我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用seaborn.pairplot()。
def get_training_image(): from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure import seaborn as sns import StringIO fig = Figure() df = pd.DataFrame.from_dict(get_flattened_training_data()) features = [f for f in df.columns if f not in ['mac', 'location']] df = df.rename(columns=dict(zip(features, [POWER_SLAVE_PREFIX + f for f in features]))) sns_plot = sns.pairplot(df, hue="location", vars=[POWER_SLAVE_PREFIX + f for f in features]) png_output = StringIO.StringIO() sns_plot.savefig(png_output, format='png') canvas = FigureCanvas(fig) canvas.print_png(png_output) print png_output.getvalue() return
def visualize_housing_data(df): sns.set(style='whitegrid', context='notebook') cols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV'] sns.pairplot(df[cols], size=2.5) plt.show() correlation_matrix = np.corrcoef(df[cols].values.T) sns.set(font_scale=1.5) heatmap = sns.heatmap( correlation_matrix, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols, ) plt.show()
def plot_pairplots(data, labels, alpha, mis, column_label, topk=5, prefix='', focus=''): cmap = sns.cubehelix_palette(as_cmap=True, light=.9) plt.rcParams.update({'font.size': 32}) m, nv = mis.shape for j in range(m): inds = np.where(np.logical_and(alpha[j] > 0, mis[j] > 0.))[0] inds = inds[np.argsort(- alpha[j, inds] * mis[j, inds])][:topk] if focus in column_label: ifocus = column_label.index(focus) if not ifocus in inds: inds = np.insert(inds, 0, ifocus) if len(inds) >= 2: plt.clf() subdata = data[:, inds] columns = [column_label[i] for i in inds] subdata = pd.DataFrame(data=subdata, columns=columns) try: sns.pairplot(subdata, kind="reg", diag_kind="kde", size=5, dropna=True) filename = '{}/pairplots_regress/group_num={}.pdf'.format(prefix, j) if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) plt.suptitle("Latent factor {}".format(j), y=1.01) plt.savefig(filename, bbox_inches='tight') plt.clf() except: pass subdata['Latent factor'] = labels[:,j] try: sns.pairplot(subdata, kind="scatter", dropna=True, vars=subdata.columns.drop('Latent factor'), hue="Latent factor", diag_kind="kde", size=5) filename = '{}/pairplots/group_num={}.pdf'.format(prefix, j) if not os.path.exists(os.path.dirname(filename)): os.makedirs(os.path.dirname(filename)) plt.suptitle("Latent factor {}".format(j), y=1.01) plt.savefig(filename, bbox_inches='tight') plt.close('all') except: pass
def stock(): #?????????????????, ????????? stock_list = {"zsyh":"600036","jsyh":"601939","szzs":"000001","pfyh":"600000","msyh":"600061"} for stock, code in stock_list.items(): globals()[stock] = tsh.get_hist_data(code,start="2015-01-01",end="2016-04-16") stock_list2 = stock_list.keys() #print(stock_list2) sl = [globals()[st]["close"] for st in stock_list2] df_close = pd.concat(sl,axis=1,join='inner') df_close.columns = stock_list2 #print(df_close) df_close.sort_index(ascending=True,inplace=True) #ascending ?????????????????? pc_ret = df_close.pct_change() #???????????????? print(pc_ret) make_end_line() print(pc_ret.mean()) make_end_line() #???????????? plt.show(sns.jointplot("zsyh","jsyh",pc_ret,kind="hex")) #?? ????????1?????????? 0????? -1???????? plt.show(sns.jointplot("zsyh","jsyh",pc_ret,kind="scatter")) plt.show(sns.jointplot("zsyh","szzs",pc_ret,kind="scatter")) plt.show(sns.pairplot(pc_ret[["jsyh","zsyh","pfyh","msyh"]].dropna())) #?????????? print(pc_ret.std()) #???????????????????????????? make_end_line() rets = pc_ret.dropna() print(rets.mean()) make_end_line() area = np.pi *20 #???? plt.scatter(rets.mean(),rets.std()) #???rets?????????xy? plt.xlabel("Expected Return")#????xy???? plt.ylabel("Risk") for label,x,y in zip(rets.columns,rets.mean(),rets.std()): plt.annotate( label, xy = (x,y),xytext = (50,50), textcoords = "offset points",ha = "right",va = "bottom", arrowprops = dict(arrowstyle = "-",connectionstyle = "arc3,rad=-0.3")) plt.show()
