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Bokeh Hovertool stacked barchart

I have constructed a Bokeh stacked barchart by the code below. The chart shows the different tree types for the districts of Copenhagen. At the moment I have a hoverTool which shows the excat amount of trees (corrosponding to the columns with the tree names) for the tree type but I also want it to show the percentage (the columns with _pat the end), but how can I do this with the stacked bar chart?
A reduced part of the data frame:
temp=pd.DataFrame( {'bydelsnavn': {0: 'Amager Vest', 1: 'Amager Øst', 2: 'Bispebjerg', 3: 'Brønshøj-Husum', 4: 'Indre By', 5: 'Nørrebro', 6: 'Valby', 7: 'Vanløse', 8: 'Vesterbro', 9: 'Østerbro'}, 'Alder': {0: 53.0, 1: 21.0, 2: 1.0, 3: 9.0, 4: 4.0, 5: 2.0, 6: 3.0, 7: 44.0, 8: 46.0, 9: 59.0}, 'Alderm': {0: 63.0, 1: 32.0, 2: 49.0, 3: 13.0, 4: 45.0, 5: 55.0, 6: 104.0, 7: 0.0, 8: 50.0, 9: 4.0}, 'Apple': {0: 94.0, 1: 109.0, 2: 115.0, 3: 12.0, 4: 22.0, 5: 81.0, 6: 41.0, 7: 3.0, 8: 132.0, 9: 51.0}, 'Alder_p': {0: 21.9, 1: 8.68, 2: 0.41, 3: 3.72, 4: 1.65, 5: 0.83, 6: 1.24, 7: 18.18, 8: 19.01, 9: 24.38}, 'Alderm_p': {0: 15.18, 1: 7.71, 2: 11.81, 3: 3.13, 4: 10.84, 5: 13.25, 6: 25.06, 7: 0.0, 8: 12.05, 9: 0.96}, 'Apple_p': {0: 14.24, 1: 16.52, 2: 17.42, 3: 1.82, 4: 3.33, 5: 12.27, 6: 6.21, 7: 0.45, 8: 20.0, 9: 7.73}})
My code:
treeName = ['Alder','Alderm','Apple']
treeName_p = ['Alder_p','Alderm_p','Apple_p']
colornames = named.__all__
colornames = colornames[:len(treeName)]
# Create an empty figure
p = figure(x_range = temp['bydelsnavn'].values,plot_width = 700, plot_height=400,
title='Tree pr. district', toolbar_sticky = False,
tools = 'pan,wheel_zoom,reset')
# Stacked bar chart
renderers = p.vbar_stack(stackers=treeName,x='bydelsnavn',source=temp,
width=0.8, color = colornames)
# Add the hover tool
for r in renderers:
tree = r.name
hover = HoverTool(tooltips=[
("%s" % tree, "#{%s}" % tree)
], renderers = [r])
p.add_tools(hover)
# remove the grid
p.xgrid.grid_line_color=None
p.ygrid.grid_line_color=None
# Make sure bars stat at 0
p.y_range.start = 0
# remove - y-axis
p.yaxis.visible = False
# Remove the grey box around the plot
p.outline_line_color = None
# Turn the x-labels
p.xaxis.major_label_orientation = 0.5
# Remove tool bar logo
p.toolbar.logo = None
# Move the border of the left side to show "Amager"
p.min_border_left = 30
show(p)
My current chart looks like this:

Assuming that the values of the _p columns are actually in the data source, you can just add another tooltip to the HoverTool:
for r in renderers:
tree = r.name
p.add_tools(HoverTool(tooltips=[(tree, "#$name"),
(f"{tree} %", f"#{tree}_p")],
renderers=[r]))
Notice how #$name is used in there - not that necessary in this particular case but sometimes comes in handy.

