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AttributeError: 'DataFrame' object has no attribute 'cat' and a series of exceptions

I'm trying to draw a boxplot from data of a csv file by forming a Pandas dataframe. Here is the number of rows and columns of the CSV:
data_final = pd.read_csv('/home/hp/Myo_dataset/final_mean.csv', header=None )
total_rows=len(data_final.axes[0])
total_cols=len(data_final.axes[1])
print("Number of Rows: "+str(total_rows))
print("Number of Columns: "+str(total_cols))
Output
Number of Rows: 400
Number of Columns: 9
Here's the code for boxplot:
plt.figure(figsize=(10,10))
sns.boxplot(x=data_final.iloc[:,8], y=data_final.iloc[:,0],data=data_final, showfliers=False, saturation=1)
plt.xlabel('Classes of Gestures')
plt.ylabel('Feature extracted from Sensor 1')
plt.show()
I'm getting the following error log:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/utils.py in categorical_order(values, order)
525 try:
--> 526 order = values.cat.categories
527 except (TypeError, AttributeError):
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/generic.py in __getattr__(self, name)
3613 return self[name]
-> 3614 return object.__getattribute__(self, name)
3615
AttributeError: 'DataFrame' object has no attribute 'cat'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/utils.py in categorical_order(values, order)
528 try:
--> 529 order = values.unique()
530 except AttributeError:
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/generic.py in __getattr__(self, name)
3613 return self[name]
-> 3614 return object.__getattribute__(self, name)
3615
AttributeError: 'DataFrame' object has no attribute 'unique'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-33-9b1bba723aae> in <module>()
1
2 plt.figure(figsize=(10,10))
----> 3 sns.boxplot(x=data_final.iloc[:,8], y=data_final.iloc[:,0],data=data_final, showfliers=False, saturation=1)
4 plt.xlabel('Classes of Gestures')
5 plt.ylabel('Feature extracted from Sensor 1')
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/categorical.py in boxplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, notch, ax, **kwargs)
2229 plotter = _BoxPlotter(x, y, hue, data, order, hue_order,
2230 orient, color, palette, saturation,
-> 2231 width, dodge, fliersize, linewidth)
2232
2233 if ax is None:
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/categorical.py in __init__(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)
444 width, dodge, fliersize, linewidth):
445
--> 446 self.establish_variables(x, y, hue, data, orient, order, hue_order)
447 self.establish_colors(color, palette, saturation)
448
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/categorical.py in establish_variables(self, x, y, hue, data, orient, order, hue_order, units)
201
202 # Get the order on the categorical axis
--> 203 group_names = categorical_order(groups, order)
204
205 # Group the numeric data
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/seaborn/utils.py in categorical_order(values, order)
529 order = values.unique()
530 except AttributeError:
--> 531 order = pd.unique(values)
532 try:
533 np.asarray(values).astype(np.float)
~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/algorithms.py in unique(values)
362
363 table = htable(len(values))
--> 364 uniques = table.unique(values)
365 uniques = _reconstruct_data(uniques, dtype, original)
366
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.unique()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
<matplotlib.figure.Figure at 0x7ff27f995a58>
If I change the data for X axis of the boxplot, it runs smoothly (however, that's not my intention):
sns.boxplot(x=data_final.iloc[:,7], y=data_final.iloc[:,0],data=data_final, showfliers=False, saturation=1)
Here's a subset of the dataframe data_final and the column 8 :
print(data_final.head())
print('--------------------------------------------')
print(data_final.iloc[:,8].head())
Output:
0 1 2 3 4 5 6 7 8
258 5650.0 7627.0 7906.0 11137.0 4229.0 4455.0 19328.0 25212.0 6
391 25734.0 36965.0 40745.0 27203.0 22482.0 9187.0 29116.0 28223.0 8
394 15436.0 18634.0 34686.0 24717.0 20505.0 8869.0 25787.0 23753.0 8
132 41836.0 8416.0 7861.0 6466.0 6324.0 17479.0 21717.0 35430.0 3
144 53333.0 10018.0 8223.0 7736.0 7686.0 21683.0 23300.0 48648.0 3
--------------------------------------------
258 6
391 8
394 8
132 3
144 3
Name: 8, dtype: int64

Try in this way:
df = pd.read_csv("final_mean.csv", header=None)
plt.figure(figsize=(10,10))
sns.boxplot(x=8, y=0,data=df, showfliers=False, saturation=1)
plt.xlabel('Classes of Gestures')
plt.ylabel('Feature extracted from Sensor 1')
plt.show()
Or:
sns.boxplot(x=df.iloc[:,8], y=df.iloc[:,0], showfliers=False, saturation=1)
Output:

