def Evt_Bootstrap_Plot(self, event):
from pandas.tools.plotting import bootstrap_plot
page = self.New_Tab.GetSelection()
panel = self.New_Tab.GetPage(page)
self.selected_checkbox()
panel.canvas.figure.clf()
if len(self.selected_checkboxes) == 1:
bootstrap_plot(self.data[self.selected_checkboxes[0][1]][self.minimum: self.maximum], fig= panel.canvas.figure)
panel.canvas.draw()
else:
raise KeyError("Bootstrap plot can only be drawn with One variable")
return
def _doplot(self, data, ax, kind, subplots, kwargs):
"""Do core plotting"""
cols = data.columns
rows = int(round(np.sqrt(len(data.columns)),0))
if len(data.columns) == 1:
kwargs['subplots'] = 0
if kind == 'pie':
kwargs['subplots'] = True
if subplots == 0:
layout = None
else:
layout=(rows,-1)
if kind == 'bar':
if len(data) > 50:
ax.get_xaxis().set_visible(False)
if len(data) > 400:
print ('too many bars to plot')
return
if kind == 'scatter':
axs = self.scatter(data, ax, **kwargs)
if kwargs['sharey'] == 1:
lims = self.fig.axes[0].get_ylim()
for a in self.fig.axes:
a.set_ylim(lims)
elif kind == 'boxplot':
axs = data.boxplot(ax=ax, rot=kwargs['rot'], grid=kwargs['grid'])
#boxplot won't accept required kwargs?
if kwargs['logy'] == 1:
ax.set_yscale('log')
elif kind == 'histogram':
bins = int(kwargs['bins'])
axs = data.plot(kind='hist',layout=layout, ax=ax, **kwargs)
elif kind == 'heatmap':
axs = self.heatmap(data, ax, kwargs)
elif kind == 'bootstrap':
axs = plotting.bootstrap_plot(data)
elif kind == 'scatter_matrix':
axs = pd.scatter_matrix(data, ax=ax, **kwargs)
elif kind == 'hexbin':
x = cols[0]
y = cols[1]
axs = data.plot(x,y,ax=ax,kind='hexbin',gridsize=20,**kwargs)
else:
axs = data.plot(ax=ax, layout=layout, **kwargs)
return axs
def DrawGraph6():
from pandas.tools.plotting import bootstrap_plot
#fig, axes = plt.subplots(2,1,figsize=(24,16))
#DF_stock['Volume'].plot(ax=axes[0], lw=1, style="b-")
bootstrap_plot(DF_stock['Close'], size=10, samples=500, color="grey", lw=1)
plt.show()
# In[247]: returns[1:10] # In[248]: tsplots.plot_pacf(returns, lags= 20) plt.show() # In[249]: from pandas.tools.plotting import bootstrap_plot bootstrap_plot(returns, size = 50) plt.show() # Find out the following facts about the data set # - Total number of data points # - Number of positive returns # - Number of negative returns # - Average annualized returns # - Standard deviation # - Sharpe ratio # - Cumulative returns # In[250]: total_data = len(returns)
def _doplot(self, data, ax, kind, subplots, errorbars, useindex, bw, kwargs):
"""Core plotting method"""
kwargs = kwargs.copy()
cols = data.columns
if kind == "line":
data = data.sort_index()
rows = int(round(np.sqrt(len(data.columns)), 0))
if len(data.columns) == 1 and kind not in ["pie"]:
kwargs["subplots"] = 0
if "colormap" in kwargs:
cmap = plt.cm.get_cmap(kwargs["colormap"])
else:
cmap = None
# change some things if we are plotting in b&w
styles = []
if bw == True and kind not in ["pie", "heatmap"]:
cmap = None
kwargs["color"] = "k"
kwargs["colormap"] = None
styles = ["-", "--", "-.", ":"]
if "linestyle" in kwargs:
del kwargs["linestyle"]
if subplots == 0:
layout = None
else:
layout = (rows, -1)
if errorbars == True:
yerr = data[data.columns[1::2]]
data = data[data.columns[0::2]]
yerr.columns = data.columns
else:
yerr = None
if kind == "bar":
if len(data) > 50:
ax.get_xaxis().set_visible(False)
if len(data) > 300:
self.showWarning("too many bars to plot")
return
if kind == "scatter":
axs = self.scatter(data, ax, **kwargs)
if kwargs["sharey"] == 1:
lims = self.fig.axes[0].get_ylim()
for a in self.fig.axes:
a.set_ylim(lims)
elif kind == "boxplot":
axs = data.boxplot(ax=ax, rot=kwargs["rot"], grid=kwargs["grid"], patch_artist=True)
lw = kwargs["linewidth"]
plt.setp(axs["boxes"], color="black", lw=lw)
plt.setp(axs["whiskers"], color="black", lw=lw)
plt.setp(axs["fliers"], color="black", marker="+", lw=lw)
clr = cmap(0.5)
for patch in axs["boxes"]:
patch.set_facecolor(clr)
