Making a dataframe of parent IDs
I am quite new to scripting, and here is my first code I wrote. The goal of the code is to extract a parent ID based on a child ID. I would appreciate some constructive criticism and aggressive checkup. The codes works and does what it's supposed to do. But how can I make it more pythonic? more passionate?
import codecs
import numpy as np
import pandas as pd
def import_file(path):
'''imports the .csv files as a pandas dataframe
Args:
path (csv file): Takes in the path for the .csv file
Returns:
Returns a pandas dataframe
'''
with codecs.open(path, "r", encoding='utf-8', errors='ignore') as fdata:
df = pd.read_csv(fdata)
return df
def appends_address_before_name(file):
'''Appends the address before the name ID name
Returns:
Returns the file, with address appened to column name.
'''
file['ID'] = [address + str(col) for col in file['ID']]
return file
def create_parent_name(file, column_name: str):
'''This will create a parent name based on the ID column
Args:
file: takes in the dataFrame created from appends_address_before_name function
column_name: Takes in the column name where the parent name
will be extracted from.The logic is to split it on the last dot.
[[parentname].[+ childname]]
Returns:
Returns a pandas dataframe with a new column called parentID
'''
file['parentID'] = [
x.rsplit('.', 1)[0] if '.' in x else x[:-1] for x in file[column_name]
]
return file
address = 'New_Jersey_'
file_1 = import_file(r'C:humans.csv')
file_2= appends_address_before_name(file=file_1)
file_3= create_parent_name(file=file_2 , column_name = 'ID')
print(file_3)
The input CSV is a column of values separated with decimal places like the following:
ID
99.99.9
100.42.3
Example output
parentID
New_Jersey_99.99
New_Jersey_100.42
Furthermore, I feel like the way I am passing variables between the functions at the end of the code seems quite basic and terrible. What can I improve in the above code and how can I improve it ?
A screenshot of the code working
python beginner pandas
add a comment |
I am quite new to scripting, and here is my first code I wrote. The goal of the code is to extract a parent ID based on a child ID. I would appreciate some constructive criticism and aggressive checkup. The codes works and does what it's supposed to do. But how can I make it more pythonic? more passionate?
import codecs
import numpy as np
import pandas as pd
def import_file(path):
'''imports the .csv files as a pandas dataframe
Args:
path (csv file): Takes in the path for the .csv file
Returns:
Returns a pandas dataframe
'''
with codecs.open(path, "r", encoding='utf-8', errors='ignore') as fdata:
df = pd.read_csv(fdata)
return df
def appends_address_before_name(file):
'''Appends the address before the name ID name
Returns:
Returns the file, with address appened to column name.
'''
file['ID'] = [address + str(col) for col in file['ID']]
return file
def create_parent_name(file, column_name: str):
'''This will create a parent name based on the ID column
Args:
file: takes in the dataFrame created from appends_address_before_name function
column_name: Takes in the column name where the parent name
will be extracted from.The logic is to split it on the last dot.
[[parentname].[+ childname]]
Returns:
Returns a pandas dataframe with a new column called parentID
'''
file['parentID'] = [
x.rsplit('.', 1)[0] if '.' in x else x[:-1] for x in file[column_name]
]
return file
address = 'New_Jersey_'
file_1 = import_file(r'C:humans.csv')
file_2= appends_address_before_name(file=file_1)
file_3= create_parent_name(file=file_2 , column_name = 'ID')
print(file_3)
The input CSV is a column of values separated with decimal places like the following:
ID
99.99.9
100.42.3
Example output
parentID
New_Jersey_99.99
New_Jersey_100.42
Furthermore, I feel like the way I am passing variables between the functions at the end of the code seems quite basic and terrible. What can I improve in the above code and how can I improve it ?
A screenshot of the code working
python beginner pandas
@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
1
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
What is the reason behind dropping the last character if there is no'.'
in the ID? It seems to result in weird things like'NewJersey_"
.
– Graipher
Dec 7 at 14:25
@Graipher you are right, the parentID for this one will be justNewJersey
, I added an extra underscore in theaddress
, thats wrong.
