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Average of every x rows with a step size of y per each subset using pandas

I have a pandas data frame like this:
Subset Position Value
1 1 2
1 10 3
1 15 0.285714
1 43 1
1 48 0
1 89 2
1 132 2
1 152 0.285714
1 189 0.133333
1 200 0
2 1 0.133333
2 10 0
2 15 2
2 33 2
2 36 0.285714
2 72 2
2 132 0.133333
2 152 0.133333
2 220 3
2 250 8
2 350 6
2 750 0
I want to know how can I get the mean of values for every "x" row with "y" step size per subset in pandas?
For example, mean of every 5 rows (step size =2) for value column in each subset like this:
Subset Start_position End_position Mean
1 1 48 1.2571428
1 15 132 1.0571428
1 48 189 0.8838094
2 1 36 0.8838094
2 15 132 1.2838094
2 36 220 1.110476
2 132 350 3.4533332

Is this what you were looking for:
df = pd.DataFrame({'Subset': [1]*10+[2]*12,
'Position': [1,10,15,43,48,89,132,152,189,200,1,10,15,33,36,72,132,152,220,250,350,750],
'Value': [2,3,.285714,1,0,2,2,.285714,.1333333,0,0.133333,0,2,2,.285714,2,.133333,.133333,3,8,6,0]})
averaged_df = pd.DataFrame(columns=['Subset', 'Start_position', 'End_position', 'Mean'])
window = 5
step_size = 2
for subset in df.Subset.unique():
subset_df = df[df.Subset==subset].reset_index(drop=True)
for i in range(0,len(df),step_size):
window_rows = subset_df.iloc[i:i+window]
if len(window_rows) < window:
continue
window_average = {'Subset': window_rows.Subset.loc[0+i],
'Start_position': window_rows.Position[0+i],
'End_position': window_rows.Position.iloc[-1],
'Mean': window_rows.Value.mean()}
averaged_df = averaged_df.append(window_average,ignore_index=True)
Some notes about the code:
It assumes all subsets are in order in the original df (1,1,2,1,2,2 will behave as if it was 1,1,1,2,2,2)
If there is a group left that's smaller than a window, it will skip it (e.g. 1, 132, 200, 0,60476 is not included`)

One version specific answer would be, using pandas.api.indexers.FixedForwardWindowIndexer introduced in pandas 1.1.0:
>>> window=5
>>> step=2
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=window)
>>> df2 = df.join(df.Position.shift(-(window-1)), lsuffix='_start', rsuffix='_end')
>>> df2 = df2.assign(Mean=df2.pop('Value').rolling(window=indexer).mean()).iloc[::step]
>>> df2 = df2[df2.Position_start.lt(df2.Position_end)].dropna()
>>> df2['Position_end'] = df2['Position_end'].astype(int)
>>> df2
Subset Position_start Position_end Mean
0 1 1 48 1.257143
2 1 15 132 1.057143
4 1 48 189 0.883809
10 2 1 36 0.883809
12 2 15 132 1.283809
14 2 36 220 1.110476
16 2 132 350 3.453333

Pandas Python highest 2 rows of every 3 and tabling the results

Suppose I have the following dataframe:
. Column1 Column2
0 25 1
1 89 2
2 59 3
3 78 10
4 99 20
5 38 30
6 89 100
7 57 200
8 87 300
Im not sure if what I want to do is impossible or not. But I want to compare every three rows of column1 and then take the highest 2 out the three rows and assign the corresponding 2 Column2 values to a new column. The values in column 3 does not matter if they are joined or not. It does not matter if they are arranged or not for I know every 2 rows of column 3 belong to every 3 rows of column 1.
. Column1 Column2 Column3
0 25 1 2
1 89 2 3
2 59 3
3 78 10 20
4 99 20 10
5 38 30
6 89 100 100
7 57 200 300
8 87 300

