About Dataset¶

This meticulously curated dataset offers a panoramic view of education on a global scale , delivering profound insights into the dynamic landscape of education across diverse countries and regions. Spanning a rich tapestry of educational aspects, it encapsulates crucial metrics including out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and primary and tertiary education enrollment statistics. A treasure trove of knowledge, this dataset is an indispensable asset for discerning researchers, dedicated educators, and forward-thinking policymakers, enabling them to embark on a transformative journey of assessing, enhancing, and reshaping education systems worldwide.

This dataset provides a comprehensive global perspective on education, offering vital insights into diverse education systems worldwide. It covers essential metrics like out-of-school rates, completion rates, proficiency levels, literacy rates, birth rates, and enrollment in primary and tertiary education. It's a valuable resource for researchers, educators, and policymakers looking to assess and improve education systems globally.

Dataset Link -Kaggle link

Data Dictionary¶

Countries and Areas: Name of the countries and areas.
Latitude: Latitude coordinates of the geographical location.
Longitude: Longitude coordinates of the geographical location.
OOSR_Pre0Primary_Age_Male: Out-of-school rate for pre-primary age males.
OOSR_Pre0Primary_Age_Female: Out-of-school rate for pre-primary age females.
OOSR_Primary_Age_Male: Out-of-school rate for primary age males.
OOSR_Primary_Age_Female: Out-of-school rate for primary age females.
OOSR_Lower_Secondary_Age_Male: Out-of-school rate for lower secondary age males.
OOSR_Lower_Secondary_Age_Female: Out-of-school rate for lower secondary age females.
OOSR_Upper_Secondary_Age_Male: Out-of-school rate for upper secondary age males.
OOSR_Upper_Secondary_Age_Female: Out-of-school rate for upper secondary age females.
Completion_Rate_Primary_Male: Completion rate for primary education among males.
Completion_Rate_Primary_Female: Completion rate for primary education among females.
Completion_Rate_Lower_Secondary_Male: Completion rate for lower secondary education among males.
Completion_Rate_Lower_Secondary_Female: Completion rate for lower secondary education among females.
Completion_Rate_Upper_Secondary_Male: Completion rate for upper secondary education among males.
Completion_Rate_Upper_Secondary_Female: Completion rate for upper secondary education among females.
Grade_2_3_Proficiency_Reading: Proficiency in reading for grade 2-3 students.
Grade_2_3_Proficiency_Math: Proficiency in math for grade 2-3 students.
Primary_End_Proficiency_Reading: Proficiency in reading at the end of primary education.
Primary_End_Proficiency_Math: Proficiency in math at the end of primary education.
Lower_Secondary_End_Proficiency_Reading: Proficiency in reading at the end of lower secondary education.
Lower_Secondary_End_Proficiency_Math: Proficiency in math at the end of lower secondary education.
Youth_15_24_Literacy_Rate_Male: Literacy rate among male youths aged 15-24.
Youth_15_24_Literacy_Rate_Female: Literacy rate among female youths aged 15-24.
Birth_Rate: Birth rate in the respective countries/areas.
Gross_Primary_Education_Enrollment: Gross enrollment in primary education.
Gross_Tertiary_Education_Enrollment: Gross enrollment in tertiary education.
Unemployment_Rate: Unemployment rate in the respective countries/areas.

Installing dependency¶

👉Ignore It if already installed

1. !pip install numpy
2. !pip install pandas
3. !pip install matplotlib
4. !pip install seaborn

step -1 Data Preprocessing and Cleaning¶

Importing Required library¶

In [1]:

# perform linear operations
import numpy as np

# Data manipulation
import pandas as pd

#Data Visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Remove warnings
import warnings
warnings.filterwarnings('ignore')

In [2]:

#Load the dataset
education = pd.read_csv(r"C:\Users\Lenovo\Downloads\content\World Education Data Analysis\Global_Education.csv", encoding='ISO-8859-1')


# Print top 5 rows
education.head()

Out[2]:

