)
[100 212 300]
print (df[df['id'].eq(ids[0])])
id name
0 100 A
1 100 B
2 100 C
3 100 D
4 100 pp;
print (df[df['id'].eq(ids[1])])
id name
5 212 E
6 212 F
7 212 ds
8 212 G
9 212 dsds
10 212 sas

Related

Pandas DataFrame subtract values

Im new to python
I have a data frame (df) which has the following structure:
ID
rate
Sequential number
a
150
1
a
150
1
a
50
2
b
250
1
c
25
1
d
25
1
d
40
2
d
25
3
The ID are customers, the value are monthly rates and Sequential number is a number that always increases by 1, if the customer changes the monthly rate
I want to do the following:
for every ID find the maximum value in the column Sequential number, take the associated value in the column rate, find the minimum value in the column Sequential number and take associated value in the column rate and subtracting the rates.
At the end I want to have a additional column to my data frame with the difference of the rates. Maybe the loop could do the following:
for id in df()
find max() in column Sequential number and get value in rates -
min () in column Sequential number and get value in rates
return difference
The new df_new should be this
ID
rate
Sequential number
rate_diff
a
150
1
0
a
150
1
0
a
50
2
-100
b
250
1
0
c
25
1
0
d
25
1
0
d
40
2
0
d
30
3
5
If an ID has only one entry, the rate_diff should be 0
I tried already the lambda Function:
df['diff_rate'] = df.groupby('ID')['rate'].transform(lambda x : x-x.min())
but this returns
ID
rate
Sequential number
rate_diff
a
150
1
100
a
150
1
100
a
50
2
0
b
250
1
0
c
25
1
0
d
25
1
0
d
40
2
15
d
30
3
10
Maybe someone of you have a small workaround for this! :-)
One approach with indexing:
g = df.groupby('ID')['Sequential number']
IMAX = g.idxmax()
IMIN = g.idxmin()
df['rate_diff'] = 0
df.loc[IMAX, 'rate_diff'] = (df.loc[IMAX, 'rate'].to_numpy()
-df.loc[IMIN, 'rate'].to_numpy()
)
Another with groupby.transform+where:
g = df.sort_values(by=['ID', 'Sequential number']).groupby('ID')
m = g['Sequential number'].idxmax()
df['rate_diff'] = (g['rate'].transform(lambda x: x.iloc[-1]-x.iloc[0])
.where(df.index.isin(m), 0)
)
output:
ID rate Sequential number rate_diff
0 a 150 1 0
1 a 150 1 0
2 a 50 2 -100
3 b 250 1 0
4 c 25 1 0
5 d 25 1 0
6 d 40 2 0
7 d 30 3 5

Append new column to DF after sum?

I have a sample dataframe below:
sn C1-1 C1-2 C1-3 H2-1 H2-2 K3-1 K3-2
1 4 3 5 4 1 4 2
2 2 2 0 2 0 1 2
3 1 2 0 0 2 1 2
I will like to sum based on the prefix of C1, H2, K3 and output three new columns with the total sum. The final result is this:
sn total_c1 total_h2 total_k3
1 12 5 6
2 4 2 3
3 3 2 3
What I have tried on my original df:
lst = ["C1", "H2", "K3"]
lst2 = ["total_c1", "total_h2", "total_k3"]
for k in lst:
idx = df.columns.str.startswith(i)
for j in lst2:
df[j] = df.iloc[:,idx].sum(axis=1)
df1 = df.append(df, sort=False)
But I kept getting error
IndexError: Item wrong length 35 instead of 36.
I can't figure out how to append the new total column to produce my end result in the loop.
Any help will be appreciated (or better suggestion as oppose to loop). Thank you.
You can use groupby:
# columns of interest
cols = df.columns[1:]
col_groups = cols.str.split('-').str[0]
out_df = df[['sn']].join(df[cols].groupby(col_groups, axis=1)
.sum()
.add_prefix('total_')
)
Output:
sn total_C1 total_H2 total_K3
0 1 12 5 6
1 2 4 2 3
2 3 3 2 3
Let us try ,split then groupby with it with axis=1
out = df.groupby(df.columns.str.split('-').str[0],axis=1).sum().set_index('sn').add_prefix('Total_').reset_index()
Out[84]:
sn Total_C1 Total_H2 Total_K3
0 1 12 5 6
1 2 4 2 3
2 3 3 2 3
Another option, where we create a dictionary to groupby the columns:
mapping = {entry: f"total_{entry[:2]}" for entry in df.columns[1:]}
result = df.groupby(mapping, axis=1).sum()
result.insert(0, "sn", df.sn)
result
sn total_C1 total_H2 total_K3
0 1 12 5 6
1 2 4 2 3
2 3 3 2 3

