Spaces:
Sleeping
Sleeping
Commit ·
da952bf
0
Parent(s):
Initial deployment: Full-featured EDA Gapminder dashboard
Browse files- Comprehensive Streamlit EDA tool with 800+ lines
- 7 descriptive statistics tabs
- Interactive visualizations and animations
- Pre-loaded Gapminder dataset
- Docker SDK deployment
- Dockerfile +25 -0
- README.md +41 -0
- app.py +803 -0
- gapminder.tsv +1705 -0
- requirements.txt +3 -0
Dockerfile
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FROM python:3.11-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements and install Python dependencies
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COPY requirements.txt .
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RUN pip3 install --no-cache-dir -r requirements.txt
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# Copy application files
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COPY . .
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# Expose Streamlit port
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EXPOSE 8501
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# Health check
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HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1
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# Run Streamlit
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ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
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README.md
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---
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title: EDA Gapminder
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emoji: 📊
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colorFrom: green
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colorTo: blue
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sdk: docker
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app_port: 8501
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pinned: false
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---
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# Exploratory Data Analysis Gapminder
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Interactive exploratory data analysis tool for CSV datasets.
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## Features
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- 📋 Data preview with first 10 rows
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- 📊 Automatic summary statistics
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- 🔍 Column information with null counts
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- 📈 Interactive scatter plots
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- 📊 Distribution histograms
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- 🔥 Correlation heatmap
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## Tech Stack
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- Streamlit for UI
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- Plotly for interactive visualizations
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- pandas for data processing
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## Usage
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1. Upload any CSV file
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2. View automatic data preview and statistics
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3. Select columns for visualization
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4. Explore scatter plots and distributions
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5. Analyze correlations between numeric columns
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## Supported Datasets
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Works with any CSV file containing:
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- Numeric data (int, float)
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- Categorical data (strings)
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- Mixed data types
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Best results with datasets that have at least 2 numeric columns.
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app.py
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|
| 1 |
+
import streamlit as st
|
| 2 |
+
import pandas as pd
|
| 3 |
+
import numpy as np
|
| 4 |
+
import plotly.express as px
|
| 5 |
+
import plotly.graph_objects as go
|
| 6 |
+
from plotly.subplots import make_subplots
|
| 7 |
+
|
| 8 |
+
# Set page config
|
| 9 |
+
st.set_page_config(page_title="Gapminder EDA v2", layout="wide")
|
| 10 |
+
|
| 11 |
+
# Custom CSS to set Helvetica for body text while keeping headers in Source Sans Pro
|
| 12 |
+
st.markdown("""
|
| 13 |
+
<style>
|
| 14 |
+
/* Body text, paragraphs, and general content */
|
| 15 |
+
.stMarkdown p, .stMarkdown li, .stMarkdown span {
|
| 16 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 17 |
+
}
|
| 18 |
+
|
| 19 |
+
/* Dataframe text */
|
| 20 |
+
.stDataFrame, .stDataFrame * {
|
| 21 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
/* Metric values */
|
| 25 |
+
.stMetric {
|
| 26 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 27 |
+
}
|
| 28 |
+
|
| 29 |
+
/* Select boxes and inputs */
|
| 30 |
+
.stSelectbox, .stMultiSelect {
|
| 31 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 32 |
+
}
|
| 33 |
+
|
| 34 |
+
/* Tab labels */
|
| 35 |
+
.stTabs [data-baseweb="tab"] {
|
| 36 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 37 |
+
}
|
| 38 |
+
|
| 39 |
+
/* Info boxes */
|
| 40 |
+
.stAlert {
|
| 41 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 42 |
+
}
|
| 43 |
+
|
| 44 |
+
/* Caption text */
|
| 45 |
+
.stCaption {
|
| 46 |
+
font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif !important;
|
| 47 |
+
}
|
| 48 |
+
|
| 49 |
+
/* Keep headers in Source Sans Pro (default Streamlit font) */
|
| 50 |
+
h1, h2, h3, h4, h5, h6 {
|
| 51 |
+
font-family: 'Source Sans Pro', sans-serif !important;
|
| 52 |
+
}
|
| 53 |
+
</style>
|
| 54 |
+
""", unsafe_allow_html=True)
|
| 55 |
+
|
| 56 |
+
# Title
|
| 57 |
+
st.title("🌍 Gapminder Dataset - Exploratory Data Analysis v2")
|
| 58 |
+
|
| 59 |
+
# Load data
|
| 60 |
+
@st.cache_data
|
| 61 |
+
def load_data():
|
| 62 |
+
"""Load Gapminder dataset from local file with fallback to URL"""
|
| 63 |
+
# For HuggingFace Spaces, use gapminder.tsv in the same directory
|
| 64 |
+
local_path = 'gapminder.tsv'
|
| 65 |
+
|
| 66 |
+
try:
|
| 67 |
+
# Try loading from local file first
|
| 68 |
+
df = pd.read_csv(local_path, sep='\t')
|
| 69 |
+
except FileNotFoundError:
|
| 70 |
+
# Fallback to data/ directory (for local development)
|
| 71 |
+
try:
|
| 72 |
+
df = pd.read_csv('data/gapminder.tsv', sep='\t')
|
| 73 |
+
except FileNotFoundError:
|
| 74 |
+
# Final fallback to downloading from GitHub
|
| 75 |
+
url = "https://raw.githubusercontent.com/jennybc/gapminder/master/inst/extdata/gapminder.tsv"
|
| 76 |
+
df = pd.read_csv(url, sep='\t')
|
| 77 |
+
|
| 78 |
+
return df
|
| 79 |
+
|
| 80 |
+
df = load_data()
|
| 81 |
+
|
| 82 |
+
# Display basic info
|
| 83 |
+
st.header("Dataset Overview")
|
| 84 |
+
col1, col2, col3 = st.columns(3)
|
| 85 |
+
with col1:
|
| 86 |
+
st.metric("Total Rows", df.shape[0])
|
| 87 |
+
with col2:
|
| 88 |
+
st.metric("Total Columns", df.shape[1])
|
| 89 |
+
with col3:
|
| 90 |
+
st.metric("Countries", df['country'].nunique())
|
| 91 |
+
|
| 92 |
+
st.subheader("Sample Data")
|
| 93 |
+
st.dataframe(df.head(10))
|
| 94 |
+
|
| 95 |
+
# Descriptive Statistics Section
|
| 96 |
+
st.header("📊 Descriptive Statistics")
|
| 97 |
+
|
| 98 |
+
# Select numerical columns
|
| 99 |
+
numerical_cols = ['lifeExp', 'pop', 'gdpPercap']
|
| 100 |
+
|
| 101 |
+
# Create tabs for each metric
|
| 102 |
+
tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
|
| 103 |
+
"Mean", "Median", "Mode", "Standard Deviation",
|
| 104 |
+
"Range & IQR", "Coefficient of Variation", "Percentiles"
|
| 105 |
+
])
|
| 106 |
+
|
| 107 |
+
with tab1:
|
| 108 |
+
st.subheader("Mean (Average)")
|
| 109 |
+
st.write("The mean represents the average value of the data.")
|
| 110 |
+
|
| 111 |
+
mean_data = {
|
| 112 |
+
'Metric': numerical_cols,
|
| 113 |
+
'Mean Value': [df[col].mean() for col in numerical_cols]
|
| 114 |
+
}
|
| 115 |
+
mean_df = pd.DataFrame(mean_data)
|
| 116 |
+
|
| 117 |
+
col1, col2 = st.columns([1, 2])
|
| 118 |
+
with col1:
|
| 119 |
+
st.dataframe(mean_df, hide_index=True)
|
| 120 |
+
with col2:
|
| 121 |
+
fig = px.bar(mean_df, x='Metric', y='Mean Value',
|
| 122 |
+
title='Mean Values by Metric',
|
| 123 |
+
color='Metric')
|
| 124 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 125 |
+
|
| 126 |
+
with tab2:
|
| 127 |
+
st.subheader("Median")
|
| 128 |
+
st.write("The median represents the middle value when data is sorted.")
|
| 129 |
+
|
| 130 |
+
median_data = {
|
| 131 |
+
'Metric': numerical_cols,
|
| 132 |
+
'Median Value': [df[col].median() for col in numerical_cols]
|
| 133 |
+
}
|
| 134 |
+
median_df = pd.DataFrame(median_data)
|
| 135 |
+
|
| 136 |
+
col1, col2 = st.columns([1, 2])
|
| 137 |
+
with col1:
|
| 138 |
+
st.dataframe(median_df, hide_index=True)
|
| 139 |
+
with col2:
|
| 140 |
+
fig = px.bar(median_df, x='Metric', y='Median Value',
|
| 141 |
+
title='Median Values by Metric',
|
| 142 |
+
color='Metric')
|
| 143 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 144 |
+
|
| 145 |
+
with tab3:
|
| 146 |
+
st.subheader("Mode")
|
| 147 |
+
st.write("The mode represents the most frequently occurring value(s).")
|
| 148 |
+
|
| 149 |
+
mode_results = []
|
| 150 |
+
for col in numerical_cols:
|
| 151 |
+
mode_val = df[col].mode()
|
| 152 |
+
if len(mode_val) > 0:
|
| 153 |
+
mode_results.append({
|
| 154 |
+
'Metric': col,
|
| 155 |
+
'Mode Value': mode_val[0],
|
| 156 |
+
'Frequency': (df[col] == mode_val[0]).sum()
|
| 157 |
+
})
|
| 158 |
+
|
| 159 |
+
mode_df = pd.DataFrame(mode_results)
|
| 160 |
+
st.dataframe(mode_df, hide_index=True)
|
| 161 |
+
|
| 162 |
+
st.info("Note: For continuous data like life expectancy and GDP, mode may not be very meaningful as values rarely repeat exactly.")
|
| 163 |
+
|
| 164 |
+
with tab4:
|
| 165 |
+
st.subheader("Standard Deviation")
|
| 166 |
+
st.write("Standard deviation measures the amount of variation or dispersion in the data.")
|
| 167 |
+
|
| 168 |
+
std_data = {
|
| 169 |
+
'Metric': numerical_cols,
|
| 170 |
+
'Standard Deviation': [df[col].std() for col in numerical_cols],
|
| 171 |
+
'Variance': [df[col].var() for col in numerical_cols]
|
| 172 |
+
}
|
| 173 |
+
std_df = pd.DataFrame(std_data)
|
| 174 |
+
|
| 175 |
+
col1, col2 = st.columns([1, 2])
|
| 176 |
+
with col1:
|
| 177 |
+
st.dataframe(std_df, hide_index=True)
|
| 178 |
+
with col2:
|
| 179 |
+
fig = px.bar(std_df, x='Metric', y='Standard Deviation',
|
| 180 |
+
title='Standard Deviation by Metric',
|
| 181 |
+
color='Metric')
|
| 182 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 183 |
+
|
| 184 |
+
with tab5:
|
| 185 |
+
st.subheader("Range & Interquartile Range (IQR)")
|
| 186 |
+
st.write("Range shows the total spread (Max - Min), while IQR shows the spread of the middle 50% of data.")
|
| 187 |
+
|
| 188 |
+
range_data = {
|
| 189 |
+
'Metric': numerical_cols,
|
| 190 |
+
'Min': [df[col].min() for col in numerical_cols],
|
| 191 |
+
'Q1 (25%)': [df[col].quantile(0.25) for col in numerical_cols],
|
| 192 |
+
'Q3 (75%)': [df[col].quantile(0.75) for col in numerical_cols],
|
| 193 |
+
'Max': [df[col].max() for col in numerical_cols],
|
| 194 |
+
'Range': [df[col].max() - df[col].min() for col in numerical_cols],
|
| 195 |
+
'IQR': [df[col].quantile(0.75) - df[col].quantile(0.25) for col in numerical_cols]
|
| 196 |
+
}
|
| 197 |
+
range_df = pd.DataFrame(range_data)
|
| 198 |
+
|
| 199 |
+
st.dataframe(range_df, hide_index=True)
|
| 200 |
+
|
| 201 |
+
# Visualizations
|
| 202 |
+
col1, col2 = st.columns(2)
|
| 203 |
+
|
| 204 |
+
with col1:
|
| 205 |
+
fig = px.bar(range_df, x='Metric', y='Range',
|
| 206 |
+
title='Range by Metric',
|
| 207 |
+
color='Metric')
|
| 208 |
+
fig.update_layout(transition={'duration': 500})
|
| 209 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 210 |
+
|
| 211 |
+
with col2:
|
| 212 |
+
fig = px.bar(range_df, x='Metric', y='IQR',
|
| 213 |
+
title='Interquartile Range (IQR) by Metric',
|
| 214 |
+
color='Metric')
|
| 215 |
+
fig.update_layout(transition={'duration': 500})
|
| 216 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 217 |
+
|
| 218 |
+
st.info("💡 IQR is more robust to outliers than Range. A larger IQR indicates more variability in the middle 50% of the data.")
|
| 219 |
+
|
| 220 |
+
with tab6:
|
| 221 |
+
st.subheader("Coefficient of Variation (CV)")
|
| 222 |
+
st.write("CV = (Standard Deviation / Mean) × 100. It allows comparison of variability across different scales.")
|
| 223 |
+
|
| 224 |
+
cv_data = {
|
| 225 |
+
'Metric': numerical_cols,
|
| 226 |
+
'Mean': [df[col].mean() for col in numerical_cols],
|
| 227 |
+
'Std Dev': [df[col].std() for col in numerical_cols],
|
| 228 |
+
'CV (%)': [(df[col].std() / df[col].mean()) * 100 for col in numerical_cols]
|
| 229 |
+
}
|
| 230 |
+
cv_df = pd.DataFrame(cv_data)
|
| 231 |
+
|
| 232 |
+
col1, col2 = st.columns([1, 2])
|
| 233 |
+
|
| 234 |
+
with col1:
|
| 235 |
+
st.dataframe(cv_df, hide_index=True)
|
| 236 |
+
|
| 237 |
+
st.markdown("**Interpretation:**")
|
| 238 |
+
st.markdown("- CV < 15%: Low variability")
|
| 239 |
+
st.markdown("- CV 15-30%: Moderate variability")
|
| 240 |
+
st.markdown("- CV > 30%: High variability")
|
| 241 |
+
|
| 242 |
+
with col2:
|
| 243 |
+
fig = px.bar(cv_df, x='Metric', y='CV (%)',
|
| 244 |
+
title='Coefficient of Variation by Metric',
|
| 245 |
+
color='Metric')
|
| 246 |
+
fig.update_layout(
|
| 247 |
+
yaxis_title="CV (%)",
|
| 248 |
+
transition={'duration': 500}
|
| 249 |
+
)
|
| 250 |
+
|
| 251 |
+
# Add reference lines for interpretation
|
| 252 |
+
fig.add_hline(y=15, line_dash="dash", line_color="green",
|
| 253 |
+
annotation_text="Low variability threshold")
|
| 254 |
+
fig.add_hline(y=30, line_dash="dash", line_color="orange",
|
| 255 |
+
annotation_text="High variability threshold")
|
| 256 |
+
|
| 257 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 258 |
+
|
| 259 |
+
st.info("💡 Higher CV indicates greater relative variability. This is especially useful when comparing metrics with different units or scales.")
|
| 260 |
+
|
| 261 |
+
with tab7:
|
| 262 |
+
st.subheader("Percentiles Analysis")
|
| 263 |
+
st.write("Percentiles divide the data into 100 equal parts. Key percentiles help understand data distribution.")
|
| 264 |
+
|
| 265 |
+
percentiles = [0, 10, 25, 50, 75, 90, 100]
|
| 266 |
+
percentile_data = {'Percentile': [f'{p}th' if p not in [0, 100] else ('Min' if p == 0 else 'Max')
|
| 267 |
+
for p in percentiles]}
|
| 268 |
+
|
| 269 |
+
for col in numerical_cols:
|
| 270 |
+
percentile_data[col] = [df[col].quantile(p/100) for p in percentiles]
|
| 271 |
+
|
| 272 |
+
percentile_df = pd.DataFrame(percentile_data)
|
| 273 |
+
|
| 274 |
+
st.dataframe(percentile_df, hide_index=True)
|
| 275 |
+
|
| 276 |
+
# Visualizations
|
| 277 |
+
st.subheader("Percentile Visualizations")
|
| 278 |
+
|
| 279 |
+
selected_percentile_metric = st.selectbox(
|
| 280 |
+
"Select metric for percentile visualization",
|
| 281 |
+
options=numerical_cols,
|
| 282 |
+
format_func=lambda x: {
|
| 283 |
+
'lifeExp': 'Life Expectancy',
|
| 284 |
+
'pop': 'Population',
|
| 285 |
+
'gdpPercap': 'GDP per Capita'
|
| 286 |
+
}[x],
|
| 287 |
+
key='percentile_metric'
|
| 288 |
+
)
|
| 289 |
+
|
| 290 |
+
col1, col2 = st.columns(2)
|
| 291 |
+
|
| 292 |
+
with col1:
|
| 293 |
+
# Line chart showing percentile progression
|
| 294 |
+
perc_viz_data = pd.DataFrame({
|
| 295 |
+
'Percentile': percentiles,
|
| 296 |
+
'Value': [df[selected_percentile_metric].quantile(p/100) for p in percentiles]
|
| 297 |
+
})
|
| 298 |
+
|
| 299 |
+
fig = px.line(perc_viz_data, x='Percentile', y='Value',
|
| 300 |
+
title=f'Percentile Progression - {selected_percentile_metric}',
|
| 301 |
+
markers=True)
|
| 302 |
+
fig.update_traces(line=dict(width=3), marker=dict(size=10))
|
| 303 |
+
fig.update_layout(transition={'duration': 600})
|
| 304 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 305 |
+
|
| 306 |
+
with col2:
|
| 307 |
+
# Enhanced box plot with percentile annotations
|
| 308 |
+
fig = px.box(df, y=selected_percentile_metric,
|
| 309 |
+
title=f'Box Plot with Percentiles - {selected_percentile_metric}')
|
| 310 |
+
|
| 311 |
+
# Add percentile annotations
|
| 312 |
+
for p in [10, 25, 50, 75, 90]:
|
| 313 |
+
val = df[selected_percentile_metric].quantile(p/100)
|
| 314 |
+
fig.add_hline(y=val, line_dash="dot", line_color="red",
|
| 315 |
+
annotation_text=f"{p}th percentile",
|
| 316 |
+
annotation_position="right")
|
| 317 |
+
|
| 318 |
+
fig.update_layout(transition={'duration': 600})
|
| 319 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 320 |
+
|
| 321 |
+
st.info("💡 The 50th percentile is the median. The difference between 75th and 25th percentiles is the IQR.")
|
| 322 |
+
|
| 323 |
+
# Comprehensive Statistics Table
|
| 324 |
+
st.header("📈 Complete Statistical Summary")
|
| 325 |
+
summary_stats = df[numerical_cols].describe().T
|
| 326 |
+
summary_stats['mode'] = [df[col].mode()[0] if len(df[col].mode()) > 0 else np.nan for col in numerical_cols]
|
| 327 |
+
summary_stats = summary_stats[['count', 'mean', 'mode', 'std', '50%', 'min', 'max']]
|
| 328 |
+
summary_stats.columns = ['Count', 'Mean', 'Mode', 'Std Dev', 'Median', 'Min', 'Max']
|
| 329 |
+
st.dataframe(summary_stats)
|
| 330 |
+
|
| 331 |
+
# Add Min/Max country details
|
| 332 |
+
st.subheader("🌟 Extreme Values - Countries")
|
| 333 |
+
st.write("Discover which countries hold the minimum and maximum values for each metric")
|
| 334 |
+
|
| 335 |
+
extreme_cols = st.columns(3)
|
| 336 |
+
|
| 337 |
+
for idx, col in enumerate(numerical_cols):
|
| 338 |
+
with extreme_cols[idx]:
|
| 339 |
+
col_name = {
|
| 340 |
+
'lifeExp': 'Life Expectancy',
|
| 341 |
+
'pop': 'Population',
|
| 342 |
+
'gdpPercap': 'GDP per Capita'
|
| 343 |
+
}[col]
|
| 344 |
+
|
| 345 |
+
# Find min and max
|
| 346 |
+
min_val = df[col].min()
|
| 347 |
+
max_val = df[col].max()
|
| 348 |
+
|
| 349 |
+
min_row = df[df[col] == min_val].iloc[0]
|
| 350 |
+
max_row = df[df[col] == max_val].iloc[0]
|
| 351 |
+
|
| 352 |
+
st.markdown(f"**{col_name}**")
|
| 353 |
+
|
| 354 |
+
# Min value
|
| 355 |
+
st.markdown(f"**📉 Minimum:** {min_val:,.2f}")
|
| 356 |
+
st.caption(f"📍 {min_row['country']} ({min_row['continent']}) in {int(min_row['year'])}")
|
| 357 |
+
|
| 358 |
+
# Max value
|
| 359 |
+
st.markdown(f"**📈 Maximum:** {max_val:,.2f}")
|
| 360 |
+
st.caption(f"📍 {max_row['country']} ({max_row['continent']}) in {int(max_row['year'])}")
|
| 361 |
+
|
| 362 |
+
st.markdown("---")
|
| 363 |
+
|
| 364 |
+
# Correlation Analysis
|
| 365 |
+
st.header("🔗 Correlation Analysis")
|
| 366 |
+
st.write("Correlation measures the strength and direction of relationships between variables (-1 to +1).")
