Comparing column values within a row within a Pandas Dataframe
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I have a specific and a general problem I'm trying to solve.
Specific Problem:
I want to create a new column in a data frame that gives a 1 if column C1 is 8 and all other values in the row are less than 8.How do I logically negate all of the other columns at the same time? Here is the code from my flawed attempt:
df["C1is_8"] = df.apply(lambda row:(row['C1']==8)& ~(row['C1']<8) ,axis=1).astype(int)
The code below produces the dataframe for the code above.
dict = { 'C1':[4,3,0,0,2,3,4,5,8,8,8,8],
'C2':[8,3,3,7,6,5,3,5,6,8,8,8],
'C3':[2,3,6,4,5,0,0,4,6,7,8,8],
'C4':[8,5,4,4,4,3,2,1,4,2,6,8]
}
columns = ['C1','C2','C3','C4']
Index = [1,2,3,4,5,6,7,8,9,10,11,12]
df = pd.DataFrame(dict,index = Index,columns = columns)
df = OGdf[::-1]
df
General Problem: How do I rewrite some version of the code above so that I can generalize it (i.e. row[ i ] ) so that it could apply to any column not just 'C1'?
python pandas
asked Nov 17 '18 at 0:48
rer49rer49
1017
add a comment |
I have a specific and a general problem I'm trying to solve.
Specific Problem:
I want to create a new column in a data frame that gives a 1 if column C1 is 8 and all other values in the row are less than 8.How do I logically negate all of the other columns at the same time? Here is the code from my flawed attempt:
df["C1is_8"] = df.apply(lambda row:(row['C1']==8)& ~(row['C1']<8) ,axis=1).astype(int)
The code below produces the dataframe for the code above.
dict = { 'C1':[4,3,0,0,2,3,4,5,8,8,8,8],
'C2':[8,3,3,7,6,5,3,5,6,8,8,8],
'C3':[2,3,6,4,5,0,0,4,6,7,8,8],
'C4':[8,5,4,4,4,3,2,1,4,2,6,8]
}
columns = ['C1','C2','C3','C4']
Index = [1,2,3,4,5,6,7,8,9,10,11,12]
df = pd.DataFrame(dict,index = Index,columns = columns)
df = OGdf[::-1]
df
General Problem: How do I rewrite some version of the code above so that I can generalize it (i.e. row[ i ] ) so that it could apply to any column not just 'C1'?
python pandas
asked Nov 17 '18 at 0:48
rer49rer49
1017
add a comment |
I have a specific and a general problem I'm trying to solve.
Specific Problem:
I want to create a new column in a data frame that gives a 1 if column C1 is 8 and all other values in the row are less than 8.How do I logically negate all of the other columns at the same time? Here is the code from my flawed attempt:
df["C1is_8"] = df.apply(lambda row:(row['C1']==8)& ~(row['C1']<8) ,axis=1).astype(int)
The code below produces the dataframe for the code above.
dict = { 'C1':[4,3,0,0,2,3,4,5,8,8,8,8],
'C2':[8,3,3,7,6,5,3,5,6,8,8,8],
'C3':[2,3,6,4,5,0,0,4,6,7,8,8],
'C4':[8,5,4,4,4,3,2,1,4,2,6,8]
}
columns = ['C1','C2','C3','C4']
Index = [1,2,3,4,5,6,7,8,9,10,11,12]
df = pd.DataFrame(dict,index = Index,columns = columns)
df = OGdf[::-1]
df
General Problem: How do I rewrite some version of the code above so that I can generalize it (i.e. row[ i ] ) so that it could apply to any column not just 'C1'?
python pandas
asked Nov 17 '18 at 0:48
rer49rer49
1017
I have a specific and a general problem I'm trying to solve.
Specific Problem:
I want to create a new column in a data frame that gives a 1 if column C1 is 8 and all other values in the row are less than 8.How do I logically negate all of the other columns at the same time? Here is the code from my flawed attempt:
df["C1is_8"] = df.apply(lambda row:(row['C1']==8)& ~(row['C1']<8) ,axis=1).astype(int)
The code below produces the dataframe for the code above.
dict = { 'C1':[4,3,0,0,2,3,4,5,8,8,8,8],
'C2':[8,3,3,7,6,5,3,5,6,8,8,8],
'C3':[2,3,6,4,5,0,0,4,6,7,8,8],
'C4':[8,5,4,4,4,3,2,1,4,2,6,8]
}
columns = ['C1','C2','C3','C4']
Index = [1,2,3,4,5,6,7,8,9,10,11,12]
df = pd.DataFrame(dict,index = Index,columns = columns)
df = OGdf[::-1]
df
General Problem: How do I rewrite some version of the code above so that I can generalize it (i.e. row[ i ] ) so that it could apply to any column not just 'C1'?
