Translate function from python to pyspark
2
I would like to compare two pyspark dataframes and get the differences in a new table.
I tested the way to do it using python:
my dataframe
data = {'name': ['NO_VALUE', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, -999999, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df3 = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df3
my reference dataframe
data_ref = {'name': ['Jaso', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 202, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df_ref3 = pd.DataFrame(data_ref, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df_ref3
Compare rows:
def compare_datasets(df, df_ref):
ne_stacked = (df != df_ref).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']
difference_locations = np.where(df != df_ref)
changed_from = df.values[difference_locations]
changed_to = df_ref.values[difference_locations]
error_test = pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)
return error_test
compare_datasets(df3, df_ref3)
However, I would like to do this in pyspark. Does someone have an idea?
Thanks!
python pyspark pyspark-sql
edited Nov 23 '18 at 10:41
Ali AzG
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
add a comment |
2
I would like to compare two pyspark dataframes and get the differences in a new table.
I tested the way to do it using python:
my dataframe
data = {'name': ['NO_VALUE', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, -999999, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df3 = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df3
my reference dataframe
data_ref = {'name': ['Jaso', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 202, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df_ref3 = pd.DataFrame(data_ref, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df_ref3
Compare rows:
def compare_datasets(df, df_ref):
ne_stacked = (df != df_ref).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']
difference_locations = np.where(df != df_ref)
changed_from = df.values[difference_locations]
changed_to = df_ref.values[difference_locations]
error_test = pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)
return error_test
compare_datasets(df3, df_ref3)
However, I would like to do this in pyspark. Does someone have an idea?
Thanks!
python pyspark pyspark-sql
edited Nov 23 '18 at 10:41
Ali AzG
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
It seems you are usingpandasdataframe, notpyspark! inpysparkyou have to convert your function into anUDF!
– Ali AzG
Nov 23 '18 at 10:20
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27
add a comment |
2
2
2
I would like to compare two pyspark dataframes and get the differences in a new table.
I tested the way to do it using python:
my dataframe
data = {'name': ['NO_VALUE', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, -999999, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df3 = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df3
my reference dataframe
data_ref = {'name': ['Jaso', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 202, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df_ref3 = pd.DataFrame(data_ref, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df_ref3
Compare rows:
def compare_datasets(df, df_ref):
ne_stacked = (df != df_ref).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']
difference_locations = np.where(df != df_ref)
changed_from = df.values[difference_locations]
changed_to = df_ref.values[difference_locations]
error_test = pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)
return error_test
compare_datasets(df3, df_ref3)
However, I would like to do this in pyspark. Does someone have an idea?
Thanks!
python pyspark pyspark-sql
edited Nov 23 '18 at 10:41
Ali AzG
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
I would like to compare two pyspark dataframes and get the differences in a new table.
I tested the way to do it using python:
my dataframe
data = {'name': ['NO_VALUE', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, -999999, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df3 = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df3
my reference dataframe
data_ref = {'name': ['Jaso', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 202, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3]}
df_ref3 = pd.DataFrame(data_ref, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'])
df_ref3
Compare rows:
def compare_datasets(df, df_ref):
ne_stacked = (df != df_ref).stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['id', 'col']
difference_locations = np.where(df != df_ref)
changed_from = df.values[difference_locations]
changed_to = df_ref.values[difference_locations]
error_test = pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)
return error_test
compare_datasets(df3, df_ref3)
However, I would like to do this in pyspark. Does someone have an idea?
Thanks!
python pyspark pyspark-sql
python pyspark pyspark-sql
edited Nov 23 '18 at 10:41
Ali AzG
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
edited Nov 23 '18 at 10:41
Ali AzG
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
edited Nov 23 '18 at 10:41
Ali AzG
581515
edited Nov 23 '18 at 10:41
Ali AzG
581515
edited Nov 23 '18 at 10:41
Ali AzG
581515
581515
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
333
It seems you are usingpandasdataframe, notpyspark! inpysparkyou have to convert your function into anUDF!