# boxplot won't accept required kwargs?
if kwargs["logy"] == 1:
ax.set_yscale("log")
elif kind == "histogram":
bins = int(kwargs["bins"])
axs = data.plot(kind="hist", layout=layout, ax=ax, **kwargs)
elif kind == "heatmap":
axs = self.heatmap(data, ax, kwargs)
elif kind == "bootstrap":
axs = plotting.bootstrap_plot(data)
elif kind == "scatter_matrix":
axs = pd.scatter_matrix(data, ax=ax, **kwargs)
elif kind == "hexbin":
x = cols[0]
y = cols[1]
axs = data.plot(x, y, ax=ax, kind="hexbin", gridsize=20, **kwargs)
elif kind == "contour":
xi, yi, zi = self.contourData(data)
cs = ax.contour(xi, yi, zi, 15, linewidths=0.5, colors="k")
# plt.clabel(cs,fontsize=9)
cs = ax.contourf(xi, yi, zi, 15, cmap=cmap)
self.fig.colorbar(cs, ax=ax)
axs = ax
elif kind == "imshow":
xi, yi, zi = self.contourData(data)
im = ax.imshow(zi, interpolation="nearest", cmap=cmap, alpha=kwargs["alpha"])
self.fig.colorbar(im, ax=ax)
axs = ax
elif kind == "pie":
if kwargs["legend"] == True:
lbls = None
else:
lbls = list(data.index)
axs = data.plot(ax=ax, kind="pie", labels=lbls, layout=layout, autopct="%1.1f%%", subplots=True, **kwargs)
if lbls == None:
axs[0].legend(labels=data.index)
elif kind == "barh":
lw = kwargs["linewidth"]
axs = data.plot(ax=ax, layout=layout, xerr=yerr, **kwargs)
else:
# line, bar and area plots
if useindex == False:
x = data.columns[0]
data.set_index(x, inplace=True)
data = data.sort()
if len(data.columns) == 0:
msg = "Not enough data.\nIf 'use index' is off select at least 2 columns"
self.showWarning(msg)
return
# adjust colormap to avoid white lines
if cmap != None:
cmap = util.adjustColorMap(cmap, 0.15, 1.0)
del kwargs["colormap"]
axs = data.plot(ax=ax, layout=layout, yerr=yerr, style=styles, cmap=cmap, **kwargs)
return axs
def DrawGraph6():
from pandas.tools.plotting import bootstrap_plot
# fig, axes = plt.subplots(2,1,figsize=(24,16))
# df['Volume'].plot(ax=axes[0], lw=1, style='b-')
bootstrap_plot(df['Close'], size=10, samples=500, color='grey', lw=1)
plt.show()
def _doplot(self, data, ax, kind, subplots, errorbars, useindex, kwargs):
"""Do core plotting"""
cols = data.columns
if kind == 'line':
data = data.sort_index()
rows = int(round(np.sqrt(len(data.columns)),0))
if len(data.columns) == 1 and kind not in ['pie']:
kwargs['subplots'] = 0
if 'colormap' in kwargs:
cmap = plt.cm.get_cmap(kwargs['colormap'])
if subplots == 0:
layout = None
else:
layout=(rows,-1)
if errorbars == True:
yerr = data[data.columns[1::2]]
data = data[data.columns[0::2]]
yerr.columns = data.columns
else:
yerr = None
if kind == 'bar':
if len(data) > 50:
ax.get_xaxis().set_visible(False)
if len(data) > 300:
self.showWarning('too many bars to plot')
return
if kind == 'scatter':
axs = self.scatter(data, ax, **kwargs)
if kwargs['sharey'] == 1:
lims = self.fig.axes[0].get_ylim()
for a in self.fig.axes:
a.set_ylim(lims)
elif kind == 'boxplot':
axs = data.boxplot(ax=ax, rot=kwargs['rot'], grid=kwargs['grid'],
patch_artist=True)
lw = kwargs['linewidth']
plt.setp(axs['boxes'], color='black', lw=lw)
plt.setp(axs['whiskers'], color='black', lw=lw)
plt.setp(axs['fliers'], color='black', marker='+', lw=lw)
clr = cmap(0.5)
for patch in axs['boxes']:
patch.set_facecolor(clr)
#boxplot won't accept required kwargs?