def loadDataSet(filename): df = pd.read_excel(filename,sheetname=[1], header=None, skiprows=1)[1] df = df.fillna(0) df[2] = df[2]/100000000 # ????? # zeros = df[df[0]==0] # df = df.drop(zeros.index,axis=0) df[2] = standard(df[2]) #????? df[3] = standard(df[3]) #???? df[4] = standard(df[4]) #???? df[6] = standard(df[6]) #?????? df[9] = df[9].apply(map_01) # ????? df[11] = df[11].apply(map_01) # ?????? df[14] = standard(df[14]) #??????? df[15] = standard(df[15].apply(map_rate)) #??????? df[16] = standard(df[16].apply(map_sub_rate)) #????????? df[17] = standard(df[17]) #?????? prov_coding,province_dict = transcoding(df[10]) # province enter_coding,enter_dict = transcoding(df[13]) # enterprise target = df[2] data = df[[3,4,6,9,11,14,15,16,17]] data = pd.concat([data,prov_coding],axis=1) data = pd.concat([data,enter_coding],axis=1) import seaborn as sns # sns.pairplot(df, x_vars=[3,4,6,9,11,14,15,16,17], y_vars=2, size=7, aspect=0.8, kind='reg') return mat(data),mat(target).T
def loadDataSet(filename): sheet = 1 df = pd.read_excel(filename,sheetname=[sheet], header=None, skiprows=1)[sheet] df = df.dropna(how='any',thresh=df.shape[1]/2) # drop those rows df = df.dropna(how='any') df = df.fillna(0) df[2] = df[2]/100000000 # ????? df = df.sort_values(2).reset_index() # zeros = df[df[0]==0] # df = df.drop(zeros.index,axis=0) df[2] = (df[2]) #????? df[3] = standard(df[3]) #???? df[4] = standard(df[4]) #???? rate_type,rate_dict = transcoding(df[5]) # ???? df[6] = standard(df[6]) #?????? # market,market_dict = transcoding(df[7]) #???? platform,platform_dict = transcoding(df[8]) #???? df[9] = df[9].apply(map_01) # ????? df[11] = df[11].apply(map_01) # ?????? nature,nature_dict = transcoding(df[12]) #???? df[14] = standard(df[14]) #??????? print( df.groupby(15).size()) df[15] = standard(df[15].apply(map_rate)) #??????? print( df.groupby(15).size()) df[16] = standard(df[16].apply(map_sub_rate)) #????????? df[17] = standard(df[17]) #?????? target = df[2] data = df[[3,4,6,9,11,14,15,16,17]] # data = pd.concat([data,rate_type,platform,nature],axis=1) import seaborn as sns sns.pairplot(df, x_vars=[3,17,4,14,15,16,6,9,11], y_vars=2, size=5, aspect=0.8, kind='reg') # sns.pairplot(df, vars=[2,4,14,15,17]) return np.mat(data),np.mat(target).T
def plot_auxiliary(all_vars, filename, table_size=4): # All variables need to be (batch_size, sequence_length, dimension) for i, a in enumerate(all_vars): if a.ndim == 2: all_vars[i] = np.expand_dims(a, 0) dim = all_vars[0].shape[-1] if dim == 2: f, ax = plt.subplots(table_size, table_size, sharex='col', sharey='row', figsize=[12, 12]) idx = 0 for x in range(table_size): for y in range(table_size): for a in all_vars: # Loop over the batch dimension ax[x, y].plot(a[idx, :, 0], a[idx, :, 1], linestyle='-', marker='o', markersize=3) # Plot starting point of the trajectory ax[x, y].plot(a[idx, 0, 0], a[idx, 0, 1], 'r.', ms=12) idx += 1 # plt.show() plt.savefig(filename, format='png', bbox_inches='tight', dpi=80) plt.close() else: df_list = [] for i, a in enumerate(all_vars): df = pd.DataFrame(all_vars[i].reshape(-1, dim)) df['class'] = i df_list.append(df) df_all = pd.concat(df_list) sns_plot = sns.pairplot(df_all, hue="class", vars=range(dim)) sns_plot.savefig(filename) plt.close()
