我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用seaborn.countplot()。
def factor_plot(dataFrame, factors, prediction, color="Set3"): # First, plot the total for each factor. Then, plot the total for each # factor for the prediction variable (so in a conversion example, how # many people converted, revenue per country, etc.) # These refer to the rows and columns of the axis numpy array; not the # data itself. row = 0 column = 0 sns.set(style="whitegrid") # TODO: Set the width based on the max number of unique # values for the factors. plots = plt.subplots(len(factors), 2, figsize=(8,12)) # It should for factor in factors: sns.countplot(x=factor, palette="Set3", data=dataFrame, ax=plots[1][row][column]) # Then print the total for each prediction sns.barplot(x=factor, y=prediction, data=dataFrame, ax=plots[1][row][column+1]) row += 1 plt.tight_layout() # Need this or else plots will crash into each other
def weather_distribution(self): data_dir = g_singletonDataFilePath.getTrainDir() self.gapdf = self.load_weatherdf(data_dir) print self.gapdf['weather'].describe() # sns.distplot(self.gapdf['gap'],kde=False, bins=100); sns.countplot(x="weather", data=self.gapdf, palette="Greens_d"); plt.title('Countplot of Weather') # self.gapdf['weather'].plot(kind='bar') # plt.xlabel('Weather') # plt.title('Histogram of Weather') return
def PlotBarChart(CategoricalVar, data, XName): sns.countplot(CategoricalVar, data=data) plt.xlabel(XName) plt.title( XName + ' Bar Chart') plt.show()
def explore_feature_variation(self, col=None, use_target=False, **kwargs): ''' Produces univariate plots of a given set of columns. Barplots are used for categorical columns while histograms (with fitted density functinos) are used for numerical columns. If use_target is true, then the variation of the given set of columns with respect to the response variable are used (e.g., 2d scatter plots, boxplots, etc). Parameters ---------- col : a string of a column name, or a list of many columns names or None (default). If col is None, all columns will be used. use_target : bool, default False Whether to use the target column in the plots. **kwargs: additional arguments to be passed to seaborn's distplot or to pandas's plotting utilities.. ''' self._validate_params(params_list = {'col':col}, expected_types= {'col':[str,list,type(None)]}) if type(col) is str: col = [col] if col is None: col = self._get_all_features() if use_target == False: for column in col: if self.is_numeric(self.df[column]) == True: plt.figure(column) #sns.despine(left=True) sns.distplot(self.df[column], color="m", **kwargs) plt.title(column) plt.tight_layout() #plt.figure('boxplot') #sns.boxplot(x=self.df[col], palette="PRGn") #sns.despine(offset=10, trim=True) elif self.is_categorical(self.df[column]) == True: #print self.df[column].describe() plt.figure(column) #sns.despine(left=True) if len(self.df[column].unique()) > 30: self.df[column].value_counts()[:20][::-1].plot.barh(**kwargs) #top = pd.DataFrame(data=top) #sns.barplot(y=top.index, x=top) else: self.df[column].value_counts()[::-1].plot.barh(**kwargs) #sns.countplot(y=self.df[column]) plt.title(column) plt.tight_layout() else: raise TypeError('TYPE IS NOT SUPPORTED') else: # use target variable for column in col: self.explore_features_covariation(col1=column, col2=self.y, **kwargs)
def countplots(wine_set): wine_set["quality"] = pd.Categorical(wine_set["quality"]) seaborn.countplot(x="quality", data=wine_set) plt.xlabel("Quality level of wine (0-10 scale)") plt.show()
def variants_chrom(self): ''' Countplot of number of variants identified across all chromosomes. ''' self.pdvcf.remove_scaffolds() plt.style.use('seaborn-deep') fig, ax = plt.subplots(figsize=(14, 7)) sns.countplot(data=self.pdvcf.vcf, x='CHROM', palette='GnBu_d') ax.tick_params(labelsize=15) ax.set_ylabel('Variants', fontsize=20) ax.set_xlabel('Chromosome', fontsize=20) ax.set_title('Variants Identified Across Chromosomes', fontsize=25) return ax
