How to use the seaborn.boxplot function in seaborn

def draw_group_boxplot(name_list,data_list1,data_list2, label ='Dice Score',titile=None, fpth=None ):
    df = get_df_from_list(name_list,data_list1,data_list2)
    df = df[['Group', 'Longitudinal', 'Cross-subject']]
    dd = pd.melt(df, id_vars=['Group'], value_vars=['Longitudinal', 'Cross-subject'], var_name='task')
    fig, ax = plt.subplots(figsize=(15, 8))
    sn=sns.boxplot(x='Group', y='value', data=dd, hue='task', palette='Set2',ax=ax)
    #sns.palplot(sns.color_palette("Set2"))
    sn.set_xlabel('')
    sn.set_ylabel(label)
    # plt.xticks(rotation=45)
    ax.yaxis.grid(True)
    leg=plt.legend(prop={'size': 18},loc=4)
    leg.get_frame().set_alpha(0.2)
    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
                 ax.get_xticklabels() + ax.get_yticklabels()):
        item.set_fontsize(20)
    for tick in ax.get_xticklabels():
        tick.set_rotation(30)
    if fpth is not None:
        plt.savefig(fpth,dpi=500, bbox_inches = 'tight')
        plt.close('all')
    else:

def barcode_length_boxplot(result_dict, datafame_dict, main, my_dpi, result_directory, desc):
    """
    Plot boxplot of the 1D pass and fail read length for each barcode indicated in the sample sheet
    """
    output_file = result_directory + '/' + '_'.join(main.split()) + '.png'

    plt.figure(figsize=(figure_image_width / my_dpi, figure_image_height / my_dpi), dpi=my_dpi)
    plt.subplot()

    ax = sns.boxplot(data=datafame_dict['barcode_selection_sequence_length_melted_dataframe'],
                     x='barcodes', y='length', hue='passes_filtering',
                     showfliers=False, palette={True: "yellowgreen", False: "orangered"},
                     hue_order=[True, False])

    handles, _ = ax.get_legend_handles_labels()
    plt.legend(bbox_to_anchor=(0.905, 0.98), loc=2, borderaxespad=0., labels=["Pass", "Fail"], handles=handles)
    plt.xlabel('Barcodes')
    plt.ylabel('Read length(bp)')

    df = datafame_dict['barcode_selection_sequence_length_dataframe']
    all_read = df.describe().T
    read_pass = df.loc[df['passes_filtering'] == bool(True)].describe().T
    read_fail = df.loc[df['passes_filtering'] == bool(False)].describe().T
    concat = pd.concat([all_read, read_pass, read_fail], keys=['1D', '1D pass', '1D fail'])
    dataframe = concat.T

"""
Grouped boxplots
================

_thumb: .66, .45

"""
import seaborn as sns
sns.set(style="ticks", palette="pastel")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Draw a nested boxplot to show bills by day and time
sns.boxplot(x="day", y="total_bill",
            hue="smoker", palette=["m", "g"],
            data=tips)
sns.despine(offset=10, trim=True)

def paper_plot(corrs, filename):

    fig, ax = plt.subplots(figsize=(3.3, 3.0))

    colors = sb.color_palette("PRGn")

    order = ['APS', 'TPS', 'SAR', 'ISR', 'SIR', 'IPS']
    sb.boxplot(y='metric', x='corr',
               order=order,
               dodge=False,
               data=corrs,
               whis=0,
               fliersize=0,
               ax=ax,
               )

    # iterate over boxes
    for i, box in enumerate(ax.artists):
        box.set_edgecolor('black')
        box.set_facecolor('white')

    # Add some small jitter
    np.random.seed(1111)
    corrs['corr'] += np.random.uniform(-0.01, 0.01, size=len(corrs))

meds['normalised_rating2'] = meds2['normalised_rating']

func = ln.utils.concordance

concordance = meds.groupby(['experiment', 'page']).apply(
    lambda g: func(g['normalised_rating'].values,
                   g['normalised_rating2'].values
                   )
).reset_index()

concordance.boxplot(column=0, by=['experiment'])
plt.show()

error = (meds['normalised_rating'] - meds['normalised_rating2']).abs()
print(error.quantile(0.95))
sb.boxplot(error)
plt.show()

g.map(qqplot, "total_bill", "tip", s=40, edgecolor="w")
g.add_legend();

