groupby all results without resorting - pandas

Question

Sort in groupby does not work the way I thought it would.
In the following example, I do not want to group "USA" together because there is one row of "Russia".
from io import StringIO
myst="""india, 905034 , 19:44
USA, 905094 , 19:33
Russia, 905154 , 21:56
USA, 345345, 45:55
USA, 34535, 65:45
"""
u_cols=['country', 'index', 'current_tm']
myf = StringIO(myst)
import pandas as pd
df = pd.read_csv(StringIO(myst), sep=',', names = u_cols)
When I use groupby I get the following:
df.groupby('country', sort=False).size()
country
india 1
USA 3
Russia 1
dtype: int64
Is there anyway I can get results something like this...
country
india 1
USA 1
Russia 1
USA 2

Answer 1

You could try this bit of code instead of a direct groupby:
country = [] #initialising lists
count = []
for i, g in df.groupby([(df.country != df.country.shift()).cumsum()]): #Creating a list that increases by 1 for every time a unique value appears in the dataframe country column.
country.append(g.country.tolist()[0]) #Adding the name of country to list.
count.append(len(g.country.tolist())) #Adding the number of times that country appears to list.
pd.DataFrame(data = {'country': country, 'count':count}) #Binding the lists all into a dataframe.
This df.groupby([(df.country != df.country.shift()).cumsum()]) creates a dataframe that gives a unique number (cumulatively) to every change of country in the country column.
In the for loop, i represents the unique cumulative number assigned to each country appearance and g represents the corresponding full row(s) from your original dataframe.
g.country.tolist() outputs a list of the country names for each unique appearance (aka i) i.e.
['india']
['USA']
['Russia']
['USA', 'USA']
for your given data.
Therefore, the first item is the name of the country and the length represents the number of appearances. This info can then be (recorded in a list and then) put together into a dataframe and give the output you require.
You could also use list comprehensions rather than the for loop:
cumulative_df = df.groupby([(df.country != df.country.shift()).cumsum()]) #The cumulative count dataframe
country = [g.country.tolist()[0] for i,g in cumulative_df] #List comprehension for getting country names.
count = [len(g.country.tolist()) for i,g in cumulative_df] #List comprehension for getting count for each country.
Reference: Pandas DataFrame: How to groupby consecutive values

Answer 2

Using the trick given in #user2285236 's comment
df['Group'] = (df.country != df.country.shift()).cumsum()
df.groupby(['country', 'Group'], sort=False).size()