– Ali AzG
Nov 23 '18 at 10:20
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27
add a comment |
It seems you are usingpandasdataframe, notpyspark! inpysparkyou have to convert your function into anUDF!
– Ali AzG
Nov 23 '18 at 10:20
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27
It seems you are using
pandas dataframe, not pyspark! in pyspark you have to convert your function into an UDF!– Ali AzG
Nov 23 '18 at 10:20
It seems you are using
pandas dataframe, not pyspark! in pyspark you have to convert your function into an UDF!– Ali AzG
Nov 23 '18 at 10:20
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27
add a comment |
1 Answer
1
active
oldest
votes
0
It is probably difficult to reproduce exactly this behavior.
I offer you one partial solution :
df.show()
+----------+--------+-------+-------+
| index| name| year|reports|
+----------+--------+-------+-------+
| Cochice|NO_VALUE| 2012| 4|
| Pima| Molly|-999999| 24|
|Santa Cruz| Tina| 2013| 31|
| Maricopa| Jake| 2014| 2|
| Yuma| Amy| 2014| 3|
+----------+--------+-------+-------+
df_ref.show()
+----------+-----+----+-------+
| index| name|year|reports|
+----------+-----+----+-------+
| Cochice| Jaso|2012| 4|
| Pima|Molly|2012| 24|
|Santa Cruz| Tina|2013| 31|
| Maricopa| Jake|2014| 2|
| Yuma| Amy|2014| 3|
+----------+-----+----+-------+
df.subtract(df_ref).show()
+-------+--------+-------+-------+
| index| name| year|reports|
+-------+--------+-------+-------+
| Pima| Molly|-999999| 24|
|Cochice|NO_VALUE| 2012| 4|
+-------+--------+-------+-------+
Or you can do the slow one :
for col in df_ref.columns:
out = df.select(col).subtract(df_ref.select(col))
if out.first():
print(out.collect())
[Row(name=u'NO_VALUE')]
[Row(year=-999999)]
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
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
0
It is probably difficult to reproduce exactly this behavior.
I offer you one partial solution :
df.show()
+----------+--------+-------+-------+
| index| name| year|reports|
+----------+--------+-------+-------+
| Cochice|NO_VALUE| 2012| 4|
| Pima| Molly|-999999| 24|
|Santa Cruz| Tina| 2013| 31|
| Maricopa| Jake| 2014| 2|
| Yuma| Amy| 2014| 3|
+----------+--------+-------+-------+
df_ref.show()
+----------+-----+----+-------+
| index| name|year|reports|
+----------+-----+----+-------+
| Cochice| Jaso|2012| 4|
| Pima|Molly|2012| 24|
|Santa Cruz| Tina|2013| 31|
| Maricopa| Jake|2014| 2|
| Yuma| Amy|2014| 3|
+----------+-----+----+-------+
df.subtract(df_ref).show()
+-------+--------+-------+-------+
| index| name| year|reports|
+-------+--------+-------+-------+
| Pima| Molly|-999999| 24|
|Cochice|NO_VALUE| 2012| 4|
+-------+--------+-------+-------+
Or you can do the slow one :
for col in df_ref.columns:
out = df.select(col).subtract(df_ref.select(col))
if out.first():
print(out.collect())
[Row(name=u'NO_VALUE')]
[Row(year=-999999)]
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
add a comment |
0
It is probably difficult to reproduce exactly this behavior.