You can use np.arange with np.repeat to create a grouping array which groups every 3 values.
Then use GroupBy.nlargest then extract indices of those values using pd.Index.get_level_values, then assign them to Column3 pandas handles index alignment.
n_grps = len(df)/3
g = np.repeat(np.arange(n_grps), 3)
idx = df.groupby(g)['Column1'].nlargest(2).index.get_level_values(1)
vals = df.loc[idx, 'Column2']
vals
# 1 2
# 2 3
# 4 20
# 3 10
# 6 100
# 8 300
# Name: Column2, dtype: int64
df['Column3'] = vals
df
Column1 Column2 Column3
0 25 1 NaN
1 89 2 2.0
2 59 3 3.0
3 78 10 10.0
4 99 20 20.0
5 38 30 NaN
6 89 100 100.0
7 57 200 NaN
8 87 300 300.0
To get output like you mentioned in the question you have to sort and push NaN to last then you have perform this additional step.
df['Column3'] = df.groupby(g)['Column3'].apply(lambda x:x.sort_values()).values
Column1 Column2 Column3
0 25 1 2.0
1 89 2 3.0
2 59 3 NaN
3 78 10 10.0
4 99 20 20.0
5 38 30 NaN
6 89 100 100.0
7 57 200 300.0
8 87 300 NaN

Create column that sums the last x occurrences of another column

I'm trying to create a new column, lets call it "HomeForm", that is the sum of the last 5 values of "FTHG" for each of the entries in the "HomeTeam" column.
Say for Team 0, the idea would be to populate the cell on the new column with the sum of the last 5 values of "FTHG" that correspond to Team 0. The table is ordered by date.
How can it be done in Python?
HomeTeam FTHG HomeForm
Date
136 0 4
135 2 0
135 4 2
135 5 0
135 6 1
135 13 0
135 17 3
135 18 1
134 11 4
134 12 0
128 1 0
128 3 0
128 8 2
128 9 1
128 13 3
128 14 1
128 15 0
127 7 1
127 16 1
126 10 1
Thanks.

You'll groupby on HomeTeam and perform a rolling sum here, summing for a minimum of 1 period, and maximum of 5.
First, define a function -
def f(x):
return x.shift().rolling(window=5, min_periods=1).sum()
This function performs the rolling sum of the previous 5 games (hence the shift). Pass this function to dfGroupBy.transform -
df['HomeForm'] = df.groupby('HomeTeam', sort=False).FTHG.transform(f)
df
HomeTeam FTHG HomeForm
Date
136 0 4 NaN
135 2 0 NaN
135 4 2 NaN
135 5 0 NaN
135 6 1 NaN
135 13 0 NaN
135 17 3 NaN
135 18 1 NaN
134 11 4 NaN
134 12 0 NaN
128 1 0 NaN
128 3 0 NaN
128 8 2 NaN
128 9 1 NaN
128 13 3 0.0
128 14 1 NaN
128 15 0 NaN
127 7 1 NaN
127 16 1 NaN
126 10 1 NaN
If needed, fill the NaNs with zeros and convert to integer -
df['HomeForm'] = df['HomeForm'].fillna(0).astype(int)

Pandas difference between groupby-size and unique

The goal here is to see how many unique values i have in my database. This is the code i have written:
apps = pd.read_csv('ConcatOwned1_900.csv', sep='\t', usecols=['appid'])
apps[('appid')] = apps[('appid')].astype(int)
apps_list=apps['appid'].unique()
b = apps.groupby('appid').size()
blist = b.unique()
print len(apps_list), len(blist), len(set(b))
>>>7672 2164 2164
Why is there difference in those two methods?
Due to request i am posting some of my data:
Unnamed: 0 StudID No appid work work2
0 0 76561193665298433 0 10 nan 0
1 1 76561193665298433 1 20 nan 0
2 2 76561193665298433 2 30 nan 0
3 3 76561193665298433 3 40 nan 0
4 4 76561193665298433 4 50 nan 0
5 5 76561193665298433 5 60 nan 0
6 6 76561193665298433 6 70 nan 0
7 7 76561193665298433 7 80 nan 0
8 8 76561193665298433 8 100 nan 0
9 9 76561193665298433 9 130 nan 0
10 10 76561193665298433 10 220 nan 0
11 11 76561193665298433 11 240 nan 0
12 12 76561193665298433 12 280 nan 0
13 13 76561193665298433 13 300 nan 0
14 14 76561193665298433 14 320 nan 0
15 15 76561193665298433 15 340 nan 0
16 16 76561193665298433 16 360 nan 0
17 17 76561193665298433 17 380 nan 0
18 18 76561193665298433 18 400 nan 0
19 19 76561193665298433 19 420 nan 0
20 20 76561193665298433 20 500 nan 0
21 21 76561193665298433 21 550 nan 0
22 22 76561193665298433 22 620 6.0 3064
33 33 76561193665298434 0 10 nan 837
34 34 76561193665298434 1 20 nan 27
35 35 76561193665298434 2 30 nan 9
36 36 76561193665298434 3 40 nan 5
37 37 76561193665298434 4 50 nan 2
38 38 76561193665298434 5 60 nan 0
39 39 76561193665298434 6 70 nan 403
40 40 76561193665298434 7 130 nan 0
41 41 76561193665298434 8 80 nan 6
42 42 76561193665298434 9 100 nan 10
43 43 76561193665298434 10 220 nan 14

