why doesn't bokeh boxplot appear?

Question

table I am working on:

https://github.com/KeithGalli/pandas/blob/master/pokemon_data.csv

When I create a boxplot for the 'HP' column with 'Generation' as the category it works fine

def box_plot(df, vals, label, ylabel=None):

    """
    Make a Bokeh box plot from a tidy DataFrame.
    
    Parameters
    ----------
    df : tidy Pandas DataFrame
        DataFrame to be used for plotting
    vals : hashable object
        Column of DataFrame containing data to be used.
    label : hashable object
        Column of DataFrame use to categorize.
    ylabel : str, default None
        Text for y-axis label
        
    Returns
    -------
    output : Bokeh plotting object
        Bokeh plotting object that can be rendered with
        bokeh.io.show()
        
    Notes
    -----
    .. Based largely on example code found here:
     https://github.com/bokeh/bokeh/blob/master/examples/plotting/file/boxplot.py
    """
    # Get the categories
    cats = list(df[label].unique())
    
    # Group Data frame
    df_gb = df.groupby(label)

    # Compute quartiles for each group
    q1 = df_gb[vals].quantile(q=0.25)
    q2 = df_gb[vals].quantile(q=0.5)
    q3 = df_gb[vals].quantile(q=0.75)
                       
    # Compute interquartile region and upper and lower bounds for outliers
    iqr = q3 - q1
    upper_cutoff = q3 + 1.5*iqr
    lower_cutoff = q1 - 1.5*iqr

    # Find the outliers for each category
    def outliers(group):
        cat = group.name
        outlier_inds = (group[vals] > upper_cutoff[cat]) \
                                     | (group[vals] < lower_cutoff[cat])
        return group[vals][outlier_inds]

    # Apply outlier finder
    out = df_gb.apply(outliers).dropna()

    # Points of outliers for plotting
    outx = []
    outy = []
    for cat in cats:
        # only add outliers if they exist
        if not out[cat].empty:
            for value in out[cat]:
                outx.append(cat)
                outy.append(value) 
                
    # If outliers, shrink whiskers to smallest and largest non-outlier
    qmin = df_gb[vals].min()
    qmax = df_gb[vals].max()
    upper = [min([x,y]) for (x,y) in zip(qmax, upper_cutoff)]
    lower = [max([x,y]) for (x,y) in zip(qmin, lower_cutoff)]

    # Build figure
    p = figure(sizing_mode='stretch_width')
    p.ygrid.grid_line_color = 'white'
    p.xgrid.grid_line_color = None
    p.ygrid.grid_line_width = 2
    p.yaxis.axis_label = ylabel
    
    # stems
    p.segment(cats, upper, cats, q3, line_width=2, line_color="black")
    p.segment(cats, lower, cats, q1, line_width=2, line_color="black")

    # boxes
    p.rect(cats, (q3 + q1)/2, 0.5, q3 - q1, fill_color="red", 
           alpha=0.7, line_width=2, line_color="black")

    # median (almost-0 height rects simpler than segments)
    p.rect(cats, q2, 0.5, 0.01, line_color="black", line_width=2)

    # whiskers (almost-0 height rects simpler than segments)
    p.rect(cats, lower, 0.2, 0.01, line_color="black")
    p.rect(cats, upper, 0.2, 0.01, line_color="black")

    # outliers
    p.circle(outx, outy, size=6, color="black")

    return p

p = box_plot(df, 'HP', 'Generation', ylabel='HP')
show(p)

however if I change the parameters at the end to, to say:

p = box_plot(df, 'Attack', 'Generation', ylabel='HP')
show(p)

results in the error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [21], in <cell line: 96>()
     92     p.circle(outx, outy, size=6, color="black")
     94     return p
---> 96 p = box_plot(df, 'Attack', 'Generation', ylabel='HP')
     97 show(p)

Input In [21], in box_plot(df, vals, label, ylabel)
     55 outy = []
     56 for cat in cats:
     57     # only add outliers if they exist
---> 58     if not out[cat].empty:
     59         for value in out[cat]:
     60             outx.append(cat)

File ~\Anaconda3\lib\site-packages\pandas\core\series.py:958, in Series.__getitem__(self, key)
    955     return self._values[key]
    957 elif key_is_scalar:
--> 958     return self._get_value(key)
    960 if is_hashable(key):
    961     # Otherwise index.get_value will raise InvalidIndexError
    962     try:
    963         # For labels that don't resolve as scalars like tuples and frozensets

File ~\Anaconda3\lib\site-packages\pandas\core\series.py:1069, in Series._get_value(self, label, takeable)
   1066     return self._values[label]
   1068 # Similar to Index.get_value, but we do not fall back to positional
-> 1069 loc = self.index.get_loc(label)
   1070 return self.index._get_values_for_loc(self, loc, label)

File ~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py:2871, in MultiIndex.get_loc(self, key, method)
   2868     return mask
   2870 if not isinstance(key, tuple):
-> 2871     loc = self._get_level_indexer(key, level=0)
   2872     return _maybe_to_slice(loc)
   2874 keylen = len(key)

File ~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py:3251, in MultiIndex._get_level_indexer(self, key, level, indexer)
   3247     end = level_codes.searchsorted(idx, side="right")
   3249 if start == end:
   3250     # The label is present in self.levels[level] but unused:
-> 3251     raise KeyError(key)
   3252 return slice(start, end)

KeyError: 5

it only seems to work with the HP column and Generation category. Another example below

if i Change the category, to say 'Type 1', it once again fails to execute.