Bokeh: remove Hovertool from toolbar in stacked bar chart

In my stacked barchart I have specified which tools I want in the toolbar. But when I add a hovertool this seems to overwrite my command and adds a hovertool for every element in the stacked barchart. How can I remove the hovertool tool in the toolbar?
Data example:
temp=pd.DataFrame( {'bydelsnavn': {0: 'Amager Vest', 1: 'Amager Øst', 2: 'Bispebjerg', 3: 'Brønshøj-Husum', 4: 'Indre By', 5: 'Nørrebro', 6: 'Valby', 7: 'Vanløse', 8: 'Vesterbro', 9: 'Østerbro'}, 'Alder': {0: 53.0, 1: 21.0, 2: 1.0, 3: 9.0, 4: 4.0, 5: 2.0, 6: 3.0, 7: 44.0, 8: 46.0, 9: 59.0}, 'Alderm': {0: 63.0, 1: 32.0, 2: 49.0, 3: 13.0, 4: 45.0, 5: 55.0, 6: 104.0, 7: 0.0, 8: 50.0, 9: 4.0}, 'Apple': {0: 94.0, 1: 109.0, 2: 115.0, 3: 12.0, 4: 22.0, 5: 81.0, 6: 41.0, 7: 3.0, 8: 132.0, 9: 51.0}, 'Alder_p': {0: 21.9, 1: 8.68, 2: 0.41, 3: 3.72, 4: 1.65, 5: 0.83, 6: 1.24, 7: 18.18, 8: 19.01, 9: 24.38}, 'Alderm_p': {0: 15.18, 1: 7.71, 2: 11.81, 3: 3.13, 4: 10.84, 5: 13.25, 6: 25.06, 7: 0.0, 8: 12.05, 9: 0.96}, 'Apple_p': {0: 14.24, 1: 16.52, 2: 17.42, 3: 1.82, 4: 3.33, 5: 12.27, 6: 6.21, 7: 0.45, 8: 20.0, 9: 7.73}})
My code:
treeName = ['Alder','Alderm','Apple']
treeName_p = ['Alder_p','Alderm_p','Apple_p']
colornames = named.__all__
colornames = colornames[:len(treeName)]
# Create an empty figure
p = figure(x_range = temp['bydelsnavn'].values,plot_width = 700, plot_height=400,
title='Tree pr. district', toolbar_sticky = False,
tools = 'pan,wheel_zoom,reset')
# Stacked bar chart
renderers = p.vbar_stack(stackers=treeName,x='bydelsnavn',source=temp,
width=0.8, color = colornames)
# Add the hover tool
for r in renderers:
tree = r.name
hover = HoverTool(tooltips=[
("%s" % tree, "#{%s}" % tree)
], renderers = [r])
p.add_tools(hover)
# remove the grid
p.xgrid.grid_line_color=None
p.ygrid.grid_line_color=None
# Make sure bars stat at 0
p.y_range.start = 0
# remove - y-axis
p.yaxis.visible = False
# Remove the grey box around the plot
p.outline_line_color = None
# Turn the x-labels
p.xaxis.major_label_orientation = 0.5
# Remove tool bar logo
p.toolbar.logo = None
# Move the border of the left side to show "Amager"
p.min_border_left = 30
show(p)
In the picture the tools I would like to aviod are pointed out:

You can hide the tool buttons completely by passing toggleable=False to HoverTool.

Conditionally summing multiple columns

I would like to sum certain rows based on a condition in a different row.
So I have a columns for points
{'secondBoxer1': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'secondBoxer2': {0: 0.0, 1: 0.0, 2: 10.0, 3: 0.0, 4: 0.0},
'secondBoxer3': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'secondBoxer4': {0: 15.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'secondBoxer5': {0: 15.0, 1: 53.57142857142857, 2: 0.0, 3: 0.0, 4: 0.0},
'secondBoxer6': {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0},
'secondBoxer7': {0: 0.0, 1: 0.0, 2: 0.0, 3: 50.0, 4: 0.0},
'secondBoxer8': {0: 0.0, 1: 0.0, 2: 0.0, 3: 37.142857142857146, 4: 0.0}}
and a column with the outcome of each fight
{'outcome1': {0: 'win ', 1: 'win ', 2: 'win ', 3: 'draw ', 4: 'win '},
'outcome2': {0: 'win ', 1: 'win ', 2: 'win ', 3: 'win ', 4: 'win '},
'outcome3': {0: 'win ', 1: 'win ', 2: 'win ', 3: 'win ', 4: 'scheduled '},
'outcome4': {0: 'win ', 1: 'win ', 2: 'nan', 3: 'loss ', 4: 'nan'},
'outcome5': {0: 'win ', 1: 'draw ', 2: 'nan', 3: 'win ', 4: 'nan'},
'outcome6': {0: 'nan', 1: 'nan', 2: 'nan', 3: 'loss ', 4: 'nan'},
'outcome7': {0: 'nan', 1: 'nan', 2: 'nan', 3: 'loss ', 4: 'nan'},
'outcome8': {0: 'nan', 1: 'nan', 2: 'nan', 3: 'win ', 4: 'nan'}}
I would like to sum the points in the first columns (points columns) in cases where the outcome is equals to a win.
I have written this code, where opp_names is the list of columns with the points and outcome_cols is a list of columns with the outcomes
data[opp_names].sum(axis=1).where(data[outcome_cols] == 'win')
The problem with the output from this code is that it returns a total sum of points that is not conditional