	Countries and areas	Latitude	Longitude	OOSR_Pre0Primary_Age_Male	OOSR_Pre0Primary_Age_Female	OOSR_Primary_Age_Male	OOSR_Primary_Age_Female	OOSR_Lower_Secondary_Age_Male	OOSR_Lower_Secondary_Age_Female	OOSR_Upper_Secondary_Age_Male	...	Primary_End_Proficiency_Reading	Primary_End_Proficiency_Math	Lower_Secondary_End_Proficiency_Reading	Lower_Secondary_End_Proficiency_Math	Youth_15_24_Literacy_Rate_Male	Youth_15_24_Literacy_Rate_Female	Birth_Rate	Gross_Primary_Education_Enrollment	Gross_Tertiary_Education_Enrollment	Unemployment_Rate
0	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	44	...	13	11	0	0	74	56	32.49	104.0	9.7	11.12
1	Albania	41.153332	20.168331	4	2	6	3	6	1	21	...	0	0	48	58	99	100	11.78	107.0	55.0	12.33
2	Algeria	28.033886	1.659626	0	0	0	0	0	0	0	...	0	0	21	19	98	97	24.28	109.9	51.4	11.70
3	Andorra	42.506285	1.521801	0	0	0	0	0	0	0	...	0	0	0	0	0	0	7.20	106.4	0.0	0.00
4	Angola	11.202692	17.873887	31	39	0	0	0	0	0	...	0	0	0	0	0	0	40.73	113.5	9.3	6.89

5 rows × 29 columns

In [3]:

education

Out[3]:

	Countries and areas	Latitude	Longitude	OOSR_Pre0Primary_Age_Male	OOSR_Pre0Primary_Age_Female	OOSR_Primary_Age_Male	OOSR_Primary_Age_Female	OOSR_Lower_Secondary_Age_Male	OOSR_Lower_Secondary_Age_Female	OOSR_Upper_Secondary_Age_Male	...	Primary_End_Proficiency_Reading	Primary_End_Proficiency_Math	Lower_Secondary_End_Proficiency_Reading	Lower_Secondary_End_Proficiency_Math	Youth_15_24_Literacy_Rate_Male	Youth_15_24_Literacy_Rate_Female	Birth_Rate	Gross_Primary_Education_Enrollment	Gross_Tertiary_Education_Enrollment	Unemployment_Rate
0	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	44	...	13	11	0	0	74	56	32.49	104.0	9.7	11.12
1	Albania	41.153332	20.168331	4	2	6	3	6	1	21	...	0	0	48	58	99	100	11.78	107.0	55.0	12.33
2	Algeria	28.033886	1.659626	0	0	0	0	0	0	0	...	0	0	21	19	98	97	24.28	109.9	51.4	11.70
3	Andorra	42.506285	1.521801	0	0	0	0	0	0	0	...	0	0	0	0	0	0	7.20	106.4	0.0	0.00
4	Angola	11.202692	17.873887	31	39	0	0	0	0	0	...	0	0	0	0	0	0	40.73	113.5	9.3	6.89
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
197	Venezuela	6.423750	66.589730	14	14	10	10	15	13	28	...	0	0	0	0	0	0	17.88	97.2	79.3	8.80
198	Vietnam	14.058324	108.277199	0	0	0	0	0	0	0	...	55	51	86	81	98	98	16.75	110.6	28.5	2.01
199	Yemen	15.552727	48.516388	96	96	10	21	23	34	46	...	0	0	0	0	0	0	30.45	93.6	10.2	12.91
200	Zambia	13.133897	27.849332	0	0	17	13	0	0	0	...	0	0	5	2	93	92	36.19	98.7	4.1	11.43
201	Zimbabwe	19.015438	29.154857	60	58	0	0	0	0	45	...	0	0	0	0	0	0	30.68	109.9	10.0	4.95

202 rows × 29 columns

In [4]:

education.shape

Out[4]:

(202, 29)

From above cell we see that the dataset contain 202 observations and 29 columns

In [5]:

#Check info of each colummn
education.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 202 entries, 0 to 201
Data columns (total 29 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   Countries and areas                      202 non-null    object 
 1   Latitude                                 202 non-null    float64
 2   Longitude                                202 non-null    float64
 3   OOSR_Pre0Primary_Age_Male                202 non-null    int64  
 4   OOSR_Pre0Primary_Age_Female              202 non-null    int64  
 5   OOSR_Primary_Age_Male                    202 non-null    int64  
 6   OOSR_Primary_Age_Female                  202 non-null    int64  
 7   OOSR_Lower_Secondary_Age_Male            202 non-null    int64  
 8   OOSR_Lower_Secondary_Age_Female          202 non-null    int64  
 9   OOSR_Upper_Secondary_Age_Male            202 non-null    int64  
 10  OOSR_Upper_Secondary_Age_Female          202 non-null    int64  
 11  Completion_Rate_Primary_Male             202 non-null    int64  
 12  Completion_Rate_Primary_Female           202 non-null    int64  
 13  Completion_Rate_Lower_Secondary_Male     202 non-null    int64  
 14  Completion_Rate_Lower_Secondary_Female   202 non-null    int64  
 15  Completion_Rate_Upper_Secondary_Male     202 non-null    int64  
 16  Completion_Rate_Upper_Secondary_Female   202 non-null    int64  
 17  Grade_2_3_Proficiency_Reading            202 non-null    int64  
 18  Grade_2_3_Proficiency_Math               202 non-null    int64  
 19  Primary_End_Proficiency_Reading          202 non-null    int64  
 20  Primary_End_Proficiency_Math             202 non-null    int64  
 21  Lower_Secondary_End_Proficiency_Reading  202 non-null    int64  
 22  Lower_Secondary_End_Proficiency_Math     202 non-null    int64  
 23  Youth_15_24_Literacy_Rate_Male           202 non-null    int64  
 24  Youth_15_24_Literacy_Rate_Female         202 non-null    int64  
 25  Birth_Rate                               202 non-null    float64
 26  Gross_Primary_Education_Enrollment       202 non-null    float64
 27  Gross_Tertiary_Education_Enrollment      202 non-null    float64
 28  Unemployment_Rate                        202 non-null    float64
dtypes: float64(6), int64(22), object(1)
memory usage: 45.9+ KB