def visualize_hist_pairplot(X,y,selected_feature1,selected_feature2,features,diag_kind): """ Visualize the pairwise relationships (Histograms and Density Funcions) between classes and respective attributes Keyword arguments: X -- The feature vectors y -- The target vector selected_feature1 - First feature selected_feature1 - Second feature diag_kind -- Type of plot in the diagonal (Histogram or Density Function) """ #create data joint_data=np.column_stack((X,y)) column_names=features #create dataframe df=pd.DataFrame(data=joint_data,columns=column_names) #plot palette = sea.hls_palette() splot=sea.pairplot(df, hue="Y", palette={0:palette[2],1:palette[0]},vars=[selected_feature1,selected_feature2],diag_kind=diag_kind) splot.fig.suptitle('Pairwise relationship: '+selected_feature1+" vs "+selected_feature2) splot.set(xticklabels=[]) # plt.subplots_adjust(right=0.94, top=0.94) #save fig output_dir = "img" save_fig(output_dir,'{}/{}_{}_hist_pairplot.png'.format(output_dir,selected_feature1,selected_feature2)) # plt.show()
def plot_scatter(df, features, target, tag='eda', directory=None): r"""Plot a scatterplot matrix, also known as a pair plot. Parameters ---------- df : pandas.DataFrame The dataframe containing the features. features: list of str The features to compare in the scatterplot. target : str The target variable for contrast. tag : str Unique identifier for the plot. directory : str, optional The full specification of the plot location. Returns ------- None : None. References ---------- https://seaborn.pydata.org/examples/scatterplot_matrix.html """ logger.info("Generating Scatter Plot") # Get the feature subset features.append(target) df = df[features] # Generate the pair plot sns.set() sns_plot = sns.pairplot(df, hue=target) # Save the plot write_plot('seaborn', sns_plot, 'scatter_plot', tag, directory) # # Function plot_facet_grid #
def comparison_1dim(by='Country', include_WEPP=True, include_VRE=False, year=2015, how='hbar', figsize=(7,5)): """ Plots a horizontal bar chart with capacity on x-axis, ``by`` on y-axis. Parameters ---------- by : string, defines how to group data Allowed values: 'Country' or 'Fueltype' """ red_w_wepp, red_wo_wepp, wepp, statistics = gather_comparison_data(include_WEPP=include_WEPP, include_VRE=include_VRE, year=year) if include_WEPP: stats = lookup([red_w_wepp, red_wo_wepp, wepp, statistics], keys=['Matched dataset w/ WEPP', 'Matched dataset w/o WEPP', 'WEPP only', 'Statistics OPSD'], by=by)/1000 else: stats = lookup([red_wo_wepp, statistics], keys=['Matched dataset w/o WEPP', 'Statistics OPSD'], by=by)/1000 if how == 'hbar': with sns.axes_style('darkgrid'): font={'size' : 24} plt.rc('font', **font) fig, ax = plt.subplots(figsize=figsize) stats.plot.barh(ax=ax, stacked=False, colormap='jet') ax.set_xlabel('Installed Capacity [GW]') ax.yaxis.label.set_visible(False) #ax.set_facecolor('#d9d9d9') # gray background ax.set_axisbelow(True) # puts the grid behind the bars ax.grid(color='white', linestyle='dotted') # adds white dotted grid ax.legend(loc='best') ax.invert_yaxis() return fig, ax if how == 'scatter': stats.loc[:, by] = stats.index.astype(str) #Needed for seaborne if len(stats.columns)-1 >= 3: g = sns.pairplot(stats, diag_kind='kde', hue=by, palette='Set2', size=figsize[1], aspect=figsize[0]/figsize[1]) else: g = sns.pairplot(stats, diag_kind='kde', hue=by, palette='Set2', size=figsize[1], aspect=figsize[0]/figsize[1], x_vars=stats.columns[0], y_vars=stats.columns[1]) for i in range(0, len(g.axes)): for j in range(0, len(g.axes[0])): g.axes[i,j].set(xscale='log', yscale='log', xlim=(1,200), ylim=(1,200)) return g.fig, g.axes
