def plotCorrelation(stats):
#columnsToDrop = ['sleep_interval_max_len', 'sleep_interval_min_len',
# 'sleep_interval_avg_len', 'sleep_inefficiency',
# 'sleep_hours', 'total_hours']
#stats = stats.drop(columnsToDrop, axis=1)
g = sns.PairGrid(stats)
def corrfunc(x, y, **kws):
r, p = scipystats.pearsonr(x, y)
ax = plt.gca()
ax.annotate("r = {:.2f}".format(r),xy=(.1, .9), xycoords=ax.transAxes)
ax.annotate("p = {:.2f}".format(p),xy=(.2, .8), xycoords=ax.transAxes)
if p>0.04:
ax.patch.set_alpha(0.1)
g.map_upper(plt.scatter)
g.map_diag(plt.hist)
g.map_lower(sns.kdeplot, cmap="Blues_d")
g.map_upper(corrfunc)
sns.plt.show()
python类PairGrid()的实例源码
def histogramnd(ax, data, **kwargs):
"""n-dimensional histogram seaborn based
"""
scatter_data_raw = data
scatter_data_cols = ["x_%d" % (i,) for i in range(data.shape[1])]
# prepare dataframe
df = pd.DataFrame(scatter_data_raw, columns=scatter_data_cols)
g = sns.PairGrid(df)
# g.map_diag(plt.hist)
g.map_diag(sns.kdeplot)
g.map_offdiag(plt.hexbin, cmap="gray", gridsize=30, bins="log");
# logger.log(loglevel_debug, "dir(g)", dir(g))
# print g.diag_axes
# print g.axes
# for i in range(data.shape[1]):
# for j in range(data.shape[1]): # 1, 2; 0, 2; 0, 1
# if i == j:
# continue
# # column gives x axis, row gives y axis, thus need to reverse the selection for plotting goal
# # g.axes[i,j].plot(df["%s%d" % (self.cols_goal_base, j)], df["%s%d" % (self.cols_goal_base, i)], "ro", alpha=0.5)
# g.axes[i,j].plot(df["x_%d" % (j,)], df["x_%d" % (i,)], "ro", alpha=0.5)
plt.show()
# run sns scattermatrix on dataframe
# plot_scattermatrix(df, ax = None)
def bubble(data,x,y,hue,bsize,palette='Reds',xscale='linear',yscale='linear',title='',suptitle=0):
if suptitle == 0:
suptitle = bsize
sns.set(style="whitegrid")
sns.set_context("notebook", font_scale=3, rc={"lines.linewidth": 0.3})
sns.set_color_codes("bright")
size = (1500 / float(data[bsize].max()))
size = data[bsize] * size
g = sns.PairGrid(data, hue=hue, palette=palette, y_vars=y, x_vars=x, size=12, aspect=3)
g.map(plt.scatter, s=size);
g.set(xscale=xscale)
g.set(yscale=yscale)
g.add_legend(title=hue, bbox_to_anchor=(0.9, 0.6))
plt.title(title, fontsize=48, y=1.12, color="gray");
plt.suptitle("size = " + suptitle, verticalalignment='top', fontsize=35, y=1.01, x=0.48, color="gray")
plt.xlabel(x, fontsize=38, labelpad=30, color="gray");
plt.ylabel(y, fontsize=38, labelpad=30, color="gray");
plt.tick_params(axis='both', which='major', pad=25)
plt.axhline(linewidth=2.5, color="black");
plt.axvline(linewidth=2.5, color="black");
plt.ylim(0,);
plt.xlim(0,);
def bubble(data,x,y,hue,bsize,palette='Reds',xscale='linear',yscale='linear',title='',suptitle=0,xlim_start=0,ylim_start=0,xlim_end=0,ylim_end=0):
import seaborn as sns
import matplotlib.pyplot as plt
"""
x > should be int or float
y > should be int or float
hue > should be boolean or category
bsize > should be int or float
"""
if suptitle == 0:
suptitle = bsize
y_modifier = (data[y].max() - data[y].min()) * 0.1
x_modifier = (data[x].max() - data[x].min()) * 0.1
if ylim_start == 0:
ylim_start = data[y].min()
if xlim_start == 0:
xlim_start = data[x].min()
if ylim_end == 0:
ylim_end = data[y].max() + y_modifier
if xlim_end == 0:
xlim_end = data[x].max() + (x_modifier * 2)
sns.set(style="whitegrid")
sns.set_context("notebook", font_scale=3, rc={"lines.linewidth": 0.3})
sns.set_color_codes("bright")
size = (1500 / float(data[bsize].max()))
size = data[bsize] * size
g = sns.PairGrid(data, hue=hue, palette=palette, y_vars=y, x_vars=x, size=12, aspect=3)
g.map(plt.scatter, s=5000);
g.set(xscale=xscale)
g.set(yscale=yscale)
g.add_legend(title=hue, bbox_to_anchor=(0.9, 0.6))
plt.title(title, fontsize=48, y=1.12, color="gray");
plt.suptitle("size = " + suptitle, verticalalignment='top', fontsize=35, y=1.01, x=0.48, color="gray")
plt.xlabel(x, fontsize=38, labelpad=30, color="gray");
plt.ylabel(y, fontsize=38, labelpad=30, color="gray");
plt.tick_params(axis='both', which='major', pad=25)
plt.axhline(linewidth=2.5, color="black");
plt.axvline(linewidth=2.5, color="black");
plt.ylim(ylim_start,ylim_end);
plt.xlim(xlim_start,xlim_end);
def plot_kde_matrix(df, w, limits=None, colorbar=True):
"""
Plot a KDE matrix.
Parameters
----------
df: Pandas Dataframe
The rows are the observations, the columns the variables.
w: np.narray
The corresponding weights.
colorbar: bool
Whether to plot the colorbars or not.
limits: dictionary, optional
Dictionary of the form ``{"name": (lower_limit, upper_limit)}``.
"""
grid = sns.PairGrid(df, diag_sharey=False)
if limits is None:
limits = {}
default = (None, None)
def off_diagonal(x, y, **kwargs):
df = pd.concat((x, y), axis=1)
plot_kde_2d(df, w,
x.name, y.name,
xmin=limits.get(x.name, default)[0],
xmax=limits.get(x.name, default)[1],
ymin=limits.get(y.name, default)[0],
ymax=limits.get(y.name, default)[1],
ax=plt.gca(), title=False, colorbar=colorbar)
def scatter(x, y, **kwargs):
alpha = w / w.max()
colors = np.zeros((alpha.size, 4))
colors[:, 3] = alpha
plt.gca().scatter(x, y, color="k")
plt.gca().set_xlim(*limits.get(x.name, default))
plt.gca().set_ylim(*limits.get(y.name, default))
def diagonal(x, **kwargs):
df = pd.concat((x,), axis=1)
plot_kde_1d(df, w, x.name,
xmin=limits.get(x.name, default)[0],
xmax=limits.get(x.name, default)[1],
ax=plt.gca())
grid.map_diag(diagonal)
grid.map_upper(scatter)
grid.map_lower(off_diagonal)
return grid