if kwargs['logy'] == 1:
ax.set_yscale('log')
elif kind == 'histogram':
bins = int(kwargs['bins'])
axs = data.plot(kind='hist',layout=layout, ax=ax, **kwargs)
elif kind == 'heatmap':
axs = self.heatmap(data, ax, kwargs)
elif kind == 'bootstrap':
axs = plotting.bootstrap_plot(data)
elif kind == 'scatter_matrix':
axs = pd.scatter_matrix(data, ax=ax, **kwargs)
elif kind == 'hexbin':
x = cols[0]
y = cols[1]
axs = data.plot(x,y,ax=ax,kind='hexbin',gridsize=20,**kwargs)
elif kind == 'contour':
xi,yi,zi = self.contourData(data)
cs = ax.contour(xi,yi,zi,15,linewidths=.5,colors='k')
#plt.clabel(cs,fontsize=9)
cs = ax.contourf(xi,yi,zi,15,cmap=cmap)
self.fig.colorbar(cs,ax=ax)
axs = ax
elif kind == 'imshow':
xi,yi,zi = self.contourData(data)
im = ax.imshow(zi, interpolation="nearest",
cmap=cmap, alpha=kwargs['alpha'])
self.fig.colorbar(im,ax=ax)
axs = ax
elif kind == 'pie':
if kwargs['legend'] == True:
lbls=None
else:
lbls = list(data.index)
axs = data.plot(ax=ax,kind='pie', labels=lbls, layout=layout,
autopct='%1.1f%%', subplots=True, **kwargs)
if lbls == None:
axs[0].legend(labels=data.index)
elif kind == 'barh':
lw = kwargs['linewidth']
axs = data.plot(ax=ax, layout=layout, xerr=yerr, **kwargs)
else:
if useindex == False:
x=data.columns[0]
data.set_index(x,inplace=True)
data=data.sort()
if len(data.columns) == 0:
msg = "Not enough data.\nIf 'use index' is off select at least 2 columns"
self.showWarning(msg)
return
axs = data.plot(ax=ax, layout=layout, yerr=yerr, **kwargs)
return axs
data = Series(randn(1000))
data.hist(by=randint(0, 4, 1000), figsize=(6, 4))
plt.show()
plt.savefig("image9.png")
from pandas.tools.plotting import scatter_matrix
df = DataFrame(randn(1000, 4), columns=['a', 'b', 'c', 'd'])
scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')
plt.show()
plt.savefig("image11.png")
from pandas.tools.plotting import bootstrap_plot
data = Series(rand(1000))
bootstrap_plot(data, size=50, samples=500, color='grey')
plt.show()
plt.savefig("image12.png")
df = DataFrame(randn(1000, 10), index=ts.index)
df = df.cumsum()
plt.figure()
df.plot(colormap='jet')
plt.show()
plt.savefig("image13.png")
dd = DataFrame(randn(10, 10)).applymap(abs)
dd = dd.cumsum()
plt.figure()
dd.plot(kind='bar', colormap='Greens')
plt.show()
def _doplot(self, data, ax, kind, subplots, errorbars, useindex, bw, kwargs):
"""Core plotting method"""
kwargs = kwargs.copy()
cols = data.columns
if kind == 'line':
data = data.sort_index()
rows = int(round(np.sqrt(len(data.columns)),0))
if len(data.columns) == 1 and kind not in ['pie']:
kwargs['subplots'] = 0
if 'colormap' in kwargs:
cmap = plt.cm.get_cmap(kwargs['colormap'])
else:
cmap = None
#change some things if we are plotting in b&w
styles = []
if bw == True and kind not in ['pie','heatmap']:
cmap = None