|
| 367 |
+
|
| 368 |
+
col1, col2 = st.columns([1, 1])
|
| 369 |
+
|
| 370 |
+
with col1:
|
| 371 |
+
# Calculate correlation matrix
|
| 372 |
+
correlation_matrix = df[numerical_cols].corr()
|
| 373 |
+
|
| 374 |
+
st.subheader("Correlation Matrix")
|
| 375 |
+
st.dataframe(correlation_matrix.round(3))
|
| 376 |
+
|
| 377 |
+
st.markdown("**Interpretation:**")
|
| 378 |
+
st.markdown("- **+1**: Perfect positive correlation")
|
| 379 |
+
st.markdown("- **0**: No correlation")
|
| 380 |
+
st.markdown("- **-1**: Perfect negative correlation")
|
| 381 |
+
st.markdown("- **|r| > 0.7**: Strong correlation")
|
| 382 |
+
st.markdown("- **|r| 0.3-0.7**: Moderate correlation")
|
| 383 |
+
st.markdown("- **|r| < 0.3**: Weak correlation")
|
| 384 |
+
|
| 385 |
+
with col2:
|
| 386 |
+
# Heatmap visualization
|
| 387 |
+
fig = px.imshow(correlation_matrix,
|
| 388 |
+
text_auto='.2f',
|
| 389 |
+
aspect='auto',
|
| 390 |
+
color_continuous_scale='RdYlGn',
|
| 391 |
+
color_continuous_midpoint=0,
|
| 392 |
+
title='Correlation Heatmap',
|
| 393 |
+
labels=dict(color="Correlation"))
|
| 394 |
+
|
| 395 |
+
fig.update_layout(
|
| 396 |
+
height=400,
|
| 397 |
+
xaxis_title="",
|
| 398 |
+
yaxis_title=""
|
| 399 |
+
)
|
| 400 |
+
|
| 401 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 402 |
+
|
| 403 |
+
# Detailed correlation insights
|
| 404 |
+
st.subheader("🔍 Key Correlation Insights")
|
| 405 |
+
|
| 406 |
+
col1, col2, col3 = st.columns(3)
|
| 407 |
+
|
| 408 |
+
# Get correlations
|
| 409 |
+
corr_life_gdp = correlation_matrix.loc['lifeExp', 'gdpPercap']
|
| 410 |
+
corr_life_pop = correlation_matrix.loc['lifeExp', 'pop']
|
| 411 |
+
corr_gdp_pop = correlation_matrix.loc['gdpPercap', 'pop']
|
| 412 |
+
|
| 413 |
+
with col1:
|
| 414 |
+
st.metric(
|
| 415 |
+
"Life Expectancy ↔ GDP per Capita",
|
| 416 |
+
f"{corr_life_gdp:.3f}",
|
| 417 |
+
delta="Strong positive" if abs(corr_life_gdp) > 0.7 else "Moderate" if abs(corr_life_gdp) > 0.3 else "Weak"
|
| 418 |
+
)
|
| 419 |
+
st.caption("Higher GDP tends to correlate with longer life expectancy")
|
| 420 |
+
|
| 421 |
+
with col2:
|
| 422 |
+
st.metric(
|
| 423 |
+
"Life Expectancy ↔ Population",
|
| 424 |
+
f"{corr_life_pop:.3f}",
|
| 425 |
+
delta="Strong" if abs(corr_life_pop) > 0.7 else "Moderate" if abs(corr_life_pop) > 0.3 else "Weak"
|
| 426 |
+
)
|
| 427 |
+
st.caption("Relationship between population size and life expectancy")
|
| 428 |
+
|
| 429 |
+
with col3:
|
| 430 |
+
st.metric(
|
| 431 |
+
"GDP per Capita ↔ Population",
|
| 432 |
+
f"{corr_gdp_pop:.3f}",
|
| 433 |
+
delta="Strong" if abs(corr_gdp_pop) > 0.7 else "Moderate" if abs(corr_gdp_pop) > 0.3 else "Weak"
|
| 434 |
+
)
|
| 435 |
+
st.caption("Relationship between wealth and population size")
|
| 436 |
+
|
| 437 |
+
st.info("💡 Correlation does not imply causation! High correlation indicates variables move together, but one doesn't necessarily cause the other.")
|
| 438 |
+
|
| 439 |
+
# Filter by Year or Country
|
| 440 |
+
st.header("🔎 Filter & Analyze")
|
| 441 |
+
col1, col2 = st.columns(2)
|
| 442 |
+
|
| 443 |
+
with col1:
|
| 444 |
+
selected_year = st.selectbox("Select Year", sorted(df['year'].unique()))
|
| 445 |
+
with col2:
|
| 446 |
+
selected_continent = st.multiselect("Select Continent(s)",
|
| 447 |
+
df['continent'].unique(),
|
| 448 |
+
default=df['continent'].unique())
|
| 449 |
+
|
| 450 |
+
filtered_df = df[(df['year'] == selected_year) & (df['continent'].isin(selected_continent))]
|
| 451 |
+
|
| 452 |
+
if not filtered_df.empty:
|
| 453 |
+
st.subheader(f"Statistics for {selected_year} - {', '.join(selected_continent)}")
|
| 454 |
+
|
| 455 |
+
# Metrics in columns
|
| 456 |
+
col1, col2, col3 = st.columns(3)
|
| 457 |
+
|
| 458 |
+
with col1:
|
| 459 |
+
st.metric("Life Expectancy (Mean)", f"{filtered_df['lifeExp'].mean():.2f}")
|
| 460 |
+
st.metric("Life Expectancy (Median)", f"{filtered_df['lifeExp'].median():.2f}")
|
| 461 |
+
st.metric("Life Expectancy (Std Dev)", f"{filtered_df['lifeExp'].std():.2f}")
|
| 462 |
+
|
| 463 |
+
with col2:
|
| 464 |
+
st.metric("Population (Mean)", f"{filtered_df['pop'].mean():,.0f}")
|
| 465 |
+
st.metric("Population (Median)", f"{filtered_df['pop'].median():,.0f}")
|
| 466 |
+
st.metric("Population (Std Dev)", f"{filtered_df['pop'].std():,.0f}")
|
| 467 |
+
|
| 468 |
+
with col3:
|
| 469 |
+
st.metric("GDP per Capita (Mean)", f"${filtered_df['gdpPercap'].mean():,.2f}")
|
| 470 |
+
st.metric("GDP per Capita (Median)", f"${filtered_df['gdpPercap'].median():.2f}")
|
| 471 |
+
st.metric("GDP per Capita (Std Dev)", f"${filtered_df['gdpPercap'].std():,.2f}")
|
| 472 |
+
|
| 473 |
+
# NEW: Add visualizations for filtered data
|
| 474 |
+
st.subheader("📊 Visual Analysis")
|
| 475 |
+
|
| 476 |
+
# Create tabs for different visualizations
|
| 477 |
+
viz_tab1, viz_tab2, viz_tab3, viz_tab4, viz_tab5 = st.tabs([
|
| 478 |
+
"Comparative Bar Charts",
|
| 479 |
+
"Scatter Analysis",
|
| 480 |
+
"Geographic Distribution",
|
| 481 |
+
"Statistical Distribution",
|
| 482 |
+
"Animated Timeline"
|
| 483 |
+
])
|
| 484 |
+
|
| 485 |
+
with viz_tab1:
|
| 486 |
+
st.write("Compare Mean, Median, and Standard Deviation across metrics")
|
| 487 |
+
|
| 488 |
+
# Create comparison charts
|
| 489 |
+
col1, col2 = st.columns(2)
|
| 490 |
+
|
| 491 |
+
with col1:
|
| 492 |
+
# Mean vs Median comparison
|
| 493 |
+
comparison_data = pd.DataFrame({
|
| 494 |
+
'Metric': numerical_cols * 2,
|
| 495 |
+
'Statistic': ['Mean']*3 + ['Median']*3,
|
| 496 |
+
'Value': [
|
| 497 |
+
filtered_df['lifeExp'].mean(),
|
| 498 |
+
filtered_df['pop'].mean(),
|
| 499 |
+
filtered_df['gdpPercap'].mean(),
|
| 500 |
+
filtered_df['lifeExp'].median(),
|
| 501 |
+
filtered_df['pop'].median(),
|
| 502 |
+
filtered_df['gdpPercap'].median()
|
| 503 |
+
]
|
| 504 |
+
})
|
| 505 |
+
|
| 506 |
+
fig = px.bar(comparison_data, x='Metric', y='Value',
|
| 507 |
+
color='Statistic', barmode='group',
|
| 508 |
+
title='Mean vs Median Comparison',
|
| 509 |
+
labels={'Value': 'Value'},
|
| 510 |
+
animation_frame=None)
|
| 511 |
+
fig.update_layout(transition={'duration': 500})
|
| 512 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 513 |
+
|
| 514 |
+
with col2:
|
| 515 |
+
# Standard Deviation
|
| 516 |
+
std_data = pd.DataFrame({
|
| 517 |
+
'Metric': numerical_cols,
|
| 518 |
+
'Std Dev': [
|
| 519 |
+
filtered_df['lifeExp'].std(),
|
| 520 |
+
filtered_df['pop'].std(),
|
| 521 |
+
filtered_df['gdpPercap'].std()
|
| 522 |
+
]
|
| 523 |
+
})
|
| 524 |
+
|
| 525 |
+
fig = px.bar(std_data, x='Metric', y='Std Dev',
|
| 526 |
+
title='Standard Deviation by Metric',
|
| 527 |
+
color='Metric')
|
| 528 |
+
fig.update_layout(transition={'duration': 500})
|
| 529 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 530 |
+
|
| 531 |
+
with viz_tab2:
|
| 532 |
+
st.write("Explore relationships between different metrics")
|
| 533 |
+
|
| 534 |
+
col1, col2 = st.columns(2)
|
| 535 |
+
|
| 536 |
+
with col1:
|
| 537 |
+
# GDP vs Life Expectancy
|
| 538 |
+
fig = px.scatter(filtered_df, x='gdpPercap', y='lifeExp',
|
| 539 |
+
size='pop', color='continent',
|
| 540 |
+
hover_name='country',
|
| 541 |
+
title=f'GDP per Capita vs Life Expectancy ({selected_year})',
|
| 542 |
+
labels={'gdpPercap': 'GDP per Capita',
|
| 543 |
+
'lifeExp': 'Life Expectancy'},
|
| 544 |
+
log_x=True)
|
| 545 |
+
fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')))
|
| 546 |
+
fig.update_layout(transition={'duration': 800, 'easing': 'cubic-in-out'})
|
| 547 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 548 |
+
|
| 549 |
+
with col2:
|
| 550 |
+
# Population vs Life Expectancy
|
| 551 |
+
fig = px.scatter(filtered_df, x='pop', y='lifeExp',
|
| 552 |
+
size='gdpPercap', color='continent',
|
| 553 |
+
hover_name='country',
|
| 554 |
+
title=f'Population vs Life Expectancy ({selected_year})',
|
| 555 |
+
labels={'pop': 'Population',
|
| 556 |
+
'lifeExp': 'Life Expectancy'},
|
| 557 |
+
log_x=True)
|
| 558 |
+
fig.update_traces(marker=dict(line=dict(width=1, color='DarkSlateGrey')))
|
| 559 |
+
fig.update_layout(transition={'duration': 800, 'easing': 'cubic-in-out'})
|
| 560 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 561 |
+
|
| 562 |
+
with viz_tab3:
|
| 563 |
+
st.write("Geographic distribution of metrics across countries")
|
| 564 |
+
|
| 565 |
+
# Bar chart by country
|
| 566 |
+
metric_choice = st.selectbox(
|
| 567 |
+
"Select metric to visualize by country",
|
| 568 |
+
options=['lifeExp', 'pop', 'gdpPercap'],
|
| 569 |
+
format_func=lambda x: {
|
| 570 |
+
'lifeExp': 'Life Expectancy',
|
| 571 |
+
'pop': 'Population',
|
| 572 |
+
'gdpPercap': 'GDP per Capita'
|
| 573 |
+
}[x]
|
| 574 |
+
)
|
| 575 |
+
|
| 576 |
+
# Sort by selected metric
|
| 577 |
+
sorted_df = filtered_df.sort_values(metric_choice, ascending=False)
|
| 578 |
+
|
| 579 |
+
fig = px.bar(sorted_df, x='country', y=metric_choice,
|
| 580 |
+
color='continent',
|
| 581 |
+
title=f'{metric_choice.replace("_", " ").title()} by Country ({selected_year})',
|
| 582 |
+
labels={metric_choice: metric_choice.replace('_', ' ').title()})
|
| 583 |
+
fig.update_layout(
|
| 584 |
+
xaxis_tickangle=-45,
|
| 585 |
+
height=500,
|
| 586 |
+
transition={'duration': 500, 'easing': 'cubic-in-out'}
|
| 587 |
+
)
|
| 588 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 589 |
+
|
| 590 |
+
# Top 10 and Bottom 10
|
| 591 |
+
col1, col2 = st.columns(2)
|
| 592 |
+
|
| 593 |
+
with col1:
|
| 594 |
+
st.write(f"🏆 Top 10 Countries - {metric_choice.replace('_', ' ').title()}")
|
| 595 |
+
top_10 = sorted_df[['country', metric_choice, 'continent']].head(10)
|
| 596 |
+
st.dataframe(top_10, hide_index=True)
|
| 597 |
+
|
| 598 |
+
with col2:
|
| 599 |
+
st.write(f"⚠️ Bottom 10 Countries - {metric_choice.replace('_', ' ').title()}")
|
| 600 |
+
bottom_10 = sorted_df[['country', metric_choice, 'continent']].tail(10)
|
| 601 |
+
st.dataframe(bottom_10, hide_index=True)
|
| 602 |
+
|
| 603 |
+
with viz_tab4:
|
| 604 |
+
st.write("Distribution patterns and box plots for filtered data")
|
| 605 |
+
|
| 606 |
+
# Box plots by continent
|
| 607 |
+
selected_metric_dist = st.selectbox(
|
| 608 |
+
"Select metric for distribution analysis",
|
| 609 |
+
options=['lifeExp', 'pop', 'gdpPercap'],
|
| 610 |
+
format_func=lambda x: {
|
| 611 |
+
'lifeExp': 'Life Expectancy',
|
| 612 |
+
'pop': 'Population',
|
| 613 |
+
'gdpPercap': 'GDP per Capita'
|
| 614 |
+
}[x],
|
| 615 |
+
key='dist_metric'
|
| 616 |
+
)
|
| 617 |
+
|
| 618 |
+
col1, col2 = st.columns(2)
|
| 619 |
+
|
| 620 |
+
with col1:
|
| 621 |
+
# Box plot
|
| 622 |
+
fig = px.box(filtered_df, x='continent', y=selected_metric_dist,
|
| 623 |
+
color='continent',
|
| 624 |
+
title=f'{selected_metric_dist.replace("_", " ").title()} Distribution by Continent',
|
| 625 |
+
labels={selected_metric_dist: selected_metric_dist.replace('_', ' ').title()})
|
| 626 |
+
fig.update_layout(transition={'duration': 600})
|
| 627 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 628 |
+
|
| 629 |
+
with col2:
|
| 630 |
+
# Violin plot
|
| 631 |
+
fig = px.violin(filtered_df, x='continent', y=selected_metric_dist,
|
| 632 |
+
color='continent', box=True,
|
| 633 |
+
title=f'{selected_metric_dist.replace("_", " ").title()} Violin Plot',
|
| 634 |
+
labels={selected_metric_dist: selected_metric_dist.replace('_', ' ').title()})
|
| 635 |
+
fig.update_layout(transition={'duration': 600})
|
| 636 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 637 |
+
|
| 638 |
+
with viz_tab5:
|
| 639 |
+
st.write("🎬 Watch how metrics evolve over time with animated visualizations")
|
| 640 |
+
|
| 641 |
+
# Animation type selector
|
| 642 |
+
anim_type = st.radio(
|
| 643 |
+
"Select Animation Type",
|
| 644 |
+
options=["Scatter Plot Timeline", "Bar Chart Race", "Line Chart Evolution"],
|
| 645 |
+
horizontal=True
|
| 646 |
+
)
|
| 647 |
+
|
| 648 |
+
if anim_type == "Scatter Plot Timeline":
|
| 649 |
+
# Animated scatter plot over all years
|
| 650 |
+
st.subheader("GDP vs Life Expectancy Over Time")
|
| 651 |
+
|
| 652 |
+
# Filter by selected continents
|
| 653 |
+
anim_df = df[df['continent'].isin(selected_continent)]
|
| 654 |
+
|
| 655 |
+
fig = px.scatter(anim_df,
|
| 656 |
+
x='gdpPercap',
|
| 657 |
+
y='lifeExp',
|
| 658 |
+
animation_frame='year',
|
| 659 |
+
animation_group='country',
|
| 660 |
+
size='pop',
|
| 661 |
+
color='continent',
|
| 662 |
+
hover_name='country',
|
| 663 |
+
log_x=True,
|
| 664 |
+
size_max=60,
|
| 665 |
+
range_x=[100, 120000],
|
| 666 |
+
range_y=[20, 90],
|
| 667 |
+
title='GDP per Capita vs Life Expectancy (Animated)',
|
| 668 |
+
labels={'gdpPercap': 'GDP per Capita',
|
| 669 |
+
'lifeExp': 'Life Expectancy',
|
| 670 |
+
'pop': 'Population'})
|
| 671 |
+
|
| 672 |
+
fig.update_layout(
|
| 673 |
+
height=600,
|
| 674 |
+
transition={'duration': 800}
|
| 675 |
+
)
|
| 676 |
+
|
| 677 |
+
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 800
|
| 678 |
+
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 500
|
| 679 |
+
|
| 680 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 681 |
+
st.info("Click the Play button to watch the animation! Each frame represents a year from 1952 to 2007.")
|
| 682 |
+
|
| 683 |
+
elif anim_type == "Bar Chart Race":
|
| 684 |
+
# Animated bar chart race
|
| 685 |
+
st.subheader("Top Countries Bar Chart Race")
|
| 686 |
+
|
| 687 |
+
metric_race = st.selectbox(
|
| 688 |
+
"Select metric for bar chart race",
|
| 689 |
+
options=['lifeExp', 'pop', 'gdpPercap'],
|
| 690 |
+
format_func=lambda x: {
|
| 691 |
+
'lifeExp': 'Life Expectancy',
|
| 692 |
+
'pop': 'Population',
|
| 693 |
+
'gdpPercap': 'GDP per Capita'
|
| 694 |
+
}[x],
|
| 695 |
+
key='race_metric'
|
| 696 |
+
)
|
| 697 |
+
|
| 698 |
+
# Get top 15 countries for the latest year
|
| 699 |
+
latest_year_df = df[df['year'] == df['year'].max()]
|
| 700 |
+
top_countries = latest_year_df.nlargest(15, metric_race)['country'].tolist()
|
| 701 |
+
|
| 702 |
+
# Filter data for these countries
|
| 703 |
+
race_df = df[df['country'].isin(top_countries) & df['continent'].isin(selected_continent)]
|
| 704 |
+
|
| 705 |
+
fig = px.bar(race_df,
|
| 706 |
+
x=metric_race,
|
| 707 |
+
y='country',
|
| 708 |
+
color='continent',
|
| 709 |
+
animation_frame='year',
|
| 710 |
+
orientation='h',
|
| 711 |
+
title=f'Top Countries by {metric_race.replace("_", " ").title()} Over Time',
|
| 712 |
+
labels={metric_race: metric_race.replace('_', ' ').title()},
|
| 713 |
+
range_x=[0, race_df[metric_race].max() * 1.1])
|
| 714 |
+
|
| 715 |
+
fig.update_layout(
|
| 716 |
+
height=600,
|
| 717 |
+
yaxis={'categoryorder': 'total ascending'}
|
| 718 |
+
)
|
| 719 |
+
|
| 720 |
+
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 1000
|
| 721 |
+
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 500
|
| 722 |
+
|
| 723 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 724 |
+
st.info("Watch the ranking change over time! Countries rise and fall based on their performance.")