– Matthew
Dec 7 at 14:27
add a comment |
I am quite new to scripting, and here is my first code I wrote. The goal of the code is to extract a parent ID based on a child ID. I would appreciate some constructive criticism and aggressive checkup. The codes works and does what it's supposed to do. But how can I make it more pythonic? more passionate?
import codecs
import numpy as np
import pandas as pd
def import_file(path):
'''imports the .csv files as a pandas dataframe
Args:
path (csv file): Takes in the path for the .csv file
Returns:
Returns a pandas dataframe
'''
with codecs.open(path, "r", encoding='utf-8', errors='ignore') as fdata:
df = pd.read_csv(fdata)
return df
def appends_address_before_name(file):
'''Appends the address before the name ID name
Returns:
Returns the file, with address appened to column name.
'''
file['ID'] = [address + str(col) for col in file['ID']]
return file
def create_parent_name(file, column_name: str):
'''This will create a parent name based on the ID column
Args:
file: takes in the dataFrame created from appends_address_before_name function
column_name: Takes in the column name where the parent name
will be extracted from.The logic is to split it on the last dot.
[[parentname].[+ childname]]
Returns:
Returns a pandas dataframe with a new column called parentID
'''
file['parentID'] = [
x.rsplit('.', 1)[0] if '.' in x else x[:-1] for x in file[column_name]
]
return file
address = 'New_Jersey_'
file_1 = import_file(r'C:humans.csv')
file_2= appends_address_before_name(file=file_1)
file_3= create_parent_name(file=file_2 , column_name = 'ID')
print(file_3)
The input CSV is a column of values separated with decimal places like the following:
ID
99.99.9
100.42.3
Example output
parentID
New_Jersey_99.99
New_Jersey_100.42
Furthermore, I feel like the way I am passing variables between the functions at the end of the code seems quite basic and terrible. What can I improve in the above code and how can I improve it ?
A screenshot of the code working
python beginner pandas
I am quite new to scripting, and here is my first code I wrote. The goal of the code is to extract a parent ID based on a child ID. I would appreciate some constructive criticism and aggressive checkup. The codes works and does what it's supposed to do. But how can I make it more pythonic? more passionate?
import codecs
import numpy as np
import pandas as pd
def import_file(path):
'''imports the .csv files as a pandas dataframe
Args:
path (csv file): Takes in the path for the .csv file
Returns:
Returns a pandas dataframe
'''
with codecs.open(path, "r", encoding='utf-8', errors='ignore') as fdata:
df = pd.read_csv(fdata)
return df
def appends_address_before_name(file):
'''Appends the address before the name ID name
Returns:
Returns the file, with address appened to column name.
'''
file['ID'] = [address + str(col) for col in file['ID']]
return file
def create_parent_name(file, column_name: str):
'''This will create a parent name based on the ID column
Args:
file: takes in the dataFrame created from appends_address_before_name function
column_name: Takes in the column name where the parent name
will be extracted from.The logic is to split it on the last dot.
[[parentname].[+ childname]]
Returns:
Returns a pandas dataframe with a new column called parentID
'''
file['parentID'] = [
x.rsplit('.', 1)[0] if '.' in x else x[:-1] for x in file[column_name]
]
return file
address = 'New_Jersey_'
file_1 = import_file(r'C:humans.csv')
file_2= appends_address_before_name(file=file_1)
file_3= create_parent_name(file=file_2 , column_name = 'ID')
print(file_3)
The input CSV is a column of values separated with decimal places like the following:
ID
99.99.9
100.42.3
Example output
parentID
New_Jersey_99.99
New_Jersey_100.42
Furthermore, I feel like the way I am passing variables between the functions at the end of the code seems quite basic and terrible. What can I improve in the above code and how can I improve it ?
A screenshot of the code working
python beginner pandas
python beginner pandas
edited Dec 7 at 14:23
asked Dec 7 at 13:03
Matthew
284
284
@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
1
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
What is the reason behind dropping the last character if there is no'.'
in the ID? It seems to result in weird things like'NewJersey_"
.
– Graipher
Dec 7 at 14:25
@Graipher you are right, the parentID for this one will be justNewJersey
, I added an extra underscore in theaddress
, thats wrong.
– Matthew
Dec 7 at 14:27
add a comment |
@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
1
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
What is the reason behind dropping the last character if there is no'.'
in the ID? It seems to result in weird things like'NewJersey_"
.