Adding legend to matplotlib scatterplot

I am following the Randy Olson approach to make beautiful time trends graphs (see here).
When I plot the following code:
tableau20 = [(31, 119, 180), (174, 199, 232)]
for i in range(len(tableau20)):
r, g, b = tableau20[i]
tableau20[i] = (r / 255., g / 255., b / 255.)
plt.figure(figsize=(12, 14))
# Remove the plot frame lines. They are unnecessary chartjunk.
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# Ensure that the axis ticks only show up on the bottom and left of the plot.
# Ticks on the right and top of the plot are generally unnecessary chartjunk.
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
majors = ['Number of findings (total)', 'Business Ethics Findings']
for rank, column in enumerate(majors):
plt.plot(monthly_df.argrc__End_Date__c.values, monthly_df[column.replace("\n", " ")].values,
lw=2.5, color=tableau20[rank])
y_pos = monthly_df[column.replace("\n", " ")].values[-1] - 0.5
if column == "Number of findings (total)":
y_pos += 0.5
elif column == 'Business Ethics Findings':
y_pos -= 0.5
plt.text(2018.1, y_pos, column, fontsize=12, color=tableau20[rank])
I get this error:
If I eliminate
plt.text(2016, y_pos, column, fontsize=12, color=tableau20[rank])
Instead I get the two lines plotted on the graph correctly, but with no legend. How do I show the names of my columns?
EDIT
I am adding here the traceback for further information. I hope this is helpful.
C:\Users\filippo.sebastio\Anaconda3\lib\site-packages\matplotlib\cbook\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
warnings.warn(message, mplDeprecation, stacklevel=1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
~\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig)
239
240 if 'png' in formats:
--> 241 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
242 if 'retina' in formats or 'png2x' in formats:
243 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
~\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
123
124 bytes_io = BytesIO()
--> 125 fig.canvas.print_figure(bytes_io, **kw)
126 data = bytes_io.getvalue()
127 if fmt == 'svg':
~\Anaconda3\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
2261 orientation=orientation,
2262 bbox_inches_restore=_bbox_inches_restore,
-> 2263 **kwargs)
2264 finally:
2265 if bbox_inches and restore_bbox:
~\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
515
516 def print_png(self, filename_or_obj, *args, **kwargs):
--> 517 FigureCanvasAgg.draw(self)
518 renderer = self.get_renderer()
519 original_dpi = renderer.dpi
~\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self)
427 Draw the figure using the renderer
428 """
--> 429 self.renderer = self.get_renderer(cleared=True)
430 # acquire a lock on the shared font cache
431 RendererAgg.lock.acquire()
~\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in get_renderer(self, cleared)
452
453 if need_new_renderer:
--> 454 self.renderer = RendererAgg(w, h, self.figure.dpi)
455 self._lastKey = key
456 elif cleared:
~\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in __init__(self, width, height, dpi)
99 self.width = width
100 self.height = height
--> 101 self._renderer = _RendererAgg(int(width), int(height), dpi)
102 self._filter_renderers = []
103
ValueError: Image size of 312943x821 pixels is too large. It must be less than 2^16 in each direction.
<Figure size 864x1008 with 1 Axes>

matplotlib how to plot multiple lines with defined colours?

