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I've got a dataframe outcome2 that I generate a grouped boxplot with in the following manner:
In [11]: outcome2.boxplot(column='Hospital 30-Day Death (Mortality) Rates from Heart Attack',by='State')
plt.ylabel('30 Day Death Rate')
plt.title('30 Day Death Rate by State')
Out [11]:
What I'd like to do is sort the plot by the median for each state, instead of alphabetically. Not sure how to go about doing so.
854
The key step to get boxplots in descending order is to get the sorted index in descending order. We can do that by changing the default ascending=True to asending=False while using Pandas sort_values() to sort the mean or median values.
You can sort by column values in pandas DataFrame using sort_values() method. To specify the order, you have to use ascending boolean property; False for descending and True for ascending. By default, it is set to True.
Seaborn's boxplot() function easily allows us to choose the order of boxplots using the argument “order”. The argument order takes a list ordered in the way we want. Here we manually specify the order of boxes using order as order=[“Professional”,”Less than bachelor's”,”Bachelor's”,”Master's”, 'PhD'].
If you want to see the median, you can use df. describe(). The 50% value is the median.
To sort by the median, just compute the median, then sort it and use the resulting Index to slice the DataFrame:
In [45]: df.iloc[:10, :5]
Out[45]:
AK AL AR AZ CA
0 0.047 0.199 0.969 -0.205 1.053
1 0.206 0.132 -0.712 0.111 -0.254
2 0.638 0.233 -0.907 1.284 1.193
3 1.234 0.046 0.624 0.485 -0.048
4 -1.362 -0.559 1.108 -0.501 0.111
5 1.276 -0.954 0.653 -0.175 -0.287
6 0.524 -1.785 -0.887 1.354 -0.431
7 0.111 0.762 -0.514 0.808 0.728
8 1.301 0.619 0.957 1.542 -0.087
9 -0.892 2.327 1.363 -1.537 0.142
In [46]: med = df.median()
In [47]: med.sort()
In [48]: newdf = df[med.index]
In [49]: newdf.iloc[:10, :5]
Out[49]:
PA CT LA RI MO
0 -0.667 0.774 -0.999 -0.938 0.155
1 0.822 0.390 -0.014 -2.228 0.570
2 -1.037 0.838 -0.673 2.038 0.809
3 0.620 2.845 -0.523 -0.151 -0.955
4 -0.918 1.043 0.613 0.698 -0.446
5 -0.767 0.869 -0.496 -0.925 -0.374
6 -0.495 0.437 1.245 -1.046 0.894
7 -1.283 0.358 0.016 0.137 0.511
8 -0.018 -0.047 -0.639 -0.385 0.080
9 -1.705 0.986 0.605 0.295 0.302
In [50]: med.head()
Out[50]:
PA -0.117
CT -0.077
LA -0.072
RI -0.069
MO -0.053
dtype: float64
The resulting figure:
105
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