From above cell we see that there are 1 object column and 22 integer and 6 column contain float values

In [6]:

# Checking null values
education.isnull().sum()

Out[6]:

Countries and areas                        0
Latitude                                   0
Longitude                                  0
OOSR_Pre0Primary_Age_Male                  0
OOSR_Pre0Primary_Age_Female                0
OOSR_Primary_Age_Male                      0
OOSR_Primary_Age_Female                    0
OOSR_Lower_Secondary_Age_Male              0
OOSR_Lower_Secondary_Age_Female            0
OOSR_Upper_Secondary_Age_Male              0
OOSR_Upper_Secondary_Age_Female            0
Completion_Rate_Primary_Male               0
Completion_Rate_Primary_Female             0
Completion_Rate_Lower_Secondary_Male       0
Completion_Rate_Lower_Secondary_Female     0
Completion_Rate_Upper_Secondary_Male       0
Completion_Rate_Upper_Secondary_Female     0
Grade_2_3_Proficiency_Reading              0
Grade_2_3_Proficiency_Math                 0
Primary_End_Proficiency_Reading            0
Primary_End_Proficiency_Math               0
Lower_Secondary_End_Proficiency_Reading    0
Lower_Secondary_End_Proficiency_Math       0
Youth_15_24_Literacy_Rate_Male             0
Youth_15_24_Literacy_Rate_Female           0
Birth_Rate                                 0
Gross_Primary_Education_Enrollment         0
Gross_Tertiary_Education_Enrollment        0
Unemployment_Rate                          0
dtype: int64

From above cell we see that there are no missing values in the dataset

In [7]:

# check for duplicate
education.duplicated().sum()

Out[7]:

From above cell we see that there is no duplicate present in our dataset

In [8]:

# find the summary satistics of our data
education.describe()

Out[8]:

	Latitude	Longitude	OOSR_Pre0Primary_Age_Male	OOSR_Pre0Primary_Age_Female	OOSR_Primary_Age_Male	OOSR_Primary_Age_Female	OOSR_Lower_Secondary_Age_Male	OOSR_Lower_Secondary_Age_Female	OOSR_Upper_Secondary_Age_Male	OOSR_Upper_Secondary_Age_Female	...	Primary_End_Proficiency_Reading	Primary_End_Proficiency_Math	Lower_Secondary_End_Proficiency_Reading	Lower_Secondary_End_Proficiency_Math	Youth_15_24_Literacy_Rate_Male	Youth_15_24_Literacy_Rate_Female	Birth_Rate	Gross_Primary_Education_Enrollment	Gross_Tertiary_Education_Enrollment	Unemployment_Rate
count	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	...	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000	202.000000
mean	25.081422	55.166928	19.658416	19.282178	5.282178	5.569307	8.707921	8.831683	20.292079	19.975248	...	10.717822	10.376238	25.787129	24.450495	35.801980	35.084158	18.914010	94.942574	34.392574	6.000000
std	16.813639	45.976287	25.007604	25.171147	9.396442	10.383092	13.258203	14.724717	21.485592	23.140376	...	24.866101	22.484423	33.181384	31.965467	45.535186	45.249643	10.828184	29.769338	29.978206	5.273136
min	0.023559	0.824782	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	11.685062	18.665678	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.250000	0.250000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	10.355000	97.200000	9.000000	2.302500
50%	21.207861	43.518091	9.000000	7.000000	1.000000	1.000000	2.000000	2.000000	15.000000	12.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	17.550000	101.850000	24.850000	4.585000
75%	39.901792	77.684945	31.000000	30.000000	6.000000	6.750000	12.750000	10.750000	32.750000	30.000000	...	0.000000	0.000000	56.750000	50.750000	94.000000	96.750000	27.692500	107.300000	59.975000	8.655000
max	64.963051	178.065032	96.000000	96.000000	58.000000	67.000000	61.000000	70.000000	84.000000	89.000000	...	99.000000	89.000000	89.000000	94.000000	100.000000	100.000000	46.080000	142.500000	136.600000	28.180000