Pandas column merging on condition

This is my pandas df:
Id Protein A_Egg B_Meat C_Milk Category
A 10 10 20 0 egg
B 20 10 0 10 milk
C 20 10 10 10 meat
D 25 20 10 0 egg
I wish to merge protein column with other column based on "Category"
My output is
Id Protein_final
A 20
B 30
C 30
D 45
Ideally, I would like to show how I am approaching but, I am frankly clueless!!
EDIT: Also, How to handle is the category is blank or does meet one of the column (in that can final should be same as initial value in protein column)
Use DataFrame.lookup with some preprocessing with remove values in columns names before _ and lowercase, last add to column:
arr = df.rename(columns=lambda x: x.split('_')[-1].lower()).lookup(df.index, df['Category'])
df['Protein'] += arr
print (df)
Id Protein A_Egg B_Meat C_Milk Category
0 A 20 10 20 0 egg
1 B 30 10 0 10 milk
2 C 30 10 10 10 meat
3 D 45 20 10 0 egg
If need only 2 columns finally:
df = df[['Id','Protein']]
You can melt the dataframe, and filter for rows where category equals the variable column, and sum the final columns :
(
df
.melt(["Id", "Protein", "Category"])
.assign(variable=lambda x: x.variable.str[2:].str.lower(),
Protein_final=lambda x: x.Protein + x.value)
.query("Category == variable")
.filter(["Id", "Protein_final"])
)
Id Protein_final
0 A 20
3 D 45
6 C 30
9 B 30

Apply function with arguments across Multiindex levels

I would like to apply a custom function to each level within a multiindex.
For example, I have the dataframe
df = pd.DataFrame(np.arange(16).reshape((4,4)),
columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY']]))
of which I want to add a column for each level 0 column, called "Value" which is the result of the following function;
def my_func(df, scale):
return df['QTY']*df['PRICE']*scale
where the user supplies the "scale" value.
Even in setting up this example, I am not sure how to show the result I want. But I know I want the final dataframe's multiindex column to be
pd.DataFrame(columns=pd.MultiIndex.from_product([['OP','PK'],['PRICE','QTY','Value']]))
Even if that wasn't had enough, I want to apply one "scale" value for the "OP" level 0 column and a different "scale" value to the "PK" column.
Use:
def my_func(df, scale):
#select second level of columns
df1 = df.xs('QTY', axis=1, level=1).values *df.xs('PRICE', axis=1, level=1) * scale
#create MultiIndex in columns
df1.columns = pd.MultiIndex.from_product([df1.columns, ['val']])
#join to original
return pd.concat([df, df1], axis=1).sort_index(axis=1)
print (my_func(df, 10))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100
EDIT:
For multiple by scaled values different for each level is possible use list of values:
print (my_func(df, [10, 20]))
OP PK
PRICE QTY val PRICE QTY val
0 0 1 0 2 3 120
1 4 5 200 6 7 840
2 8 9 720 10 11 2200
3 12 13 1560 14 15 4200
Use groupby + agg, and then concatenate the pieces together with pd.concat.
scale = 10
v = df.groupby(level=0, axis=1).agg(lambda x: x.values.prod(1) * scale)
v.columns = pd.MultiIndex.from_product([v.columns, ['value']])
pd.concat([df, v], axis=1).sort_index(axis=1, level=0)
OP PK
PRICE QTY value PRICE QTY value
0 0 1 0 2 3 60
1 4 5 200 6 7 420
2 8 9 720 10 11 1100
3 12 13 1560 14 15 2100

Pandas: keep the first three rows containing a value for each unique value [duplicate]