In your case we use mask :d is your first dict , d1 is your 2nd dict
pd.DataFrame(d).mask(pd.DataFrame(d1).ne('win ').to_numpy()).sum(1)
Out[164]:
0 30.000000
1 0.000000
2 10.000000
3 37.142857
4 0.000000
dtype: float64

Number of labels does not match samples on decision tree regression

Trying to run a decision tree regressor on my data, but whenever I try and run my code, I get this error
ValueError: Number of labels=78177 does not match number of samples=312706
#feature selection
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
target = ['sale_price']
train, test = train_test_split(housing_data, test_size=0.2)
regression_tree = DecisionTreeRegressor(criterion="entropy",random_state=100,
max_depth=4,min_samples_leaf=5)
regression_tree.fit(train,test)
I have added a sample of my code, hopefully this gives you more context to help better understand my question and problem:
{'Age of House at Sale': {0: 6,
1: 2016,
2: 92,
3: 42,
4: 90,
5: 2012,
6: 89,
7: 3,
8: 2015,
9: 104},
'AreaSource': {0: 2.0,
1: 7.0,
2: 2.0,
3: 2.0,
4: 2.0,
5: 2.0,
6: 2.0,
7: 2.0,
8: 2.0,
9: 2.0},
'AssessLand': {0: 9900.0,
1: 1571850.0,
2: 1548000.0,
3: 36532350.0,
4: 2250000.0,
5: 3110400.0,
6: 2448000.0,
7: 1354500.0,
8: 1699200.0,
9: 1282500.0},
'AssessTot': {0: 34380.0,
1: 1571850.0,
2: 25463250.0,
3: 149792400.0,
4: 27166050.0,
5: 5579990.0,
6: 28309500.0,
7: 23965650.0,
8: 3534300.0,
9: 11295000.0},
'BldgArea': {0: 2688.0,
1: 0.0,
2: 304650.0,
3: 2548000.0,
4: 356000.0,
5: 382746.0,
6: 290440.0,
7: 241764.0,
8: 463427.0,
9: 547000.0},
'BldgClass': {0: 72,
1: 89,
2: 80,
3: 157,
4: 150,
5: 44,
6: 92,
7: 43,
8: 39,
9: 61},
'BldgDepth': {0: 50.0,
1: 0.0,
2: 92.0,
3: 0.0,
4: 100.33,
5: 315.0,
6: 125.0,
7: 100.0,
8: 0.0,
9: 80.92},
'BldgFront': {0: 20.0,
1: 0.0,
2: 335.0,
3: 0.0,
4: 202.0,
5: 179.0,
6: 92.0,
7: 500.0,
8: 0.0,
9: 304.0},
'BsmtCode': {0: 5.0,
1: 5.0,
2: 5.0,
3: 5.0,
4: 2.0,
5: 5.0,
6: 2.0,
7: 2.0,
8: 5.0,
9: 5.0},
'CD': {0: 310.0,
1: 302.0,
2: 302.0,
3: 318.0,
4: 302.0,
5: 301.0,
6: 302.0,
7: 301.0,
8: 301.0,
9: 302.0},
'ComArea': {0: 0.0,
1: 0.0,
2: 304650.0,
3: 2548000.0,
4: 30000.0,
5: 11200.0,
6: 290440.0,
7: 27900.0,
8: 4884.0,
9: 547000.0},
'CommFAR': {0: 0.0,
1: 2.0,
2: 2.0,
3: 2.0,
4: 0.0,
5: 0.0,
6: 10.0,
7: 2.0,
8: 0.0,
9: 2.0},
'Council': {0: 41.0,
1: 33.0,
2: 33.0,
3: 46.0,
4: 33.0,
5: 33.0,
6: 33.0,
7: 33.0,
8: 33.0,
9: 35.0},
'Easements': {0: 0.0,
1: 0.0,
2: 0.0,
3: 1.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0},
'ExemptLand': {0: 0.0,
1: 1571850.0,
2: 0.0,
3: 0.0,
4: 2250000.0,
5: 0.0,
6: 0.0,
7: 932847.0,
8: 0.0,
9: 0.0},
'ExemptTot': {0: 0.0,
