python pandas consolidate rows with same values in a sequence and reorder (drop duplicates in a sequence)

up vote
1
down vote

favorite

Lets say i have following table:

ID    FRUIT    ORDER

01    apple    1

01    apple    2

01    peach    3

01    apple    4

02    melon    1

02    apple    2

02    apple    3

02    apple    4

Now i want to consolidate rows within same ID when the values are equal in a iterative manner (drop duplicates if they are in a sequence) and redefine the order number, e.g.

ID    FRUIT    ORDER

01    apple    1

01    peach    2

01    apple    3

02    melon    1

02    apple    2

EDIT: I forgot to reorder. Like above: the order should be re-arranged in an iterative manner

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

add a comment |

up vote
1
down vote

favorite

Lets say i have following table:

ID    FRUIT    ORDER

01    apple    1

01    apple    2

01    peach    3

01    apple    4

02    melon    1

02    apple    2

02    apple    3

02    apple    4

Now i want to consolidate rows within same ID when the values are equal in a iterative manner (drop duplicates if they are in a sequence) and redefine the order number, e.g.

ID    FRUIT    ORDER

01    apple    1

01    peach    2

01    apple    3

02    melon    1

02    apple    2

EDIT: I forgot to reorder. Like above: the order should be re-arranged in an iterative manner

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

add a comment |

up vote
1
down vote

favorite

Lets say i have following table:

ID    FRUIT    ORDER

01    apple    1

01    apple    2

01    peach    3

01    apple    4

02    melon    1

02    apple    2

02    apple    3

02    apple    4

Now i want to consolidate rows within same ID when the values are equal in a iterative manner (drop duplicates if they are in a sequence) and redefine the order number, e.g.

ID    FRUIT    ORDER

01    apple    1

01    peach    2

01    apple    3

02    melon    1

02    apple    2

EDIT: I forgot to reorder. Like above: the order should be re-arranged in an iterative manner

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

Lets say i have following table:

ID    FRUIT    ORDER

01    apple    1

01    apple    2

01    peach    3

01    apple    4

02    melon    1

02    apple    2

02    apple    3

02    apple    4

Now i want to consolidate rows within same ID when the values are equal in a iterative manner (drop duplicates if they are in a sequence) and redefine the order number, e.g.

ID    FRUIT    ORDER

01    apple    1

01    peach    2

01    apple    3

02    melon    1

02    apple    2

EDIT: I forgot to reorder. Like above: the order should be re-arranged in an iterative manner

python pandas group-by

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

edited Nov 12 at 10:29

asked Nov 12 at 10:14

kxell2001

144

asked Nov 12 at 10:14

kxell2001

144

asked Nov 12 at 10:14

kxell2001

144

add a comment |

2 Answers
2

active

oldest

votes

up vote
1
down vote

accepted

Use boolean indexing for filter only first consecutive values with cumcount for new ordering:

a = df['ID'] + df['FRUIT']

#if necessary

#a = df['ID'].astype(str) + df['FRUIT']

df = df[a.ne(a.shift())]

df['ORDER'] = df.groupby('ID').cumcount().add(1)

print (df)

   ID  FRUIT ORDER

0  01  apple     1

2  01  peach     2

3  01  apple     3

4  02  melon     1

5  02  apple     2

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

add a comment |

up vote
0
down vote

I believe this will be easy one to go :

>>> df

   ID  FRUIT  ORDER

0  01  apple      1

1  01  apple      2

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

6  02  apple      3

7  02  apple      4



>>> df[df['FRUIT'] != df['FRUIT'].shift(1)]

   ID  FRUIT  ORDER

0  01  apple      1

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

add a comment |

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2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

up vote
1
down vote

accepted

Use boolean indexing for filter only first consecutive values with cumcount for new ordering:

a = df['ID'] + df['FRUIT']

#if necessary

#a = df['ID'].astype(str) + df['FRUIT']

df = df[a.ne(a.shift())]

df['ORDER'] = df.groupby('ID').cumcount().add(1)

print (df)

   ID  FRUIT ORDER

0  01  apple     1

2  01  peach     2

3  01  apple     3

4  02  melon     1

5  02  apple     2

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

add a comment |

up vote
1
down vote

accepted

Use boolean indexing for filter only first consecutive values with cumcount for new ordering:

a = df['ID'] + df['FRUIT']

#if necessary

#a = df['ID'].astype(str) + df['FRUIT']

df = df[a.ne(a.shift())]

df['ORDER'] = df.groupby('ID').cumcount().add(1)

print (df)

   ID  FRUIT ORDER

0  01  apple     1

2  01  peach     2

3  01  apple     3

4  02  melon     1

5  02  apple     2

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

add a comment |

up vote
1
down vote

accepted

Use boolean indexing for filter only first consecutive values with cumcount for new ordering:

a = df['ID'] + df['FRUIT']

#if necessary

#a = df['ID'].astype(str) + df['FRUIT']

df = df[a.ne(a.shift())]

df['ORDER'] = df.groupby('ID').cumcount().add(1)

print (df)

   ID  FRUIT ORDER

0  01  apple     1

2  01  peach     2

3  01  apple     3

4  02  melon     1

5  02  apple     2

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

Use boolean indexing for filter only first consecutive values with cumcount for new ordering:

a = df['ID'] + df['FRUIT']

#if necessary

#a = df['ID'].astype(str) + df['FRUIT']

df = df[a.ne(a.shift())]

df['ORDER'] = df.groupby('ID').cumcount().add(1)

print (df)

   ID  FRUIT ORDER

0  01  apple     1

2  01  peach     2

3  01  apple     3

4  02  melon     1

5  02  apple     2

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

edited Nov 12 at 15:38

answered Nov 12 at 10:26

jezrael

316k22256333

answered Nov 12 at 10:26

jezrael

316k22256333

answered Nov 12 at 10:26

jezrael

316k22256333

add a comment |

up vote
0
down vote

I believe this will be easy one to go :

>>> df

   ID  FRUIT  ORDER

0  01  apple      1

1  01  apple      2

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

6  02  apple      3

7  02  apple      4



>>> df[df['FRUIT'] != df['FRUIT'].shift(1)]

   ID  FRUIT  ORDER

0  01  apple      1

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

add a comment |

up vote
0
down vote

I believe this will be easy one to go :

>>> df

   ID  FRUIT  ORDER

0  01  apple      1

1  01  apple      2

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

6  02  apple      3

7  02  apple      4



>>> df[df['FRUIT'] != df['FRUIT'].shift(1)]

   ID  FRUIT  ORDER

0  01  apple      1

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

add a comment |

up vote
0
down vote

I believe this will be easy one to go :

>>> df

   ID  FRUIT  ORDER

0  01  apple      1

1  01  apple      2

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

6  02  apple      3

7  02  apple      4



>>> df[df['FRUIT'] != df['FRUIT'].shift(1)]

   ID  FRUIT  ORDER

0  01  apple      1

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

I believe this will be easy one to go :

>>> df

   ID  FRUIT  ORDER

0  01  apple      1

1  01  apple      2

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

6  02  apple      3

7  02  apple      4



>>> df[df['FRUIT'] != df['FRUIT'].shift(1)]

   ID  FRUIT  ORDER

0  01  apple      1

2  01  peach      3

3  01  apple      4

4  02  melon      1

5  02  apple      2

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

edited Nov 12 at 10:55

answered Nov 12 at 10:45

pygo

1,7361416

answered Nov 12 at 10:45

pygo

1,7361416

answered Nov 12 at 10:45

pygo

1,7361416

add a comment |

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