CODEWITHSUNDEEP
pythonpandasmergeduplicates
Zesima29
Zesima29

Reputation: 184

Pivot duplicates rows into new columns Pandas

I have a data frame like this and I'm trying reshape my data frame using Pivot from Pandas in a way that I can keep some values from the original rows while making the duplicates row into columns and renaming them. Sometimes I have rows with 5 duplicates

I have been trying, but I don't get it.

import pandas as pd
df = pd.read_csv("C:dummy")

df = df.pivot(index=["ID"], columns=["Zone","PTC"], values=["Zone","PTC"])

# Rename columns and reset the index.
df.columns = [["PTC{}","Zone{}"],.format(c) for c in df.columns]
df.reset_index(inplace=True)
# Drop duplicates
df.drop(["PTC","Zone"], axis=1, inplace=True)

Input

ID  Agent   OV  Zone Value  PTC
1   10      26   M1   10    100
2   26.5    8    M2   50    95
2   26.5    8    M1   6     5
3   4.5     6    M3   4     40
3   4.5     6    M4   6     60
4   1.2    0.8   M1   8     100
5   2      0.4   M1   6     10
5   2      0.4   M2   41    86
5   2      0.4   M4   2     4

Output

ID  Agent   OV  Zone1   Value1  PTC1    Zone2   Value2  PTC2    Zone3   Value3  PTC3
1   10      26  M_1     10       100    0          0      0      0         0      0
2   26.5    8   M_2     50        95    M_1        6      5      0         0      0
3   4.5     6   M_3     4         40    M_4        6     60      0         0      0
4   1.2    0.8  M_1     8        100    0          0      0      0         0      0
5   2      0.4  M_1     6         10    M_2        41    86     M_4        2      4

Upvotes: 7

Views: 2593

Answers (2)

jezrael
jezrael

Reputation: 862511

Use cumcount for count groups, create MultiIndex by set_index with unstack and last flatten values of columns:

g = df.groupby(["ID","Agent", "OV"]).cumcount().add(1)
df = df.set_index(["ID","Agent","OV", g]).unstack(fill_value=0).sort_index(axis=1, level=1)
df.columns = ["{}{}".format(a, b) for a, b in df.columns]

df = df.reset_index()
print (df)
   ID  Agent    OV Zone1  Value1  PTC1 Zone2  Value2  PTC2 Zone3  Value3  PTC3
0   1   10.0  26.0    M1      10   100     0       0     0     0       0     0
1   2   26.5   8.0    M2      50    95    M1       6     5     0       0     0
2   3    4.5   6.0    M3       4    40    M4       6    60     0       0     0
3   4    1.2   0.8    M1       8   100     0       0     0     0       0     0
4   5    2.0   0.4    M1       6    10    M2      41    86    M4       2     4

If want replace to 0 only numeric columns:

g = df.groupby(["ID","Agent"]).cumcount().add(1)
df = df.set_index(["ID","Agent","OV", g]).unstack().sort_index(axis=1, level=1)

idx = pd.IndexSlice
df.loc[:, idx[['Value','PTC']]] = df.loc[:, idx[['Value','PTC']]].fillna(0).astype(int)
df.columns = ["{}{}".format(a, b) for a, b in df.columns]

df = df.fillna('').reset_index()
print (df)
   ID  Agent    OV Zone1  Value1  PTC1 Zone2  Value2  PTC2 Zone3  Value3  PTC3
0   1   10.0  26.0    M1      10   100             0     0             0     0
1   2   26.5   8.0    M2      50    95    M1       6     5             0     0
2   3    4.5   6.0    M3       4    40    M4       6    60             0     0
3   4    1.2   0.8    M1       8   100             0     0             0     0
4   5    2.0   0.4    M1       6    10    M2      41    86    M4       2     4

Upvotes: 5

BENY
BENY

Reputation: 323226

You can using cumcount create the help key , then we do unstack with multiple index flatten (PS : you can add fillna(0) at the end , I did not add it cause I do not think for Zone value 0 is correct )

df['New']=df.groupby(['ID','Agent','OV']).cumcount()+1
new_df=df.set_index(['ID','Agent','OV','New']).unstack('New').sort_index(axis=1 , level=1)
new_df.columns=new_df.columns.map('{0[0]}{0[1]}'.format) 
new_df
Out[40]: 
              Zone1  Value1   PTC1 Zone2  Value2  PTC2 Zone3  Value3  PTC3
ID Agent OV                                                               
1  10.0  26.0    M1    10.0  100.0  None     NaN   NaN  None     NaN   NaN
2  26.5  8.0     M2    50.0   95.0    M1     6.0   5.0  None     NaN   NaN
3  4.5   6.0     M3     4.0   40.0    M4     6.0  60.0  None     NaN   NaN
4  1.2   0.8     M1     8.0  100.0  None     NaN   NaN  None     NaN   NaN
5  2.0   0.4     M1     6.0   10.0    M2    41.0  86.0    M4     2.0   4.0

Upvotes: 2

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