1: 1571850.0,
2: 0.0,
3: 0.0,
4: 27166050.0,
5: 0.0,
6: 11304900.0,
7: 23543997.0,
8: 0.0,
9: 0.0},
'FacilFAR': {0: 0.0,
1: 6.5,
2: 0.0,
3: 0.0,
4: 4.8,
5: 4.8,
6: 10.0,
7: 3.0,
8: 5.0,
9: 4.8},
'FactryArea': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 547000.0},
'GarageArea': {0: 0.0,
1: 0.0,
2: 0.0,
3: 1285000.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 22200.0,
8: 0.0,
9: 0.0},
'HealthArea': {0: 6410.0,
1: 1000.0,
2: 2300.0,
3: 8822.0,
4: 2300.0,
5: 400.0,
6: 2300.0,
7: 700.0,
8: 500.0,
9: 9300.0},
'HealthCent': {0: 35.0,
1: 36.0,
2: 38.0,
3: 35.0,
4: 38.0,
5: 30.0,
6: 38.0,
7: 30.0,
8: 30.0,
9: 36.0},
'IrrLotCode': {0: 1, 1: 1, 2: 0, 3: 0, 4: 1, 5: 1, 6: 0, 7: 1, 8: 0, 9: 0},
'LandUse': {0: 2.0,
1: 10.0,
2: 5.0,
3: 5.0,
4: 8.0,
5: 4.0,
6: 5.0,
7: 3.0,
8: 3.0,
9: 6.0},
'LotArea': {0: 2252.0,
1: 134988.0,
2: 32000.0,
3: 905000.0,
4: 20267.0,
5: 57600.0,
6: 12500.0,
7: 50173.0,
8: 44704.0,
9: 113800.0},
'LotDepth': {0: 100.0,
1: 275.33,
2: 335.92,
3: 859.0,
4: 100.33,
5: 320.0,
6: 125.0,
7: 200.0,
8: 281.86,
9: 204.0},
'LotFront': {0: 24.0,
1: 490.5,
2: 92.42,
3: 930.0,
4: 202.0,
5: 180.0,
6: 100.0,
7: 521.25,
8: 225.08,
9: 569.0},
'LotType': {0: 5.0,
1: 5.0,
2: 3.0,
3: 3.0,
4: 3.0,
5: 3.0,
6: 3.0,
7: 1.0,
8: 5.0,
9: 3.0},
'NumBldgs': {0: 1.0,
1: 0.0,
2: 1.0,
3: 4.0,
4: 1.0,
5: 1.0,
6: 1.0,
7: 1.0,
8: 2.0,
9: 13.0},
'NumFloors': {0: 2.0,
1: 0.0,
2: 13.0,
3: 2.0,
4: 15.0,
5: 0.0,
6: 37.0,
7: 6.0,
8: 20.0,
9: 8.0},
'OfficeArea': {0: 0.0,
1: 0.0,
2: 264750.0,
3: 0.0,
4: 30000.0,
5: 1822.0,
6: 274500.0,
7: 4200.0,
8: 0.0,
9: 0.0},
'OtherArea': {0: 0.0,
1: 0.0,
2: 39900.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0},
'PolicePrct': {0: 70.0,
1: 84.0,
2: 84.0,
3: 63.0,
4: 84.0,
5: 90.0,
6: 84.0,
7: 94.0,
8: 90.0,
9: 88.0},
'ProxCode': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 1.0,
8: 0.0,
9: 0.0},
'ResArea': {0: 2172.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 371546.0,
6: 0.0,
7: 213864.0,
8: 458543.0,
9: 0.0},
'ResidFAR': {0: 2.0,
1: 7.2,
2: 0.0,
3: 0.0,
4: 2.43,
5: 2.43,
6: 10.0,
7: 3.0,
8: 5.0,
9: 0.0},
'RetailArea': {0: 0.0,
1: 0.0,
2: 0.0,
3: 1263000.0,
4: 0.0,
5: 9378.0,
6: 15940.0,
7: 0.0,
8: 4884.0,
9: 0.0},
'SHAPE_Area': {0: 2316.8863224,
1: 140131.577176,
2: 34656.4472405,
3: 797554.847834,
4: 21360.1476315,
5: 58564.8643115,
6: 12947.145471,
7: 50772.624868800005,
8: 47019.5677861,
9: 118754.78573699998},
'SHAPE_Leng': {0: 249.41135038849998,
1: 1559.88914353,
2: 890.718521021,
3: 3729.78685686,
4: 620.761169374,
5: 1006.33799946,
6: 460.03168012300006,
7: 1385.27352839,
8: 992.915660585,
9: 1565.91477261},