kwargs['color'] = 'k'
kwargs['colormap'] = None
styles = ["-","--","-.",":"]
if 'linestyle' in kwargs:
del kwargs['linestyle']
if subplots == 0:
layout = None
else:
layout=(rows,-1)
if errorbars == True:
yerr = data[data.columns[1::2]]
data = data[data.columns[0::2]]
yerr.columns = data.columns
else:
yerr = None
if kind == 'bar' or kind == 'barh':
if len(data) > 50:
ax.get_xaxis().set_visible(False)
if len(data) > 300:
self.showWarning('too many bars to plot')
return
if kind == 'scatter':
axs = self.scatter(data, ax, **kwargs)
if kwargs['sharey'] == 1:
lims = self.fig.axes[0].get_ylim()
for a in self.fig.axes:
a.set_ylim(lims)
elif kind == 'boxplot':
axs = data.boxplot(ax=ax, rot=kwargs['rot'], grid=kwargs['grid'],
patch_artist=True)
lw = kwargs['linewidth']
plt.setp(axs['boxes'], color='black', lw=lw)
plt.setp(axs['whiskers'], color='black', lw=lw)
plt.setp(axs['fliers'], color='black', marker='+', lw=lw)
clr = cmap(0.5)
for patch in axs['boxes']:
patch.set_facecolor(clr)
#boxplot won't accept required kwargs?
if kwargs['logy'] == 1:
ax.set_yscale('log')
elif kind == 'histogram':
bins = int(kwargs['bins'])
axs = data.plot(kind='hist',layout=layout, ax=ax, **kwargs)
elif kind == 'heatmap':
axs = self.heatmap(data, ax, kwargs)
elif kind == 'bootstrap':
axs = plotting.bootstrap_plot(data)
elif kind == 'scatter_matrix':
axs = pd.scatter_matrix(data, ax=ax, **kwargs)
elif kind == 'hexbin':
x = cols[0]
y = cols[1]
axs = data.plot(x,y,ax=ax,kind='hexbin',gridsize=20,**kwargs)
elif kind == 'contour':
xi,yi,zi = self.contourData(data)
cs = ax.contour(xi,yi,zi,15,linewidths=.5,colors='k')
#plt.clabel(cs,fontsize=9)
cs = ax.contourf(xi,yi,zi,15,cmap=cmap)
self.fig.colorbar(cs,ax=ax)
axs = ax
elif kind == 'imshow':
xi,yi,zi = self.contourData(data)
im = ax.imshow(zi, interpolation="nearest",
cmap=cmap, alpha=kwargs['alpha'])
self.fig.colorbar(im,ax=ax)
axs = ax
elif kind == 'pie':
if kwargs['legend'] == True:
lbls=None
else:
lbls = list(data.index)
axs = data.plot(ax=ax,kind='pie', labels=lbls, layout=layout,
autopct='%1.1f%%', subplots=True, **kwargs)
if lbls == None:
axs[0].legend(labels=data.index)
#elif kind == 'barh':
# lw = kwargs['linewidth']
# axs = data.plot(ax=ax, layout=layout, xerr=yerr, **kwargs)
elif kind == 'venn':
axs = self.venn(data, ax, **kwargs)
else:
#line, bar and area plots
if useindex == False:
x=data.columns[0]
data.set_index(x,inplace=True)
#data=data.sort()
if len(data.columns) == 0:
msg = "Not enough data.\nIf 'use index' is off select at least 2 columns"
self.showWarning(msg)
return
#adjust colormap to avoid white lines
if cmap != None:
cmap = util.adjustColorMap(cmap, 0.15,1.0)
del kwargs['colormap']
axs = data.plot(ax=ax, layout=layout, yerr=yerr, style=styles, cmap=cmap,
**kwargs)
return axs
