CODEWITHSUNDEEP
pandas
Johnson Francis
Johnson Francis

Reputation: 271

Repeat rows in pandas data frame with a sequential change in a column value

I want to repaet the rows in my df in a time sequence with forward filling.

Original df:

     A   B   C Year
 0   ABC 0   A 1950
 1   CDE 1   A 1950
 2   XYZ 1   B 1954
 3   123 1   C 1954
 4   X12 1   B 1956
 5   123 1   D 1956
 6   124 1   D 1956

Desired df:

     A   B   C Year
 0   ABC 0   A 1950
 1   CDE 1   A 1950
 2   ABC 0   A 1951
 3   CDE 1   A 1951
 4   ABC 0   A 1952
 5   CDE 1   A 1952
 6   ABC 0   A 1953
 7   CDE 1   A 1953
 8   XYZ 1   B 1954
 9   123 1   C 1954
10   XYZ 1   B 1955
11   123 1   C 1955
12   X12 1   B 1956
13   123 1   D 1956
14   124 1   D 1956

I have tried converting the Year column to datetime and used a resampling yearwise with forward fill. But that didn't work as resample gives only one row for each year if resample year wise.

df.resample('YS').first().ffill().reset_index()

Desired df:

     A   B   C Year
 0   ABC 0   A 1950
 1   CDE 1   A 1950
 2   ABC 0   A 1951
 3   CDE 1   A 1951
 4   ABC 0   A 1952
 5   CDE 1   A 1952
 6   ABC 0   A 1953
 7   CDE 1   A 1953
 8   XYZ 1   B 1954
 9   123 1   C 1954
10   XYZ 1   B 1955
11   123 1   C 1955
12   X12 1   B 1956
13   123 1   D 1956
14   124 1   D 1956

Upvotes: 2

Views: 1013

Answers (3)

Johnson Francis
Johnson Francis

Reputation: 271

I took a different approach by pivoting & melting.. Seems to be working.. Any body sees an issue..?

data = {'year': ['2000', '2000', '2005', '2005', '2007', '2007', '2007', '2009'],
'country':['UK', 'US', 'FR','US','UK','FR','US','UK'],
'sales': [10, 21, 20, 10,12,20, 10,12],
'rep': ['john', 'john', 'claire','claire', 'kyle','kyle','kyle','amy']
}
df=pd.DataFrame(data)


    year    country sales   rep
0   2000    UK  10  john
1   2000    US  21  john
2   2005    FR  20  claire
3   2005    US  10  claire
4   2007    UK  12  kyle
5   2007    FR  20  kyle
6   2007    US  10  kyle
7   2009    UK  12  amy

First doing a pivot...

dfp=pd.pivot_table(df,index=['country','rep'],values=['sales'],columns=['year']).fillna(0)
dfp=dfp.xs('sales', axis=1, drop_level=True)

    year    2000    2005    2007    2009
country rep             
FR  claire  0.0 20.0    0.0 0.0
kyle    0.0 0.0 20.0    0.0
UK  amy 0.0 0.0 0.0 12.0
john    10.0    0.0 0.0 0.0
kyle    0.0 0.0 12.0    0.0
US  claire  0.0 10.0    0.0 0.0
john    21.0    0.0 0.0 0.0
kyle    0.0 0.0 10.0    0.0

Then a little logic to replicate the columns..

cols=dfp.columns.astype(int).values
dft=dfp.copy()
i=0
for col in cols :
    if col != cols[-1]:
        for newcol in range(col+1,cols[i+1]):
            dft[str(newcol)]=dft[str(col)]
    i+=1

    year    2000    2005    2007    2009    2001    2002    2003    2004    2006    2008
country rep                                     
FR  claire  0.0 20.0    0.0 0.0 0.0 0.0 0.0 0.0 20.0    0.0
kyle    0.0 0.0 20.0    0.0 0.0 0.0 0.0 0.0 0.0 20.0
UK  amy 0.0 0.0 0.0 12.0    0.0 0.0 0.0 0.0 0.0 0.0
john    10.0    0.0 0.0 0.0 10.0    10.0    10.0    10.0    0.0 0.0
kyle    0.0 0.0 12.0    0.0 0.0 0.0 0.0 0.0 0.0 12.0
US  claire  0.0 10.0    0.0 0.0 0.0 0.0 0.0 0.0 10.0    0.0
john    21.0    0.0 0.0 0.0 21.0    21.0    21.0    21.0    0.0 0.0
kyle    0.0 0.0 10.0    0.0 0.0 0.0 0.0 0.0 0.0 10.0

Then did a melt get them back into original format..

dfm=dft.reset_index()
dfm=dfm.melt(id_vars=['country','rep'],value_vars=dfm.columns.values[2:],var_name='Year',value_name='sales')
dfm=dfm.loc[dfm.sales>0].reset_index(drop='True')

    country rep Year    sales
0   UK  john    2000    10.0
1   US  john    2000    21.0
2   FR  claire  2005    20.0
3   US  claire  2005    10.0
4   FR  kyle    2007    20.0
5   UK  kyle    2007    12.0
6   US  kyle    2007    10.0
7   UK  amy     2009    12.0
8   UK  john    2001    10.0
9   US  john    2001    21.0
10  UK  john    2002    10.0
11  US  john    2002    21.0
12  UK  john    2003    10.0
13  US  john    2003    21.0
14  UK  john    2004    10.0
15  US  john    2004    21.0
16  FR  claire  2006    20.0
17  US  claire  2006    10.0
18  FR  kyle    2008    20.0
19  UK  kyle    2008    12.0
20  US  kyle    2008    10.0

Upvotes: 0

BENY
BENY

Reputation: 323226

I feel like this is a unnesting problem 

s=df.astype(str).groupby('Year').agg(list)
s.index=s.index.astype(int)
s1=s.reindex(np.arange(s.index.min(),s.index.max()+1),method='ffill')
yourdf=unnesting(s1,list('ABC')).reset_index()
yourdf
Out[117]: 
    Year    A  B  C
0   1950  ABC  0  A
1   1950  CDE  1  A
2   1951  ABC  0  A
3   1951  CDE  1  A
4   1952  ABC  0  A
5   1952  CDE  1  A
6   1953  ABC  0  A
7   1953  CDE  1  A
8   1954  XYZ  1  B
9   1954  123  1  C
10  1955  XYZ  1  B
11  1955  123  1  C
12  1956  X12  1  B
13  1956  123  1  D
14  1956  124  1  D

def unnesting(df, explode):
    idx = df.index.repeat(df[explode[0]].str.len())
    df1 = pd.concat([
        pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
    df1.index = idx
    return df1.join(df.drop(explode, 1), how='left')

Upvotes: 3

Scott Boston
Scott Boston

Reputation: 153460

You can try this:

df_out = df.set_index([pd.to_datetime(df['Year'], format='%Y'),'A','B','C'])\
           .unstack([1,2,3]).resample('A').ffill()\
           .stack([1,2,3]).reset_index([1,2,3])

df_out = df_out.assign(Year=pd.to_datetime(df_out.index).year).reset_index(drop=True)
df_out

Output:

      A  B  C  Year
0   ABC  0  A  1950
1   CDE  1  A  1950
2   ABC  0  A  1951
3   CDE  1  A  1951
4   ABC  0  A  1952
5   CDE  1  A  1952
6   ABC  0  A  1953
7   CDE  1  A  1953
8   123  1  C  1954
9   XYZ  1  B  1954
10  123  1  C  1955
11  XYZ  1  B  1955
12  123  1  D  1956
13  X12  1  B  1956

Upvotes: 2

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