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
6651616
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
6651616
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
6651616
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
6651616
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
edited Nov 23 '18 at 10:41
Ali AzG
6651616
asked Nov 23 '18 at 9:57
MVachelardMVachelard
333
edited Nov 23 '18 at 10:41
Ali AzG
6651616
edited Nov 23 '18 at 10:41
Ali AzG
6651616
edited Nov 23 '18 at 10:41
Ali AzG
6651616
6651616
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
2,67711234
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53444371%2ftranslate-function-from-python-to-pyspark%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
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
2,67711234
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
2,67711234
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
2,67711234
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
2,67711234
edited Nov 23 '18 at 14:09
answered Nov 23 '18 at 13:48
StevenSteven
2,67711234
answered Nov 23 '18 at 13:48
StevenSteven
2,67711234
answered Nov 23 '18 at 13:48
StevenSteven
2,67711234
2,67711234
add a comment |
add a comment |
draft saved
draft discarded
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
draft saved
draft discarded
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53444371%2ftranslate-function-from-python-to-pyspark%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
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