|
| 725 |
+
|
| 726 |
+
else: # Line Chart Evolution
|
| 727 |
+
st.subheader("Metric Evolution by Continent")
|
| 728 |
+
|
| 729 |
+
metric_line = st.selectbox(
|
| 730 |
+
"Select metric for time evolution",
|
| 731 |
+
options=['lifeExp', 'pop', 'gdpPercap'],
|
| 732 |
+
format_func=lambda x: {
|
| 733 |
+
'lifeExp': 'Life Expectancy',
|
| 734 |
+
'pop': 'Population',
|
| 735 |
+
'gdpPercap': 'GDP per Capita'
|
| 736 |
+
}[x],
|
| 737 |
+
key='line_metric'
|
| 738 |
+
)
|
| 739 |
+
|
| 740 |
+
# Calculate mean by continent and year
|
| 741 |
+
line_df = df[df['continent'].isin(selected_continent)].groupby(['year', 'continent'])[metric_line].mean().reset_index()
|
| 742 |
+
|
| 743 |
+
fig = px.line(line_df,
|
| 744 |
+
x='year',
|
| 745 |
+
y=metric_line,
|
| 746 |
+
color='continent',
|
| 747 |
+
markers=True,
|
| 748 |
+
title=f'Average {metric_line.replace("_", " ").title()} Evolution by Continent',
|
| 749 |
+
labels={metric_line: f'Average {metric_line.replace("_", " ").title()}',
|
| 750 |
+
'year': 'Year'})
|
| 751 |
+
|
| 752 |
+
fig.update_traces(
|
| 753 |
+
mode='lines+markers',
|
| 754 |
+
line=dict(width=3),
|
| 755 |
+
marker=dict(size=8)
|
| 756 |
+
)
|
| 757 |
+
|
| 758 |
+
fig.update_layout(
|
| 759 |
+
height=500,
|
| 760 |
+
hovermode='x unified',
|
| 761 |
+
transition={'duration': 600}
|
| 762 |
+
)
|
| 763 |
+
|
| 764 |
+
# Add animation on load
|
| 765 |
+
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightGray')
|
| 766 |
+
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightGray')
|
| 767 |
+
|
| 768 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 769 |
+
st.info("Hover over the chart to see detailed values for each year and continent.")
|
| 770 |
+
|
| 771 |
+
# Distribution Visualizations
|
| 772 |
+
st.header("📉 Overall Data Distributions")
|
| 773 |
+
selected_metric = st.selectbox("Select metric to visualize", numerical_cols,
|
| 774 |
+
format_func=lambda x: {
|
| 775 |
+
'lifeExp': 'Life Expectancy',
|
| 776 |
+
'pop': 'Population',
|
| 777 |
+
'gdpPercap': 'GDP per Capita'
|
| 778 |
+
}[x])
|
| 779 |
+
|
| 780 |
+
col1, col2 = st.columns(2)
|
| 781 |
+
|
| 782 |
+
with col1:
|
| 783 |
+
fig = px.histogram(df, x=selected_metric, nbins=50,
|
| 784 |
+
title=f'Distribution of {selected_metric}',
|
| 785 |
+
labels={selected_metric: selected_metric.replace('_', ' ').title()})
|
| 786 |
+
fig.add_vline(x=df[selected_metric].mean(), line_dash="dash",
|
| 787 |
+
line_color="red", annotation_text="Mean")
|
| 788 |
+
fig.add_vline(x=df[selected_metric].median(), line_dash="dash",
|
| 789 |
+
line_color="green", annotation_text="Median")
|
| 790 |
+
fig.update_layout(transition={'duration': 700})
|
| 791 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 792 |
+
|
| 793 |
+
with col2:
|
| 794 |
+
fig = px.box(df, y=selected_metric, x='continent',
|
| 795 |
+
title=f'{selected_metric} by Continent',
|
| 796 |
+
labels={selected_metric: selected_metric.replace('_', ' ').title()})
|
| 797 |
+
fig.update_layout(transition={'duration': 700})
|
| 798 |
+
st.plotly_chart(fig, use_container_width=True)
|
| 799 |
+
|
| 800 |
+
# Footer
|
| 801 |
+
st.markdown("---")
|
| 802 |
+
st.markdown("Data source: [Gapminder](https://www.gapminder.org/)")
|
| 803 |
+
st.markdown("**Version 2** - Enhanced with interactive visualizations in Filter & Analyze section")
|
gapminder.tsv
ADDED
|
@@ -0,0 +1,1705 @@
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|
| 1 |
+
country continent year lifeExp pop gdpPercap
|
| 2 |
+
Afghanistan Asia 1952 28.801 8425333 779.4453145
|
| 3 |
+
Afghanistan Asia 1957 30.332 9240934 820.8530296
|
| 4 |
+
Afghanistan Asia 1962 31.997 10267083 853.10071
|
| 5 |
+
Afghanistan Asia 1967 34.02 11537966 836.1971382
|
| 6 |
+
Afghanistan Asia 1972 36.088 13079460 739.9811058
|
| 7 |
+
Afghanistan Asia 1977 38.438 14880372 786.11336
|
| 8 |
+
Afghanistan Asia 1982 39.854 12881816 978.0114388
|
| 9 |
+
Afghanistan Asia 1987 40.822 13867957 852.3959448
|
| 10 |
+
Afghanistan Asia 1992 41.674 16317921 649.3413952
|
| 11 |
+
Afghanistan Asia 1997 41.763 22227415 635.341351
|
| 12 |
+
Afghanistan Asia 2002 42.129 25268405 726.7340548
|
| 13 |
+
Afghanistan Asia 2007 43.828 31889923 974.5803384
|
| 14 |
+
Albania Europe 1952 55.23 1282697 1601.056136
|
| 15 |
+
Albania Europe 1957 59.28 1476505 1942.284244
|
| 16 |
+
Albania Europe 1962 64.82 1728137 2312.888958
|
| 17 |
+
Albania Europe 1967 66.22 1984060 2760.196931
|
| 18 |
+
Albania Europe 1972 67.69 2263554 3313.422188
|
| 19 |
+
Albania Europe 1977 68.93 2509048 3533.00391
|
| 20 |
+
Albania Europe 1982 70.42 2780097 3630.880722
|
| 21 |
+
Albania Europe 1987 72 3075321 3738.932735
|
| 22 |
+
Albania Europe 1992 71.581 3326498 2497.437901
|
| 23 |
+
Albania Europe 1997 72.95 3428038 3193.054604
|
| 24 |
+
Albania Europe 2002 75.651 3508512 4604.211737
|
| 25 |
+
Albania Europe 2007 76.423 3600523 5937.029526
|
| 26 |
+
Algeria Africa 1952 43.077 9279525 2449.008185
|
| 27 |
+
Algeria Africa 1957 45.685 10270856 3013.976023
|
| 28 |
+
Algeria Africa 1962 48.303 11000948 2550.81688
|
| 29 |
+
Algeria Africa 1967 51.407 12760499 3246.991771
|
| 30 |
+
Algeria Africa 1972 54.518 14760787 4182.663766
|
| 31 |
+
Algeria Africa 1977 58.014 17152804 4910.416756
|
| 32 |
+
Algeria Africa 1982 61.368 20033753 5745.160213
|
| 33 |
+
Algeria Africa 1987 65.799 23254956 5681.358539
|
| 34 |
+
Algeria Africa 1992 67.744 26298373 5023.216647
|
| 35 |
+
Algeria Africa 1997 69.152 29072015 4797.295051
|
| 36 |
+
Algeria Africa 2002 70.994 31287142 5288.040382
|
| 37 |
+
Algeria Africa 2007 72.301 33333216 6223.367465
|
| 38 |
+
Angola Africa 1952 30.015 4232095 3520.610273
|
| 39 |
+
Angola Africa 1957 31.999 4561361 3827.940465
|
| 40 |
+
Angola Africa 1962 34 4826015 4269.276742
|
| 41 |
+
Angola Africa 1967 35.985 5247469 5522.776375
|
| 42 |
+
Angola Africa 1972 37.928 5894858 5473.288005
|
| 43 |
+
Angola Africa 1977 39.483 6162675 3008.647355
|
| 44 |
+
Angola Africa 1982 39.942 7016384 2756.953672
|
| 45 |
+
Angola Africa 1987 39.906 7874230 2430.208311
|
| 46 |
+
Angola Africa 1992 40.647 8735988 2627.845685
|
| 47 |
+
Angola Africa 1997 40.963 9875024 2277.140884
|
| 48 |
+
Angola Africa 2002 41.003 10866106 2773.287312
|
| 49 |
+
Angola Africa 2007 42.731 12420476 4797.231267
|
| 50 |
+
Argentina Americas 1952 62.485 17876956 5911.315053
|
| 51 |
+
Argentina Americas 1957 64.399 19610538 6856.856212
|
| 52 |
+
Argentina Americas 1962 65.142 21283783 7133.166023
|
| 53 |
+
Argentina Americas 1967 65.634 22934225 8052.953021
|
| 54 |
+
Argentina Americas 1972 67.065 24779799 9443.038526
|
| 55 |
+
Argentina Americas 1977 68.481 26983828 10079.02674
|
| 56 |
+
Argentina Americas 1982 69.942 29341374 8997.897412
|
| 57 |
+
Argentina Americas 1987 70.774 31620918 9139.671389
|
| 58 |
+
Argentina Americas 1992 71.868 33958947 9308.41871
|
| 59 |
+
Argentina Americas 1997 73.275 36203463 10967.28195
|
| 60 |
+
Argentina Americas 2002 74.34 38331121 8797.640716
|
| 61 |
+
Argentina Americas 2007 75.32 40301927 12779.37964
|
| 62 |
+
Australia Oceania 1952 69.12 8691212 10039.59564
|
| 63 |
+
Australia Oceania 1957 70.33 9712569 10949.64959
|
| 64 |
+
Australia Oceania 1962 70.93 10794968 12217.22686
|
| 65 |
+
Australia Oceania 1967 71.1 11872264 14526.12465
|
| 66 |
+
Australia Oceania 1972 71.93 13177000 16788.62948
|
| 67 |
+
Australia Oceania 1977 73.49 14074100 18334.19751
|
| 68 |
+
Australia Oceania 1982 74.74 15184200 19477.00928
|
| 69 |
+
Australia Oceania 1987 76.32 16257249 21888.88903
|
| 70 |
+
Australia Oceania 1992 77.56 17481977 23424.76683
|
| 71 |
+
Australia Oceania 1997 78.83 18565243 26997.93657
|
| 72 |
+
Australia Oceania 2002 80.37 19546792 30687.75473
|
| 73 |
+
Australia Oceania 2007 81.235 20434176 34435.36744
|
| 74 |
+
Austria Europe 1952 66.8 6927772 6137.076492
|
| 75 |
+
Austria Europe 1957 67.48 6965860 8842.59803
|
| 76 |
+
Austria Europe 1962 69.54 7129864 10750.72111
|
| 77 |
+
Austria Europe 1967 70.14 7376998 12834.6024
|
| 78 |
+
Austria Europe 1972 70.63 7544201 16661.6256
|
| 79 |
+
Austria Europe 1977 72.17 7568430 19749.4223
|
| 80 |
+
Austria Europe 1982 73.18 7574613 21597.08362
|
| 81 |
+
Austria Europe 1987 74.94 7578903 23687.82607
|
| 82 |
+
Austria Europe 1992 76.04 7914969 27042.01868
|
| 83 |
+
Austria Europe 1997 77.51 8069876 29095.92066
|
| 84 |
+
Austria Europe 2002 78.98 8148312 32417.60769
|
| 85 |
+
Austria Europe 2007 79.829 8199783 36126.4927
|
| 86 |
+
Bahrain Asia 1952 50.939 120447 9867.084765
|
| 87 |
+
Bahrain Asia 1957 53.832 138655 11635.79945
|
| 88 |
+
Bahrain Asia 1962 56.923 171863 12753.27514
|
| 89 |
+
Bahrain Asia 1967 59.923 202182 14804.6727
|
| 90 |
+
Bahrain Asia 1972 63.3 230800 18268.65839
|
| 91 |
+
Bahrain Asia 1977 65.593 297410 19340.10196
|
| 92 |
+
Bahrain Asia 1982 69.052 377967 19211.14731
|
| 93 |
+
Bahrain Asia 1987 70.75 454612 18524.02406
|
| 94 |
+
Bahrain Asia 1992 72.601 529491 19035.57917
|
| 95 |
+
Bahrain Asia 1997 73.925 598561 20292.01679
|
| 96 |
+
Bahrain Asia 2002 74.795 656397 23403.55927
|
| 97 |
+
Bahrain Asia 2007 75.635 708573 29796.04834
|
| 98 |
+
Bangladesh Asia 1952 37.484 46886859 684.2441716
|
| 99 |
+
Bangladesh Asia 1957 39.348 51365468 661.6374577
|
| 100 |
+
Bangladesh Asia 1962 41.216 56839289 686.3415538
|
| 101 |
+
Bangladesh Asia 1967 43.453 62821884 721.1860862
|
| 102 |
+
Bangladesh Asia 1972 45.252 70759295 630.2336265
|
| 103 |
+
Bangladesh Asia 1977 46.923 80428306 659.8772322
|
| 104 |
+
Bangladesh Asia 1982 50.009 93074406 676.9818656
|
| 105 |
+
Bangladesh Asia 1987 52.819 103764241 751.9794035
|
| 106 |
+
Bangladesh Asia 1992 56.018 113704579 837.8101643
|
| 107 |
+
Bangladesh Asia 1997 59.412 123315288 972.7700352
|
| 108 |
+
Bangladesh Asia 2002 62.013 135656790 1136.39043
|
| 109 |
+
Bangladesh Asia 2007 64.062 150448339 1391.253792
|
| 110 |
+
Belgium Europe 1952 68 8730405 8343.105127
|
| 111 |
+
Belgium Europe 1957 69.24 8989111 9714.960623
|
| 112 |
+
Belgium Europe 1962 70.25 9218400 10991.20676
|
| 113 |
+
Belgium Europe 1967 70.94 9556500 13149.04119
|
| 114 |
+
Belgium Europe 1972 71.44 9709100 16672.14356
|
| 115 |
+
Belgium Europe 1977 72.8 9821800 19117.97448
|
| 116 |
+
Belgium Europe 1982 73.93 9856303 20979.84589
|
| 117 |
+
Belgium Europe 1987 75.35 9870200 22525.56308
|
| 118 |
+
Belgium Europe 1992 76.46 10045622 25575.57069
|
| 119 |
+
Belgium Europe 1997 77.53 10199787 27561.19663
|
| 120 |
+
Belgium Europe 2002 78.32 10311970 30485.88375
|
| 121 |
+
Belgium Europe 2007 79.441 10392226 33692.60508
|
| 122 |
+
Benin Africa 1952 38.223 1738315 1062.7522
|
| 123 |
+
Benin Africa 1957 40.358 1925173 959.6010805
|
| 124 |
+
Benin Africa 1962 42.618 2151895 949.4990641
|
| 125 |
+
Benin Africa 1967 44.885 2427334 1035.831411
|
| 126 |
+
Benin Africa 1972 47.014 2761407 1085.796879
|
| 127 |
+
Benin Africa 1977 49.19 3168267 1029.161251
|
| 128 |
+
Benin Africa 1982 50.904 3641603 1277.897616
|
| 129 |
+
Benin Africa 1987 52.337 4243788 1225.85601
|
| 130 |
+
Benin Africa 1992 53.919 4981671 1191.207681
|
| 131 |
+
Benin Africa 1997 54.777 6066080 1232.975292
|
| 132 |
+
Benin Africa 2002 54.406 7026113 1372.877931
|
| 133 |
+
Benin Africa 2007 56.728 8078314 1441.284873
|
| 134 |
+
Bolivia Americas 1952 40.414 2883315 2677.326347
|
| 135 |
+
Bolivia Americas 1957 41.89 3211738 2127.686326
|
| 136 |
+
Bolivia Americas 1962 43.428 3593918 2180.972546
|
| 137 |
+
Bolivia Americas 1967 45.032 4040665 2586.886053
|
| 138 |
+
Bolivia Americas 1972 46.714 4565872 2980.331339
|
| 139 |
+
Bolivia Americas 1977 50.023 5079716 3548.097832
|
| 140 |
+
Bolivia Americas 1982 53.859 5642224 3156.510452
|
| 141 |
+
Bolivia Americas 1987 57.251 6156369 2753.69149
|
| 142 |
+
Bolivia Americas 1992 59.957 6893451 2961.699694
|
| 143 |
+
Bolivia Americas 1997 62.05 7693188 3326.143191
|
| 144 |
+
Bolivia Americas 2002 63.883 8445134 3413.26269
|
| 145 |
+
Bolivia Americas 2007 65.554 9119152 3822.137084
|
| 146 |
+
Bosnia and Herzegovina Europe 1952 53.82 2791000 973.5331948
|
| 147 |
+
Bosnia and Herzegovina Europe 1957 58.45 3076000 1353.989176
|
| 148 |
+
Bosnia and Herzegovina Europe 1962 61.93 3349000 1709.683679
|
| 149 |
+
Bosnia and Herzegovina Europe 1967 64.79 3585000 2172.352423
|
| 150 |
+
Bosnia and Herzegovina Europe 1972 67.45 3819000 2860.16975
|
| 151 |
+
Bosnia and Herzegovina Europe 1977 69.86 4086000 3528.481305
|
| 152 |
+
Bosnia and Herzegovina Europe 1982 70.69 4172693 4126.613157
|
| 153 |
+
Bosnia and Herzegovina Europe 1987 71.14 4338977 4314.114757
|
| 154 |
+
Bosnia and Herzegovina Europe 1992 72.178 4256013 2546.781445
|
| 155 |
+
Bosnia and Herzegovina Europe 1997 73.244 3607000 4766.355904
|
| 156 |
+
Bosnia and Herzegovina Europe 2002 74.09 4165416 6018.975239
|
| 157 |
+
Bosnia and Herzegovina Europe 2007 74.852 4552198 7446.298803
|
| 158 |
+
Botswana Africa 1952 47.622 442308 851.2411407
|
| 159 |
+
Botswana Africa 1957 49.618 474639 918.2325349
|
| 160 |
+
Botswana Africa 1962 51.52 512764 983.6539764
|
| 161 |
+
Botswana Africa 1967 53.298 553541 1214.709294
|
| 162 |
+
Botswana Africa 1972 56.024 619351 2263.611114
|
| 163 |
+
Botswana Africa 1977 59.319 781472 3214.857818
|
| 164 |
+
Botswana Africa 1982 61.484 970347 4551.14215
|
| 165 |
+
Botswana Africa 1987 63.622 1151184 6205.88385
|
| 166 |
+
Botswana Africa 1992 62.745 1342614 7954.111645
|
| 167 |
+
Botswana Africa 1997 52.556 1536536 8647.142313
|
| 168 |
+
Botswana Africa 2002 46.634 1630347 11003.60508
|
| 169 |
+
Botswana Africa 2007 50.728 1639131 12569.85177
|
| 170 |
+
Brazil Americas 1952 50.917 56602560 2108.944355
|
| 171 |
+
Brazil Americas 1957 53.285 65551171 2487.365989
|
| 172 |
+
Brazil Americas 1962 55.665 76039390 3336.585802
|
| 173 |
+
Brazil Americas 1967 57.632 88049823 3429.864357
|
| 174 |
+
Brazil Americas 1972 59.504 100840058 4985.711467
|
| 175 |
+
