<jupyter_start><jupyter_text>Sierra Leone df_Sierra_Leone<jupyter_code>plt.figure(figsize=(15,6))
plt.ylabel('Count')
plt.xlabel('Administrated Region')
sns.set_style('white')
plt.title('Relation Between Administrated Regions in Sierra Leone and Number of Acutecases of Lassa Fever')
sns.countplot(x='Admin_region', data=df_Sierra_Leone)
plt.savefig('serraleonecountcerto1.png')

plt.figure(figsize=(12,8))
plt.ylabel('Year of end')
plt.xlabel('Year of Start')
sns.lineplot(x=df_Sierra_Leone['Year_start'],
            y=df_Sierra_Leone['Year_end'])
sns.line

plt.figure(figsize=(12,8))
sns.kdeplot(df_Sierra_Leone['Acute_cases'])
sns.rugplot(df_Sierra_Leone['Acute_cases'])
sns.set_context("paper")
plt.figure(figsize=(12,8))
sns.boxplot(x='Country', y='Acute_cases', data=df_Sierra_Leone)

plt.figure(figsize=(12,8))
sns.distplot(df_Sierra_Leone['Acute_cases'], bins=50)
plt.savefig('estesavefig.png')
plt.figure(figsize=(12,8))
sns.jointplot(x='Year_start', y='Acute_cases', data=df_Sierra_Leone, kind='hex')

plt.figure(figsize=(12,8))
sns.stripplot(x='Year_start', y='Acute_cases', data=df_Sierra_Leone, jitter=True)
plt.figure(figsize=(12,8))
sns.jointplot(x='Year_start', y='Year_end', data=df_Sierra_Leone, kind='hex')
plt.savefig('fig3plot.png')
plt.figure(figsize=(12,8))
sns.swarmplot(x='Year_start', y='Acute_cases', data=df_Sierra_Leone, color='black')
sns.violinplot(x='Year_start', y='Acute_cases', data=df_Sierra_Leone,)
plt.savefig('violinplotserra.png')
df1 = pd.DataFrame(df_Sierra_Leone.groupby('Admin_region').sum()['Acute_cases'])

df1.reset_index(inplace=True)

df1
plt.figure(figsize=(25,10))
sns.barplot(x=df1['Admin_region'], y=df1['Acute_cases'], data=df1, palette="deep")
df2 = pd.DataFrame(df_Sierra_Leone.groupby('Year_start').sum()['Acute_cases'])

df2.reset_index(inplace=True)

df2


plt.figure(figsize=(12,8))
sns.lineplot(x=df2['Year_start'], y=df2['Acute_cases'])
plt.figure(figsize=(12,8))
sns.jointplot(x='Year_start', y='Acute_cases', data=df2, color="k").plot_joint(sns.kdeplot, zorder=0, n_levels=6)
plt.figure(figsize=(12,8))
sns.jointplot(x='Year_start', y='Acute_cases', data=df2, kind='reg')
df3 = pd.DataFrame(df_Sierra_Leone.groupby('Year_end').sum()['Acute_cases'])

df3.reset_index(inplace=True)

df3
plt.figure(figsize=(12,8))
sns.lineplot(x=df3['Year_end'], y=df3['Acute_cases'])
plt.figure(figsize=(12,8))
sns.jointplot(x='Year_end', y='Acute_cases', data=df4, kind='reg')
df4 = pd.DataFrame(df_Sierra_Leone.groupby('Town').sum()['Acute_cases'])

df4.reset_index(inplace=True)

df4
plt.figure(figsize=(25,10))
sns.barplot(x=df4['Town'], y=df4['Acute_cases'], data=df4, palette="deep")<jupyter_output><empty_output><jupyter_text>Nigeria<jupyter_code>df_Nigeria = df_Acute[df_Acute['Country']=='Nigeria']
df_Nigeria.head()
df_Guinea = df_Acute[df_Acute['Country']=='Guinea']
df_Guinea.head()
df_Liberia  = df_Acute[df_Acute['Country']=='Liberia']
df_Liberia.head()
df_Ghana  = df_Acute[df_Acute['Country']=='Ghana']
df_Ghana
df_Mali  = df_Acute[df_Acute['Country']=='Mali']
df_Mali
df_Cote_dIvoire  = df_Acute[df_Acute['Country']=="Cote d'Ivoire"]
df_Cote_dIvoire
df_Benin  = df_Acute[df_Acute['Country']=="Benin"]
df_Benin
df_Burkina_Faso  = df_Acute[df_Acute['Country']=="Burkina Faso"]
df_Burkina_Faso<jupyter_output><empty_output><jupyter_text>Nigeria<jupyter_code>import pandas as pd
import matplotlib.pyplot as plt 
import numpy as np
import seaborn as sns
%matplotlib inline


df = pd.read_csv("convertcsv.csv")
df_Acute = df[df['Acutedisease_or_seroprevalence']=='Acute']
df['Country'].unique()
array(['Nigeria', 'Liberia', "Cote d'Ivoire", 'Sierra Leone', 'Mali',
       'Burkina Faso', 'Guinea', 'Ghana', 'Benin'], dtype=object)
df_seroprevalence = df[df['Acutedisease_or_seroprevalence']=='seroprevalence']
df['Acutedisease_or_seroprevalence']=='seroprevalence'
df_Nigeria = df_Acute[df_Acute['Country']=='Nigeria']
df_Nigeria.drop(['ID', 'Rodent_or_human', 'Acutedisease_or_seroprevalence'], axis=1, inplace=True)
df_Nigeria['Acute_cases'] = df_Nigeria['Acute_cases'].astype(int)
df_Nigeria.reset_index(inplace=True)
df_Nigeria.drop(['Number_acutecases'], axis=1, inplace=True)
df_Nigeria.drop('index', axis=1, inplace=True)
plt.figure(figsize=(20,8))
plt.ylabel('Count')
plt.xlabel('Administrated Region')
sns.set_style('white')
plt.title('Relation Between Administrated Regions in Nigeria and Number of Acutecases of Lassa Fever')
sns.countplot(x='Admin_region', data=df_Nigeria)

plt.savefig('nigbars2.png')
plt.figure(figsize=(12,8))
plt.ylabel('Year of end')
plt.xlabel('Year of Start')
sns.lineplot(x=df_Nigeria['Year_start'],
            y=df_Nigeria['Year_end'])<jupyter_output><empty_output>
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