8 rows × 28 columns

In [9]:

education.columns

Out[9]:

Index(['Countries and areas', 'Latitude ', 'Longitude',
       'OOSR_Pre0Primary_Age_Male', 'OOSR_Pre0Primary_Age_Female',
       'OOSR_Primary_Age_Male', 'OOSR_Primary_Age_Female',
       'OOSR_Lower_Secondary_Age_Male', 'OOSR_Lower_Secondary_Age_Female',
       'OOSR_Upper_Secondary_Age_Male', 'OOSR_Upper_Secondary_Age_Female',
       'Completion_Rate_Primary_Male', 'Completion_Rate_Primary_Female',
       'Completion_Rate_Lower_Secondary_Male',
       'Completion_Rate_Lower_Secondary_Female',
       'Completion_Rate_Upper_Secondary_Male',
       'Completion_Rate_Upper_Secondary_Female',
       'Grade_2_3_Proficiency_Reading', 'Grade_2_3_Proficiency_Math',
       'Primary_End_Proficiency_Reading', 'Primary_End_Proficiency_Math',
       'Lower_Secondary_End_Proficiency_Reading',
       'Lower_Secondary_End_Proficiency_Math',
       'Youth_15_24_Literacy_Rate_Male', 'Youth_15_24_Literacy_Rate_Female',
       'Birth_Rate', 'Gross_Primary_Education_Enrollment',
       'Gross_Tertiary_Education_Enrollment', 'Unemployment_Rate'],
      dtype='object')

Step -2 Data analysis¶

Let's Solve some practice Questions

Regional Disparities:¶

How do completion rates vary across different countries?¶

In [10]:

grouped_data = education.groupby('Countries and areas')

# Compute average completion rates for each countries and education level
completion_rates = grouped_data[['Completion_Rate_Primary_Male', 'Completion_Rate_Primary_Female',
                                'Completion_Rate_Lower_Secondary_Male', 'Completion_Rate_Lower_Secondary_Female',
                                'Completion_Rate_Upper_Secondary_Male', 'Completion_Rate_Upper_Secondary_Female']].mean()
top_10_countries_completion_rates=completion_rates.sort_values(by=['Completion_Rate_Primary_Male', 'Completion_Rate_Primary_Female',
                                'Completion_Rate_Lower_Secondary_Male', 'Completion_Rate_Lower_Secondary_Female',
                                'Completion_Rate_Upper_Secondary_Male', 'Completion_Rate_Upper_Secondary_Female'],ascending=False).head(10)
top_10_countries_completion_rates

Out[10]:

	Completion_Rate_Primary_Male	Completion_Rate_Primary_Female	Completion_Rate_Lower_Secondary_Male	Completion_Rate_Lower_Secondary_Female	Completion_Rate_Upper_Secondary_Male	Completion_Rate_Upper_Secondary_Female
Countries and areas
North Korea	100.0	100.0	100.0	100.0	100.0	100.0
Kazakhstan	100.0	100.0	100.0	100.0	95.0	96.0
Belarus	100.0	100.0	100.0	100.0	91.0	94.0
Turkmenistan	100.0	100.0	99.0	100.0	93.0	95.0
Georgia	100.0	100.0	98.0	98.0	79.0	83.0
Ukraine	100.0	99.0	100.0	100.0	97.0	97.0
Cuba	100.0	98.0	95.0	98.0	85.0	86.0
Kyrgyzstan	99.0	100.0	99.0	99.0	89.0	85.0
Serbia	99.0	100.0	99.0	99.0	71.0	81.0
Bosnia and Herzegovina	99.0	100.0	97.0	97.0	92.0	92.0

In [11]:

# Plot the data
plt.figure(figsize=(14, 5))
sns.heatmap(top_10_countries_completion_rates, annot=True, cmap="YlGnBu", fmt=".0f", cbar_kws={'label': 'Average Completion Rate'})
plt.title('Average Completion Rates of top 10 countries and Education Levels')
plt.show()

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Exploratory Data Analysis on World Education Data in Python