Brazil Americas 1977 61.489 114313951 6660.118654
|
| 176 |
+
Brazil Americas 1982 63.336 128962939 7030.835878
|
| 177 |
+
Brazil Americas 1987 65.205 142938076 7807.095818
|
| 178 |
+
Brazil Americas 1992 67.057 155975974 6950.283021
|
| 179 |
+
Brazil Americas 1997 69.388 168546719 7957.980824
|
| 180 |
+
Brazil Americas 2002 71.006 179914212 8131.212843
|
| 181 |
+
Brazil Americas 2007 72.39 190010647 9065.800825
|
| 182 |
+
Bulgaria Europe 1952 59.6 7274900 2444.286648
|
| 183 |
+
Bulgaria Europe 1957 66.61 7651254 3008.670727
|
| 184 |
+
Bulgaria Europe 1962 69.51 8012946 4254.337839
|
| 185 |
+
Bulgaria Europe 1967 70.42 8310226 5577.0028
|
| 186 |
+
Bulgaria Europe 1972 70.9 8576200 6597.494398
|
| 187 |
+
Bulgaria Europe 1977 70.81 8797022 7612.240438
|
| 188 |
+
Bulgaria Europe 1982 71.08 8892098 8224.191647
|
| 189 |
+
Bulgaria Europe 1987 71.34 8971958 8239.854824
|
| 190 |
+
Bulgaria Europe 1992 71.19 8658506 6302.623438
|
| 191 |
+
Bulgaria Europe 1997 70.32 8066057 5970.38876
|
| 192 |
+
Bulgaria Europe 2002 72.14 7661799 7696.777725
|
| 193 |
+
Bulgaria Europe 2007 73.005 7322858 10680.79282
|
| 194 |
+
Burkina Faso Africa 1952 31.975 4469979 543.2552413
|
| 195 |
+
Burkina Faso Africa 1957 34.906 4713416 617.1834648
|
| 196 |
+
Burkina Faso Africa 1962 37.814 4919632 722.5120206
|
| 197 |
+
Burkina Faso Africa 1967 40.697 5127935 794.8265597
|
| 198 |
+
Burkina Faso Africa 1972 43.591 5433886 854.7359763
|
| 199 |
+
Burkina Faso Africa 1977 46.137 5889574 743.3870368
|
| 200 |
+
Burkina Faso Africa 1982 48.122 6634596 807.1985855
|
| 201 |
+
Burkina Faso Africa 1987 49.557 7586551 912.0631417
|
| 202 |
+
Burkina Faso Africa 1992 50.26 8878303 931.7527731
|
| 203 |
+
Burkina Faso Africa 1997 50.324 10352843 946.2949618
|
| 204 |
+
Burkina Faso Africa 2002 50.65 12251209 1037.645221
|
| 205 |
+
Burkina Faso Africa 2007 52.295 14326203 1217.032994
|
| 206 |
+
Burundi Africa 1952 39.031 2445618 339.2964587
|
| 207 |
+
Burundi Africa 1957 40.533 2667518 379.5646281
|
| 208 |
+
Burundi Africa 1962 42.045 2961915 355.2032273
|
| 209 |
+
Burundi Africa 1967 43.548 3330989 412.9775136
|
| 210 |
+
Burundi Africa 1972 44.057 3529983 464.0995039
|
| 211 |
+
Burundi Africa 1977 45.91 3834415 556.1032651
|
| 212 |
+
Burundi Africa 1982 47.471 4580410 559.603231
|
| 213 |
+
Burundi Africa 1987 48.211 5126023 621.8188189
|
| 214 |
+
Burundi Africa 1992 44.736 5809236 631.6998778
|
| 215 |
+
Burundi Africa 1997 45.326 6121610 463.1151478
|
| 216 |
+
Burundi Africa 2002 47.36 7021078 446.4035126
|
| 217 |
+
Burundi Africa 2007 49.58 8390505 430.0706916
|
| 218 |
+
Cambodia Asia 1952 39.417 4693836 368.4692856
|
| 219 |
+
Cambodia Asia 1957 41.366 5322536 434.0383364
|
| 220 |
+
Cambodia Asia 1962 43.415 6083619 496.9136476
|
| 221 |
+
Cambodia Asia 1967 45.415 6960067 523.4323142
|
| 222 |
+
Cambodia Asia 1972 40.317 7450606 421.6240257
|
| 223 |
+
Cambodia Asia 1977 31.22 6978607 524.9721832
|
| 224 |
+
Cambodia Asia 1982 50.957 7272485 624.4754784
|
| 225 |
+
Cambodia Asia 1987 53.914 8371791 683.8955732
|
| 226 |
+
Cambodia Asia 1992 55.803 10150094 682.3031755
|
| 227 |
+
Cambodia Asia 1997 56.534 11782962 734.28517
|
| 228 |
+
Cambodia Asia 2002 56.752 12926707 896.2260153
|
| 229 |
+
Cambodia Asia 2007 59.723 14131858 1713.778686
|
| 230 |
+
Cameroon Africa 1952 38.523 5009067 1172.667655
|
| 231 |
+
Cameroon Africa 1957 40.428 5359923 1313.048099
|
| 232 |
+
Cameroon Africa 1962 42.643 5793633 1399.607441
|
| 233 |
+
Cameroon Africa 1967 44.799 6335506 1508.453148
|
| 234 |
+
Cameroon Africa 1972 47.049 7021028 1684.146528
|
| 235 |
+
Cameroon Africa 1977 49.355 7959865 1783.432873
|
| 236 |
+
Cameroon Africa 1982 52.961 9250831 2367.983282
|
| 237 |
+
Cameroon Africa 1987 54.985 10780667 2602.664206
|
| 238 |
+
Cameroon Africa 1992 54.314 12467171 1793.163278
|
| 239 |
+
Cameroon Africa 1997 52.199 14195809 1694.337469
|
| 240 |
+
Cameroon Africa 2002 49.856 15929988 1934.011449
|
| 241 |
+
Cameroon Africa 2007 50.43 17696293 2042.09524
|
| 242 |
+
Canada Americas 1952 68.75 14785584 11367.16112
|
| 243 |
+
Canada Americas 1957 69.96 17010154 12489.95006
|
| 244 |
+
Canada Americas 1962 71.3 18985849 13462.48555
|
| 245 |
+
Canada Americas 1967 72.13 20819767 16076.58803
|
| 246 |
+
Canada Americas 1972 72.88 22284500 18970.57086
|
| 247 |
+
Canada Americas 1977 74.21 23796400 22090.88306
|
| 248 |
+
Canada Americas 1982 75.76 25201900 22898.79214
|
| 249 |
+
Canada Americas 1987 76.86 26549700 26626.51503
|
| 250 |
+
Canada Americas 1992 77.95 28523502 26342.88426
|
| 251 |
+
Canada Americas 1997 78.61 30305843 28954.92589
|
| 252 |
+
Canada Americas 2002 79.77 31902268 33328.96507
|
| 253 |
+
Canada Americas 2007 80.653 33390141 36319.23501
|
| 254 |
+
Central African Republic Africa 1952 35.463 1291695 1071.310713
|
| 255 |
+
Central African Republic Africa 1957 37.464 1392284 1190.844328
|
| 256 |
+
Central African Republic Africa 1962 39.475 1523478 1193.068753
|
| 257 |
+
Central African Republic Africa 1967 41.478 1733638 1136.056615
|
| 258 |
+
Central African Republic Africa 1972 43.457 1927260 1070.013275
|
| 259 |
+
Central African Republic Africa 1977 46.775 2167533 1109.374338
|
| 260 |
+
Central African Republic Africa 1982 48.295 2476971 956.7529907
|
| 261 |
+
Central African Republic Africa 1987 50.485 2840009 844.8763504
|
| 262 |
+
Central African Republic Africa 1992 49.396 3265124 747.9055252
|
| 263 |
+
Central African Republic Africa 1997 46.066 3696513 740.5063317
|
| 264 |
+
Central African Republic Africa 2002 43.308 4048013 738.6906068
|
| 265 |
+
Central African Republic Africa 2007 44.741 4369038 706.016537
|
| 266 |
+
Chad Africa 1952 38.092 2682462 1178.665927
|
| 267 |
+
Chad Africa 1957 39.881 2894855 1308.495577
|
| 268 |
+
Chad Africa 1962 41.716 3150417 1389.817618
|
| 269 |
+
Chad Africa 1967 43.601 3495967 1196.810565
|
| 270 |
+
Chad Africa 1972 45.569 3899068 1104.103987
|
| 271 |
+
Chad Africa 1977 47.383 4388260 1133.98495
|
| 272 |
+
Chad Africa 1982 49.517 4875118 797.9081006
|
| 273 |
+
Chad Africa 1987 51.051 5498955 952.386129
|
| 274 |
+
Chad Africa 1992 51.724 6429417 1058.0643
|
| 275 |
+
Chad Africa 1997 51.573 7562011 1004.961353
|
| 276 |
+
Chad Africa 2002 50.525 8835739 1156.18186
|
| 277 |
+
Chad Africa 2007 50.651 10238807 1704.063724
|
| 278 |
+
Chile Americas 1952 54.745 6377619 3939.978789
|
| 279 |
+
Chile Americas 1957 56.074 7048426 4315.622723
|
| 280 |
+
Chile Americas 1962 57.924 7961258 4519.094331
|
| 281 |
+
Chile Americas 1967 60.523 8858908 5106.654313
|
| 282 |
+
Chile Americas 1972 63.441 9717524 5494.024437
|
| 283 |
+
Chile Americas 1977 67.052 10599793 4756.763836
|
| 284 |
+
Chile Americas 1982 70.565 11487112 5095.665738
|
| 285 |
+
Chile Americas 1987 72.492 12463354 5547.063754
|
| 286 |
+
Chile Americas 1992 74.126 13572994 7596.125964
|
| 287 |
+
Chile Americas 1997 75.816 14599929 10118.05318
|
| 288 |
+
Chile Americas 2002 77.86 15497046 10778.78385
|
| 289 |
+
Chile Americas 2007 78.553 16284741 13171.63885
|
| 290 |
+
China Asia 1952 44 556263527 400.448611
|
| 291 |
+
China Asia 1957 50.54896 637408000 575.9870009
|
| 292 |
+
China Asia 1962 44.50136 665770000 487.6740183
|
| 293 |
+
China Asia 1967 58.38112 754550000 612.7056934
|
| 294 |
+
China Asia 1972 63.11888 862030000 676.9000921
|
| 295 |
+
China Asia 1977 63.96736 943455000 741.2374699
|
| 296 |
+
China Asia 1982 65.525 1000281000 962.4213805
|
| 297 |
+
China Asia 1987 67.274 1084035000 1378.904018
|
| 298 |
+
China Asia 1992 68.69 1164970000 1655.784158
|
| 299 |
+
China Asia 1997 70.426 1230075000 2289.234136
|
| 300 |
+
China Asia 2002 72.028 1280400000 3119.280896
|
| 301 |
+
China Asia 2007 72.961 1318683096 4959.114854
|
| 302 |
+
Colombia Americas 1952 50.643 12350771 2144.115096
|
| 303 |
+
Colombia Americas 1957 55.118 14485993 2323.805581
|
| 304 |
+
Colombia Americas 1962 57.863 17009885 2492.351109
|
| 305 |
+
Colombia Americas 1967 59.963 19764027 2678.729839
|
| 306 |
+
Colombia Americas 1972 61.623 22542890 3264.660041
|
| 307 |
+
Colombia Americas 1977 63.837 25094412 3815.80787
|
| 308 |
+
Colombia Americas 1982 66.653 27764644 4397.575659
|
| 309 |
+
Colombia Americas 1987 67.768 30964245 4903.2191
|
| 310 |
+
Colombia Americas 1992 68.421 34202721 5444.648617
|
| 311 |
+
Colombia Americas 1997 70.313 37657830 6117.361746
|
| 312 |
+
Colombia Americas 2002 71.682 41008227 5755.259962
|
| 313 |
+
Colombia Americas 2007 72.889 44227550 7006.580419
|
| 314 |
+
Comoros Africa 1952 40.715 153936 1102.990936
|
| 315 |
+
Comoros Africa 1957 42.46 170928 1211.148548
|
| 316 |
+
Comoros Africa 1962 44.467 191689 1406.648278
|
| 317 |
+
Comoros Africa 1967 46.472 217378 1876.029643
|
| 318 |
+
Comoros Africa 1972 48.944 250027 1937.577675
|
| 319 |
+
Comoros Africa 1977 50.939 304739 1172.603047
|
| 320 |
+
Comoros Africa 1982 52.933 348643 1267.100083
|
| 321 |
+
Comoros Africa 1987 54.926 395114 1315.980812
|
| 322 |
+
Comoros Africa 1992 57.939 454429 1246.90737
|
| 323 |
+
Comoros Africa 1997 60.66 527982 1173.618235
|
| 324 |
+
Comoros Africa 2002 62.974 614382 1075.811558
|
| 325 |
+
Comoros Africa 2007 65.152 710960 986.1478792
|
| 326 |
+
Congo, Dem. Rep. Africa 1952 39.143 14100005 780.5423257
|
| 327 |
+
Congo, Dem. Rep. Africa 1957 40.652 15577932 905.8602303
|
| 328 |
+
Congo, Dem. Rep. Africa 1962 42.122 17486434 896.3146335
|
| 329 |
+
Congo, Dem. Rep. Africa 1967 44.056 19941073 861.5932424
|
| 330 |
+
Congo, Dem. Rep. Africa 1972 45.989 23007669 904.8960685
|
| 331 |
+
Congo, Dem. Rep. Africa 1977 47.804 26480870 795.757282
|
| 332 |
+
Congo, Dem. Rep. Africa 1982 47.784 30646495 673.7478181
|
| 333 |
+
Congo, Dem. Rep. Africa 1987 47.412 35481645 672.774812
|
| 334 |
+
Congo, Dem. Rep. Africa 1992 45.548 41672143 457.7191807
|
| 335 |
+
Congo, Dem. Rep. Africa 1997 42.587 47798986 312.188423
|
| 336 |
+
Congo, Dem. Rep. Africa 2002 44.966 55379852 241.1658765
|
| 337 |
+
Congo, Dem. Rep. Africa 2007 46.462 64606759 277.5518587
|
| 338 |
+
Congo, Rep. Africa 1952 42.111 854885 2125.621418
|
| 339 |
+
Congo, Rep. Africa 1957 45.053 940458 2315.056572
|
| 340 |
+
Congo, Rep. Africa 1962 48.435 1047924 2464.783157
|
| 341 |
+
Congo, Rep. Africa 1967 52.04 1179760 2677.939642
|
| 342 |
+
Congo, Rep. Africa 1972 54.907 1340458 3213.152683
|
| 343 |
+
Congo, Rep. Africa 1977 55.625 1536769 3259.178978
|
| 344 |
+
Congo, Rep. Africa 1982 56.695 1774735 4879.507522
|
| 345 |
+
Congo, Rep. Africa 1987 57.47 2064095 4201.194937
|
| 346 |
+
Congo, Rep. Africa 1992 56.433 2409073 4016.239529
|
| 347 |
+
Congo, Rep. Africa 1997 52.962 2800947 3484.164376
|
| 348 |
+
Congo, Rep. Africa 2002 52.97 3328795 3484.06197
|
| 349 |
+
Congo, Rep. Africa 2007 55.322 3800610 3632.557798
|
| 350 |
+
Costa Rica Americas 1952 57.206 926317 2627.009471
|
| 351 |
+
Costa Rica Americas 1957 60.026 1112300 2990.010802
|
| 352 |
+
Costa Rica Americas 1962 62.842 1345187 3460.937025
|
| 353 |
+
Costa Rica Americas 1967 65.424 1588717 4161.727834
|
| 354 |
+
Costa Rica Americas 1972 67.849 1834796 5118.146939
|
| 355 |
+
Costa Rica Americas 1977 70.75 2108457 5926.876967
|
| 356 |
+
Costa Rica Americas 1982 73.45 2424367 5262.734751
|
| 357 |
+
Costa Rica Americas 1987 74.752 2799811 5629.915318
|
| 358 |
+
Costa Rica Americas 1992 75.713 3173216 6160.416317
|
| 359 |
+
Costa Rica Americas 1997 77.26 3518107 6677.045314
|
| 360 |
+
Costa Rica Americas 2002 78.123 3834934 7723.447195
|
| 361 |
+
Costa Rica Americas 2007 78.782 4133884 9645.06142
|
| 362 |
+
Cote d'Ivoire Africa 1952 40.477 2977019 1388.594732
|
| 363 |
+
Cote d'Ivoire Africa 1957 42.469 3300000 1500.895925
|
| 364 |
+
Cote d'Ivoire Africa 1962 44.93 3832408 1728.869428
|
| 365 |
+
Cote d'Ivoire Africa 1967 47.35 4744870 2052.050473
|
| 366 |
+
Cote d'Ivoire Africa 1972 49.801 6071696 2378.201111
|
| 367 |
+
Cote d'Ivoire Africa 1977 52.374 7459574 2517.736547
|
| 368 |
+
Cote d'Ivoire Africa 1982 53.983 9025951 2602.710169
|
| 369 |
+
Cote d'Ivoire Africa 1987 54.655 10761098 2156.956069
|
| 370 |
+
Cote d'Ivoire Africa 1992 52.044 12772596 1648.073791
|
| 371 |
+
Cote d'Ivoire Africa 1997 47.991 14625967 1786.265407
|
| 372 |
+
Cote d'Ivoire Africa 2002 46.832 16252726 1648.800823
|
| 373 |
+
Cote d'Ivoire Africa 2007 48.328 18013409 1544.750112
|
| 374 |
+
Croatia Europe 1952 61.21 3882229 3119.23652
|
| 375 |
+
Croatia Europe 1957 64.77 3991242 4338.231617
|
| 376 |
+
Croatia Europe 1962 67.13 4076557 5477.890018
|
| 377 |
+
Croatia Europe 1967 68.5 4174366 6960.297861
|
| 378 |
+
Croatia Europe 1972 69.61 4225310 9164.090127
|
| 379 |
+
Croatia Europe 1977 70.64 4318673 11305.38517
|
| 380 |
+
Croatia Europe 1982 70.46 4413368 13221.82184
|
| 381 |
+
Croatia Europe 1987 71.52 4484310 13822.58394
|
| 382 |
+
Croatia Europe 1992 72.527 4494013 8447.794873
|
| 383 |
+
Croatia Europe 1997 73.68 4444595 9875.604515
|
| 384 |
+
Croatia Europe 2002 74.876 4481020 11628.38895
|
| 385 |
+
Croatia Europe 2007 75.748 4493312 14619.22272
|
| 386 |
+
Cuba Americas 1952 59.421 6007797 5586.53878
|
| 387 |
+
Cuba Americas 1957 62.325 6640752 6092.174359
|
| 388 |
+
Cuba Americas 1962 65.246 7254373 5180.75591
|
| 389 |
+
Cuba Americas 1967 68.29 8139332 5690.268015
|
| 390 |
+
Cuba Americas 1972 70.723 8831348 5305.445256
|
| 391 |
+
Cuba Americas 1977 72.649 9537988 6380.494966
|
| 392 |
+
Cuba Americas 1982 73.717 9789224 7316.918107
|
| 393 |
+
Cuba Americas 1987 74.174 10239839 7532.924763
|
| 394 |
+
Cuba Americas 1992 74.414 10723260 5592.843963
|
| 395 |
+
Cuba Americas 1997 76.151 10983007 5431.990415
|
| 396 |
+
Cuba Americas 2002 77.158 11226999 6340.646683
|
| 397 |
+
Cuba Americas 2007 78.273 11416987 8948.102923
|
| 398 |
+
Czech Republic Europe 1952 66.87 9125183 6876.14025
|
| 399 |
+
Czech Republic Europe 1957 69.03 9513758 8256.343918
|
| 400 |
+
Czech Republic Europe 1962 69.9 9620282 10136.86713
