plotting stacked bar graph on column values
up vote
1
down vote
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I have a Pandas data frame that looks like this:
ID Management Administrative
1 1 2
3 2 1
4 3 3
10 1 3
essentially the 1-3 is a grade of low medium or high. I want a stacked bar chart that has Management and Administrative on x-axis and the stacked composition of 1,2,3 of each column in percentages.
e.g. if there were only 4 entries as above, 1 would compose 50% of the height, 2 would compose 25% and 3 would compose 25% of the height of the management bar. The y axis would go up to 100%.
Hope this makes sense. Hard to explain but if unclear willing to clarify further!
python pandas bar-chart
asked Nov 21 at 16:41
user8322222
408
add a comment |
up vote
1
down vote
favorite
I have a Pandas data frame that looks like this:
ID Management Administrative
1 1 2
3 2 1
4 3 3
10 1 3
essentially the 1-3 is a grade of low medium or high. I want a stacked bar chart that has Management and Administrative on x-axis and the stacked composition of 1,2,3 of each column in percentages.
e.g. if there were only 4 entries as above, 1 would compose 50% of the height, 2 would compose 25% and 3 would compose 25% of the height of the management bar. The y axis would go up to 100%.
Hope this makes sense. Hard to explain but if unclear willing to clarify further!
python pandas bar-chart
asked Nov 21 at 16:41
user8322222
408
It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have a Pandas data frame that looks like this:
ID Management Administrative
1 1 2
3 2 1
4 3 3
10 1 3
essentially the 1-3 is a grade of low medium or high. I want a stacked bar chart that has Management and Administrative on x-axis and the stacked composition of 1,2,3 of each column in percentages.
e.g. if there were only 4 entries as above, 1 would compose 50% of the height, 2 would compose 25% and 3 would compose 25% of the height of the management bar. The y axis would go up to 100%.
Hope this makes sense. Hard to explain but if unclear willing to clarify further!
python pandas bar-chart
asked Nov 21 at 16:41
user8322222
408
I have a Pandas data frame that looks like this:
ID Management Administrative
1 1 2
3 2 1
4 3 3
10 1 3
essentially the 1-3 is a grade of low medium or high. I want a stacked bar chart that has Management and Administrative on x-axis and the stacked composition of 1,2,3 of each column in percentages.
e.g. if there were only 4 entries as above, 1 would compose 50% of the height, 2 would compose 25% and 3 would compose 25% of the height of the management bar. The y axis would go up to 100%.
Hope this makes sense. Hard to explain but if unclear willing to clarify further!
python pandas bar-chart
python pandas bar-chart
asked Nov 21 at 16:41
user8322222
408
asked Nov 21 at 16:41
user8322222
408
asked Nov 21 at 16:41
user8322222
408
asked Nov 21 at 16:41
user8322222
408
asked Nov 21 at 16:41
user8322222
408
408
It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55
add a comment |
It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55
It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55
It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55
add a comment |
1 Answer
1
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oldest
votes
up vote
0
down vote
You will need to chain several operations: First melt your dataset to move the Department as a new variable, after that you can groupby the Department and the Rating to count the number of IDs that fall into that bucket, then you groupby again by Department to calculate the percentages. Lastly you can plot your stacked bar graph:
df4.melt().rename(columns={'variable':'Dept', 'value':'Rating'}
).query('Dept!="ID"'
).groupby(['Dept','Rating']).size(
).rename('Count'
).groupby(level=0).apply(lambda x: x/sum(x)
).unstack().plot(kind='bar', stacked=True)
answered Nov 21 at 18:26
Franco Piccolo
1,335611
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
You will need to chain several operations: First melt your dataset to move the Department as a new variable, after that you can groupby the Department and the Rating to count the number of IDs that fall into that bucket, then you groupby again by Department to calculate the percentages. Lastly you can plot your stacked bar graph:
df4.melt().rename(columns={'variable':'Dept', 'value':'Rating'}
).query('Dept!="ID"'
).groupby(['Dept','Rating']).size(
).rename('Count'
).groupby(level=0).apply(lambda x: x/sum(x)
).unstack().plot(kind='bar', stacked=True)
answered Nov 21 at 18:26
Franco Piccolo
1,335611
add a comment |
up vote
0
down vote
You will need to chain several operations: First melt your dataset to move the Department as a new variable, after that you can groupby the Department and the Rating to count the number of IDs that fall into that bucket, then you groupby again by Department to calculate the percentages. Lastly you can plot your stacked bar graph:
df4.melt().rename(columns={'variable':'Dept', 'value':'Rating'}
).query('Dept!="ID"'
).groupby(['Dept','Rating']).size(
).rename('Count'
).groupby(level=0).apply(lambda x: x/sum(x)
).unstack().plot(kind='bar', stacked=True)
answered Nov 21 at 18:26
Franco Piccolo
1,335611
add a comment |
up vote
0
down vote
up vote
0
down vote
You will need to chain several operations: First melt your dataset to move the Department as a new variable, after that you can groupby the Department and the Rating to count the number of IDs that fall into that bucket, then you groupby again by Department to calculate the percentages. Lastly you can plot your stacked bar graph:
df4.melt().rename(columns={'variable':'Dept', 'value':'Rating'}
).query('Dept!="ID"'
).groupby(['Dept','Rating']).size(
).rename('Count'
).groupby(level=0).apply(lambda x: x/sum(x)
).unstack().plot(kind='bar', stacked=True)
answered Nov 21 at 18:26
Franco Piccolo
1,335611
You will need to chain several operations: First melt your dataset to move the Department as a new variable, after that you can groupby the Department and the Rating to count the number of IDs that fall into that bucket, then you groupby again by Department to calculate the percentages. Lastly you can plot your stacked bar graph:
df4.melt().rename(columns={'variable':'Dept', 'value':'Rating'}
).query('Dept!="ID"'
).groupby(['Dept','Rating']).size(
).rename('Count'
).groupby(level=0).apply(lambda x: x/sum(x)
).unstack().plot(kind='bar', stacked=True)
answered Nov 21 at 18:26
Franco Piccolo
1,335611
answered Nov 21 at 18:26
Franco Piccolo
1,335611
answered Nov 21 at 18:26
Franco Piccolo
1,335611
answered Nov 21 at 18:26
Franco Piccolo
1,335611
1,335611
add a comment |
add a comment |
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It is very unclear to me as I can't see the relation of your explanation to your dataframe. To me, for index 1, Management would compose for 33% and Administrative 66% for instance.
– IMCoins
Nov 21 at 17:55