I get an error when I plot with colours, and works fine without it. My line colours need to be restricted to 2 defined values.
This works in Jupyter Notebook
import random
xStart = random.sample(range(1, 10), 6)
xStart.sort()
xEnd = [x + random.randint(1, 6) for x in xStart]
yval = list(range(1, 7))
colours = ['r']*6
colours[1] = 'b'
print(xStart)
print(xEnd)
print(yval)
print(colours)
f, ax1 = plt.subplots(figsize=(6,4))
ax1.plot([xStart,xEnd], [yval,yval], '-', linewidth=1) #, color=colours)
plt.show()
This does not work.
If I uncomment the color argument, the code throws an (elaborate) error. While I can draw each line segment in a loop and colour each red or blue, I assume it will be slower than the below code. In this toy example I have 6 lines, but in reality I have 12,000 lines and it takes a few minutes, drawing one line at a time in a loop.
I think the error is related to the size of my colour argument; it is likely expecting 1 (at a time internally) whereas I am providing a list of 6.
import random
xStart = random.sample(range(1, 10), 6)
xStart.sort()
xEnd = [x + random.randint(1, 6) for x in xStart]
yval = list(range(1, 7))
colours = ['r']*6
colours[1] = 'b'
print(xStart)
print(xEnd)
print(yval)
print(colours)
f, ax1 = plt.subplots(figsize=(6,4))
ax1.plot([xStart,xEnd], [yval,yval], '-', linewidth=1, color=colours) #--> Only change from above code
plt.show()
TypeError Traceback (most recent call
last) C:\Anaconda3\lib\site-packages\matplotlib\colors.py in
to_rgba(c, alpha)
131 try:
--> 132 rgba = _colors_full_map.cache[c, alpha]
133 except (KeyError, TypeError): # Not in cache, or unhashable.
TypeError: unhashable type: 'list'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call
last) C:\Anaconda3\lib\site-packages\IPython\core\formatters.py in
call(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)
C:\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in
(fig)
236
237 if 'png' in formats:
--> 238 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
239 if 'retina' in formats or 'png2x' in formats:
240 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
C:\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in
print_figure(fig, fmt, bbox_inches, **kwargs)
120
121 bytes_io = BytesIO()
--> 122 fig.canvas.print_figure(bytes_io, **kw)
123 data = bytes_io.getvalue()
124 if fmt == 'svg':
C:\Anaconda3\lib\site-packages\matplotlib\backend_bases.py in
print_figure(self, filename, dpi, facecolor, edgecolor, orientation,
format, **kwargs) 2214 orientation=orientation,
2215 dryrun=True,
-> 2216 **kwargs) 2217 renderer = self.figure._cachedRenderer 2218 bbox_inches = self.figure.get_tightbbox(renderer)
C:\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in
print_png(self, filename_or_obj, *args, **kwargs)
505
506 def print_png(self, filename_or_obj, *args, **kwargs):
--> 507 FigureCanvasAgg.draw(self)
508 renderer = self.get_renderer()
509 original_dpi = renderer.dpi
C:\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in
draw(self)
428 # if toolbar:
429 # toolbar.set_cursor(cursors.WAIT)
--> 430 self.figure.draw(self.renderer)
431 finally:
432 # if toolbar:
C:\Anaconda3\lib\site-packages\matplotlib\artist.py in
draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:
C:\Anaconda3\lib\site-packages\matplotlib\figure.py in draw(self,
renderer) 1297 1298
mimage._draw_list_compositing_images(
-> 1299 renderer, self, artists, self.suppressComposite) 1300 1301
renderer.close_group('figure')
C:\Anaconda3\lib\site-packages\matplotlib\image.py in
_draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
C:\Anaconda3\lib\site-packages\matplotlib\artist.py in
draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:
C:\Anaconda3\lib\site-packages\matplotlib\axes_base.py in draw(self,
renderer, inframe) 2435 renderer.stop_rasterizing()
2436
-> 2437 mimage._draw_list_compositing_images(renderer, self, artists) 2438 2439 renderer.close_group('axes')
C:\Anaconda3\lib\site-packages\matplotlib\image.py in
_draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together
C:\Anaconda3\lib\site-packages\matplotlib\artist.py in
draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:
C:\Anaconda3\lib\site-packages\matplotlib\lines.py in draw(self,
renderer)
765 self._set_gc_clip(gc)
766
--> 767 ln_color_rgba = self._get_rgba_ln_color()
768 gc.set_foreground(ln_color_rgba, isRGBA=True)
769 gc.set_alpha(ln_color_rgba[3])
C:\Anaconda3\lib\site-packages\matplotlib\lines.py in
_get_rgba_ln_color(self, alt) 1267 1268 def _get_rgba_ln_color(self, alt=False):
-> 1269 return mcolors.to_rgba(self._color, self._alpha) 1270 1271 # some aliases....
C:\Anaconda3\lib\site-packages\matplotlib\colors.py in to_rgba(c,
alpha)
132 rgba = _colors_full_map.cache[c, alpha]
133 except (KeyError, TypeError): # Not in cache, or unhashable.
--> 134 rgba = _to_rgba_no_colorcycle(c, alpha)
135 try:
136 _colors_full_map.cache[c, alpha] = rgba
C:\Anaconda3\lib\site-packages\matplotlib\colors.py in
_to_rgba_no_colorcycle(c, alpha)
183 # float)andnp.array(...).astype(float)` all convert "0.5" to 0.5.
184 # Test dimensionality to reject single floats.
--> 185 raise ValueError("Invalid RGBA argument: {!r}".format(orig_c))
186 # Return a tuple to prevent the cached value from being modified.
187 c = tuple(c.astype(float))
ValueError: Invalid RGBA argument: ['r', 'b', 'r', 'r', 'r', 'r']