|
| 401 |
+
Czech Republic Europe 1967 70.38 9835109 11399.44489
|
| 402 |
+
Czech Republic Europe 1972 70.29 9862158 13108.4536
|
| 403 |
+
Czech Republic Europe 1977 70.71 10161915 14800.16062
|
| 404 |
+
Czech Republic Europe 1982 70.96 10303704 15377.22855
|
| 405 |
+
Czech Republic Europe 1987 71.58 10311597 16310.4434
|
| 406 |
+
Czech Republic Europe 1992 72.4 10315702 14297.02122
|
| 407 |
+
Czech Republic Europe 1997 74.01 10300707 16048.51424
|
| 408 |
+
Czech Republic Europe 2002 75.51 10256295 17596.21022
|
| 409 |
+
Czech Republic Europe 2007 76.486 10228744 22833.30851
|
| 410 |
+
Denmark Europe 1952 70.78 4334000 9692.385245
|
| 411 |
+
Denmark Europe 1957 71.81 4487831 11099.65935
|
| 412 |
+
Denmark Europe 1962 72.35 4646899 13583.31351
|
| 413 |
+
Denmark Europe 1967 72.96 4838800 15937.21123
|
| 414 |
+
Denmark Europe 1972 73.47 4991596 18866.20721
|
| 415 |
+
Denmark Europe 1977 74.69 5088419 20422.9015
|
| 416 |
+
Denmark Europe 1982 74.63 5117810 21688.04048
|
| 417 |
+
Denmark Europe 1987 74.8 5127024 25116.17581
|
| 418 |
+
Denmark Europe 1992 75.33 5171393 26406.73985
|
| 419 |
+
Denmark Europe 1997 76.11 5283663 29804.34567
|
| 420 |
+
Denmark Europe 2002 77.18 5374693 32166.50006
|
| 421 |
+
Denmark Europe 2007 78.332 5468120 35278.41874
|
| 422 |
+
Djibouti Africa 1952 34.812 63149 2669.529475
|
| 423 |
+
Djibouti Africa 1957 37.328 71851 2864.969076
|
| 424 |
+
Djibouti Africa 1962 39.693 89898 3020.989263
|
| 425 |
+
Djibouti Africa 1967 42.074 127617 3020.050513
|
| 426 |
+
Djibouti Africa 1972 44.366 178848 3694.212352
|
| 427 |
+
Djibouti Africa 1977 46.519 228694 3081.761022
|
| 428 |
+
Djibouti Africa 1982 48.812 305991 2879.468067
|
| 429 |
+
Djibouti Africa 1987 50.04 311025 2880.102568
|
| 430 |
+
Djibouti Africa 1992 51.604 384156 2377.156192
|
| 431 |
+
Djibouti Africa 1997 53.157 417908 1895.016984
|
| 432 |
+
Djibouti Africa 2002 53.373 447416 1908.260867
|
| 433 |
+
Djibouti Africa 2007 54.791 496374 2082.481567
|
| 434 |
+
Dominican Republic Americas 1952 45.928 2491346 1397.717137
|
| 435 |
+
Dominican Republic Americas 1957 49.828 2923186 1544.402995
|
| 436 |
+
Dominican Republic Americas 1962 53.459 3453434 1662.137359
|
| 437 |
+
Dominican Republic Americas 1967 56.751 4049146 1653.723003
|
| 438 |
+
Dominican Republic Americas 1972 59.631 4671329 2189.874499
|
| 439 |
+
Dominican Republic Americas 1977 61.788 5302800 2681.9889
|
| 440 |
+
Dominican Republic Americas 1982 63.727 5968349 2861.092386
|
| 441 |
+
Dominican Republic Americas 1987 66.046 6655297 2899.842175
|
| 442 |
+
Dominican Republic Americas 1992 68.457 7351181 3044.214214
|
| 443 |
+
Dominican Republic Americas 1997 69.957 7992357 3614.101285
|
| 444 |
+
Dominican Republic Americas 2002 70.847 8650322 4563.808154
|
| 445 |
+
Dominican Republic Americas 2007 72.235 9319622 6025.374752
|
| 446 |
+
Ecuador Americas 1952 48.357 3548753 3522.110717
|
| 447 |
+
Ecuador Americas 1957 51.356 4058385 3780.546651
|
| 448 |
+
Ecuador Americas 1962 54.64 4681707 4086.114078
|
| 449 |
+
Ecuador Americas 1967 56.678 5432424 4579.074215
|
| 450 |
+
Ecuador Americas 1972 58.796 6298651 5280.99471
|
| 451 |
+
Ecuador Americas 1977 61.31 7278866 6679.62326
|
| 452 |
+
Ecuador Americas 1982 64.342 8365850 7213.791267
|
| 453 |
+
Ecuador Americas 1987 67.231 9545158 6481.776993
|
| 454 |
+
Ecuador Americas 1992 69.613 10748394 7103.702595
|
| 455 |
+
Ecuador Americas 1997 72.312 11911819 7429.455877
|
| 456 |
+
Ecuador Americas 2002 74.173 12921234 5773.044512
|
| 457 |
+
Ecuador Americas 2007 74.994 13755680 6873.262326
|
| 458 |
+
Egypt Africa 1952 41.893 22223309 1418.822445
|
| 459 |
+
Egypt Africa 1957 44.444 25009741 1458.915272
|
| 460 |
+
Egypt Africa 1962 46.992 28173309 1693.335853
|
| 461 |
+
Egypt Africa 1967 49.293 31681188 1814.880728
|
| 462 |
+
Egypt Africa 1972 51.137 34807417 2024.008147
|
| 463 |
+
Egypt Africa 1977 53.319 38783863 2785.493582
|
| 464 |
+
Egypt Africa 1982 56.006 45681811 3503.729636
|
| 465 |
+
Egypt Africa 1987 59.797 52799062 3885.46071
|
| 466 |
+
Egypt Africa 1992 63.674 59402198 3794.755195
|
| 467 |
+
Egypt Africa 1997 67.217 66134291 4173.181797
|
| 468 |
+
Egypt Africa 2002 69.806 73312559 4754.604414
|
| 469 |
+
Egypt Africa 2007 71.338 80264543 5581.180998
|
| 470 |
+
El Salvador Americas 1952 45.262 2042865 3048.3029
|
| 471 |
+
El Salvador Americas 1957 48.57 2355805 3421.523218
|
| 472 |
+
El Salvador Americas 1962 52.307 2747687 3776.803627
|
| 473 |
+
El Salvador Americas 1967 55.855 3232927 4358.595393
|
| 474 |
+
El Salvador Americas 1972 58.207 3790903 4520.246008
|
| 475 |
+
El Salvador Americas 1977 56.696 4282586 5138.922374
|
| 476 |
+
El Salvador Americas 1982 56.604 4474873 4098.344175
|
| 477 |
+
El Salvador Americas 1987 63.154 4842194 4140.442097
|
| 478 |
+
El Salvador Americas 1992 66.798 5274649 4444.2317
|
| 479 |
+
El Salvador Americas 1997 69.535 5783439 5154.825496
|
| 480 |
+
El Salvador Americas 2002 70.734 6353681 5351.568666
|
| 481 |
+
El Salvador Americas 2007 71.878 6939688 5728.353514
|
| 482 |
+
Equatorial Guinea Africa 1952 34.482 216964 375.6431231
|
| 483 |
+
Equatorial Guinea Africa 1957 35.983 232922 426.0964081
|
| 484 |
+
Equatorial Guinea Africa 1962 37.485 249220 582.8419714
|
| 485 |
+
Equatorial Guinea Africa 1967 38.987 259864 915.5960025
|
| 486 |
+
Equatorial Guinea Africa 1972 40.516 277603 672.4122571
|
| 487 |
+
Equatorial Guinea Africa 1977 42.024 192675 958.5668124
|
| 488 |
+
Equatorial Guinea Africa 1982 43.662 285483 927.8253427
|
| 489 |
+
Equatorial Guinea Africa 1987 45.664 341244 966.8968149
|
| 490 |
+
Equatorial Guinea Africa 1992 47.545 387838 1132.055034
|
| 491 |
+
Equatorial Guinea Africa 1997 48.245 439971 2814.480755
|
| 492 |
+
Equatorial Guinea Africa 2002 49.348 495627 7703.4959
|
| 493 |
+
Equatorial Guinea Africa 2007 51.579 551201 12154.08975
|
| 494 |
+
Eritrea Africa 1952 35.928 1438760 328.9405571
|
| 495 |
+
Eritrea Africa 1957 38.047 1542611 344.1618859
|
| 496 |
+
Eritrea Africa 1962 40.158 1666618 380.9958433
|
| 497 |
+
Eritrea Africa 1967 42.189 1820319 468.7949699
|
| 498 |
+
Eritrea Africa 1972 44.142 2260187 514.3242082
|
| 499 |
+
Eritrea Africa 1977 44.535 2512642 505.7538077
|
| 500 |
+
Eritrea Africa 1982 43.89 2637297 524.8758493
|
| 501 |
+
Eritrea Africa 1987 46.453 2915959 521.1341333
|
| 502 |
+
Eritrea Africa 1992 49.991 3668440 582.8585102
|
| 503 |
+
Eritrea Africa 1997 53.378 4058319 913.47079
|
| 504 |
+
Eritrea Africa 2002 55.24 4414865 765.3500015
|
| 505 |
+
Eritrea Africa 2007 58.04 4906585 641.3695236
|
| 506 |
+
Ethiopia Africa 1952 34.078 20860941 362.1462796
|
| 507 |
+
Ethiopia Africa 1957 36.667 22815614 378.9041632
|
| 508 |
+
Ethiopia Africa 1962 40.059 25145372 419.4564161
|
| 509 |
+
Ethiopia Africa 1967 42.115 27860297 516.1186438
|
| 510 |
+
Ethiopia Africa 1972 43.515 30770372 566.2439442
|
| 511 |
+
Ethiopia Africa 1977 44.51 34617799 556.8083834
|
| 512 |
+
Ethiopia Africa 1982 44.916 38111756 577.8607471
|
| 513 |
+
Ethiopia Africa 1987 46.684 42999530 573.7413142
|
| 514 |
+
Ethiopia Africa 1992 48.091 52088559 421.3534653
|
| 515 |
+
Ethiopia Africa 1997 49.402 59861301 515.8894013
|
| 516 |
+
Ethiopia Africa 2002 50.725 67946797 530.0535319
|
| 517 |
+
Ethiopia Africa 2007 52.947 76511887 690.8055759
|
| 518 |
+
Finland Europe 1952 66.55 4090500 6424.519071
|
| 519 |
+
Finland Europe 1957 67.49 4324000 7545.415386
|
| 520 |
+
Finland Europe 1962 68.75 4491443 9371.842561
|
| 521 |
+
Finland Europe 1967 69.83 4605744 10921.63626
|
| 522 |
+
Finland Europe 1972 70.87 4639657 14358.8759
|
| 523 |
+
Finland Europe 1977 72.52 4738902 15605.42283
|
| 524 |
+
Finland Europe 1982 74.55 4826933 18533.15761
|
| 525 |
+
Finland Europe 1987 74.83 4931729 21141.01223
|
| 526 |
+
Finland Europe 1992 75.7 5041039 20647.16499
|
| 527 |
+
Finland Europe 1997 77.13 5134406 23723.9502
|
| 528 |
+
Finland Europe 2002 78.37 5193039 28204.59057
|
| 529 |
+
Finland Europe 2007 79.313 5238460 33207.0844
|
| 530 |
+
France Europe 1952 67.41 42459667 7029.809327
|
| 531 |
+
France Europe 1957 68.93 44310863 8662.834898
|
| 532 |
+
France Europe 1962 70.51 47124000 10560.48553
|
| 533 |
+
France Europe 1967 71.55 49569000 12999.91766
|
| 534 |
+
France Europe 1972 72.38 51732000 16107.19171
|
| 535 |
+
France Europe 1977 73.83 53165019 18292.63514
|
| 536 |
+
France Europe 1982 74.89 54433565 20293.89746
|
| 537 |
+
France Europe 1987 76.34 55630100 22066.44214
|
| 538 |
+
France Europe 1992 77.46 57374179 24703.79615
|
| 539 |
+
France Europe 1997 78.64 58623428 25889.78487
|
| 540 |
+
France Europe 2002 79.59 59925035 28926.03234
|
| 541 |
+
France Europe 2007 80.657 61083916 30470.0167
|
| 542 |
+
Gabon Africa 1952 37.003 420702 4293.476475
|
| 543 |
+
Gabon Africa 1957 38.999 434904 4976.198099
|
| 544 |
+
Gabon Africa 1962 40.489 455661 6631.459222
|
| 545 |
+
Gabon Africa 1967 44.598 489004 8358.761987
|
| 546 |
+
Gabon Africa 1972 48.69 537977 11401.94841
|
| 547 |
+
Gabon Africa 1977 52.79 706367 21745.57328
|
| 548 |
+
Gabon Africa 1982 56.564 753874 15113.36194
|
| 549 |
+
Gabon Africa 1987 60.19 880397 11864.40844
|
| 550 |
+
Gabon Africa 1992 61.366 985739 13522.15752
|
| 551 |
+
Gabon Africa 1997 60.461 1126189 14722.84188
|
| 552 |
+
Gabon Africa 2002 56.761 1299304 12521.71392
|
| 553 |
+
Gabon Africa 2007 56.735 1454867 13206.48452
|
| 554 |
+
Gambia Africa 1952 30 284320 485.2306591
|
| 555 |
+
Gambia Africa 1957 32.065 323150 520.9267111
|
| 556 |
+
Gambia Africa 1962 33.896 374020 599.650276
|
| 557 |
+
Gambia Africa 1967 35.857 439593 734.7829124
|
| 558 |
+
Gambia Africa 1972 38.308 517101 756.0868363
|
| 559 |
+
Gambia Africa 1977 41.842 608274 884.7552507
|
| 560 |
+
Gambia Africa 1982 45.58 715523 835.8096108
|
| 561 |
+
Gambia Africa 1987 49.265 848406 611.6588611
|
| 562 |
+
Gambia Africa 1992 52.644 1025384 665.6244126
|
| 563 |
+
Gambia Africa 1997 55.861 1235767 653.7301704
|
| 564 |
+
Gambia Africa 2002 58.041 1457766 660.5855997
|
| 565 |
+
Gambia Africa 2007 59.448 1688359 752.7497265
|
| 566 |
+
Germany Europe 1952 67.5 69145952 7144.114393
|
| 567 |
+
Germany Europe 1957 69.1 71019069 10187.82665
|
| 568 |
+
Germany Europe 1962 70.3 73739117 12902.46291
|
| 569 |
+
Germany Europe 1967 70.8 76368453 14745.62561
|
| 570 |
+
Germany Europe 1972 71 78717088 18016.18027
|
| 571 |
+
Germany Europe 1977 72.5 78160773 20512.92123
|
| 572 |
+
Germany Europe 1982 73.8 78335266 22031.53274
|
| 573 |
+
Germany Europe 1987 74.847 77718298 24639.18566
|
| 574 |
+
Germany Europe 1992 76.07 80597764 26505.30317
|
| 575 |
+
Germany Europe 1997 77.34 82011073 27788.88416
|
| 576 |
+
Germany Europe 2002 78.67 82350671 30035.80198
|
| 577 |
+
Germany Europe 2007 79.406 82400996 32170.37442
|
| 578 |
+
Ghana Africa 1952 43.149 5581001 911.2989371
|
| 579 |
+
Ghana Africa 1957 44.779 6391288 1043.561537
|
| 580 |
+
Ghana Africa 1962 46.452 7355248 1190.041118
|
| 581 |
+
Ghana Africa 1967 48.072 8490213 1125.69716
|
| 582 |
+
Ghana Africa 1972 49.875 9354120 1178.223708
|
| 583 |
+
Ghana Africa 1977 51.756 10538093 993.2239571
|
| 584 |
+
Ghana Africa 1982 53.744 11400338 876.032569
|
| 585 |
+
Ghana Africa 1987 55.729 14168101 847.0061135
|
| 586 |
+
Ghana Africa 1992 57.501 16278738 925.060154
|
| 587 |
+
Ghana Africa 1997 58.556 18418288 1005.245812
|
| 588 |
+
Ghana Africa 2002 58.453 20550751 1111.984578
|
| 589 |
+
Ghana Africa 2007 60.022 22873338 1327.60891
|
| 590 |
+
Greece Europe 1952 65.86 7733250 3530.690067
|
| 591 |
+
Greece Europe 1957 67.86 8096218 4916.299889
|
| 592 |
+
Greece Europe 1962 69.51 8448233 6017.190733
|
| 593 |
+
Greece Europe 1967 71 8716441 8513.097016
|
| 594 |
+
Greece Europe 1972 72.34 8888628 12724.82957
|
| 595 |
+
Greece Europe 1977 73.68 9308479 14195.52428
|
| 596 |
+
Greece Europe 1982 75.24 9786480 15268.42089
|
| 597 |
+
Greece Europe 1987 76.67 9974490 16120.52839
|
| 598 |
+
Greece Europe 1992 77.03 10325429 17541.49634
|
| 599 |
+
Greece Europe 1997 77.869 10502372 18747.69814
|
| 600 |
+
Greece Europe 2002 78.256 10603863 22514.2548
|
| 601 |
+
Greece Europe 2007 79.483 10706290 27538.41188
|
| 602 |
+
Guatemala Americas 1952 42.023 3146381 2428.237769
|
| 603 |
+
Guatemala Americas 1957 44.142 3640876 2617.155967
|
| 604 |
+
Guatemala Americas 1962 46.954 4208858 2750.364446
|
| 605 |
+
Guatemala Americas 1967 50.016 4690773 3242.531147
|
| 606 |
+
Guatemala Americas 1972 53.738 5149581 4031.408271
|
| 607 |
+
Guatemala Americas 1977 56.029 5703430 4879.992748
|
| 608 |
+
Guatemala Americas 1982 58.137 6395630 4820.49479
|
| 609 |
+
Guatemala Americas 1987 60.782 7326406 4246.485974
|
| 610 |
+
Guatemala Americas 1992 63.373 8486949 4439.45084
|
| 611 |
+
Guatemala Americas 1997 66.322 9803875 4684.313807
|
| 612 |
+
Guatemala Americas 2002 68.978 11178650 4858.347495
|
| 613 |
+
Guatemala Americas 2007 70.259 12572928 5186.050003
|
| 614 |
+
Guinea Africa 1952 33.609 2664249 510.1964923
|
| 615 |
+
Guinea Africa 1957 34.558 2876726 576.2670245
|
| 616 |
+
Guinea Africa 1962 35.753 3140003 686.3736739
|
| 617 |
+
Guinea Africa 1967 37.197 3451418 708.7595409
|
| 618 |
+
Guinea Africa 1972 38.842 3811387 741.6662307
|
| 619 |
+
Guinea Africa 1977 40.762 4227026 874.6858643
|
| 620 |
+
Guinea Africa 1982 42.891 4710497 857.2503577
|
| 621 |
+
Guinea Africa 1987 45.552 5650262 805.5724718
|
| 622 |
+
Guinea Africa 1992 48.576 6990574 794.3484384
|
| 623 |
+
Guinea Africa 1997 51.455 8048834 869.4497668
|
| 624 |
+
Guinea Africa 2002 53.676 8807818 945.5835837
|
| 625 |
+
Guinea Africa 2007 56.007 9947814 942.6542111
|
| 626 |
+
Guinea-Bissau Africa 1952 32.5 580653 299.850319
|
| 627 |
+
Guinea-Bissau Africa 1957 33.489 601095 431.7904566
|
| 628 |
+
Guinea-Bissau Africa 1962 34.488 627820 522.0343725
|
| 629 |
+
Guinea-Bissau Africa 1967 35.492 601287 715.5806402
|
| 630 |
+
Guinea-Bissau Africa 1972 36.486 625361 820.2245876
