Group rows based on the current occurance of a variable - python-3.x

Question

I am trying to group a dataframe based on the occurrence a variable. For example take this dataframe
| col_1 | col_2
---------------------
0 | 1 | 1
1 | 0 | 1
2 | 0 | 1
3 | 0 | -1
4 | 0 | -1
5 | 0 | -1
6 | 0 | NaN
7 | -1 | NaN
8 | 0 | NaN
9 | 0 | -1
10| 0 | -1
11| 0 | -1
I want to group variable based on the current occurrence of a variable in column_2 to a dataframe and get the next sequence into another dataframe and likewise till the end of dataframe while also ignoring NaN.
So the final output would be like:
ones_1 =
| col_1 | col_2
---------------------
0 | 1 | 1
1 | 0 | 1
2 | 0 | 1
mones_1 =
3 | 0 | -1
4 | 0 | -1
5 | 0 | -1
mones_2 =
9 | 0 | -1
10| 0 | -1
11| 0 | -1

Answer 1

I suggest create dictionary of DataFrames:
#only non missing rows
mask = df['col_2'].notna()
#create unique groups
g = df['col_2'].ne(df['col_2'].shift()).cumsum()
#create counter of filtered g
g = g[mask].groupby(df['col_2']).transform(lambda x:pd.factorize(x)[0]) + 1
#map positive and negative values to strings and add counter values
g = df.loc[mask, 'col_2'].map({-1:'mones_',1:'ones_'}) + g.astype(str)
#generally groups
#g = 'val' + df.loc[mask, 'col_2'].astype(str) + ' no' + g.astype(str)
print (g)
0 ones_1
1 ones_1
2 ones_1
3 mones_1
4 mones_1
5 mones_1
9 mones_2
10 mones_2
11 mones_2
Name: col_2, dtype: object
#create dictionary of DataFrames
dfs = dict(tuple(df.groupby(g)))
print (dfs)
{'mones_1': col_1 col_2
3 0 -1.0
4 0 -1.0
5 0 -1.0, 'mones_2': col_1 col_2
9 0 -1.0
10 0 -1.0
11 0 -1.0, 'ones_1': col_1 col_2
0 1 1.0
1 0 1.0
2 0 1.0}
#select by keys
print (dfs['ones_1'])
col_1 col_2
0 1 1.0
1 0 1.0
2 0 1.0
It is not recommended, but possible create DataFrames by groups with variable names:
for i, g in df.groupby(g):
globals()[i] = g
print (ones_1)
col_1 col_2
0 1 1.0
1 0 1.0
2 0 1.0

Answer 2

here is another logic (keeping them in dictionary is the idea again):
m=df[df.col_2.notna()] #filter out the NaN rows
#check if the index are in sequence along with that check if values changes per row
s=m.col_2.ne(m.col_2.shift())|m.index.to_series().diff().fillna(1).gt(1)
dfs={f'df_{int(i)}':g for i , g in df.groupby(s.cumsum())} #groupby and store in dict
Access the dataframes by accessing the keys:
print(dfs['df_1'])
print('---------------------------------')
print(dfs['df_2'])
print('---------------------------------')
print(dfs['df_3'])
col_1 col_2
0 1 1.0
1 0 1.0
2 0 1.0
---------------------------------
col_1 col_2
3 0 -1.0
4 0 -1.0
5 0 -1.0
---------------------------------
col_1 col_2
9 0 -1.0
10 0 -1.0
11 0 -1.0