Count number of true and false condition in spark data frame
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I am coming from a MATLAB background, and I can simply do this
age_sum_error = sum(age > prediction - 4 & age < prediction + 4);
This will count the number of age values for which the prediction (+4/-4) is true, I want to do something similar in spark data frame.
Say that below is my spark data frame
+--------------------------+
|age | gender | prediction |
+----+--------+------------+
|35 | M | 30 |
|40 | F | 42 |
|45 | F | 38 |
|26 | F | 29 |
+----+--------+------------+
I want my result to look something like this
+------+----------+
|false | positive |
+------+----------+
|2 | 2 |
+------+----------+
python apache-spark pyspark apache-spark-sql
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
add a comment |
0
I am coming from a MATLAB background, and I can simply do this
age_sum_error = sum(age > prediction - 4 & age < prediction + 4);
This will count the number of age values for which the prediction (+4/-4) is true, I want to do something similar in spark data frame.
Say that below is my spark data frame
+--------------------------+
|age | gender | prediction |
+----+--------+------------+
|35 | M | 30 |
|40 | F | 42 |
|45 | F | 38 |
|26 | F | 29 |
+----+--------+------------+
I want my result to look something like this
+------+----------+
|false | positive |
+------+----------+
|2 | 2 |
+------+----------+
python apache-spark pyspark apache-spark-sql
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
add a comment |
0
0
0
I am coming from a MATLAB background, and I can simply do this
age_sum_error = sum(age > prediction - 4 & age < prediction + 4);
This will count the number of age values for which the prediction (+4/-4) is true, I want to do something similar in spark data frame.
Say that below is my spark data frame
+--------------------------+
|age | gender | prediction |
+----+--------+------------+
|35 | M | 30 |
|40 | F | 42 |
|45 | F | 38 |
|26 | F | 29 |
+----+--------+------------+
I want my result to look something like this
+------+----------+
|false | positive |
+------+----------+
|2 | 2 |
+------+----------+
python apache-spark pyspark apache-spark-sql
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
I am coming from a MATLAB background, and I can simply do this
age_sum_error = sum(age > prediction - 4 & age < prediction + 4);
This will count the number of age values for which the prediction (+4/-4) is true, I want to do something similar in spark data frame.
Say that below is my spark data frame
+--------------------------+
|age | gender | prediction |
+----+--------+------------+
|35 | M | 30 |
|40 | F | 42 |
|45 | F | 38 |
|26 | F | 29 |
+----+--------+------------+
I want my result to look something like this
+------+----------+
|false | positive |
+------+----------+
|2 | 2 |
+------+----------+
python apache-spark pyspark apache-spark-sql
python apache-spark pyspark apache-spark-sql
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
asked Nov 24 '18 at 21:13
Jam1Jam1
306315
306315
add a comment |
add a comment |
2 Answers
2
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oldest
votes
1
First calculate the condition, and then aggregate the result by summing up the 1s and 0s:
df.selectExpr(
'cast(abs(age - prediction) < 4 as int) as condition'
).selectExpr(
'sum(condition) as positive',
'sum(1-condition) as negative'
).show()
+--------+--------+
|positive|negative|
+--------+--------+
| 2| 2|
+--------+--------+
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
add a comment |
0
Its a lot more code than matlab, but here's how I would do it.
import numpy as np
ages = [35, 40, 45, 26]
pred = [30, 42, 38, 29]
tolerance = 4
# get boolean array of people older and younger than limits
is_older = np.greater(ages, pred-tolerance) # a boolean array
is_younger = np.less(ages, pred+tolerance) # a boolean array
# convert these boolean arrays to ints then multiply. True = 1, False = 0.
in_range = is_older.astype(int)*is_younger.astype(int) # 0's cancel 1's
# add upp the indixes that are still 1
senior_count = np.sum(in_range)
Hope this helps.