|
| 631 |
+
Guinea-Bissau Africa 1977 37.465 745228 764.7259628
|
| 632 |
+
Guinea-Bissau Africa 1982 39.327 825987 838.1239671
|
| 633 |
+
Guinea-Bissau Africa 1987 41.245 927524 736.4153921
|
| 634 |
+
Guinea-Bissau Africa 1992 43.266 1050938 745.5398706
|
| 635 |
+
Guinea-Bissau Africa 1997 44.873 1193708 796.6644681
|
| 636 |
+
Guinea-Bissau Africa 2002 45.504 1332459 575.7047176
|
| 637 |
+
Guinea-Bissau Africa 2007 46.388 1472041 579.231743
|
| 638 |
+
Haiti Americas 1952 37.579 3201488 1840.366939
|
| 639 |
+
Haiti Americas 1957 40.696 3507701 1726.887882
|
| 640 |
+
Haiti Americas 1962 43.59 3880130 1796.589032
|
| 641 |
+
Haiti Americas 1967 46.243 4318137 1452.057666
|
| 642 |
+
Haiti Americas 1972 48.042 4698301 1654.456946
|
| 643 |
+
Haiti Americas 1977 49.923 4908554 1874.298931
|
| 644 |
+
Haiti Americas 1982 51.461 5198399 2011.159549
|
| 645 |
+
Haiti Americas 1987 53.636 5756203 1823.015995
|
| 646 |
+
Haiti Americas 1992 55.089 6326682 1456.309517
|
| 647 |
+
Haiti Americas 1997 56.671 6913545 1341.726931
|
| 648 |
+
Haiti Americas 2002 58.137 7607651 1270.364932
|
| 649 |
+
Haiti Americas 2007 60.916 8502814 1201.637154
|
| 650 |
+
Honduras Americas 1952 41.912 1517453 2194.926204
|
| 651 |
+
Honduras Americas 1957 44.665 1770390 2220.487682
|
| 652 |
+
Honduras Americas 1962 48.041 2090162 2291.156835
|
| 653 |
+
Honduras Americas 1967 50.924 2500689 2538.269358
|
| 654 |
+
Honduras Americas 1972 53.884 2965146 2529.842345
|
| 655 |
+
Honduras Americas 1977 57.402 3055235 3203.208066
|
| 656 |
+
Honduras Americas 1982 60.909 3669448 3121.760794
|
| 657 |
+
Honduras Americas 1987 64.492 4372203 3023.096699
|
| 658 |
+
Honduras Americas 1992 66.399 5077347 3081.694603
|
| 659 |
+
Honduras Americas 1997 67.659 5867957 3160.454906
|
| 660 |
+
Honduras Americas 2002 68.565 6677328 3099.72866
|
| 661 |
+
Honduras Americas 2007 70.198 7483763 3548.330846
|
| 662 |
+
Hong Kong, China Asia 1952 60.96 2125900 3054.421209
|
| 663 |
+
Hong Kong, China Asia 1957 64.75 2736300 3629.076457
|
| 664 |
+
Hong Kong, China Asia 1962 67.65 3305200 4692.648272
|
| 665 |
+
Hong Kong, China Asia 1967 70 3722800 6197.962814
|
| 666 |
+
Hong Kong, China Asia 1972 72 4115700 8315.928145
|
| 667 |
+
Hong Kong, China Asia 1977 73.6 4583700 11186.14125
|
| 668 |
+
Hong Kong, China Asia 1982 75.45 5264500 14560.53051
|
| 669 |
+
Hong Kong, China Asia 1987 76.2 5584510 20038.47269
|
| 670 |
+
Hong Kong, China Asia 1992 77.601 5829696 24757.60301
|
| 671 |
+
Hong Kong, China Asia 1997 80 6495918 28377.63219
|
| 672 |
+
Hong Kong, China Asia 2002 81.495 6762476 30209.01516
|
| 673 |
+
Hong Kong, China Asia 2007 82.208 6980412 39724.97867
|
| 674 |
+
Hungary Europe 1952 64.03 9504000 5263.673816
|
| 675 |
+
Hungary Europe 1957 66.41 9839000 6040.180011
|
| 676 |
+
Hungary Europe 1962 67.96 10063000 7550.359877
|
| 677 |
+
Hungary Europe 1967 69.5 10223422 9326.64467
|
| 678 |
+
Hungary Europe 1972 69.76 10394091 10168.65611
|
| 679 |
+
Hungary Europe 1977 69.95 10637171 11674.83737
|
| 680 |
+
Hungary Europe 1982 69.39 10705535 12545.99066
|
| 681 |
+
Hungary Europe 1987 69.58 10612740 12986.47998
|
| 682 |
+
Hungary Europe 1992 69.17 10348684 10535.62855
|
| 683 |
+
Hungary Europe 1997 71.04 10244684 11712.7768
|
| 684 |
+
Hungary Europe 2002 72.59 10083313 14843.93556
|
| 685 |
+
Hungary Europe 2007 73.338 9956108 18008.94444
|
| 686 |
+
Iceland Europe 1952 72.49 147962 7267.688428
|
| 687 |
+
Iceland Europe 1957 73.47 165110 9244.001412
|
| 688 |
+
Iceland Europe 1962 73.68 182053 10350.15906
|
| 689 |
+
Iceland Europe 1967 73.73 198676 13319.89568
|
| 690 |
+
Iceland Europe 1972 74.46 209275 15798.06362
|
| 691 |
+
Iceland Europe 1977 76.11 221823 19654.96247
|
| 692 |
+
Iceland Europe 1982 76.99 233997 23269.6075
|
| 693 |
+
Iceland Europe 1987 77.23 244676 26923.20628
|
| 694 |
+
Iceland Europe 1992 78.77 259012 25144.39201
|
| 695 |
+
Iceland Europe 1997 78.95 271192 28061.09966
|
| 696 |
+
Iceland Europe 2002 80.5 288030 31163.20196
|
| 697 |
+
Iceland Europe 2007 81.757 301931 36180.78919
|
| 698 |
+
India Asia 1952 37.373 372000000 546.5657493
|
| 699 |
+
India Asia 1957 40.249 409000000 590.061996
|
| 700 |
+
India Asia 1962 43.605 454000000 658.3471509
|
| 701 |
+
India Asia 1967 47.193 506000000 700.7706107
|
| 702 |
+
India Asia 1972 50.651 567000000 724.032527
|
| 703 |
+
India Asia 1977 54.208 634000000 813.337323
|
| 704 |
+
India Asia 1982 56.596 708000000 855.7235377
|
| 705 |
+
India Asia 1987 58.553 788000000 976.5126756
|
| 706 |
+
India Asia 1992 60.223 872000000 1164.406809
|
| 707 |
+
India Asia 1997 61.765 959000000 1458.817442
|
| 708 |
+
India Asia 2002 62.879 1034172547 1746.769454
|
| 709 |
+
India Asia 2007 64.698 1110396331 2452.210407
|
| 710 |
+
Indonesia Asia 1952 37.468 82052000 749.6816546
|
| 711 |
+
Indonesia Asia 1957 39.918 90124000 858.9002707
|
| 712 |
+
Indonesia Asia 1962 42.518 99028000 849.2897701
|
| 713 |
+
Indonesia Asia 1967 45.964 109343000 762.4317721
|
| 714 |
+
Indonesia Asia 1972 49.203 121282000 1111.107907
|
| 715 |
+
Indonesia Asia 1977 52.702 136725000 1382.702056
|
| 716 |
+
Indonesia Asia 1982 56.159 153343000 1516.872988
|
| 717 |
+
Indonesia Asia 1987 60.137 169276000 1748.356961
|
| 718 |
+
Indonesia Asia 1992 62.681 184816000 2383.140898
|
| 719 |
+
Indonesia Asia 1997 66.041 199278000 3119.335603
|
| 720 |
+
Indonesia Asia 2002 68.588 211060000 2873.91287
|
| 721 |
+
Indonesia Asia 2007 70.65 223547000 3540.651564
|
| 722 |
+
Iran Asia 1952 44.869 17272000 3035.326002
|
| 723 |
+
Iran Asia 1957 47.181 19792000 3290.257643
|
| 724 |
+
Iran Asia 1962 49.325 22874000 4187.329802
|
| 725 |
+
Iran Asia 1967 52.469 26538000 5906.731805
|
| 726 |
+
Iran Asia 1972 55.234 30614000 9613.818607
|
| 727 |
+
Iran Asia 1977 57.702 35480679 11888.59508
|
| 728 |
+
Iran Asia 1982 59.62 43072751 7608.334602
|
| 729 |
+
Iran Asia 1987 63.04 51889696 6642.881371
|
| 730 |
+
Iran Asia 1992 65.742 60397973 7235.653188
|
| 731 |
+
Iran Asia 1997 68.042 63327987 8263.590301
|
| 732 |
+
Iran Asia 2002 69.451 66907826 9240.761975
|
| 733 |
+
Iran Asia 2007 70.964 69453570 11605.71449
|
| 734 |
+
Iraq Asia 1952 45.32 5441766 4129.766056
|
| 735 |
+
Iraq Asia 1957 48.437 6248643 6229.333562
|
| 736 |
+
Iraq Asia 1962 51.457 7240260 8341.737815
|
| 737 |
+
Iraq Asia 1967 54.459 8519282 8931.459811
|
| 738 |
+
Iraq Asia 1972 56.95 10061506 9576.037596
|
| 739 |
+
Iraq Asia 1977 60.413 11882916 14688.23507
|
| 740 |
+
Iraq Asia 1982 62.038 14173318 14517.90711
|
| 741 |
+
Iraq Asia 1987 65.044 16543189 11643.57268
|
| 742 |
+
Iraq Asia 1992 59.461 17861905 3745.640687
|
| 743 |
+
Iraq Asia 1997 58.811 20775703 3076.239795
|
| 744 |
+
Iraq Asia 2002 57.046 24001816 4390.717312
|
| 745 |
+
Iraq Asia 2007 59.545 27499638 4471.061906
|
| 746 |
+
Ireland Europe 1952 66.91 2952156 5210.280328
|
| 747 |
+
Ireland Europe 1957 68.9 2878220 5599.077872
|
| 748 |
+
Ireland Europe 1962 70.29 2830000 6631.597314
|
| 749 |
+
Ireland Europe 1967 71.08 2900100 7655.568963
|
| 750 |
+
Ireland Europe 1972 71.28 3024400 9530.772896
|
| 751 |
+
Ireland Europe 1977 72.03 3271900 11150.98113
|
| 752 |
+
Ireland Europe 1982 73.1 3480000 12618.32141
|
| 753 |
+
Ireland Europe 1987 74.36 3539900 13872.86652
|
| 754 |
+
Ireland Europe 1992 75.467 3557761 17558.81555
|
| 755 |
+
Ireland Europe 1997 76.122 3667233 24521.94713
|
| 756 |
+
Ireland Europe 2002 77.783 3879155 34077.04939
|
| 757 |
+
Ireland Europe 2007 78.885 4109086 40675.99635
|
| 758 |
+
Israel Asia 1952 65.39 1620914 4086.522128
|
| 759 |
+
Israel Asia 1957 67.84 1944401 5385.278451
|
| 760 |
+
Israel Asia 1962 69.39 2310904 7105.630706
|
| 761 |
+
Israel Asia 1967 70.75 2693585 8393.741404
|
| 762 |
+
Israel Asia 1972 71.63 3095893 12786.93223
|
| 763 |
+
Israel Asia 1977 73.06 3495918 13306.61921
|
| 764 |
+
Israel Asia 1982 74.45 3858421 15367.0292
|
| 765 |
+
Israel Asia 1987 75.6 4203148 17122.47986
|
| 766 |
+
Israel Asia 1992 76.93 4936550 18051.52254
|
| 767 |
+
Israel Asia 1997 78.269 5531387 20896.60924
|
| 768 |
+
Israel Asia 2002 79.696 6029529 21905.59514
|
| 769 |
+
Israel Asia 2007 80.745 6426679 25523.2771
|
| 770 |
+
Italy Europe 1952 65.94 47666000 4931.404155
|
| 771 |
+
Italy Europe 1957 67.81 49182000 6248.656232
|
| 772 |
+
Italy Europe 1962 69.24 50843200 8243.58234
|
| 773 |
+
Italy Europe 1967 71.06 52667100 10022.40131
|
| 774 |
+
Italy Europe 1972 72.19 54365564 12269.27378
|
| 775 |
+
Italy Europe 1977 73.48 56059245 14255.98475
|
| 776 |
+
Italy Europe 1982 74.98 56535636 16537.4835
|
| 777 |
+
Italy Europe 1987 76.42 56729703 19207.23482
|
| 778 |
+
Italy Europe 1992 77.44 56840847 22013.64486
|
| 779 |
+
Italy Europe 1997 78.82 57479469 24675.02446
|
| 780 |
+
Italy Europe 2002 80.24 57926999 27968.09817
|
| 781 |
+
Italy Europe 2007 80.546 58147733 28569.7197
|
| 782 |
+
Jamaica Americas 1952 58.53 1426095 2898.530881
|
| 783 |
+
Jamaica Americas 1957 62.61 1535090 4756.525781
|
| 784 |
+
Jamaica Americas 1962 65.61 1665128 5246.107524
|
| 785 |
+
Jamaica Americas 1967 67.51 1861096 6124.703451
|
| 786 |
+
Jamaica Americas 1972 69 1997616 7433.889293
|
| 787 |
+
Jamaica Americas 1977 70.11 2156814 6650.195573
|
| 788 |
+
Jamaica Americas 1982 71.21 2298309 6068.05135
|
| 789 |
+
Jamaica Americas 1987 71.77 2326606 6351.237495
|
| 790 |
+
Jamaica Americas 1992 71.766 2378618 7404.923685
|
| 791 |
+
Jamaica Americas 1997 72.262 2531311 7121.924704
|
| 792 |
+
Jamaica Americas 2002 72.047 2664659 6994.774861
|
| 793 |
+
Jamaica Americas 2007 72.567 2780132 7320.880262
|
| 794 |
+
Japan Asia 1952 63.03 86459025 3216.956347
|
| 795 |
+
Japan Asia 1957 65.5 91563009 4317.694365
|
| 796 |
+
Japan Asia 1962 68.73 95831757 6576.649461
|
| 797 |
+
Japan Asia 1967 71.43 100825279 9847.788607
|
| 798 |
+
Japan Asia 1972 73.42 107188273 14778.78636
|
| 799 |
+
Japan Asia 1977 75.38 113872473 16610.37701
|
| 800 |
+
Japan Asia 1982 77.11 118454974 19384.10571
|
| 801 |
+
Japan Asia 1987 78.67 122091325 22375.94189
|
| 802 |
+
Japan Asia 1992 79.36 124329269 26824.89511
|
| 803 |
+
Japan Asia 1997 80.69 125956499 28816.58499
|
| 804 |
+
Japan Asia 2002 82 127065841 28604.5919
|
| 805 |
+
Japan Asia 2007 82.603 127467972 31656.06806
|
| 806 |
+
Jordan Asia 1952 43.158 607914 1546.907807
|
| 807 |
+
Jordan Asia 1957 45.669 746559 1886.080591
|
| 808 |
+
Jordan Asia 1962 48.126 933559 2348.009158
|
| 809 |
+
Jordan Asia 1967 51.629 1255058 2741.796252
|
| 810 |
+
Jordan Asia 1972 56.528 1613551 2110.856309
|
| 811 |
+
Jordan Asia 1977 61.134 1937652 2852.351568
|
| 812 |
+
Jordan Asia 1982 63.739 2347031 4161.415959
|
| 813 |
+
Jordan Asia 1987 65.869 2820042 4448.679912
|
| 814 |
+
Jordan Asia 1992 68.015 3867409 3431.593647
|
| 815 |
+
Jordan Asia 1997 69.772 4526235 3645.379572
|
| 816 |
+
Jordan Asia 2002 71.263 5307470 3844.917194
|
| 817 |
+
Jordan Asia 2007 72.535 6053193 4519.461171
|
| 818 |
+
Kenya Africa 1952 42.27 6464046 853.540919
|
| 819 |
+
Kenya Africa 1957 44.686 7454779 944.4383152
|
| 820 |
+
Kenya Africa 1962 47.949 8678557 896.9663732
|
| 821 |
+
Kenya Africa 1967 50.654 10191512 1056.736457
|
| 822 |
+
Kenya Africa 1972 53.559 12044785 1222.359968
|
| 823 |
+
Kenya Africa 1977 56.155 14500404 1267.613204
|
| 824 |
+
Kenya Africa 1982 58.766 17661452 1348.225791
|
| 825 |
+
Kenya Africa 1987 59.339 21198082 1361.936856
|
| 826 |
+
Kenya Africa 1992 59.285 25020539 1341.921721
|
| 827 |
+
Kenya Africa 1997 54.407 28263827 1360.485021
|
| 828 |
+
Kenya Africa 2002 50.992 31386842 1287.514732
|
| 829 |
+
Kenya Africa 2007 54.11 35610177 1463.249282
|
| 830 |
+
Korea, Dem. Rep. Asia 1952 50.056 8865488 1088.277758
|
| 831 |
+
Korea, Dem. Rep. Asia 1957 54.081 9411381 1571.134655
|
| 832 |
+
Korea, Dem. Rep. Asia 1962 56.656 10917494 1621.693598
|
| 833 |
+
Korea, Dem. Rep. Asia 1967 59.942 12617009 2143.540609
|
| 834 |
+
Korea, Dem. Rep. Asia 1972 63.983 14781241 3701.621503
|
| 835 |
+
Korea, Dem. Rep. Asia 1977 67.159 16325320 4106.301249
|
| 836 |
+
Korea, Dem. Rep. Asia 1982 69.1 17647518 4106.525293
|
| 837 |
+
Korea, Dem. Rep. Asia 1987 70.647 19067554 4106.492315
|
| 838 |
+
Korea, Dem. Rep. Asia 1992 69.978 20711375 3726.063507
|
| 839 |
+
Korea, Dem. Rep. Asia 1997 67.727 21585105 1690.756814
|
| 840 |
+
Korea, Dem. Rep. Asia 2002 66.662 22215365 1646.758151
|
| 841 |
+
Korea, Dem. Rep. Asia 2007 67.297 23301725 1593.06548
|
| 842 |
+
Korea, Rep. Asia 1952 47.453 20947571 1030.592226
|
| 843 |
+
Korea, Rep. Asia 1957 52.681 22611552 1487.593537
|
| 844 |
+
Korea, Rep. Asia 1962 55.292 26420307 1536.344387
|
| 845 |
+
Korea, Rep. Asia 1967 57.716 30131000 2029.228142
|
| 846 |
+
Korea, Rep. Asia 1972 62.612 33505000 3030.87665
|
| 847 |
+
Korea, Rep. Asia 1977 64.766 36436000 4657.22102
|
| 848 |
+
Korea, Rep. Asia 1982 67.123 39326000 5622.942464
|
| 849 |
+
Korea, Rep. Asia 1987 69.81 41622000 8533.088805
|
| 850 |
+
Korea, Rep. Asia 1992 72.244 43805450 12104.27872
|
| 851 |
+
Korea, Rep. Asia 1997 74.647 46173816 15993.52796
|
| 852 |
+
Korea, Rep. Asia 2002 77.045 47969150 19233.98818
|
| 853 |
+
Korea, Rep. Asia 2007 78.623 49044790 23348.13973
|
| 854 |
+
Kuwait Asia 1952 55.565 160000 108382.3529
|
| 855 |
+
Kuwait Asia 1957 58.033 212846 113523.1329
|
| 856 |
+
Kuwait Asia 1962 60.47 358266 95458.11176
|
| 857 |
+
Kuwait Asia 1967 64.624 575003 80894.88326
|
| 858 |
+
Kuwait Asia 1972 67.712 841934 109347.867
|
| 859 |
+
Kuwait Asia 1977 69.343 1140357 59265.47714
|
| 860 |
+
Kuwait Asia 1982 71.309 1497494 31354.03573
|
| 861 |
+
Kuwait Asia 1987 74.174 1891487 28118.42998
|
| 862 |
+
Kuwait Asia 1992 75.19 1418095 34932.91959
|
| 863 |
+
Kuwait Asia 1997 76.156 1765345 40300.61996
|
| 864 |
+
Kuwait Asia 2002 76.904 2111561 35110.10566
|
| 865 |
+
Kuwait Asia 2007 77.588 2505559 47306.98978
|
| 866 |
+
Lebanon Asia 1952 55.928 1439529 4834.804067
|
| 867 |
+
