How to convert Pandas dataframe column into bin string data?
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1
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I have a Pandas dataframe called odf that looks like this:
Customer Employees
A 2
B 100
C 5
D 1000
I have created custom bins for the employee data:
df = odf['Employees']
bins = [0,5,1000]
df.value_counts(bins=bins)
(-0.001, 5.0] 2
(5.0, 1000] 2
Name:Employees, dtype: int64
now I'd like to 'join' this data but am unsure how to do this, or if there is an easier way to accomplish what I need. I want the end result to look like this:
Customer Employees NewBinColumn
A 2 -0.001, 5.0
B 100 5.0, 1000
C 5 -0.001, 5.0
D 1000 5.0, 1000
That way I can see the bin column next to the original dataframe columns
here is what I tried that did not work:
ndf = odf.join(df, lsuffix='Employees', rsuffix='Employees', how='left')
ndf
And while it does join the two, what I get is this:
Customer EmployeesEmployees Employees
A 2 2
B 100 100
C 5 5
D 1000 1000
If this was SQL I'd use a case statement to get the new column, but I was hoping there is an easier way to dynamically do this without writing out a really long statement.
pandas dataframe join bins
asked Nov 9 at 19:45
user76595
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add a comment |
up vote
1
down vote
favorite
I have a Pandas dataframe called odf that looks like this:
Customer Employees
A 2
B 100
C 5
D 1000
I have created custom bins for the employee data:
df = odf['Employees']
bins = [0,5,1000]
df.value_counts(bins=bins)
(-0.001, 5.0] 2
(5.0, 1000] 2
Name:Employees, dtype: int64
now I'd like to 'join' this data but am unsure how to do this, or if there is an easier way to accomplish what I need. I want the end result to look like this:
Customer Employees NewBinColumn
A 2 -0.001, 5.0
B 100 5.0, 1000
C 5 -0.001, 5.0
D 1000 5.0, 1000
That way I can see the bin column next to the original dataframe columns
here is what I tried that did not work:
ndf = odf.join(df, lsuffix='Employees', rsuffix='Employees', how='left')
ndf
And while it does join the two, what I get is this:
Customer EmployeesEmployees Employees
A 2 2
B 100 100
C 5 5
D 1000 1000
If this was SQL I'd use a case statement to get the new column, but I was hoping there is an easier way to dynamically do this without writing out a really long statement.
pandas dataframe join bins
asked Nov 9 at 19:45
user76595
728
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have a Pandas dataframe called odf that looks like this:
Customer Employees
A 2
B 100
C 5
D 1000
I have created custom bins for the employee data:
df = odf['Employees']
bins = [0,5,1000]
df.value_counts(bins=bins)
(-0.001, 5.0] 2
(5.0, 1000] 2
Name:Employees, dtype: int64
now I'd like to 'join' this data but am unsure how to do this, or if there is an easier way to accomplish what I need. I want the end result to look like this:
Customer Employees NewBinColumn
A 2 -0.001, 5.0
B 100 5.0, 1000
C 5 -0.001, 5.0
D 1000 5.0, 1000
That way I can see the bin column next to the original dataframe columns
here is what I tried that did not work:
ndf = odf.join(df, lsuffix='Employees', rsuffix='Employees', how='left')
ndf
And while it does join the two, what I get is this:
Customer EmployeesEmployees Employees
A 2 2
B 100 100
C 5 5
D 1000 1000
If this was SQL I'd use a case statement to get the new column, but I was hoping there is an easier way to dynamically do this without writing out a really long statement.
pandas dataframe join bins
asked Nov 9 at 19:45
user76595
728
I have a Pandas dataframe called odf that looks like this:
Customer Employees
A 2
B 100
C 5
D 1000
I have created custom bins for the employee data:
df = odf['Employees']
bins = [0,5,1000]
df.value_counts(bins=bins)
(-0.001, 5.0] 2
(5.0, 1000] 2
Name:Employees, dtype: int64
now I'd like to 'join' this data but am unsure how to do this, or if there is an easier way to accomplish what I need. I want the end result to look like this:
Customer Employees NewBinColumn
A 2 -0.001, 5.0
B 100 5.0, 1000
C 5 -0.001, 5.0
D 1000 5.0, 1000
That way I can see the bin column next to the original dataframe columns
here is what I tried that did not work:
ndf = odf.join(df, lsuffix='Employees', rsuffix='Employees', how='left')
ndf
And while it does join the two, what I get is this:
Customer EmployeesEmployees Employees
A 2 2
B 100 100
C 5 5
D 1000 1000
If this was SQL I'd use a case statement to get the new column, but I was hoping there is an easier way to dynamically do this without writing out a really long statement.
pandas dataframe join bins
pandas dataframe join bins
asked Nov 9 at 19:45
user76595
728
asked Nov 9 at 19:45
user76595
728
asked Nov 9 at 19:45
user76595
728
asked Nov 9 at 19:45
user76595
728
asked Nov 9 at 19:45
user76595
728
728
add a comment |
add a comment |
1 Answer
1
active
oldest
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up vote
1
down vote
accepted
It is not exactly the same formating that what you want, but using pd.cut on odf['Employees'] such as:
odf['NewBinColumn'] = pd.cut(odf['Employees'],bins)
will give:
Customer Employees NewBinColumn
0 A 2 (0, 5]
1 B 100 (5, 1000]
2 C 5 (0, 5]
3 D 1000 (5, 1000]
answered Nov 9 at 19:58
Ben.T
5,4822523
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
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
up vote
1
down vote
accepted
It is not exactly the same formating that what you want, but using pd.cut on odf['Employees'] such as:
odf['NewBinColumn'] = pd.cut(odf['Employees'],bins)
will give:
Customer Employees NewBinColumn
0 A 2 (0, 5]
1 B 100 (5, 1000]
2 C 5 (0, 5]
3 D 1000 (5, 1000]
answered Nov 9 at 19:58
Ben.T
5,4822523
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
add a comment |
up vote
1
down vote
accepted
It is not exactly the same formating that what you want, but using pd.cut on odf['Employees'] such as:
odf['NewBinColumn'] = pd.cut(odf['Employees'],bins)
will give:
Customer Employees NewBinColumn
0 A 2 (0, 5]
1 B 100 (5, 1000]
2 C 5 (0, 5]
3 D 1000 (5, 1000]
answered Nov 9 at 19:58
Ben.T
5,4822523
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
It is not exactly the same formating that what you want, but using pd.cut on odf['Employees'] such as:
odf['NewBinColumn'] = pd.cut(odf['Employees'],bins)
will give:
Customer Employees NewBinColumn
0 A 2 (0, 5]
1 B 100 (5, 1000]
2 C 5 (0, 5]
3 D 1000 (5, 1000]
answered Nov 9 at 19:58
Ben.T
5,4822523
It is not exactly the same formating that what you want, but using pd.cut on odf['Employees'] such as:
odf['NewBinColumn'] = pd.cut(odf['Employees'],bins)
will give:
Customer Employees NewBinColumn
0 A 2 (0, 5]
1 B 100 (5, 1000]
2 C 5 (0, 5]
3 D 1000 (5, 1000]
answered Nov 9 at 19:58
Ben.T
5,4822523
answered Nov 9 at 19:58
Ben.T
5,4822523
answered Nov 9 at 19:58
Ben.T
5,4822523
answered Nov 9 at 19:58
Ben.T
5,4822523
5,4822523
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
add a comment |
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
1
1
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
This is close enough. I'm newb enough to handle the formatting after the fact. I just tend to make things more complicated than need be. Thanks again.
– user76595
Nov 9 at 20:07
add a comment |
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