'SanitDistr': {0: 10.0,
1: 2.0,
2: 2.0,
3: 18.0,
4: 2.0,
5: 1.0,
6: 2.0,
7: 1.0,
8: 1.0,
9: 2.0},
'SanitSub': {0: 21,
1: 23,
2: 31,
3: 22,
4: 31,
5: 21,
6: 23,
7: 7,
8: 12,
9: 22},
'SchoolDist': {0: 19.0,
1: 13.0,
2: 13.0,
3: 22.0,
4: 13.0,
5: 14.0,
6: 13.0,
7: 14.0,
8: 14.0,
9: 14.0},
'SplitZone': {0: 1, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 0, 9: 1},
'StrgeArea': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 1500.0,
8: 0.0,
9: 0.0},
'UnitsRes': {0: 2.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 522.0,
6: 0.0,
7: 234.0,
8: 470.0,
9: 0.0},
'UnitsTotal': {0: 2.0,
1: 0.0,
2: 0.0,
3: 123.0,
4: 1.0,
5: 525.0,
6: 102.0,
7: 237.0,
8: 472.0,
9: 1.0},
'YearAlter1': {0: 0.0,
1: 0.0,
2: 1980.0,
3: 0.0,
4: 1998.0,
5: 0.0,
6: 2009.0,
7: 2012.0,
8: 0.0,
9: 0.0},
'YearAlter2': {0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 2000.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0},
'ZipCode': {0: 11220.0,
1: 11201.0,
2: 11201.0,
3: 11234.0,
4: 11201.0,
5: 11249.0,
6: 11241.0,
7: 11211.0,
8: 11249.0,
9: 11205.0},
'ZoneDist1': {0: 24,
1: 76,
2: 5,
3: 64,
4: 24,
5: 24,
6: 30,
7: 74,
8: 45,
9: 27},
'ZoneMap': {0: 3,
1: 19,
2: 19,
3: 22,
4: 19,
5: 19,
6: 19,
7: 2,
8: 19,
9: 19},
'building_class': {0: 141,
1: 97,
2: 87,
3: 176,
4: 168,
5: 8,
6: 102,
7: 46,
8: 97,
9: 66},
'building_class_at_sale': {0: 143,
1: 98,
2: 89,
3: 179,
4: 171,
5: 7,
6: 103,
7: 49,
8: 98,
9: 69},
'building_class_category': {0: 39,
1: 71,
2: 31,
3: 38,
4: 86,
5: 40,
6: 80,
7: 75,
8: 71,
9: 41},
'commercial_units': {0: 1,
1: 0,
2: 0,
3: 123,
4: 1,
5: 0,
6: 102,
7: 3,
8: 0,
9: 1},
'gross_sqft': {0: 0.0,
1: 0.0,
2: 304650.0,
3: 2548000.0,
4: 356000.0,
5: 0.0,
6: 290440.0,
7: 241764.0,
8: 0.0,
9: 547000.0},
'land_sqft': {0: 0.0,
1: 134988.0,
2: 32000.0,
3: 905000.0,
4: 20267.0,
5: 57600.0,
6: 12500.0,
7: 50173.0,
8: 44704.0,
9: 113800.0},
'neighborhood': {0: 43,
1: 48,
2: 6,
3: 44,
4: 6,
5: 40,
6: 6,
7: 28,
8: 40,
9: 56},
'residential_units': {0: 0,
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 234,
8: 0,
9: 0},
'sale_date': {0: 2257,
1: 4839,
2: 337,
3: 638,
4: 27,
5: 1458,
6: 2450,
7: 3276,
8: 5082,
9: 1835},
'sale_price': {0: 499401179.0,
1: 345000000.0,
2: 340000000.0,
3: 276947000.0,
4: 202500000.0,
5: 185445000.0,
6: 171000000.0,
7: 169000000.0,
8: 165000000.0,
9: 161000000.0},
'tax_class': {0: 3, 1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, 7: 7, 8: 3, 9: 3},
'total_units': {0: 1,
1: 0,
2: 0,
3: 123,
4: 1,
5: 0,
6: 102,
7: 237,
8: 0,
9: 1},
'zip_code': {0: 11201,
1: 11201,
2: 11201,
3: 11234,
4: 11201,
5: 11249,
6: 11241,
7: 11211,
8: 11249,
9: 11205}}