Lebanon Asia 1957 59.489 1647412 6089.786934
|
| 868 |
+
Lebanon Asia 1962 62.094 1886848 5714.560611
|
| 869 |
+
Lebanon Asia 1967 63.87 2186894 6006.983042
|
| 870 |
+
Lebanon Asia 1972 65.421 2680018 7486.384341
|
| 871 |
+
Lebanon Asia 1977 66.099 3115787 8659.696836
|
| 872 |
+
Lebanon Asia 1982 66.983 3086876 7640.519521
|
| 873 |
+
Lebanon Asia 1987 67.926 3089353 5377.091329
|
| 874 |
+
Lebanon Asia 1992 69.292 3219994 6890.806854
|
| 875 |
+
Lebanon Asia 1997 70.265 3430388 8754.96385
|
| 876 |
+
Lebanon Asia 2002 71.028 3677780 9313.93883
|
| 877 |
+
Lebanon Asia 2007 71.993 3921278 10461.05868
|
| 878 |
+
Lesotho Africa 1952 42.138 748747 298.8462121
|
| 879 |
+
Lesotho Africa 1957 45.047 813338 335.9971151
|
| 880 |
+
Lesotho Africa 1962 47.747 893143 411.8006266
|
| 881 |
+
Lesotho Africa 1967 48.492 996380 498.6390265
|
| 882 |
+
Lesotho Africa 1972 49.767 1116779 496.5815922
|
| 883 |
+
Lesotho Africa 1977 52.208 1251524 745.3695408
|
| 884 |
+
Lesotho Africa 1982 55.078 1411807 797.2631074
|
| 885 |
+
Lesotho Africa 1987 57.18 1599200 773.9932141
|
| 886 |
+
Lesotho Africa 1992 59.685 1803195 977.4862725
|
| 887 |
+
Lesotho Africa 1997 55.558 1982823 1186.147994
|
| 888 |
+
Lesotho Africa 2002 44.593 2046772 1275.184575
|
| 889 |
+
Lesotho Africa 2007 42.592 2012649 1569.331442
|
| 890 |
+
Liberia Africa 1952 38.48 863308 575.5729961
|
| 891 |
+
Liberia Africa 1957 39.486 975950 620.9699901
|
| 892 |
+
Liberia Africa 1962 40.502 1112796 634.1951625
|
| 893 |
+
Liberia Africa 1967 41.536 1279406 713.6036483
|
| 894 |
+
Liberia Africa 1972 42.614 1482628 803.0054535
|
| 895 |
+
Liberia Africa 1977 43.764 1703617 640.3224383
|
| 896 |
+
Liberia Africa 1982 44.852 1956875 572.1995694
|
| 897 |
+
Liberia Africa 1987 46.027 2269414 506.1138573
|
| 898 |
+
Liberia Africa 1992 40.802 1912974 636.6229191
|
| 899 |
+
Liberia Africa 1997 42.221 2200725 609.1739508
|
| 900 |
+
Liberia Africa 2002 43.753 2814651 531.4823679
|
| 901 |
+
Liberia Africa 2007 45.678 3193942 414.5073415
|
| 902 |
+
Libya Africa 1952 42.723 1019729 2387.54806
|
| 903 |
+
Libya Africa 1957 45.289 1201578 3448.284395
|
| 904 |
+
Libya Africa 1962 47.808 1441863 6757.030816
|
| 905 |
+
Libya Africa 1967 50.227 1759224 18772.75169
|
| 906 |
+
Libya Africa 1972 52.773 2183877 21011.49721
|
| 907 |
+
Libya Africa 1977 57.442 2721783 21951.21176
|
| 908 |
+
Libya Africa 1982 62.155 3344074 17364.27538
|
| 909 |
+
Libya Africa 1987 66.234 3799845 11770.5898
|
| 910 |
+
Libya Africa 1992 68.755 4364501 9640.138501
|
| 911 |
+
Libya Africa 1997 71.555 4759670 9467.446056
|
| 912 |
+
Libya Africa 2002 72.737 5368585 9534.677467
|
| 913 |
+
Libya Africa 2007 73.952 6036914 12057.49928
|
| 914 |
+
Madagascar Africa 1952 36.681 4762912 1443.011715
|
| 915 |
+
Madagascar Africa 1957 38.865 5181679 1589.20275
|
| 916 |
+
Madagascar Africa 1962 40.848 5703324 1643.38711
|
| 917 |
+
Madagascar Africa 1967 42.881 6334556 1634.047282
|
| 918 |
+
Madagascar Africa 1972 44.851 7082430 1748.562982
|
| 919 |
+
Madagascar Africa 1977 46.881 8007166 1544.228586
|
| 920 |
+
Madagascar Africa 1982 48.969 9171477 1302.878658
|
| 921 |
+
Madagascar Africa 1987 49.35 10568642 1155.441948
|
| 922 |
+
Madagascar Africa 1992 52.214 12210395 1040.67619
|
| 923 |
+
Madagascar Africa 1997 54.978 14165114 986.2958956
|
| 924 |
+
Madagascar Africa 2002 57.286 16473477 894.6370822
|
| 925 |
+
Madagascar Africa 2007 59.443 19167654 1044.770126
|
| 926 |
+
Malawi Africa 1952 36.256 2917802 369.1650802
|
| 927 |
+
Malawi Africa 1957 37.207 3221238 416.3698064
|
| 928 |
+
Malawi Africa 1962 38.41 3628608 427.9010856
|
| 929 |
+
Malawi Africa 1967 39.487 4147252 495.5147806
|
| 930 |
+
Malawi Africa 1972 41.766 4730997 584.6219709
|
| 931 |
+
Malawi Africa 1977 43.767 5637246 663.2236766
|
| 932 |
+
Malawi Africa 1982 45.642 6502825 632.8039209
|
| 933 |
+
Malawi Africa 1987 47.457 7824747 635.5173634
|
| 934 |
+
Malawi Africa 1992 49.42 10014249 563.2000145
|
| 935 |
+
Malawi Africa 1997 47.495 10419991 692.2758103
|
| 936 |
+
Malawi Africa 2002 45.009 11824495 665.4231186
|
| 937 |
+
Malawi Africa 2007 48.303 13327079 759.3499101
|
| 938 |
+
Malaysia Asia 1952 48.463 6748378 1831.132894
|
| 939 |
+
Malaysia Asia 1957 52.102 7739235 1810.066992
|
| 940 |
+
Malaysia Asia 1962 55.737 8906385 2036.884944
|
| 941 |
+
Malaysia Asia 1967 59.371 10154878 2277.742396
|
| 942 |
+
Malaysia Asia 1972 63.01 11441462 2849.09478
|
| 943 |
+
Malaysia Asia 1977 65.256 12845381 3827.921571
|
| 944 |
+
Malaysia Asia 1982 68 14441916 4920.355951
|
| 945 |
+
Malaysia Asia 1987 69.5 16331785 5249.802653
|
| 946 |
+
Malaysia Asia 1992 70.693 18319502 7277.912802
|
| 947 |
+
Malaysia Asia 1997 71.938 20476091 10132.90964
|
| 948 |
+
Malaysia Asia 2002 73.044 22662365 10206.97794
|
| 949 |
+
Malaysia Asia 2007 74.241 24821286 12451.6558
|
| 950 |
+
Mali Africa 1952 33.685 3838168 452.3369807
|
| 951 |
+
Mali Africa 1957 35.307 4241884 490.3821867
|
| 952 |
+
Mali Africa 1962 36.936 4690372 496.1743428
|
| 953 |
+
Mali Africa 1967 38.487 5212416 545.0098873
|
| 954 |
+
Mali Africa 1972 39.977 5828158 581.3688761
|
| 955 |
+
Mali Africa 1977 41.714 6491649 686.3952693
|
| 956 |
+
Mali Africa 1982 43.916 6998256 618.0140641
|
| 957 |
+
Mali Africa 1987 46.364 7634008 684.1715576
|
| 958 |
+
Mali Africa 1992 48.388 8416215 739.014375
|
| 959 |
+
Mali Africa 1997 49.903 9384984 790.2579846
|
| 960 |
+
Mali Africa 2002 51.818 10580176 951.4097518
|
| 961 |
+
Mali Africa 2007 54.467 12031795 1042.581557
|
| 962 |
+
Mauritania Africa 1952 40.543 1022556 743.1159097
|
| 963 |
+
Mauritania Africa 1957 42.338 1076852 846.1202613
|
| 964 |
+
Mauritania Africa 1962 44.248 1146757 1055.896036
|
| 965 |
+
Mauritania Africa 1967 46.289 1230542 1421.145193
|
| 966 |
+
Mauritania Africa 1972 48.437 1332786 1586.851781
|
| 967 |
+
Mauritania Africa 1977 50.852 1456688 1497.492223
|
| 968 |
+
Mauritania Africa 1982 53.599 1622136 1481.150189
|
| 969 |
+
Mauritania Africa 1987 56.145 1841240 1421.603576
|
| 970 |
+
Mauritania Africa 1992 58.333 2119465 1361.369784
|
| 971 |
+
Mauritania Africa 1997 60.43 2444741 1483.136136
|
| 972 |
+
Mauritania Africa 2002 62.247 2828858 1579.019543
|
| 973 |
+
Mauritania Africa 2007 64.164 3270065 1803.151496
|
| 974 |
+
Mauritius Africa 1952 50.986 516556 1967.955707
|
| 975 |
+
Mauritius Africa 1957 58.089 609816 2034.037981
|
| 976 |
+
Mauritius Africa 1962 60.246 701016 2529.067487
|
| 977 |
+
Mauritius Africa 1967 61.557 789309 2475.387562
|
| 978 |
+
Mauritius Africa 1972 62.944 851334 2575.484158
|
| 979 |
+
Mauritius Africa 1977 64.93 913025 3710.982963
|
| 980 |
+
Mauritius Africa 1982 66.711 992040 3688.037739
|
| 981 |
+
Mauritius Africa 1987 68.74 1042663 4783.586903
|
| 982 |
+
Mauritius Africa 1992 69.745 1096202 6058.253846
|
| 983 |
+
Mauritius Africa 1997 70.736 1149818 7425.705295
|
| 984 |
+
Mauritius Africa 2002 71.954 1200206 9021.815894
|
| 985 |
+
Mauritius Africa 2007 72.801 1250882 10956.99112
|
| 986 |
+
Mexico Americas 1952 50.789 30144317 3478.125529
|
| 987 |
+
Mexico Americas 1957 55.19 35015548 4131.546641
|
| 988 |
+
Mexico Americas 1962 58.299 41121485 4581.609385
|
| 989 |
+
Mexico Americas 1967 60.11 47995559 5754.733883
|
| 990 |
+
Mexico Americas 1972 62.361 55984294 6809.40669
|
| 991 |
+
Mexico Americas 1977 65.032 63759976 7674.929108
|
| 992 |
+
Mexico Americas 1982 67.405 71640904 9611.147541
|
| 993 |
+
Mexico Americas 1987 69.498 80122492 8688.156003
|
| 994 |
+
Mexico Americas 1992 71.455 88111030 9472.384295
|
| 995 |
+
Mexico Americas 1997 73.67 95895146 9767.29753
|
| 996 |
+
Mexico Americas 2002 74.902 102479927 10742.44053
|
| 997 |
+
Mexico Americas 2007 76.195 108700891 11977.57496
|
| 998 |
+
Mongolia Asia 1952 42.244 800663 786.5668575
|
| 999 |
+
Mongolia Asia 1957 45.248 882134 912.6626085
|
| 1000 |
+
Mongolia Asia 1962 48.251 1010280 1056.353958
|
| 1001 |
+
Mongolia Asia 1967 51.253 1149500 1226.04113
|
| 1002 |
+
Mongolia Asia 1972 53.754 1320500 1421.741975
|
| 1003 |
+
Mongolia Asia 1977 55.491 1528000 1647.511665
|
| 1004 |
+
Mongolia Asia 1982 57.489 1756032 2000.603139
|
| 1005 |
+
Mongolia Asia 1987 60.222 2015133 2338.008304
|
| 1006 |
+
Mongolia Asia 1992 61.271 2312802 1785.402016
|
| 1007 |
+
Mongolia Asia 1997 63.625 2494803 1902.2521
|
| 1008 |
+
Mongolia Asia 2002 65.033 2674234 2140.739323
|
| 1009 |
+
Mongolia Asia 2007 66.803 2874127 3095.772271
|
| 1010 |
+
Montenegro Europe 1952 59.164 413834 2647.585601
|
| 1011 |
+
Montenegro Europe 1957 61.448 442829 3682.259903
|
| 1012 |
+
Montenegro Europe 1962 63.728 474528 4649.593785
|
| 1013 |
+
Montenegro Europe 1967 67.178 501035 5907.850937
|
| 1014 |
+
Montenegro Europe 1972 70.636 527678 7778.414017
|
| 1015 |
+
Montenegro Europe 1977 73.066 560073 9595.929905
|
| 1016 |
+
Montenegro Europe 1982 74.101 562548 11222.58762
|
| 1017 |
+
Montenegro Europe 1987 74.865 569473 11732.51017
|
| 1018 |
+
Montenegro Europe 1992 75.435 621621 7003.339037
|
| 1019 |
+
Montenegro Europe 1997 75.445 692651 6465.613349
|
| 1020 |
+
Montenegro Europe 2002 73.981 720230 6557.194282
|
| 1021 |
+
Montenegro Europe 2007 74.543 684736 9253.896111
|
| 1022 |
+
Morocco Africa 1952 42.873 9939217 1688.20357
|
| 1023 |
+
Morocco Africa 1957 45.423 11406350 1642.002314
|
| 1024 |
+
Morocco Africa 1962 47.924 13056604 1566.353493
|
| 1025 |
+
Morocco Africa 1967 50.335 14770296 1711.04477
|
| 1026 |
+
Morocco Africa 1972 52.862 16660670 1930.194975
|
| 1027 |
+
Morocco Africa 1977 55.73 18396941 2370.619976
|
| 1028 |
+
Morocco Africa 1982 59.65 20198730 2702.620356
|
| 1029 |
+
Morocco Africa 1987 62.677 22987397 2755.046991
|
| 1030 |
+
Morocco Africa 1992 65.393 25798239 2948.047252
|
| 1031 |
+
Morocco Africa 1997 67.66 28529501 2982.101858
|
| 1032 |
+
Morocco Africa 2002 69.615 31167783 3258.495584
|
| 1033 |
+
Morocco Africa 2007 71.164 33757175 3820.17523
|
| 1034 |
+
Mozambique Africa 1952 31.286 6446316 468.5260381
|
| 1035 |
+
Mozambique Africa 1957 33.779 7038035 495.5868333
|
| 1036 |
+
Mozambique Africa 1962 36.161 7788944 556.6863539
|
| 1037 |
+
Mozambique Africa 1967 38.113 8680909 566.6691539
|
| 1038 |
+
Mozambique Africa 1972 40.328 9809596 724.9178037
|
| 1039 |
+
Mozambique Africa 1977 42.495 11127868 502.3197334
|
| 1040 |
+
Mozambique Africa 1982 42.795 12587223 462.2114149
|
| 1041 |
+
Mozambique Africa 1987 42.861 12891952 389.8761846
|
| 1042 |
+
Mozambique Africa 1992 44.284 13160731 410.8968239
|
| 1043 |
+
Mozambique Africa 1997 46.344 16603334 472.3460771
|
| 1044 |
+
Mozambique Africa 2002 44.026 18473780 633.6179466
|
| 1045 |
+
Mozambique Africa 2007 42.082 19951656 823.6856205
|
| 1046 |
+
Myanmar Asia 1952 36.319 20092996 331
|
| 1047 |
+
Myanmar Asia 1957 41.905 21731844 350
|
| 1048 |
+
Myanmar Asia 1962 45.108 23634436 388
|
| 1049 |
+
Myanmar Asia 1967 49.379 25870271 349
|
| 1050 |
+
Myanmar Asia 1972 53.07 28466390 357
|
| 1051 |
+
Myanmar Asia 1977 56.059 31528087 371
|
| 1052 |
+
Myanmar Asia 1982 58.056 34680442 424
|
| 1053 |
+
Myanmar Asia 1987 58.339 38028578 385
|
| 1054 |
+
Myanmar Asia 1992 59.32 40546538 347
|
| 1055 |
+
Myanmar Asia 1997 60.328 43247867 415
|
| 1056 |
+
Myanmar Asia 2002 59.908 45598081 611
|
| 1057 |
+
Myanmar Asia 2007 62.069 47761980 944
|
| 1058 |
+
Namibia Africa 1952 41.725 485831 2423.780443
|
| 1059 |
+
Namibia Africa 1957 45.226 548080 2621.448058
|
| 1060 |
+
Namibia Africa 1962 48.386 621392 3173.215595
|
| 1061 |
+
Namibia Africa 1967 51.159 706640 3793.694753
|
| 1062 |
+
Namibia Africa 1972 53.867 821782 3746.080948
|
| 1063 |
+
Namibia Africa 1977 56.437 977026 3876.485958
|
| 1064 |
+
Namibia Africa 1982 58.968 1099010 4191.100511
|
| 1065 |
+
Namibia Africa 1987 60.835 1278184 3693.731337
|
| 1066 |
+
Namibia Africa 1992 61.999 1554253 3804.537999
|
| 1067 |
+
Namibia Africa 1997 58.909 1774766 3899.52426
|
| 1068 |
+
Namibia Africa 2002 51.479 1972153 4072.324751
|
| 1069 |
+
Namibia Africa 2007 52.906 2055080 4811.060429
|
| 1070 |
+
Nepal Asia 1952 36.157 9182536 545.8657229
|
| 1071 |
+
Nepal Asia 1957 37.686 9682338 597.9363558
|
| 1072 |
+
Nepal Asia 1962 39.393 10332057 652.3968593
|
| 1073 |
+
Nepal Asia 1967 41.472 11261690 676.4422254
|
| 1074 |
+
Nepal Asia 1972 43.971 12412593 674.7881296
|
| 1075 |
+
Nepal Asia 1977 46.748 13933198 694.1124398
|
| 1076 |
+
Nepal Asia 1982 49.594 15796314 718.3730947
|
| 1077 |
+
Nepal Asia 1987 52.537 17917180 775.6324501
|
| 1078 |
+
Nepal Asia 1992 55.727 20326209 897.7403604
|
| 1079 |
+
Nepal Asia 1997 59.426 23001113 1010.892138
|
| 1080 |
+
Nepal Asia 2002 61.34 25873917 1057.206311
|
| 1081 |
+
Nepal Asia 2007 63.785 28901790 1091.359778
|
| 1082 |
+
Netherlands Europe 1952 72.13 10381988 8941.571858
|
| 1083 |
+
Netherlands Europe 1957 72.99 11026383 11276.19344
|
| 1084 |
+
Netherlands Europe 1962 73.23 11805689 12790.84956
|
| 1085 |
+
Netherlands Europe 1967 73.82 12596822 15363.25136
|
| 1086 |
+
Netherlands Europe 1972 73.75 13329874 18794.74567
|
| 1087 |
+
Netherlands Europe 1977 75.24 13852989 21209.0592
|
| 1088 |
+
Netherlands Europe 1982 76.05 14310401 21399.46046
|
| 1089 |
+
Netherlands Europe 1987 76.83 14665278 23651.32361
|
| 1090 |
+
Netherlands Europe 1992 77.42 15174244 26790.94961
|
| 1091 |
+
Netherlands Europe 1997 78.03 15604464 30246.13063
|
| 1092 |
+
Netherlands Europe 2002 78.53 16122830 33724.75778
|
| 1093 |
+
Netherlands Europe 2007 79.762 16570613 36797.93332
|
| 1094 |
+
New Zealand Oceania 1952 69.39 1994794 10556.57566
|
| 1095 |
+
New Zealand Oceania 1957 70.26 2229407 12247.39532
|
| 1096 |
+
New Zealand Oceania 1962 71.24 2488550 13175.678
|
| 1097 |
+
New Zealand Oceania 1967 71.52 2728150 14463.91893
|
| 1098 |
+
New Zealand Oceania 1972 71.89 2929100 16046.03728
|
| 1099 |
+
New Zealand Oceania 1977 72.22 3164900 16233.7177
|
| 1100 |
+
New Zealand Oceania 1982 73.84 3210650 17632.4104
|
| 1101 |
+