OK thanks to Bazingaa and this thread, How to get different colored lines for different plots in a single figure?
...the final code is as follows.
Since I am drawing multiple lines with one ax.plot() command, the colour argument will not take. IMHO it should since it logically makes sense and should be an enhancement matplotlib does. Nonetheless, here is the solution as Bazingaa pointed out to me.
For those interested, as expected this code does run a LOT faster compared with drawing 12K lines in a loop (in order to draw & color them one at a time with individual ax.plot() commands).
import random
xStart = random.sample(range(1, 10), 6)
xStart.sort()
xEnd = [x + random.randint(1, 6) for x in xStart]
yval = list(range(1, 7))
colours = ['r']*6
colours[1] = 'b'
f, ax1 = plt.subplots(figsize=(6,4))
ax1.plot([xStart,xEnd], [yval,yval], '-', linewidth=1) #, color=colours) #Leaving the color argument commented
#Add new code to colour after the fact
for idx,line in enumerate(ax1.lines):
line.set_color(colours[idx])
plt.show()

Jupyter Seaborn sns.lmplot Error

I am having trouble while trying to plot a lmplot in Jupyter.
Below you can see the first rows of my data and rest of the data goes same as well.
Year_of_Release Platform_General Platform counts Global_Sales(M#) GS_Amount/Game
1994.0 Sony_Playstation PS 1 1.27 1.270000
1996.0 Sony_Playstation PS 5 17.48 3.496000
1997.0 Sony_Playstation PS 12 30.89 2.574167
1998.0 Sony_Playstation PS 22 43.08 1.958182
1999.0 Sony_Playstation PS 25 49.02 1.960800
Below is the code that should draw the plot but
# Make a custom palette with platform colors
pal = dict(Sony_Playstation="#6495ED",Microsoft_Xbox="#F08080",Nintendo="Green")
# Show the survival proability as a function of platforms
g = sns.lmplot(x="Year_of_Release", y="GS_Amount/Game", col="Platform_General", hue="Platform_General",
data=vgs_df_pf_grouped,palette=pal, y_jitter=.02, logistic=True)
# Use more informative axis labels than are provided by default
g.set_axis_labels("Year of Release", "Sales per Game Released (M #)")
When I run this code below long error massage appears and because of I am new to data analytics I can't understand what is wrong with data or code.
I appreciate some help with this one. Thank you.
ValueError Traceback (most recent call last)
<ipython-input-68-c78b6ba34d96> in <module>()
5 # Show the survival proability as a function of age and sex
6 g = sns.lmplot(x="Year_of_Release", y="GS_Amount/Game",col="Platform_General", hue="Platform_General",
----> 7 data=vgs_df_pf_grouped,palette=pal, y_jitter=.02, logistic=True)
8
9 # Use more informative axis labels than are provided by default
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in lmplot(x, y, data, hue, col, row, palette, col_wrap, size, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws)
588 scatter_kws=scatter_kws, line_kws=line_kws,
589 )
--> 590 facets.map_dataframe(regplot, x, y, **regplot_kws)
591
592 # Add a legend
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in map_dataframe(self, func, *args, **kwargs)
795
796 # Draw the plot
--> 797 self._facet_plot(func, ax, args, kwargs)
798
799 # Finalize the annotations and layout
~/anaconda3/lib/python3.6/site-packages/seaborn/axisgrid.py in _facet_plot(self, func, ax, plot_args, plot_kwargs)
813
814 # Draw the plot
--> 815 func(*plot_args, **plot_kwargs)
816
817 # Sort out the supporting information
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in regplot(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)
788 scatter_kws["marker"] = marker
789 line_kws = {} if line_kws is None else copy.copy(line_kws)
--> 790 plotter.plot(ax, scatter_kws, line_kws)
791 return ax
792
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in plot(self, ax, scatter_kws, line_kws)
340 self.scatterplot(ax, scatter_kws)
341 if self.fit_reg:
--> 342 self.lineplot(ax, line_kws)
343
344 # Label the axes
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in lineplot(self, ax, kws)
385
386 # Fit the regression model
--> 387 grid, yhat, err_bands = self.fit_regression(ax)
388
389 # Get set default aesthetics
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in fit_regression(self, ax, x_range, grid)
198 from statsmodels.genmod.families import Binomial
199 yhat, yhat_boots = self.fit_statsmodels(grid, GLM,
--> 200 family=Binomial())
201 elif self.lowess:
202 ci = None
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in fit_statsmodels(self, grid, model, **kwargs)
258 return yhat
259
--> 260 yhat = reg_func(X, y)
261 if self.ci is None:
262 return yhat, None
~/anaconda3/lib/python3.6/site-packages/seaborn/regression.py in reg_func(_x, _y)
252 def reg_func(_x, _y):
253 try:
--> 254 yhat = model(_y, _x, **kwargs).fit().predict(grid)
255 except glm.PerfectSeparationError:
256 yhat = np.empty(len(grid))
~/anaconda3/lib/python3.6/site-packages/statsmodels/genmod/generalized_linear_model.py in fit(self, start_params, maxiter, method, tol, scale, cov_type, cov_kwds, use_t, full_output, disp, max_start_irls, **kwargs)
901 return self._fit_irls(start_params=start_params, maxiter=maxiter,
902 tol=tol, scale=scale, cov_type=cov_type,
--> 903 cov_kwds=cov_kwds, use_t=use_t, **kwargs)
904 else:
905 return self._fit_gradient(start_params=start_params,
~/anaconda3/lib/python3.6/site-packages/statsmodels/genmod/generalized_linear_model.py in _fit_irls(self, start_params, maxiter, tol, scale, cov_type, cov_kwds, use_t, **kwargs)
977 dev = self.family.deviance(self.endog, mu, self.freq_weights)
978 if np.isnan(dev):
--> 979 raise ValueError("The first guess on the deviance function "
980 "returned a nan. This could be a boundary "
981 " problem and should be reported.")
ValueError: The first guess on the deviance function returned a nan. This could be a boundary problem and should be reported.