pandas: columns must be same length as key

I'm trying to re-format several columns into strings (they contain NaNs, so I can't just read them in as integers). All of the columns are currently float64, and I want to make it so they don't have decimals.
Here is the data:
{'crash_id': {0: 201226857.0,
1: 201226857.0,
2: 2012272611.0,
3: 2012272611.0,
4: 2012298998.0},
'driver_action1': {0: 1.0, 1: 1.0, 2: 29.0, 3: 1.0, 4: 3.0},
'driver_action2': {0: 99.0, 1: 99.0, 2: 1.0, 3: 99.0, 4: 99.0},
'driver_action3': {0: 99.0, 1: 99.0, 2: 99.0, 3: 99.0, 4: 99.0},
'driver_action4': {0: 99.0, 1: 99.0, 2: 99.0, 3: 99.0, 4: 99.0},
'harmful_event1': {0: 14.0, 1: 14.0, 2: 14.0, 3: 14.0, 4: 14.0},
'harmful_event2': {0: 99.0, 1: 99.0, 2: 99.0, 3: 99.0, 4: 99.0},
'harmful_event3': {0: 99.0, 1: 99.0, 2: 99.0, 3: 99.0, 4: 99.0},
'harmful_event4': {0: 99.0, 1: 99.0, 2: 99.0, 3: 99.0, 4: 99.0},
'most_damaged_area': {0: 14.0, 1: 2.0, 2: 14.0, 3: 14.0, 4: 3.0},
'most_harmful_event': {0: 14.0, 1: 14.0, 2: 14.0, 3: 14.0, 4: 14.0},
'point_of_impact': {0: 15.0, 1: 1.0, 2: 14.0, 3: 14.0, 4: 1.0},
'vehicle_id': {0: 20121.0, 1: 20122.0, 2: 20123.0, 3: 20124.0, 4: 20125.0},
'vehicle_maneuver': {0: 3.0, 1: 1.0, 2: 4.0, 3: 1.0, 4: 1.0}}
When I try to convert those columns to string, this is what happens:
>> df[['crash_id','vehicle_id','point_of_impact','most_damaged_area','most_harmful_event','vehicle_maneuver','harmful_event1','harmful_event2','harmful_event3','harmful_event4','driver_action1','driver_action2','driver_action3','driver_action4']] = df[['crash_id','vehicle_id','point_of_impact','most_damaged_area','most_harmful_event','vehicle_maneuver','harmful_event1','harmful_event2','harmful_event3','harmful_event4','driver_action1','driver_action2','driver_action3','driver_action4']].applymap(lambda x: '{:.0f}'.format(x))
File "C:\Users\<name>\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 2376, in _setitem_array
raise ValueError('Columns must be same length as key')
ValueError: Columns must be same length as key
I've never seen this error before and feel like this is something simple...what am I doing wrong?

Your code runs for me with the dictionary you provided. Try creating a function to deal with the NaN cases separately; I think they are causing your issues.
Something basic like below:
def formatter(x):
if x == None:
return None
else:
return '{:.0f}'.format(x)