IIUC based on attached piece of the dataframe it seems that you should analyze b.index, not values of b. Just look:
b = apps.groupby('appid').size()
In [24]: b
Out[24]:
appid
10 2
20 2
30 2
40 2
50 2
60 2
70 2
80 2
100 2
130 2
220 2
240 1
280 1
300 1
320 1
340 1
360 1
380 1
400 1
420 1
500 1
550 1
620 1
dtype: int64
In [25]: set(b)
Out[25]: {1, 2}
But if you do it for b.index you'll get the same values for all 3 methods:
blist = b.index.unique()
In [30]: len(apps_list), len(blist), len(set(b.index))
Out[30]: (23, 23, 23)

pandas drop row below each row containing an 'na'

i have a dataframe with, say, 4 columns [['a','b','c','d']], to which I add another column ['total'] containing the sum of all the other columns for each row. I then add another column ['growth of total'] with the growth rate of the total.
some of the values in [['a','b','c','d']] are blank, rendering the ['total'] column invalid for these rows. I can easily get rid of these rows with df.dropna(how='any').
However, my growth rate will be invalid not only for rows with missing values in [['a','b','c','d']], but also for the following row. How do I drop all these rows?

IIUC correctly you can use notnull with all to mask off any rows with NaN and any rows that follow NaN rows:
In [43]:
df = pd.DataFrame({'a':[0,np.NaN, 2, 3,np.NaN], 'b':[np.NaN, 1,2,3,4], 'c':[0, np.NaN,2,3,4]})
df
Out[43]:
a b c
0 0 NaN 0
1 NaN 1 NaN
2 2 2 2
3 3 3 3
4 NaN 4 4
In [44]:
df[df.notnull().all(axis=1) & df.shift().notnull().all(axis=1)]
Out[44]:
a b c
3 3 3 3

Here's one option that I think does what you're looking for:
In [76]: df = pd.DataFrame(np.arange(40).reshape(10,4))
In [77]: df.ix[1,2] = np.nan
In [78]: df.ix[6,1] = np.nan
In [79]: df['total'] = df.sum(axis=1, skipna=False)
In [80]: df
Out[80]:
0 1 2 3 total
0 0 1 2 3 6
1 4 5 NaN 7 NaN
2 8 9 10 11 38
3 12 13 14 15 54
4 16 17 18 19 70
5 20 21 22 23 86
6 24 NaN 26 27 NaN
7 28 29 30 31 118
8 32 33 34 35 134
9 36 37 38 39 150
In [81]: df['growth'] = df['total'].iloc[1:] - df['total'].values[:-1]
In [82]: df
Out[82]:
0 1 2 3 total growth
0 0 1 2 3 6 NaN
1 4 5 NaN 7 NaN NaN
2 8 9 10 11 38 NaN
3 12 13 14 15 54 16
4 16 17 18 19 70 16
5 20 21 22 23 86 16
6 24 NaN 26 27 NaN NaN
7 28 29 30 31 118 NaN
8 32 33 34 35 134 16
9 36 37 38 39 150 16