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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Here we are going to replace the less common regiments with 'other'
Step1:  LLQ
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Python Code:
regimen = clinical['Regimen Type'].ix[pts].dropna()
print regimen.value_counts()
regimen = regimen[regimen.map(regimen.value_counts()) > 10]
regimen = regimen.ix[pts].fillna('Other')
regimen = regimen.str.replace(' Based','')
regimen = regimen.ix[ti(duration != 'Control')]
regimen.value_counts()
adherence = clinical['adherence %'].ix[pts].dropna()
paofi = clinical['paofi total'].ix[pts].dropna()
beck = clinical['beck total'].ix[pts].dropna()
iadl = clinical.IADL.ix[pts].dropna()
age.name = 'Age (years)'
bmi.name = 'BMI'
control_color = (0.2980392156862745, 0.4470588235294118, 0.6901960784313725)
hiv_color = (0.3333333333333333, 0.6588235294117647, 0.40784313725490196)
sns.set_style(rc={'font.family': 'sans-serif', 'font.sans-serif': ['Arial'],
                  'font.size': 14})
fig, axs = subplots(4,2, figsize=(8, 10))
sns.violinplot(age, hiv,  inner='None', ax=axs[0][0],
               order=['HIV-','HIV+'])
axs[0][0].set_yticks([0,20,40,60,80])
sns.violinplot(bmi, hiv,  inner='None', ax=axs[0][1],
               order=['HIV-','HIV+'])
duration_m.hist(ax=axs[1][0], color=hiv_color)
axs[1][0].set_xlabel('Estimated HIV duration (months)')
sns.barplot(stage.dropna(), ax=axs[1][1], color=hiv_color)
o = [u'PI/NRTI', u'NNRTI/NRTI', u'3-class','Other']
sns.barplot(regimen, ax=axs[2][0], x_order=o, color=hiv_color)
adherence.dropna().hist(ax=axs[2][1], color=hiv_color, bins=15)
axs[2][1].set_xlabel('Drug adherence (%)')
beck.dropna().hist(ax=axs[3][0], color=hiv_color)
axs[3][0].set_xlabel('Beck depression inventory')
paofi.dropna().hist(ax=axs[3][1], color=hiv_color)
axs[3][1].set_xlabel('PAOFI')
letters = list(map(chr, range(97, 123)))[:8] 
for i,ax in enumerate(axs.flatten()):
    ax.text(-0.15, 1.15, letters[i].upper(), transform=ax.transAxes,
            fontsize=20, fontweight='bold', va='top', ha='right')
    if i > 1:
        ax.set_ylabel('No. of patients')
    prettify_ax(ax)
fig.tight_layout()
fig.savefig(FIGDIR + 'sup_clinical_params.pdf')
Explanation: Here we are going to replace the less common regiments with 'other'
End of explanation
pd.crosstab(labs['LLQ PLASMA'], duration)
fisher_exact_test(duration=='HIV Long', labs['LLQ PLASMA']=='>LLQ')
c = ['LLQ PLASMA', 'PT', 'INR']
c = ['WBC','RBC','HGB','HCT','MCV','MCH','MCHC','Platelets']
fig, axs = subplots(4,2, figsize=(8,10))
axs = axs.flatten()
for i,b in enumerate(c):
    labs[b].hist(ax=axs[i], color=hiv_color)
    axs[i].set_xlabel(b)
    axs[i].set_ylabel('No. of patients')
    prettify_ax(axs[i])
letters = list(map(chr, range(97, 123)))[8:] 
for i,ax in enumerate(axs.flatten()):
    ax.text(-0.15, 1.15, letters[i].upper(), transform=ax.transAxes,
            fontsize=20, fontweight='bold', va='top', ha='right')
    prettify_ax(ax)
fig.tight_layout()
fig.savefig(FIGDIR + 'sup_clinical_blood.pdf')
Explanation: LLQ: limit of HIV rna quantification 
APRI: liver disease progression (AST to Platelet Ratio Index)
End of explanation
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