– Graipher
Dec 7 at 14:25
@Graipher you are right, the parentID for this one will be justNewJersey
, I added an extra underscore in theaddress
, thats wrong.
– Matthew
Dec 7 at 14:27
@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
1
1
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
What is the reason behind dropping the last character if there is no
'.'
in the ID? It seems to result in weird things like 'NewJersey_"
.– Graipher
Dec 7 at 14:25
What is the reason behind dropping the last character if there is no
'.'
in the ID? It seems to result in weird things like 'NewJersey_"
.– Graipher
Dec 7 at 14:25
@Graipher you are right, the parentID for this one will be just
NewJersey
, I added an extra underscore in the address
, thats wrong.– Matthew
Dec 7 at 14:27
@Graipher you are right, the parentID for this one will be just
NewJersey
, I added an extra underscore in the address
, thats wrong.– Matthew
Dec 7 at 14:27
add a comment |
1 Answer
1
active
oldest
votes
You should decide if your functions modify the object they receive or if the return a modified object. Doing both is just asking for disaster. After your code has finished, file_1
, file_2
and file_3
are all identical.
The usual convention is to return None
(implicitly or explicitly) if you mutate any of the inputs. In the rest of this answer I have decided to mutate the inputs.
Besides that, pandas
is most effective if you use its vectorized functions. For columns with strings, it has a whole lot of methods which are vectorized. You can access them with df.col_name.str
. You can find some examples in the documentation.
Your appends_address_before_name
function could be simplified a lot because string addition is vectorized:
def appends_address_before_name(file):
file["parentID"] = address + file["parentID"]
file["ID"] = address + file["ID"]
And your create_parent_name
function could be:
def create_parent_name(file, column_name: str):
file["parentID"] = file[column_name].str.split(".").str[:-1].str.join(".")
With a csv file like this:
ID
99.99.9
100.42.3
101
This produces
df = import_file(file_name)
create_parent_name(df, 'ID')
appends_address_before_name(df)
print(df)
# ID parentID
# 0 New_Jersey_99.99.9 New_Jersey_99.99
# 1 New_Jersey_100.42.3 New_Jersey_100.42
# 2 New_Jersey_101 New_Jersey_
Note that the order of the calls has changed, so that ids without a .
are handled correctly.
As for general structure:
- Seeing docstrings is very nice (I omitted them here for brevity)
- Python has an official style-guide, PEP8. It recommends writing
x = some_thing(a=3)
, so surround equal signs with spaces when assigning but not when setting keyword arguments. - You should wrap the main calling code in a
if __name__ == "__main__"
guard.
can you kindly elaborate more onobject they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?
– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.file_2
as infile_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?
– Matthew
Dec 7 at 14:29
2
@Matthew: When you dofile["ID"] = ...
you modify thefile
object in place. Afterwards your returnfile
, which is still the same object, but modified.
– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You should decide if your functions modify the object they receive or if the return a modified object. Doing both is just asking for disaster. After your code has finished, file_1
, file_2
and file_3
are all identical.
The usual convention is to return None
(implicitly or explicitly) if you mutate any of the inputs. In the rest of this answer I have decided to mutate the inputs.
Besides that, pandas
is most effective if you use its vectorized functions. For columns with strings, it has a whole lot of methods which are vectorized. You can access them with df.col_name.str
. You can find some examples in the documentation.
Your appends_address_before_name
function could be simplified a lot because string addition is vectorized:
def appends_address_before_name(file):
file["parentID"] = address + file["parentID"]
file["ID"] = address + file["ID"]
And your create_parent_name
function could be:
def create_parent_name(file, column_name: str):
file["parentID"] = file[column_name].str.split(".").str[:-1].str.join(".")
With a csv file like this:
ID
99.99.9
100.42.3
101
This produces
df = import_file(file_name)
create_parent_name(df, 'ID')
appends_address_before_name(df)
print(df)
# ID parentID
# 0 New_Jersey_99.99.9 New_Jersey_99.99
# 1 New_Jersey_100.42.3 New_Jersey_100.42
# 2 New_Jersey_101 New_Jersey_
Note that the order of the calls has changed, so that ids without a .
are handled correctly.
