Fill blank cells with data above it
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I have data which looks like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| | | | | 0 | 0 | 0 | 5.00% | 10750 | 537.5 |
The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.
The above data must ideally be like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 5.00% | 10750 | 537.5 |
As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.
I need to:
- Identify
blanksorNULLs in "ID" (column 1) - Populate that row: column 1 ot 7 with the data above that row.
python pandas
asked Nov 25 '18 at 7:05
clmnoclmno
779416
add a comment |
2
I have data which looks like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| | | | | 0 | 0 | 0 | 5.00% | 10750 | 537.5 |
The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.
The above data must ideally be like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 5.00% | 10750 | 537.5 |
As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.
I need to:
- Identify
blanksorNULLs in "ID" (column 1) - Populate that row: column 1 ot 7 with the data above that row.
python pandas
asked Nov 25 '18 at 7:05
clmnoclmno
779416
add a comment |
2
2
2
I have data which looks like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| | | | | 0 | 0 | 0 | 5.00% | 10750 | 537.5 |
The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.
The above data must ideally be like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 5.00% | 10750 | 537.5 |
As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.
I need to:
- Identify
blanksorNULLs in "ID" (column 1) - Populate that row: column 1 ot 7 with the data above that row.
python pandas
asked Nov 25 '18 at 7:05
clmnoclmno
779416
I have data which looks like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| | | | | 0 | 0 | 0 | 5.00% | 10750 | 537.5 |
The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.
The above data must ideally be like:
| ID | Name | Inv | Date | Value | PO | Type | Rate | Tax | Integ |
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 0.00% | 3000 | 0 |
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK | State | 5.00% | 10750 | 537.5 |
As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.
I need to:
- Identify
blanksorNULLs in "ID" (column 1) - Populate that row: column 1 ot 7 with the data above that row.
python pandas
python pandas
asked Nov 25 '18 at 7:05
clmnoclmno
779416
asked Nov 25 '18 at 7:05
clmnoclmno
779416
asked Nov 25 '18 at 7:05
clmnoclmno
779416
asked Nov 25 '18 at 7:05
clmnoclmno
779416
asked Nov 25 '18 at 7:05
clmnoclmno
779416
779416
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
2
Use mask with forward filling missing values:
df = df.mask(df == 0).ffill()
Or:
df = df.mask(df.isin(['', 0])).ffill()
If want also change first row filled by missing values to 0 only for numeric columns:
num = df.select_dtypes(np.number).columns
d = dict.fromkeys(num, 0)
print (d)
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
df = df.mask(df == 0).ffill().fillna(d)
print (df)
ID Name Inv Date Value PO Type Rate Tax
0 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 UK State 0.00% 3000
1 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 0 0 5.00% 10750
Integ
0 0.0
1 537.5
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
Use mask with forward filling missing values:
df = df.mask(df == 0).ffill()
Or:
df = df.mask(df.isin(['', 0])).ffill()
If want also change first row filled by missing values to 0 only for numeric columns:
num = df.select_dtypes(np.number).columns
d = dict.fromkeys(num, 0)
print (d)
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
df = df.mask(df == 0).ffill().fillna(d)
print (df)
ID Name Inv Date Value PO Type Rate Tax
0 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 UK State 0.00% 3000
1 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 0 0 5.00% 10750
Integ
0 0.0
1 537.5
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
add a comment |
2
Use mask with forward filling missing values:
df = df.mask(df == 0).ffill()
Or:
df = df.mask(df.isin(['', 0])).ffill()
If want also change first row filled by missing values to 0 only for numeric columns:
num = df.select_dtypes(np.number).columns
d = dict.fromkeys(num, 0)
print (d)
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
df = df.mask(df == 0).ffill().fillna(d)
print (df)
ID Name Inv Date Value PO Type Rate Tax
0 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 UK State 0.00% 3000
1 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 0 0 5.00% 10750
Integ
0 0.0
1 537.5
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
add a comment |
2
2
2
Use mask with forward filling missing values:
df = df.mask(df == 0).ffill()
Or:
df = df.mask(df.isin(['', 0])).ffill()
If want also change first row filled by missing values to 0 only for numeric columns:
num = df.select_dtypes(np.number).columns
d = dict.fromkeys(num, 0)
print (d)
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
df = df.mask(df == 0).ffill().fillna(d)
print (df)
ID Name Inv Date Value PO Type Rate Tax
0 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 UK State 0.00% 3000
1 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 0 0 5.00% 10750
Integ
0 0.0
1 537.5
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
Use mask with forward filling missing values:
df = df.mask(df == 0).ffill()
Or:
df = df.mask(df.isin(['', 0])).ffill()
If want also change first row filled by missing values to 0 only for numeric columns:
num = df.select_dtypes(np.number).columns
d = dict.fromkeys(num, 0)
print (d)
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
df = df.mask(df == 0).ffill().fillna(d)
print (df)
ID Name Inv Date Value PO Type Rate Tax
0 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 UK State 0.00% 3000
1 DEADBEEF CHEMICALS 321.0 19-11-2017 14288.0 0 0 5.00% 10750
Integ
0 0.0
1 537.5
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
edited Nov 25 '18 at 7:15
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
answered Nov 25 '18 at 7:06
jezraeljezrael
363k26330412
363k26330412
add a comment |
add a comment |
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