Bar chart in pandas on time series data

I am trying to do bar chart in pandas on time series data.
Documentation says it is not possible: http://pandas.pydata.org/pandas-docs/stable/visualization.html#bar-plots
Is there some workaround ?
This is my code
# there must be ORDER BY, other wise rows will not be ordered
df = sql.read_frame("SELECT * FROM hzmo_report ORDER BY datum;", cnx, index_col='datum')
df.index = pd.to_datetime(df.index) # converting to DatetimeIndex
df['korisnika'].plot(ax=axs1[0], title='SOMETHING', marker='o')
df['korisnika'].diff().plot(ax=axs1[1], title='SOMETHING', marker='o') # i would like this to be bar plot
If I do
df['korisnika'].diff().plot(kind='bar', ax=axs1[1], title='SOMETHING', marker='o')
I have just added kind='bar'
I get:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-109-d41eb2b2e3a7> in <module>()
36 fig1.suptitle('Umirovljenici', fontsize=16)
37 df['korisnika'].plot(ax=axs1[0], title='Broj korisnika mirovine', marker='o')
---> 38 ( df['korisnika'].diff() ).plot(ax=axs1[1], kind='bar', title='Apsolutna razlika naspram prethodnog mjeseca', marker='o')
39 #df['korisnika'].diff().hist()
40
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\pandas\tools\plotting.pyc in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logy, secondary_y, **kwds)
1504 secondary_y=secondary_y, **kwds)
1505
-> 1506 plot_obj.generate()
1507 plot_obj.draw()
1508
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\pandas\tools\plotting.pyc in generate(self)
731 self._compute_plot_data()
732 self._setup_subplots()
--> 733 self._make_plot()
734 self._post_plot_logic()
735 self._adorn_subplots()
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\pandas\tools\plotting.pyc in _make_plot(self)
1291 else:
1292 rect = bar_f(ax, self.ax_pos + i * 0.75 / K, y, 0.75 / K,
-> 1293 start=pos_prior, label=label, **kwds)
1294 rects.append(rect)
1295 labels.append(label)
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\pandas\tools\plotting.pyc in f(ax, x, y, w, start, **kwds)
1251 if self.kind == 'bar':
1252 def f(ax, x, y, w, start=None, **kwds):
-> 1253 return ax.bar(x, y, w, bottom=start, **kwds)
1254 elif self.kind == 'barh':
1255 def f(ax, x, y, w, start=None, **kwds):
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\matplotlib\axes.pyc in bar(self, left, height, width, bottom, **kwargs)
4779 label='_nolegend_'
4780 )
-> 4781 r.update(kwargs)
4782 r.get_path()._interpolation_steps = 100
4783 #print r.get_label(), label, 'label' in kwargs
C:\Documents and Settings\hr1ub098\Application Data\Python\Python27\site-packages\matplotlib\artist.pyc in update(self, props)
657 func = getattr(self, 'set_'+k, None)
658 if func is None or not callable(func):
--> 659 raise AttributeError('Unknown property %s'%k)
660 func(v)
661 changed = True
AttributeError: Unknown property marker

You can plot a bar-plot of a time-series. Not that useful IMHO though.
ts = Series(randn(20),date_range('20130101',periods=20))
ts.plot()
A time-series line-plot
A Bar Plot