As for general structure:
- Seeing docstrings is very nice (I omitted them here for brevity)
- Python has an official style-guide, PEP8. It recommends writing
x = some_thing(a=3)
, so surround equal signs with spaces when assigning but not when setting keyword arguments. - You should wrap the main calling code in a
if __name__ == "__main__"
guard.
can you kindly elaborate more onobject they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?
– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.file_2
as infile_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?
– Matthew
Dec 7 at 14:29
2
@Matthew: When you dofile["ID"] = ...
you modify thefile
object in place. Afterwards your returnfile
, which is still the same object, but modified.
– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
add a comment |
You should decide if your functions modify the object they receive or if the return a modified object. Doing both is just asking for disaster. After your code has finished, file_1
, file_2
and file_3
are all identical.
The usual convention is to return None
(implicitly or explicitly) if you mutate any of the inputs. In the rest of this answer I have decided to mutate the inputs.
Besides that, pandas
is most effective if you use its vectorized functions. For columns with strings, it has a whole lot of methods which are vectorized. You can access them with df.col_name.str
. You can find some examples in the documentation.
Your appends_address_before_name
function could be simplified a lot because string addition is vectorized:
def appends_address_before_name(file):
file["parentID"] = address + file["parentID"]
file["ID"] = address + file["ID"]
And your create_parent_name
function could be:
def create_parent_name(file, column_name: str):
file["parentID"] = file[column_name].str.split(".").str[:-1].str.join(".")
With a csv file like this:
ID
99.99.9
100.42.3
101
This produces
df = import_file(file_name)
create_parent_name(df, 'ID')
appends_address_before_name(df)
print(df)
# ID parentID
# 0 New_Jersey_99.99.9 New_Jersey_99.99
# 1 New_Jersey_100.42.3 New_Jersey_100.42
# 2 New_Jersey_101 New_Jersey_
Note that the order of the calls has changed, so that ids without a .
are handled correctly.
As for general structure:
- Seeing docstrings is very nice (I omitted them here for brevity)
- Python has an official style-guide, PEP8. It recommends writing
x = some_thing(a=3)
, so surround equal signs with spaces when assigning but not when setting keyword arguments. - You should wrap the main calling code in a
if __name__ == "__main__"
guard.
can you kindly elaborate more onobject they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?
– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.file_2
as infile_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?
– Matthew
Dec 7 at 14:29
2
@Matthew: When you dofile["ID"] = ...
you modify thefile
object in place. Afterwards your returnfile
, which is still the same object, but modified.
– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
add a comment |
You should decide if your functions modify the object they receive or if the return a modified object. Doing both is just asking for disaster. After your code has finished, file_1
, file_2
and file_3
are all identical.
The usual convention is to return None
(implicitly or explicitly) if you mutate any of the inputs. In the rest of this answer I have decided to mutate the inputs.
Besides that, pandas
is most effective if you use its vectorized functions. For columns with strings, it has a whole lot of methods which are vectorized. You can access them with df.col_name.str
. You can find some examples in the documentation.
Your appends_address_before_name
function could be simplified a lot because string addition is vectorized:
def appends_address_before_name(file):
file["parentID"] = address + file["parentID"]
file["ID"] = address + file["ID"]
And your create_parent_name
function could be:
def create_parent_name(file, column_name: str):
file["parentID"] = file[column_name].str.split(".").str[:-1].str.join(".")
With a csv file like this:
ID
99.99.9
100.42.3
101
This produces
df = import_file(file_name)
create_parent_name(df, 'ID')
appends_address_before_name(df)
print(df)
# ID parentID
# 0 New_Jersey_99.99.9 New_Jersey_99.99
# 1 New_Jersey_100.42.3 New_Jersey_100.42
# 2 New_Jersey_101 New_Jersey_
Note that the order of the calls has changed, so that ids without a .
are handled correctly.
As for general structure:
- Seeing docstrings is very nice (I omitted them here for brevity)
- Python has an official style-guide, PEP8. It recommends writing
x = some_thing(a=3)
, so surround equal signs with spaces when assigning but not when setting keyword arguments. - You should wrap the main calling code in a
if __name__ == "__main__"
guard.
