Pandas stacked board with grouped bars
I have data that looks like this:
topic positive negative type
0 88 0.080000 0.030000 source
1 36 0.010000 0.200000 source
2 101 0.350000 0.040000 source
3 78 0.110000 0.090000 source
4 99 0.110000 0.010000 source
5 79 0.000000 0.050000 source
6 24 0.000000 0.160000 source
7 17 0.000000 0.410000 source
8 14 0.090000 0.050000 source
9 29 0.060000 0.030000 source
0 14 0.207071 0.085859 summary
1 17 0.000000 0.738889 summary
2 24 0.000000 0.219349 summary
3 29 0.000000 0.094907 summary
4 36 0.000000 0.255808 summary
5 78 0.108333 0.194444 summary
6 79 0.000000 0.106443 summary
7 88 0.089286 0.041667 summary
8 99 0.098496 0.050877 summary
9 101 0.444444 0.055556 summary
I need to draw a dash graph that compares the positive / negative values for different type
for each topic
. I see this as a stacked (positive / negative) barcode with topic
the x-axis and the columns are grouped using the column type
. But I couldn't find a way to plot both the grouped and complex graph.
For one type it looks like this (sorry, I don't have enough reputation to post images):
polar_data.set_index(['type', 'topic']).xs('summary').plot(kind='bar', stacked=True)
And the only way I can currently compare the two different types is by simply placing the two parcels side by side with the aid seaborn.factorplot
, which makes it difficult to see trends clearly. And also I don't know how to build a complex graph with seaborn
.
print_data = pd.melt(polar_data, id_vars=['topic', 'type'], value_name='percent', var_name='polarity')
sns.factorplot("topic", 'percent', 'polarity', row="type", data=print_data, margin_titles=True, kind='bar')
So, is there a way to "concatenate" them instead of placing them side by side?
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Here's one way to do it with matplotlib. I am assuming that the sea wave will use the same structure.
In [3]: polar_data.pivot('topic', 'type')
Out[3]:
positive negative
type source summary source summary
topic
14 0.09 0.207071 0.05 0.085859
17 0.00 0.000000 0.41 0.738889
24 0.00 0.000000 0.16 0.219349
29 0.06 0.000000 0.03 0.094907
36 0.01 0.000000 0.20 0.255808
78 0.11 0.108333 0.09 0.194444
79 0.00 0.000000 0.05 0.106443
88 0.08 0.089286 0.03 0.041667
99 0.11 0.098496 0.01 0.050877
101 0.35 0.444444 0.04 0.055556
So now for positive values, you can do -
polar_data.pivot('topic', 'type')['positive'].plot(kind='bar', stacked=True)
For negative values, you can do -
polar_data.pivot('topic', 'type')['negative'].plot(kind='bar', stacked=True)
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