Suppose I have pandas DataFrame like this:
df = pd.DataFrame({'id':[1,1,1,2,2,2,2,3,4], 'value':[1,2,3,1,2,3,4,1,1]})
which looks like:
id value
0 1 1
1 1 2
2 1 3
3 2 1
4 2 2
5 2 3
6 2 4
7 3 1
8 4 1
I want to get a new DataFrame with top 2 records for each id, like this:
id value
0 1 1
1 1 2
3 2 1
4 2 2
7 3 1
8 4 1
I can do it with numbering records within group after groupby:
dfN = df.groupby('id').apply(lambda x:x['value'].reset_index()).reset_index()
which looks like:
id level_1 index value
0 1 0 0 1
1 1 1 1 2
2 1 2 2 3
3 2 0 3 1
4 2 1 4 2
5 2 2 5 3
6 2 3 6 4
7 3 0 7 1
8 4 0 8 1
then for the desired output:
dfN[dfN['level_1'] <= 1][['id', 'value']]
Output:
id value
0 1 1
1 1 2
3 2 1
4 2 2
7 3 1
8 4 1
But is there more effective/elegant approach to do this? And also is there more elegant approach to number records within each group (like SQL window function row_number()).
Did you try
df.groupby('id').head(2)
Output generated:
id value
id
1 0 1 1
1 1 2
2 3 2 1
4 2 2
3 7 3 1
4 8 4 1
(Keep in mind that you might need to order/sort before, depending on your data)
EDIT: As mentioned by the questioner, use
df.groupby('id').head(2).reset_index(drop=True)
to remove the MultiIndex and flatten the results:
id value
0 1 1
1 1 2
2 2 1
3 2 2
4 3 1
5 4 1
Since 0.14.1, you can now do nlargest and nsmallest on a groupby object:
In [23]: df.groupby('id')['value'].nlargest(2)
Out[23]:
id
1 2 3
1 2
2 6 4
5 3
3 7 1
4 8 1
dtype: int64
There's a slight weirdness that you get the original index in there as well, but this might be really useful depending on what your original index was.
If you're not interested in it, you can do .reset_index(level=1, drop=True) to get rid of it altogether.
(Note: From 0.17.1 you'll be able to do this on a DataFrameGroupBy too but for now it only works with Series and SeriesGroupBy.)
Sometimes sorting the whole data ahead is very time consuming.
We can groupby first and doing topk for each group:
g = df.groupby(['id']).apply(lambda x: x.nlargest(topk,['value'])).reset_index(drop=True)
df.groupby('id').apply(lambda x : x.sort_values(by = 'value', ascending = False).head(2).reset_index(drop = True))
Here sort values ascending false gives similar to nlargest and True gives similar to nsmallest.
The value inside the head is the same as the value we give inside nlargest to get the number of values to display for each group.
reset_index is optional and not necessary.
This works for duplicated values
If you have duplicated values in top-n values, and want only unique values, you can do like this:
import pandas as pd
ifile = "https://raw.githubusercontent.com/bhishanpdl/Shared/master/data/twitter_employee.tsv"
df = pd.read_csv(ifile,delimiter='\t')
print(df.query("department == 'Audit'")[['id','first_name','last_name','department','salary']])
id first_name last_name department salary
24 12 Shandler Bing Audit 110000
25 14 Jason Tom Audit 100000
26 16 Celine Anston Audit 100000
27 15 Michale Jackson Audit 70000
If we do not remove duplicates, for the audit department we get top 3 salaries as 110k,100k and 100k.
If we want to have not-duplicated salaries per each department, we can do this:
(df.groupby('department')['salary']
.apply(lambda ser: ser.drop_duplicates().nlargest(3))
.droplevel(level=1)
.sort_index()
.reset_index()
)
This gives
department salary
0 Audit 110000
1 Audit 100000
2 Audit 70000
3 Management 250000
4 Management 200000
5 Management 150000
6 Sales 220000
7 Sales 200000
8 Sales 150000
To get the first N rows of each group, another way is via groupby().nth[:N]. The outcome of this call is the same as groupby().head(N). For example, for the top-2 rows for each id, call:
N = 2
df1 = df.groupby('id', as_index=False).nth[:N]
To get the largest N values of each group, I suggest two approaches.
First sort by "id" and "value" (make sure to sort "id" in ascending order and "value" in descending order by using the ascending parameter appropriately) and then call groupby().nth[].
N = 2
df1 = df.sort_values(by=['id', 'value'], ascending=[True, False])
df1 = df1.groupby('id', as_index=False).nth[:N]
Another approach is to rank the values of each group and filter using these ranks.
# for the entire rows
N = 2
msk = df.groupby('id')['value'].rank(method='first', ascending=False) <= N
df1 = df[msk]
# for specific column rows
df1 = df.loc[msk, 'value']
Both of these are much faster than groupby().apply() and groupby().nlargest() calls as suggested in the other answers on here(1, 2, 3). On a sample with 100k rows and 8000 groups, a %timeit test showed that it was 24-150 times faster than those solutions.
Also, instead of slicing, you can also pass a list/tuple/range to a .nth() call:
df.groupby('id', as_index=False).nth([0,1])
# doesn't even have to be consecutive
# the following returns 1st and 3rd row of each id
df.groupby('id', as_index=False).nth([0,2])