Python Pandas: Check the value of a column over multiple rows with the same index

Question

I have a pretty complex (to me) situation where I need to process a dataframe that has multiple rows for each index that can be one of three scenarios depending on the value of a certain column.

The dataframe looks like this:

Index   Account Postfix ID  val1    val2
AA11    AA      11      aa  1       2
AA11    AA      11      aa  1       2
AA11    AA      11      aa  1       2
BB22    BB      22      bb  1       1
BB22    BB      22      NA  2       2
BB22    BB      22      NA  3       3
CC33    CC      33      NA  1       2
CC33    CC      33      NA  1       2
CC33    CC      33      NA  1       2

Each unique index can fall into one of three scenarios:

• A: ID is always populated, take val1 and val2 from first row on each index
• S: ID is sometimes populated, take val1 from row where ID != Na and val2 summed from rows where ID = Na
• N: ID is never populated, sum val1 and val2 across all rows

My first problem is that I cannot figure out how to check the value of a column across multiple rows for the same index.

I was thinking something like:

indices = df.index.unique()
for index in indices:
    df[ScenarioA] = np.all(df.loc[index, ID])
    df[ScenarioN] = np.all(np.logical_not(df.loc[index, ID]))
    df[ScenarioS] = np.logical_and(np.logical_not(df[ScenarioA]),np.logical_not(df[ScenarioN]))

But this is resulting in all rows getting tagged as ScenarioN when in actuality the result should look like this:

Index   Account Postfix ID  val1    val2  ScenarioA ScenarioS ScenarioN
AA11    AA      11      aa  1       2     True      False     False
AA11    AA      11      aa  1       2     True      False     False
AA11    AA      11      aa  1       2     True      False     False
BB22    BB      22      bb  1       1     False     True      False
BB22    BB      22      NA  2       2     False     True      False
BB22    BB      22      NA  3       3     False     True      False
CC33    CC      33      NA  1       2     False     False     True
CC33    CC      33      NA  1       2     False     False     True
CC33    CC      33      NA  1       2     False     False     True

Once I've done that I need to perform the sums and end up with something like the below but I don't think this part will be too difficult as I can go by Scenario and perform the calcs as needed:

Index   Account Postfix ID  val1    val2
AA11    AA      11      aa  1       2
BB22    BB      22      bb  1       5
CC33    CC      33      NA  3       6

What am I doing wrong in the part where I try to assign T/F to the Scenario columns?

Answer 1

not sure if this is what u r after, hopefully it can guide u on solving ur specific challenge:

grouping = df.groupby('Index').ID

#create some anonymous functions
#determine groups that completely have no null
#those that have some null
#those that have nulls all through
alls = lambda x: x.isna().all()
anys = lambda x: x.isna().any()
notnull = lambda x: x.notna().all()
all_null = grouping.apply(alls)
any_null = grouping.apply(anys)
all_not_null = grouping.apply(notnull)

#get the individual groups
full = all_not_null.index[all_not_null.array]
empty = all_null.index[all_null.array]
partially_empty = any_null.index[any_null.array].difference(empty)

#get the different dataframes for each group
step1 = df.loc[df.Index.isin(full)].groupby('Index').first()


#some nulls
cond1 = df.Index.isin(partially_empty) & (df.ID.notna())
cond2 = df.Index.isin(partially_empty) &(df.ID.isna())

step2 = df.loc[cond1]
step2 = step2.assign(val2 = df.loc[cond2,'val2'].sum())

#nulls all the way
step3 = df.loc[df.Index.isin(empty)]
temp = step3.groupby(['Index']).agg({'val1':'sum','val2':'sum'})

step3 = step3.drop_duplicates('Index')
step3 = step3.assign(val1 = temp['val1'].squeeze(), val2 = temp['val2'].squeeze())

#combine the three dataframes
pd.concat([step1.reset_index(),step2,step3],ignore_index=True)

    Index   Account Postfix ID  val1    val2
0   AA11      AA      11    aa    1      2
1   BB22      BB      22    bb    1      5
2   CC33      CC      33    NaN   3      6