TOPIC: EDA-Suicide Rate Analysis

1.1 LOADING THE LIBRARIES

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

1.2 IMPORTING THE DATASETS

data=pd.read_csv('master.csv')

1.3 Check the data head()

data.head()

	country	year	sex	age	suicides_no	population	suicides/100k pop	country-year	HDI for year	gdp_for_year ($)	gdp_per_capita ($)	generation
0	Albania	1987	male	15-24 years	21	312900	6.71	Albania1987	NaN	2,156,624,900	796	Generation X
1	Albania	1987	male	35-54 years	16	308000	5.19	Albania1987	NaN	2,156,624,900	796	Silent
2	Albania	1987	female	15-24 years	14	289700	4.83	Albania1987	NaN	2,156,624,900	796	Generation X
3	Albania	1987	male	75+ years	1	21800	4.59	Albania1987	NaN	2,156,624,900	796	G.I. Generation
4	Albania	1987	male	25-34 years	9	274300	3.28	Albania1987	NaN	2,156,624,900	796	Boomers

1.4 Check the data.describe()

data.describe()

	year	suicides_no	population	suicides/100k pop	HDI for year	gdp_per_capita ($)
count	27820.000000	27820.000000	2.782000e+04	27820.000000	8364.000000	27820.000000
mean	2001.258375	242.574407	1.844794e+06	12.816097	0.776601	16866.464414
std	8.469055	902.047917	3.911779e+06	18.961511	0.093367	18887.576472
min	1985.000000	0.000000	2.780000e+02	0.000000	0.483000	251.000000
25%	1995.000000	3.000000	9.749850e+04	0.920000	0.713000	3447.000000
50%	2002.000000	25.000000	4.301500e+05	5.990000	0.779000	9372.000000
75%	2008.000000	131.000000	1.486143e+06	16.620000	0.855000	24874.000000
max	2016.000000	22338.000000	4.380521e+07	224.970000	0.944000	126352.000000

1.5 Cleaning check if there is missing data Check the Columns

data.columns

Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',
       'suicides/100k pop', 'country-year', 'HDI for year',
       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],
      dtype='object')

1.6 Checking the shape of data

data.shape

(27820, 12)

1.7 Count the datatypes

data.dtypes.value_counts()

object     6
int64      4
float64    2
dtype: int64

1.8 Check the data set information

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27820 entries, 0 to 27819
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   country             27820 non-null  object 
 1   year                27820 non-null  int64  
 2   sex                 27820 non-null  object 
 3   age                 27820 non-null  object 
 4   suicides_no         27820 non-null  int64  
 5   population          27820 non-null  int64  
 6   suicides/100k pop   27820 non-null  float64
 7   country-year        27820 non-null  object 
 8   HDI for year        8364 non-null   float64
 9    gdp_for_year ($)   27820 non-null  object 
 10  gdp_per_capita ($)  27820 non-null  int64  
 11  generation          27820 non-null  object 
dtypes: float64(2), int64(4), object(6)
memory usage: 2.5+ MB

1.8 Checking the dataset missing values

total_missing_check=data.isnull().sum().sort_values(ascending=False)    #Total number of Null values
total_missing_check

HDI for year          19456
generation                0
gdp_per_capita ($)        0
 gdp_for_year ($)         0
country-year              0
suicides/100k pop         0
population                0
suicides_no               0
age                       0
sex                       0
year                      0
country                   0
dtype: int64

             1.9  Create Frequency table for Age  

my_tab=pd.crosstab(index=data['age'], columns='Freq')
my_tab

col_0	Freq
age
15-24 years	4642
25-34 years	4642
35-54 years	4642
5-14 years	4610
55-74 years	4642
75+ years	4642

1.10 Bar plot to check Number of Suicides by top Countries

data.groupby(by=['country'])['suicides_no'].sum().reset_index().sort_values(['suicides_no']).tail(10).plot(x='country',y='suicides_no',kind='bar', figsize=(15,5))
plt.title('Plot of Number of Suicides by top Countries')

plt.show() 

Fig 1.0: Number of suicides by countries

1.11 Bar Plot for Number of suicides by Age

plt.figure(figsize=(10,5))   #setting the figure size
ax=sns.barplot(x='age',y='suicides_no', data=data,  estimator= np.mean,ci=90, palette='rocket' )#barplot
plt.title('Plot of Number of Suicides by age')

Text(0.5, 1.0, 'Plot of Number of Suicides by age')

Fig 2.0: Bar Plot of Number of suicides by Age

1.12 Bar Plot of Number of Suicides by Sex

plt.figure(figsize=(8,4))
ax = sns.barplot(x='sex',y='suicides_no', data=data,ci=None)
plt.show()

• Based on the size of the error bar, there seems to be a significantly more suicides among men than among women

1.13 Bar plot of Number of Suicides vs generation

plt.figure(figsize=(9,5))
ax= sns.barplot(x='generation', y='suicides_no', data=data,ci=None)

Interpretation:

• The suicides rate for Baby Boomers(People born after the second world war) seems to be high compared to the others.

1.14 Scatter Plot of Number of Suicides vs Population.

This is a numerical vs numerical data, so, scatter plot will be a good preference.

figure=plt.figure(figsize=(10,4))
ax= sns.scatterplot(x='population',y='suicides_no', data=data,size='suicides_no',color='g') #scatter plot

In order to a more clearer picture to establish this relationship, we introduce a regression line.

figure=plt.figure(figsize=(20,10))
ax= sns.regplot(x='population',y='suicides_no', data=data,color='r') #scatter plot

Fig 3.0: Scatter Plot of Number of Suicides vs Population

We can also view this relationship lineraly, using a line plot

x= sns.lineplot(x='population',y='suicides_no', data=data.head(),color='m')

1.15 Effects of GDP per capita on the suicide rates of a country We use a scatter plot: +Scatter plot Number of suicides/100k population vs GDP percapita

plt.figure(figsize=(15,7))
sns.scatterplot(x='gdp_per_capita ($)', y='suicides/100k pop', data=data)  #scatter plot
plt.show()

Fig 4.0: Suicide rate vs GDP percapita +

We can use the Heatmap to check if there are any significant correlation among the two variables

Correlation among pairs of continous variables

plt.figure(figsize=(10,5))
sns.heatmap(data.corr(), annot=True, linewidths= 0.5, fmt='0.1f', center=1)

<matplotlib.axes._subplots.AxesSubplot at 0x1ee1fbed208>

The result of the heat map shows that there exist a small but insignificant correlation between the suicides rate and gdp_percapita($)=0.1

1.16 Bar plot to check Number of suicides by sex and age to generate a single plot

plt.figure(figsize=(15,5))
sns.barplot(x='sex',y='suicides_no', data=data, hue='age')
plt.show()

Fig 5.0:Bar plot to check Number of suicides by sex

1.17 Trend of suicide rates across all the years

data[['year','suicides_no']].groupby(['year']).sum().plot(figsize=(15,5))

<matplotlib.axes._subplots.AxesSubplot at 0x1ee20fd1b08>

Fig 6.0: Trend plot of suicide rates across all the years