code at the end:

p = box_plot(df, 'HP', 'Type 1', ylabel='HP')
show(p)

results in the error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Input In [22], in <cell line: 96>()
     92     p.circle(outx, outy, size=6, color="black")
     94     return p
---> 96 p = box_plot(df, 'HP', 'Type 1', ylabel='HP')
     97 show(p)

Input In [22], in box_plot(df, vals, label, ylabel)
     55 outy = []
     56 for cat in cats:
     57     # only add outliers if they exist
---> 58     if not out[cat].empty:
     59         for value in out[cat]:
     60             outx.append(cat)

File ~\Anaconda3\lib\site-packages\pandas\core\series.py:958, in Series.__getitem__(self, key)
    955     return self._values[key]
    957 elif key_is_scalar:
--> 958     return self._get_value(key)
    960 if is_hashable(key):
    961     # Otherwise index.get_value will raise InvalidIndexError
    962     try:
    963         # For labels that don't resolve as scalars like tuples and frozensets

File ~\Anaconda3\lib\site-packages\pandas\core\series.py:1069, in Series._get_value(self, label, takeable)
   1066     return self._values[label]
   1068 # Similar to Index.get_value, but we do not fall back to positional
-> 1069 loc = self.index.get_loc(label)
   1070 return self.index._get_values_for_loc(self, loc, label)

File ~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py:2871, in MultiIndex.get_loc(self, key, method)
   2868     return mask
   2870 if not isinstance(key, tuple):
-> 2871     loc = self._get_level_indexer(key, level=0)
   2872     return _maybe_to_slice(loc)
   2874 keylen = len(key)

File ~\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py:3251, in MultiIndex._get_level_indexer(self, key, level, indexer)
   3247     end = level_codes.searchsorted(idx, side="right")
   3249 if start == end:
   3250     # The label is present in self.levels[level] but unused:
-> 3251     raise KeyError(key)
   3252 return slice(start, end)

KeyError: 'Poison'

any guidance to help me adjust the code to ensure it works with all possible combinations?

@mosc9575 thank you for the reply. so how can I alter my code to make sure its compatible with all combinations? I am fairly new at all this so any further assistance would be greatly appreciated — curiouscoder, Apr 23 '22 at 12:47
`RangeIndex: 800 entries, 0 to 799 # Column Non-Null Count Dtype --- ------ ------- ----- 0 # 800 non-null int64 1 Name 800 non-null object 2 Type 1 800 non-null object 3 Type 2 414 non-null object 4 HP 800 non-null int64 5 Attack 800 non-null int64 6 Defense 800 non-null int64 7 Sp. Atk 800 non-null int64 8 Sp. Def 800 non-null int64 9 Speed 800 non-null int64 10 Generation 800 non-null int64 11 Legendary 800 non-null bool` — curiouscoder, Apr 24 '22 at 09:58
@mosc9575 from using df.info() above none of the columns have NaN value except 'Type2' which i wont be using. you have mentioned the problem lies in 'if not out[cat].empty' 'cats = list(df[label].unique())' 'out = df_gb.apply(outliers).dropna()' so rather than going back and forth can you kindly let me know how each part of this code can be explicitly change to ensure the code runs. as mentioned before I am new at this and finding this all very hard to comprehend — curiouscoder, Apr 24 '22 at 10:03
Try to run [`df = df.drop('Type2', axis=1)`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html) right after `df = pd.read_csv()`. But you should find a way do debug your code yourself. I will not debug it for you. SO is not a free coding service. There are a lot of tutorials in the web. — mosc9575, Apr 24 '22 at 15:37
@mosc9575 I have tried this but unfortunately it was unsuccessful(dropping NaN column). I understand SO is not a free coding service however I have been requesting help for this work as I dont possess the ability to debug the issue, because if I did I would not have been asking in the first place. You have tried to help me with the issue and I am grateful for that, and I will not be requesting any further help with this issue. for this particular topic there actually arent many tutorials on the web at all. I have checked other forums as well as youtube. anyway, thank you once again — curiouscoder, Apr 24 '22 at 16:34

mosc9575 · Accepted Answer · 2022-04-25T08:35:35.450

To make your code run, you have to apply a few little changes:

# old
# # Get the categories
# cats = list(df[label].unique())

# # Group Data frame
# df_gb = df.groupby(label)

# Group Data frame
df_gb = df.groupby(label)
# Get the categories
cats = list(df_gb.groups.keys())

Here the selected categories were wrong. Also change this line:

# old
# if out[cat].empty:
if cat in out and not out[cat].empty: #new

This is because not all categories have outliers and you have to skip them some how. This is the place there your code was throwing an error.

Now your code is showing a blank figure, if you run box_plot(df, 'HP', 'Type 1', ylabel='HP'). This is because the index is a string and not a number and bokeh doesn't know where to place the boxes.

To make this valid for strings apply the strings to the figure as x_range when creating the figure().

# old
# # Build figure
# p = figure(sizing_mode='stretch_width')

cats = [str(i) for i in cats]
# Build figure
p = figure(sizing_mode='stretch_width', x_range=cats)

Here all items are transformed to a string and added to the bokeh figure in the first place. So the figure can add the boxes in a proper way.

All this is done, the call p = box_plot(df, 'HP', 'Type 1', ylabel='HP') leads to this figure:

thank you very much for your help and support for my query! hopefully I can use this as a structure and apply it to different datasets going forward. I appreciate your time and efforts and am grateful for the input! — curiouscoder, Apr 25 '22 at 08:19

why doesn't bokeh boxplot appear?

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