I offer you one partial solution :
df.show()
+----------+--------+-------+-------+
| index| name| year|reports|
+----------+--------+-------+-------+
| Cochice|NO_VALUE| 2012| 4|
| Pima| Molly|-999999| 24|
|Santa Cruz| Tina| 2013| 31|
| Maricopa| Jake| 2014| 2|
| Yuma| Amy| 2014| 3|
+----------+--------+-------+-------+
df_ref.show()
+----------+-----+----+-------+
| index| name|year|reports|
+----------+-----+----+-------+
| Cochice| Jaso|2012| 4|
| Pima|Molly|2012| 24|
|Santa Cruz| Tina|2013| 31|
| Maricopa| Jake|2014| 2|
| Yuma| Amy|2014| 3|
+----------+-----+----+-------+
df.subtract(df_ref).show()
+-------+--------+-------+-------+
| index| name| year|reports|
+-------+--------+-------+-------+
| Pima| Molly|-999999| 24|
|Cochice|NO_VALUE| 2012| 4|
+-------+--------+-------+-------+
Or you can do the slow one :
for col in df_ref.columns:
out = df.select(col).subtract(df_ref.select(col))
if out.first():
print(out.collect())
[Row(name=u'NO_VALUE')]
[Row(year=-999999)]
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
add a comment |
0
0
0
It is probably difficult to reproduce exactly this behavior.
I offer you one partial solution :
df.show()
+----------+--------+-------+-------+
| index| name| year|reports|
+----------+--------+-------+-------+
| Cochice|NO_VALUE| 2012| 4|
| Pima| Molly|-999999| 24|
|Santa Cruz| Tina| 2013| 31|
| Maricopa| Jake| 2014| 2|
| Yuma| Amy| 2014| 3|
+----------+--------+-------+-------+
df_ref.show()
+----------+-----+----+-------+
| index| name|year|reports|
+----------+-----+----+-------+
| Cochice| Jaso|2012| 4|
| Pima|Molly|2012| 24|
|Santa Cruz| Tina|2013| 31|
| Maricopa| Jake|2014| 2|
| Yuma| Amy|2014| 3|
+----------+-----+----+-------+
df.subtract(df_ref).show()
+-------+--------+-------+-------+
| index| name| year|reports|
+-------+--------+-------+-------+
| Pima| Molly|-999999| 24|
|Cochice|NO_VALUE| 2012| 4|
+-------+--------+-------+-------+
Or you can do the slow one :
for col in df_ref.columns:
out = df.select(col).subtract(df_ref.select(col))
if out.first():
print(out.collect())
[Row(name=u'NO_VALUE')]
[Row(year=-999999)]
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
It is probably difficult to reproduce exactly this behavior.
I offer you one partial solution :
df.show()
+----------+--------+-------+-------+
| index| name| year|reports|
+----------+--------+-------+-------+
| Cochice|NO_VALUE| 2012| 4|
| Pima| Molly|-999999| 24|
|Santa Cruz| Tina| 2013| 31|
| Maricopa| Jake| 2014| 2|
| Yuma| Amy| 2014| 3|
+----------+--------+-------+-------+
df_ref.show()
+----------+-----+----+-------+
| index| name|year|reports|
+----------+-----+----+-------+
| Cochice| Jaso|2012| 4|
| Pima|Molly|2012| 24|
|Santa Cruz| Tina|2013| 31|
| Maricopa| Jake|2014| 2|
| Yuma| Amy|2014| 3|
+----------+-----+----+-------+
df.subtract(df_ref).show()
+-------+--------+-------+-------+
| index| name| year|reports|
+-------+--------+-------+-------+
| Pima| Molly|-999999| 24|
|Cochice|NO_VALUE| 2012| 4|
+-------+--------+-------+-------+
Or you can do the slow one :
for col in df_ref.columns:
out = df.select(col).subtract(df_ref.select(col))
if out.first():
print(out.collect())
[Row(name=u'NO_VALUE')]
[Row(year=-999999)]
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
edited Nov 23 '18 at 14:09
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
answered Nov 23 '18 at 13:48
StevenSteven
2,46311033
2,46311033
add a comment |
add a comment |
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It seems you are using
pandasdataframe, notpyspark! inpysparkyou have to convert your function into anUDF!– Ali AzG
Nov 23 '18 at 10:20
I know I have pandas dataframe. The fact is that I now want to do the same function but with pyspark dataframes and language.
– MVachelard
Nov 23 '18 at 11:27