You should decide if your functions modify the object they receive or if the return a modified object. Doing both is just asking for disaster. After your code has finished, file_1
, file_2
and file_3
are all identical.
The usual convention is to return None
(implicitly or explicitly) if you mutate any of the inputs. In the rest of this answer I have decided to mutate the inputs.
Besides that, pandas
is most effective if you use its vectorized functions. For columns with strings, it has a whole lot of methods which are vectorized. You can access them with df.col_name.str
. You can find some examples in the documentation.
Your appends_address_before_name
function could be simplified a lot because string addition is vectorized:
def appends_address_before_name(file):
file["parentID"] = address + file["parentID"]
file["ID"] = address + file["ID"]
And your create_parent_name
function could be:
def create_parent_name(file, column_name: str):
file["parentID"] = file[column_name].str.split(".").str[:-1].str.join(".")
With a csv file like this:
ID
99.99.9
100.42.3
101
This produces
df = import_file(file_name)
create_parent_name(df, 'ID')
appends_address_before_name(df)
print(df)
# ID parentID
# 0 New_Jersey_99.99.9 New_Jersey_99.99
# 1 New_Jersey_100.42.3 New_Jersey_100.42
# 2 New_Jersey_101 New_Jersey_
Note that the order of the calls has changed, so that ids without a .
are handled correctly.
As for general structure:
- Seeing docstrings is very nice (I omitted them here for brevity)
- Python has an official style-guide, PEP8. It recommends writing
x = some_thing(a=3)
, so surround equal signs with spaces when assigning but not when setting keyword arguments. - You should wrap the main calling code in a
if __name__ == "__main__"
guard.
edited Dec 7 at 15:47
answered Dec 7 at 14:19
Graipher
23.5k53585
23.5k53585
can you kindly elaborate more onobject they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?
– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.file_2
as infile_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?
– Matthew
Dec 7 at 14:29
2
@Matthew: When you dofile["ID"] = ...
you modify thefile
object in place. Afterwards your returnfile
, which is still the same object, but modified.
– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
add a comment |
can you kindly elaborate more onobject they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?
– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.file_2
as infile_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?
– Matthew
Dec 7 at 14:29
2
@Matthew: When you dofile["ID"] = ...
you modify thefile
object in place. Afterwards your returnfile
, which is still the same object, but modified.
– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
can you kindly elaborate more on
object they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?– Matthew
Dec 7 at 14:28
can you kindly elaborate more on
object they receive or if the return a modified object.
, where in my code am I returning or modifiying objective ?– Matthew
Dec 7 at 14:28
so you removed the variables, i.e.
file_2
as in file_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?– Matthew
Dec 7 at 14:29
so you removed the variables, i.e.
file_2
as in file_2= appends_address_before_name(file=file_1)
, as you modified the original file rather than returning it?– Matthew
Dec 7 at 14:29
2
2
@Matthew: When you do
file["ID"] = ...
you modify the file
object in place. Afterwards your return file
, which is still the same object, but modified.– Graipher
Dec 7 at 14:30
@Matthew: When you do
file["ID"] = ...
you modify the file
object in place. Afterwards your return file
, which is still the same object, but modified.– Graipher
Dec 7 at 14:30
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
the concept of returning a file or modifying in place was unknown to me, thanks. What more ? is the docstrings okay ? is the overall structure okay ?
– Matthew
Dec 7 at 14:38
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
@Matthew: I figured out how to get exactly your behaviour and added some comments on general structure.
– Graipher
Dec 7 at 14:41
add a comment |
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@200_success I edited the question. On the other hand, I want to improve the logic of the code and how variables are passed between functions. The code works as expected. I was wondering if this looks like something ready for production.
– Matthew
Dec 7 at 13:57
1
Thanks for adding the explanation. Downvote retracted.
– 200_success
Dec 7 at 14:02
Can you post a sample from your csv, including header?
– Graipher
Dec 7 at 14:11
What is the reason behind dropping the last character if there is no
'.'
in the ID? It seems to result in weird things like'NewJersey_"
.– Graipher
Dec 7 at 14:25
@Graipher you are right, the parentID for this one will be just
NewJersey
, I added an extra underscore in theaddress
, thats wrong.– Matthew
Dec 7 at 14:27