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
First calculate the condition, and then aggregate the result by summing up the 1s and 0s:
df.selectExpr(
'cast(abs(age - prediction) < 4 as int) as condition'
).selectExpr(
'sum(condition) as positive',
'sum(1-condition) as negative'
).show()
+--------+--------+
|positive|negative|
+--------+--------+
| 2| 2|
+--------+--------+
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
add a comment |
1
First calculate the condition, and then aggregate the result by summing up the 1s and 0s:
df.selectExpr(
'cast(abs(age - prediction) < 4 as int) as condition'
).selectExpr(
'sum(condition) as positive',
'sum(1-condition) as negative'
).show()
+--------+--------+
|positive|negative|
+--------+--------+
| 2| 2|
+--------+--------+
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
add a comment |
1
1
1
First calculate the condition, and then aggregate the result by summing up the 1s and 0s:
df.selectExpr(
'cast(abs(age - prediction) < 4 as int) as condition'
).selectExpr(
'sum(condition) as positive',
'sum(1-condition) as negative'
).show()
+--------+--------+
|positive|negative|
+--------+--------+
| 2| 2|
+--------+--------+
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
First calculate the condition, and then aggregate the result by summing up the 1s and 0s:
df.selectExpr(
'cast(abs(age - prediction) < 4 as int) as condition'
).selectExpr(
'sum(condition) as positive',
'sum(1-condition) as negative'
).show()
+--------+--------+
|positive|negative|
+--------+--------+
| 2| 2|
+--------+--------+
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
answered Nov 24 '18 at 21:57
PsidomPsidom
129k1295142
129k1295142
add a comment |
add a comment |
0
Its a lot more code than matlab, but here's how I would do it.
import numpy as np
ages = [35, 40, 45, 26]
pred = [30, 42, 38, 29]
tolerance = 4
# get boolean array of people older and younger than limits
is_older = np.greater(ages, pred-tolerance) # a boolean array
is_younger = np.less(ages, pred+tolerance) # a boolean array
# convert these boolean arrays to ints then multiply. True = 1, False = 0.
in_range = is_older.astype(int)*is_younger.astype(int) # 0's cancel 1's
# add upp the indixes that are still 1
senior_count = np.sum(in_range)
Hope this helps.
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
add a comment |
0
Its a lot more code than matlab, but here's how I would do it.
import numpy as np
ages = [35, 40, 45, 26]
pred = [30, 42, 38, 29]
tolerance = 4
# get boolean array of people older and younger than limits
is_older = np.greater(ages, pred-tolerance) # a boolean array
is_younger = np.less(ages, pred+tolerance) # a boolean array
# convert these boolean arrays to ints then multiply. True = 1, False = 0.
in_range = is_older.astype(int)*is_younger.astype(int) # 0's cancel 1's
# add upp the indixes that are still 1
senior_count = np.sum(in_range)
Hope this helps.
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
add a comment |
0
0
0
Its a lot more code than matlab, but here's how I would do it.
import numpy as np
ages = [35, 40, 45, 26]
pred = [30, 42, 38, 29]
tolerance = 4
# get boolean array of people older and younger than limits
is_older = np.greater(ages, pred-tolerance) # a boolean array
is_younger = np.less(ages, pred+tolerance) # a boolean array
# convert these boolean arrays to ints then multiply. True = 1, False = 0.
in_range = is_older.astype(int)*is_younger.astype(int) # 0's cancel 1's
# add upp the indixes that are still 1
senior_count = np.sum(in_range)
Hope this helps.
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
Its a lot more code than matlab, but here's how I would do it.
import numpy as np
ages = [35, 40, 45, 26]
pred = [30, 42, 38, 29]
tolerance = 4
# get boolean array of people older and younger than limits
is_older = np.greater(ages, pred-tolerance) # a boolean array
is_younger = np.less(ages, pred+tolerance) # a boolean array
# convert these boolean arrays to ints then multiply. True = 1, False = 0.
in_range = is_older.astype(int)*is_younger.astype(int) # 0's cancel 1's
# add upp the indixes that are still 1
senior_count = np.sum(in_range)
Hope this helps.
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
answered Nov 24 '18 at 21:54
Charles StraussCharles Strauss
912
912
add a comment |
add a comment |
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