We Keep Coding

1 item can have 2 rows because of different value in 1 column . i would like that different value to come into a single row itself

My data exists as follows-
planningOrg itemNumber sourceName supplierSplit
FVO 06-100632-01 MERRY ELECTRON 85
FVO 06-100632-01 GGEC AMERICA 15
I want GGEC America and 15 in the same row with colume name as source name 2 and supplier split 2

finding KPIs using Pandas in Python

I have datasets where I have tried to use the pandas groupby function to group the selected columns in the dataset. I would like to get the count of items in a particular column as part of the same dataframe. I can't seem to find a way. I am new to python and pandas. Thanks for the help.
example:-
country customer_no Treatment_Group Open
Atlantis 1352202109 Group A 1
Atlantis 1354540751 Group B 1
Atlantis 1354849289 Group A 1
Oceania 1356553036 Group A 1
Oceania 1356553036 Group A 1
Oceania 1356553036 Group A 1
Oceania 1356883118 Group B 0
Oceania 1356883118 Group B 0
Group Country Rate (count opened/total unique customer))
A Atlantis (2/2)*100
A Oceania (1/1)*100 rate of distinct customer number who opened
B Atlantis (1/1)*100
B Oceania *0/1)*100

lookup within filtered range

I have a dataframe with data from ecommerce panel.
It has orders and returns mixed together.
Each row has orderID - it's the same number for normal orders and for corresponding returns that come back from customers.
My data looks like this:
orderID
Shop
Revenue
Note
44
0
-32
Return
45
0
-100
Return
44
1
14
45
3
20
Something else
46
2
50
47
1
80
Something
48
2
222
For each return I want to find a 'Shop' column value that corresponds to original order.
For example : 'orderID' == 44 comes twice: once as return (with 'Shop' == 0) and once as normal order (with 'Shop' == 1).
I want to replace all the 0 values with 'Shop' column with values from earlier orders
My desired output looks like this:
orderID
Shop
Revenue
Note
44
1
-32
Return
45
3
-100
Return
44
1
14
45
3
20
Something else
46
2
50
47
1
80
Something
48
2
222
I know how to do it in Google Sheets (first I filter table removing 'Shop'==0 values and then I vlookup for numbers in this filtered array)
I know how to filter this table using Pandas but I don't know how to write it.
I assume that I will need to write a temporary column first, where I store both types of values - for normal orders (just copied) and for returns.
Original dataframe is 1 000 000+ rows
My data in .csv is available here:
https://docs.google.com/spreadsheets/d/e/2PACX-1vQAJ4tMc_Bcvv-4FsUy3E7sG0m9hm-nLTVLj-LwlSEns-YJ1pbq6gSKp5mj5lZqRI2EgHOsOutwnn1I/pub?gid=0&single=true&output=csv
Thank you for any advice!

IIUC, using map:
m = df.query('Shop != 0').set_index('orderID')['Shop']
df['Shop'] = df['orderID'].map(m)
print(df)
Output:
orderID Shop Revenue Note
0 44 1 -32 Return
1 45 3 -100 Return
2 44 1 14 NaN
3 45 3 20 Something else
4 46 2 50 NaN
5 47 1 80 Something
6 48 2 222 NaN
Create a pd.Series using query to filter out zero shops then set_index and map shops to orderID​.
This works if there is a 1-1 shop to order mapping. If you have multiple shops per order, then you'll need logic to determine which shop valid.
If you have duplicate order to the same shop, then you need to drop_duplicates first.

Filtering Dataframe in Python

I have a dataframe with 2 columns as below:
Index Year Country
0 2015 US
1 2015 US
2 2015 UK
3 2015 Indonesia
4 2015 US
5 2016 India
6 2016 India
7 2016 UK
I want to create a new dataframe containing the maximum count of country in every year.
The new dataframe will contain 3 columns as below:
Index Year Country Count
0 2015 US 3
1 2016 India 2
Is there any function in pandas where this can be done quickly?

One way can be to use groupby and along with size for finding in each category adn sort values and slice by possible number of year. You can try the following:
num_year = df['Year'].nunique()
new_df = df.groupby(['Year', 'Country']).size().rename('Count').sort_values(ascending=False).reset_index()[:num_year]
Result:
Year Country Count
0 2015 US 3
1 2016 India 2

Use:
1.
First get count of each pairs Year and Country by groupby and size.
Then get index of max value by idxmax and select row by loc:
df = df.groupby(['Year','Country']).size()
df = df.loc[df.groupby(level=0).idxmax()].reset_index(name='Count')
print (df)
Year Country Count
0 2015 US 3
1 2016 India 2
2.
Use custom function with value_counts and head:
df = df.groupby('Year')['Country']
.apply(lambda x: x.value_counts().head(1))
.rename_axis(('Year','Country'))
.reset_index(name='Count')
print (df)
Year Country Count
0 2015 US 3
1 2016 India 2

Just provide a method without groupby
Count=pd.Series(list(zip(df2.Year,df2.Country))).value_counts()
.head(2).reset_index(name='Count')
Count[['Year','Country']]=Count['index'].apply(pd.Series)
Count.drop('index',1)
Out[266]:
Count Year Country
0 3 2015 US
1 2 2016 India

Update Specific Pandas Rows with Value from Different Dataframe

I have a pandas dataframe that contains budget data but my sales data is located in another dataframe that is not the same size. How can I get my sales data updated in my budget data? How can I write conditions so that it makes these updates?
DF budget:
cust type loc rev sales spend
0 abc new north 500 0 250
1 def new south 700 0 150
2 hij old south 700 0 150
DF sales:
cust type loc sales
0 abc new north 15
1 hij old south 18
DF budget outcome:
cust type loc rev sales spend
0 abc new north 500 15 250
1 def new south 700 0 150
2 hij old south 700 18 150
Any thoughts?

Assuming that 'cust' column is unique in your other df, you can call map on the sales df after setting the index to be the 'cust' column, this will map for each 'cust' in budget df to it's sales value, additionally you will get NaN where there are missing values so you call fillna(0) to fill those values:
In [76]:
df['sales'] = df['cust'].map(df1.set_index('cust')['sales']).fillna(0)
df
Out[76]:
cust type loc rev sales spend
0 abc new north 500 15 250
1 def new south 700 0 150
2 hij old south 700 18 150