python pandas
python pandas
asked Nov 17 '18 at 0:48
rer49rer49
1017
asked Nov 17 '18 at 0:48
rer49rer49
1017
asked Nov 17 '18 at 0:48
rer49rer49
1017
asked Nov 17 '18 at 0:48
rer49rer49
1017
asked Nov 17 '18 at 0:48
rer49rer49
1017
1017
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I think this answer satisfies both your specific and your general problems, using filter and all:
# define the column you want to apply your first condition to
col = 'C1'
# Python 3.6 or above, with f-strings:
df['new_col'] = ((df[col] == 8) & (df.filter(regex=f'[^{col}]') < 8).all(1)).astype(int)
# Otherwise:
df['new_col'] = ((df[col] == 8) & (df.filter(regex='[^{}]'.format(col)) < 8).all(1)).astype(int)
>>> df
C1 C2 C3 C4 new_col
1 4 8 2 8 0
2 3 3 3 5 0
3 0 3 6 4 0
4 0 7 4 4 0
5 2 6 5 4 0
6 3 5 0 3 0
7 4 3 0 2 0
8 5 5 4 1 0
9 8 6 6 4 1
10 8 8 7 2 0
11 8 8 8 6 0
12 8 8 8 8 0
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
@ sacul, will mind explainingall, though i understandpandas.Series.all : Return True if all elements are True, i think when you sayall(1)ie across the columns
– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the< 8condition
– sacuL
Nov 20 '18 at 4:48
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
I think this answer satisfies both your specific and your general problems, using filter and all:
# define the column you want to apply your first condition to
col = 'C1'
# Python 3.6 or above, with f-strings:
df['new_col'] = ((df[col] == 8) & (df.filter(regex=f'[^{col}]') < 8).all(1)).astype(int)
# Otherwise:
df['new_col'] = ((df[col] == 8) & (df.filter(regex='[^{}]'.format(col)) < 8).all(1)).astype(int)
>>> df
C1 C2 C3 C4 new_col
1 4 8 2 8 0
2 3 3 3 5 0
3 0 3 6 4 0
4 0 7 4 4 0
5 2 6 5 4 0
6 3 5 0 3 0
7 4 3 0 2 0
8 5 5 4 1 0
9 8 6 6 4 1
10 8 8 7 2 0
11 8 8 8 6 0
12 8 8 8 8 0
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
@ sacul, will mind explainingall, though i understandpandas.Series.all : Return True if all elements are True, i think when you sayall(1)ie across the columns
– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the< 8condition
– sacuL
Nov 20 '18 at 4:48
add a comment |
I think this answer satisfies both your specific and your general problems, using filter and all:
# define the column you want to apply your first condition to
col = 'C1'
# Python 3.6 or above, with f-strings:
df['new_col'] = ((df[col] == 8) & (df.filter(regex=f'[^{col}]') < 8).all(1)).astype(int)
# Otherwise:
df['new_col'] = ((df[col] == 8) & (df.filter(regex='[^{}]'.format(col)) < 8).all(1)).astype(int)
>>> df
C1 C2 C3 C4 new_col
1 4 8 2 8 0
2 3 3 3 5 0
3 0 3 6 4 0
4 0 7 4 4 0
5 2 6 5 4 0
6 3 5 0 3 0
7 4 3 0 2 0
8 5 5 4 1 0
9 8 6 6 4 1
10 8 8 7 2 0
11 8 8 8 6 0
12 8 8 8 8 0
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
@ sacul, will mind explainingall, though i understandpandas.Series.all : Return True if all elements are True, i think when you sayall(1)ie across the columns
– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the< 8condition
– sacuL
Nov 20 '18 at 4:48
add a comment |
I think this answer satisfies both your specific and your general problems, using filter and all:
# define the column you want to apply your first condition to
col = 'C1'
# Python 3.6 or above, with f-strings:
df['new_col'] = ((df[col] == 8) & (df.filter(regex=f'[^{col}]') < 8).all(1)).astype(int)
# Otherwise:
df['new_col'] = ((df[col] == 8) & (df.filter(regex='[^{}]'.format(col)) < 8).all(1)).astype(int)
>>> df
C1 C2 C3 C4 new_col
1 4 8 2 8 0
2 3 3 3 5 0
3 0 3 6 4 0
4 0 7 4 4 0
5 2 6 5 4 0
6 3 5 0 3 0
7 4 3 0 2 0
8 5 5 4 1 0
9 8 6 6 4 1
10 8 8 7 2 0
11 8 8 8 6 0
12 8 8 8 8 0
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
I think this answer satisfies both your specific and your general problems, using filter and all:
# define the column you want to apply your first condition to
col = 'C1'
# Python 3.6 or above, with f-strings:
df['new_col'] = ((df[col] == 8) & (df.filter(regex=f'[^{col}]') < 8).all(1)).astype(int)
# Otherwise:
df['new_col'] = ((df[col] == 8) & (df.filter(regex='[^{}]'.format(col)) < 8).all(1)).astype(int)
>>> df
C1 C2 C3 C4 new_col
1 4 8 2 8 0
2 3 3 3 5 0
3 0 3 6 4 0
4 0 7 4 4 0
5 2 6 5 4 0
6 3 5 0 3 0
7 4 3 0 2 0
8 5 5 4 1 0
9 8 6 6 4 1
10 8 8 7 2 0
11 8 8 8 6 0
12 8 8 8 8 0
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
answered Nov 17 '18 at 0:58
sacuLsacuL
30.9k42144
30.9k42144
@ sacul, will mind explainingall, though i understandpandas.Series.all : Return True if all elements are True, i think when you sayall(1)ie across the columns
– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the< 8condition
– sacuL
Nov 20 '18 at 4:48
add a comment |
@ sacul, will mind explainingall, though i understandpandas.Series.all : Return True if all elements are True, i think when you sayall(1)ie across the columns
– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the< 8condition
– sacuL
Nov 20 '18 at 4:48
@ sacul, will mind explaining
all, though i understand pandas.Series.all : Return True if all elements are True , i think when you say all(1) ie across the columns– pygo
Nov 20 '18 at 4:35
@ sacul, will mind explaining
all, though i understand pandas.Series.all : Return True if all elements are True , i think when you say all(1) ie across the columns– pygo
Nov 20 '18 at 4:35
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the
< 8 condition– sacuL
Nov 20 '18 at 4:48
@pygo, that's right, it's checking that all values (besides the one filtered out) in a row (i.e. across all columns) satisfies the
< 8 condition– sacuL
Nov 20 '18 at 4:48
add a comment |
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