#
def hexbin(x, y, color, **kwargs):
    cmap = sns.light_palette(color, as_cmap=True)
    plt.hexbin(x, y, gridsize=10, cmap=cmap, **kwargs)

with sns.axes_style("dark"):
    g = sns.FacetGrid(mtcars, hue="gear", row='gear', col="cyl", height=4)
g.map(hexbin, "wt", "mpg");


#%%
cmap = sns.color_palette("Set3")
sns.boxplot(x='cyl', y='mpg', data=mtcars, palette=cmap);
plt.xticks(rotation=45);

#make maximum possible 500
        df.loc[df['score']>500,'score'] = 500

        #match plot
        df_match = df[(df['mismatch_score'] == -2) & (df['gap_score'] == -1)]

        g = sns.FacetGrid(df_match, col="match_score")
        g = g.map(sns.boxplot, "match", "score")
        sns.plt.ylim(0,400)
        sns.plt.show()

        #mismatch plot
        df_mismatch = df[(df['match_score'] == 3) & (df['gap_score'] == -1)]

        g = sns.FacetGrid(df_mismatch, col="mismatch_score")
        g = g.map(sns.boxplot, "match", "score")
        sns.plt.ylim(0,400)
        sns.plt.show()

        #gap plot
        df_gap = df[(df['match_score'] == 3) & (df['mismatch_score'] == -2)]

        g = sns.FacetGrid(df_gap, col="gap_score")
        g = g.map(sns.boxplot, "match", "score")
        sns.plt.ylim(0,400)
        sns.plt.show()

# functional ANOVA sometimes crashes with a RuntimeError, e.g., on tasks where the performance is constant
            # for all configurations (there is no variance). We will skip these tasks (like the authors did in the
            # paper).
            print('Task %d error: %s' % (task_id, e))
            continue

# transform ``fanova_results`` from a list of dicts into a DataFrame
fanova_results = pd.DataFrame(fanova_results)

##############################################################################
# make the boxplot of the variance contribution. Obviously, we can also use
# this data to make the Nemenyi plot, but this relies on the rather complex
# ``Orange`` dependency (``pip install Orange3``). For the complete example,
# the reader is referred to the more elaborate script (referred to earlier)
fig, ax = plt.subplots()
sns.boxplot(x='hyperparameter', y='fanova', data=fanova_results, ax=ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
ax.set_ylabel('Variance Contribution')
ax.set_xlabel(None)
plt.tight_layout()
plt.show()

def _plot_category(self, name):
        data = self._train_features_df.dropna(subset=[name])
        self._plot(sns.countplot(x=name, data=data))
        self._plot(sns.boxplot(x=name, y=self._target_col,
                               data=data))

b.set_xlabel('')
    b.set_ylabel('')
    b.legend(fontsize=30)
    b.tick_params(labelsize=30)
    plt.savefig(dice_file)

    plt.figure()
    b = sns.boxplot(x='struc', y='hd', hue='phase', data=df, palette="PRGn")
    b.set_xlabel('')
    b.set_ylabel('')
    b.legend(fontsize=30)
    b.tick_params(labelsize=30)
    plt.savefig(hd_file)

    plt.figure()
    b = sns.boxplot(x='struc', y='assd', hue='phase', data=df, palette="PRGn")
    b.set_xlabel('')
    b.set_ylabel('')
    b.legend(fontsize=30)
    b.tick_params(labelsize=30)
    plt.savefig(assd_file)


    print('--------------------------------------------')
    print('the following measures should be the same as online')

    for struc_name in ['LV', 'RV', 'Myo']:
        for cardiac_phase in ['ED', 'ES']:
            dat = df.loc[(df['phase'] == cardiac_phase) & (df['struc'] == struc_name)]

            print('{} {}, mean += std Dice: {:.3f} ({:.3f})'.format(cardiac_phase, struc_name, np.mean(dat['dice']), np.std(dat['dice'])))
            print('{} {}, mean Hausdorff: {:.2f} ({:.2f})'.format(cardiac_phase, struc_name, np.mean(dat['hd']), np.std(dat['hd'])))