New Zealand Oceania 1987 74.32 3317166 19007.19129
|
| 1102 |
+
New Zealand Oceania 1992 76.33 3437674 18363.32494
|
| 1103 |
+
New Zealand Oceania 1997 77.55 3676187 21050.41377
|
| 1104 |
+
New Zealand Oceania 2002 79.11 3908037 23189.80135
|
| 1105 |
+
New Zealand Oceania 2007 80.204 4115771 25185.00911
|
| 1106 |
+
Nicaragua Americas 1952 42.314 1165790 3112.363948
|
| 1107 |
+
Nicaragua Americas 1957 45.432 1358828 3457.415947
|
| 1108 |
+
Nicaragua Americas 1962 48.632 1590597 3634.364406
|
| 1109 |
+
Nicaragua Americas 1967 51.884 1865490 4643.393534
|
| 1110 |
+
Nicaragua Americas 1972 55.151 2182908 4688.593267
|
| 1111 |
+
Nicaragua Americas 1977 57.47 2554598 5486.371089
|
| 1112 |
+
Nicaragua Americas 1982 59.298 2979423 3470.338156
|
| 1113 |
+
Nicaragua Americas 1987 62.008 3344353 2955.984375
|
| 1114 |
+
Nicaragua Americas 1992 65.843 4017939 2170.151724
|
| 1115 |
+
Nicaragua Americas 1997 68.426 4609572 2253.023004
|
| 1116 |
+
Nicaragua Americas 2002 70.836 5146848 2474.548819
|
| 1117 |
+
Nicaragua Americas 2007 72.899 5675356 2749.320965
|
| 1118 |
+
Niger Africa 1952 37.444 3379468 761.879376
|
| 1119 |
+
Niger Africa 1957 38.598 3692184 835.5234025
|
| 1120 |
+
Niger Africa 1962 39.487 4076008 997.7661127
|
| 1121 |
+
Niger Africa 1967 40.118 4534062 1054.384891
|
| 1122 |
+
Niger Africa 1972 40.546 5060262 954.2092363
|
| 1123 |
+
Niger Africa 1977 41.291 5682086 808.8970728
|
| 1124 |
+
Niger Africa 1982 42.598 6437188 909.7221354
|
| 1125 |
+
Niger Africa 1987 44.555 7332638 668.3000228
|
| 1126 |
+
Niger Africa 1992 47.391 8392818 581.182725
|
| 1127 |
+
Niger Africa 1997 51.313 9666252 580.3052092
|
| 1128 |
+
Niger Africa 2002 54.496 11140655 601.0745012
|
| 1129 |
+
Niger Africa 2007 56.867 12894865 619.6768924
|
| 1130 |
+
Nigeria Africa 1952 36.324 33119096 1077.281856
|
| 1131 |
+
Nigeria Africa 1957 37.802 37173340 1100.592563
|
| 1132 |
+
Nigeria Africa 1962 39.36 41871351 1150.927478
|
| 1133 |
+
Nigeria Africa 1967 41.04 47287752 1014.514104
|
| 1134 |
+
Nigeria Africa 1972 42.821 53740085 1698.388838
|
| 1135 |
+
Nigeria Africa 1977 44.514 62209173 1981.951806
|
| 1136 |
+
Nigeria Africa 1982 45.826 73039376 1576.97375
|
| 1137 |
+
Nigeria Africa 1987 46.886 81551520 1385.029563
|
| 1138 |
+
Nigeria Africa 1992 47.472 93364244 1619.848217
|
| 1139 |
+
Nigeria Africa 1997 47.464 106207839 1624.941275
|
| 1140 |
+
Nigeria Africa 2002 46.608 119901274 1615.286395
|
| 1141 |
+
Nigeria Africa 2007 46.859 135031164 2013.977305
|
| 1142 |
+
Norway Europe 1952 72.67 3327728 10095.42172
|
| 1143 |
+
Norway Europe 1957 73.44 3491938 11653.97304
|
| 1144 |
+
Norway Europe 1962 73.47 3638919 13450.40151
|
| 1145 |
+
Norway Europe 1967 74.08 3786019 16361.87647
|
| 1146 |
+
Norway Europe 1972 74.34 3933004 18965.05551
|
| 1147 |
+
Norway Europe 1977 75.37 4043205 23311.34939
|
| 1148 |
+
Norway Europe 1982 75.97 4114787 26298.63531
|
| 1149 |
+
Norway Europe 1987 75.89 4186147 31540.9748
|
| 1150 |
+
Norway Europe 1992 77.32 4286357 33965.66115
|
| 1151 |
+
Norway Europe 1997 78.32 4405672 41283.16433
|
| 1152 |
+
Norway Europe 2002 79.05 4535591 44683.97525
|
| 1153 |
+
Norway Europe 2007 80.196 4627926 49357.19017
|
| 1154 |
+
Oman Asia 1952 37.578 507833 1828.230307
|
| 1155 |
+
Oman Asia 1957 40.08 561977 2242.746551
|
| 1156 |
+
Oman Asia 1962 43.165 628164 2924.638113
|
| 1157 |
+
Oman Asia 1967 46.988 714775 4720.942687
|
| 1158 |
+
Oman Asia 1972 52.143 829050 10618.03855
|
| 1159 |
+
Oman Asia 1977 57.367 1004533 11848.34392
|
| 1160 |
+
Oman Asia 1982 62.728 1301048 12954.79101
|
| 1161 |
+
Oman Asia 1987 67.734 1593882 18115.22313
|
| 1162 |
+
Oman Asia 1992 71.197 1915208 18616.70691
|
| 1163 |
+
Oman Asia 1997 72.499 2283635 19702.05581
|
| 1164 |
+
Oman Asia 2002 74.193 2713462 19774.83687
|
| 1165 |
+
Oman Asia 2007 75.64 3204897 22316.19287
|
| 1166 |
+
Pakistan Asia 1952 43.436 41346560 684.5971438
|
| 1167 |
+
Pakistan Asia 1957 45.557 46679944 747.0835292
|
| 1168 |
+
Pakistan Asia 1962 47.67 53100671 803.3427418
|
| 1169 |
+
Pakistan Asia 1967 49.8 60641899 942.4082588
|
| 1170 |
+
Pakistan Asia 1972 51.929 69325921 1049.938981
|
| 1171 |
+
Pakistan Asia 1977 54.043 78152686 1175.921193
|
| 1172 |
+
Pakistan Asia 1982 56.158 91462088 1443.429832
|
| 1173 |
+
Pakistan Asia 1987 58.245 105186881 1704.686583
|
| 1174 |
+
Pakistan Asia 1992 60.838 120065004 1971.829464
|
| 1175 |
+
Pakistan Asia 1997 61.818 135564834 2049.350521
|
| 1176 |
+
Pakistan Asia 2002 63.61 153403524 2092.712441
|
| 1177 |
+
Pakistan Asia 2007 65.483 169270617 2605.94758
|
| 1178 |
+
Panama Americas 1952 55.191 940080 2480.380334
|
| 1179 |
+
Panama Americas 1957 59.201 1063506 2961.800905
|
| 1180 |
+
Panama Americas 1962 61.817 1215725 3536.540301
|
| 1181 |
+
Panama Americas 1967 64.071 1405486 4421.009084
|
| 1182 |
+
Panama Americas 1972 66.216 1616384 5364.249663
|
| 1183 |
+
Panama Americas 1977 68.681 1839782 5351.912144
|
| 1184 |
+
Panama Americas 1982 70.472 2036305 7009.601598
|
| 1185 |
+
Panama Americas 1987 71.523 2253639 7034.779161
|
| 1186 |
+
Panama Americas 1992 72.462 2484997 6618.74305
|
| 1187 |
+
Panama Americas 1997 73.738 2734531 7113.692252
|
| 1188 |
+
Panama Americas 2002 74.712 2990875 7356.031934
|
| 1189 |
+
Panama Americas 2007 75.537 3242173 9809.185636
|
| 1190 |
+
Paraguay Americas 1952 62.649 1555876 1952.308701
|
| 1191 |
+
Paraguay Americas 1957 63.196 1770902 2046.154706
|
| 1192 |
+
Paraguay Americas 1962 64.361 2009813 2148.027146
|
| 1193 |
+
Paraguay Americas 1967 64.951 2287985 2299.376311
|
| 1194 |
+
Paraguay Americas 1972 65.815 2614104 2523.337977
|
| 1195 |
+
Paraguay Americas 1977 66.353 2984494 3248.373311
|
| 1196 |
+
Paraguay Americas 1982 66.874 3366439 4258.503604
|
| 1197 |
+
Paraguay Americas 1987 67.378 3886512 3998.875695
|
| 1198 |
+
Paraguay Americas 1992 68.225 4483945 4196.411078
|
| 1199 |
+
Paraguay Americas 1997 69.4 5154123 4247.400261
|
| 1200 |
+
Paraguay Americas 2002 70.755 5884491 3783.674243
|
| 1201 |
+
Paraguay Americas 2007 71.752 6667147 4172.838464
|
| 1202 |
+
Peru Americas 1952 43.902 8025700 3758.523437
|
| 1203 |
+
Peru Americas 1957 46.263 9146100 4245.256698
|
| 1204 |
+
Peru Americas 1962 49.096 10516500 4957.037982
|
| 1205 |
+
Peru Americas 1967 51.445 12132200 5788.09333
|
| 1206 |
+
Peru Americas 1972 55.448 13954700 5937.827283
|
| 1207 |
+
Peru Americas 1977 58.447 15990099 6281.290855
|
| 1208 |
+
Peru Americas 1982 61.406 18125129 6434.501797
|
| 1209 |
+
Peru Americas 1987 64.134 20195924 6360.943444
|
| 1210 |
+
Peru Americas 1992 66.458 22430449 4446.380924
|
| 1211 |
+
Peru Americas 1997 68.386 24748122 5838.347657
|
| 1212 |
+
Peru Americas 2002 69.906 26769436 5909.020073
|
| 1213 |
+
Peru Americas 2007 71.421 28674757 7408.905561
|
| 1214 |
+
Philippines Asia 1952 47.752 22438691 1272.880995
|
| 1215 |
+
Philippines Asia 1957 51.334 26072194 1547.944844
|
| 1216 |
+
Philippines Asia 1962 54.757 30325264 1649.552153
|
| 1217 |
+
Philippines Asia 1967 56.393 35356600 1814.12743
|
| 1218 |
+
Philippines Asia 1972 58.065 40850141 1989.37407
|
| 1219 |
+
Philippines Asia 1977 60.06 46850962 2373.204287
|
| 1220 |
+
Philippines Asia 1982 62.082 53456774 2603.273765
|
| 1221 |
+
Philippines Asia 1987 64.151 60017788 2189.634995
|
| 1222 |
+
Philippines Asia 1992 66.458 67185766 2279.324017
|
| 1223 |
+
Philippines Asia 1997 68.564 75012988 2536.534925
|
| 1224 |
+
Philippines Asia 2002 70.303 82995088 2650.921068
|
| 1225 |
+
Philippines Asia 2007 71.688 91077287 3190.481016
|
| 1226 |
+
Poland Europe 1952 61.31 25730551 4029.329699
|
| 1227 |
+
Poland Europe 1957 65.77 28235346 4734.253019
|
| 1228 |
+
Poland Europe 1962 67.64 30329617 5338.752143
|
| 1229 |
+
Poland Europe 1967 69.61 31785378 6557.152776
|
| 1230 |
+
Poland Europe 1972 70.85 33039545 8006.506993
|
| 1231 |
+
Poland Europe 1977 70.67 34621254 9508.141454
|
| 1232 |
+
Poland Europe 1982 71.32 36227381 8451.531004
|
| 1233 |
+
Poland Europe 1987 70.98 37740710 9082.351172
|
| 1234 |
+
Poland Europe 1992 70.99 38370697 7738.881247
|
| 1235 |
+
Poland Europe 1997 72.75 38654957 10159.58368
|
| 1236 |
+
Poland Europe 2002 74.67 38625976 12002.23908
|
| 1237 |
+
Poland Europe 2007 75.563 38518241 15389.92468
|
| 1238 |
+
Portugal Europe 1952 59.82 8526050 3068.319867
|
| 1239 |
+
Portugal Europe 1957 61.51 8817650 3774.571743
|
| 1240 |
+
Portugal Europe 1962 64.39 9019800 4727.954889
|
| 1241 |
+
Portugal Europe 1967 66.6 9103000 6361.517993
|
| 1242 |
+
Portugal Europe 1972 69.26 8970450 9022.247417
|
| 1243 |
+
Portugal Europe 1977 70.41 9662600 10172.48572
|
| 1244 |
+
Portugal Europe 1982 72.77 9859650 11753.84291
|
| 1245 |
+
Portugal Europe 1987 74.06 9915289 13039.30876
|
| 1246 |
+
Portugal Europe 1992 74.86 9927680 16207.26663
|
| 1247 |
+
Portugal Europe 1997 75.97 10156415 17641.03156
|
| 1248 |
+
Portugal Europe 2002 77.29 10433867 19970.90787
|
| 1249 |
+
Portugal Europe 2007 78.098 10642836 20509.64777
|
| 1250 |
+
Puerto Rico Americas 1952 64.28 2227000 3081.959785
|
| 1251 |
+
Puerto Rico Americas 1957 68.54 2260000 3907.156189
|
| 1252 |
+
Puerto Rico Americas 1962 69.62 2448046 5108.34463
|
| 1253 |
+
Puerto Rico Americas 1967 71.1 2648961 6929.277714
|
| 1254 |
+
Puerto Rico Americas 1972 72.16 2847132 9123.041742
|
| 1255 |
+
Puerto Rico Americas 1977 73.44 3080828 9770.524921
|
| 1256 |
+
Puerto Rico Americas 1982 73.75 3279001 10330.98915
|
| 1257 |
+
Puerto Rico Americas 1987 74.63 3444468 12281.34191
|
| 1258 |
+
Puerto Rico Americas 1992 73.911 3585176 14641.58711
|
| 1259 |
+
Puerto Rico Americas 1997 74.917 3759430 16999.4333
|
| 1260 |
+
Puerto Rico Americas 2002 77.778 3859606 18855.60618
|
| 1261 |
+
Puerto Rico Americas 2007 78.746 3942491 19328.70901
|
| 1262 |
+
Reunion Africa 1952 52.724 257700 2718.885295
|
| 1263 |
+
Reunion Africa 1957 55.09 308700 2769.451844
|
| 1264 |
+
Reunion Africa 1962 57.666 358900 3173.72334
|
| 1265 |
+
Reunion Africa 1967 60.542 414024 4021.175739
|
| 1266 |
+
Reunion Africa 1972 64.274 461633 5047.658563
|
| 1267 |
+
Reunion Africa 1977 67.064 492095 4319.804067
|
| 1268 |
+
Reunion Africa 1982 69.885 517810 5267.219353
|
| 1269 |
+
Reunion Africa 1987 71.913 562035 5303.377488
|
| 1270 |
+
Reunion Africa 1992 73.615 622191 6101.255823
|
| 1271 |
+
Reunion Africa 1997 74.772 684810 6071.941411
|
| 1272 |
+
Reunion Africa 2002 75.744 743981 6316.1652
|
| 1273 |
+
Reunion Africa 2007 76.442 798094 7670.122558
|
| 1274 |
+
Romania Europe 1952 61.05 16630000 3144.613186
|
| 1275 |
+
Romania Europe 1957 64.1 17829327 3943.370225
|
| 1276 |
+
Romania Europe 1962 66.8 18680721 4734.997586
|
| 1277 |
+
Romania Europe 1967 66.8 19284814 6470.866545
|
| 1278 |
+
Romania Europe 1972 69.21 20662648 8011.414402
|
| 1279 |
+
Romania Europe 1977 69.46 21658597 9356.39724
|
| 1280 |
+
Romania Europe 1982 69.66 22356726 9605.314053
|
| 1281 |
+
Romania Europe 1987 69.53 22686371 9696.273295
|
| 1282 |
+
Romania Europe 1992 69.36 22797027 6598.409903
|
| 1283 |
+
Romania Europe 1997 69.72 22562458 7346.547557
|
| 1284 |
+
Romania Europe 2002 71.322 22404337 7885.360081
|
| 1285 |
+
Romania Europe 2007 72.476 22276056 10808.47561
|
| 1286 |
+
Rwanda Africa 1952 40 2534927 493.3238752
|
| 1287 |
+
Rwanda Africa 1957 41.5 2822082 540.2893983
|
| 1288 |
+
Rwanda Africa 1962 43 3051242 597.4730727
|
| 1289 |
+
Rwanda Africa 1967 44.1 3451079 510.9637142
|
| 1290 |
+
Rwanda Africa 1972 44.6 3992121 590.5806638
|
| 1291 |
+
Rwanda Africa 1977 45 4657072 670.0806011
|
| 1292 |
+
Rwanda Africa 1982 46.218 5507565 881.5706467
|
| 1293 |
+
Rwanda Africa 1987 44.02 6349365 847.991217
|
| 1294 |
+
Rwanda Africa 1992 23.599 7290203 737.0685949
|
| 1295 |
+
Rwanda Africa 1997 36.087 7212583 589.9445051
|
| 1296 |
+
Rwanda Africa 2002 43.413 7852401 785.6537648
|
| 1297 |
+
Rwanda Africa 2007 46.242 8860588 863.0884639
|
| 1298 |
+
Sao Tome and Principe Africa 1952 46.471 60011 879.5835855
|
| 1299 |
+
Sao Tome and Principe Africa 1957 48.945 61325 860.7369026
|
| 1300 |
+
Sao Tome and Principe Africa 1962 51.893 65345 1071.551119
|
| 1301 |
+
Sao Tome and Principe Africa 1967 54.425 70787 1384.840593
|
| 1302 |
+
Sao Tome and Principe Africa 1972 56.48 76595 1532.985254
|
| 1303 |
+
Sao Tome and Principe Africa 1977 58.55 86796 1737.561657
|
| 1304 |
+
Sao Tome and Principe Africa 1982 60.351 98593 1890.218117
|
| 1305 |
+
Sao Tome and Principe Africa 1987 61.728 110812 1516.525457
|
| 1306 |
+
Sao Tome and Principe Africa 1992 62.742 125911 1428.777814
|
| 1307 |
+
Sao Tome and Principe Africa 1997 63.306 145608 1339.076036
|
| 1308 |
+
Sao Tome and Principe Africa 2002 64.337 170372 1353.09239
|
| 1309 |
+
Sao Tome and Principe Africa 2007 65.528 199579 1598.435089
|
| 1310 |
+
Saudi Arabia Asia 1952 39.875 4005677 6459.554823
|
| 1311 |
+
Saudi Arabia Asia 1957 42.868 4419650 8157.591248
|
| 1312 |
+
Saudi Arabia Asia 1962 45.914 4943029 11626.41975
|
| 1313 |
+
Saudi Arabia Asia 1967 49.901 5618198 16903.04886
|
| 1314 |
+
Saudi Arabia Asia 1972 53.886 6472756 24837.42865
|
| 1315 |
+
Saudi Arabia Asia 1977 58.69 8128505 34167.7626
|
| 1316 |
+
Saudi Arabia Asia 1982 63.012 11254672 33693.17525
|
| 1317 |
+
Saudi Arabia Asia 1987 66.295 14619745 21198.26136
|
| 1318 |
+
Saudi Arabia Asia 1992 68.768 16945857 24841.61777
|
| 1319 |
+
Saudi Arabia Asia 1997 70.533 21229759 20586.69019
|
| 1320 |
+
Saudi Arabia Asia 2002 71.626 24501530 19014.54118
|
| 1321 |
+
Saudi Arabia Asia 2007 72.777 27601038 21654.83194
|
| 1322 |
+
Senegal Africa 1952 37.278 2755589 1450.356983
|
| 1323 |
+
Senegal Africa 1957 39.329 3054547 1567.653006
|
| 1324 |
+
Senegal Africa 1962 41.454 3430243 1654.988723
|
| 1325 |
+
Senegal Africa 1967 43.563 3965841 1612.404632
|
| 1326 |
+
Senegal Africa 1972 45.815 4588696 1597.712056
|
| 1327 |
+
Senegal Africa 1977 48.879 5260855 1561.769116
|
| 1328 |
+
Senegal Africa 1982 52.379 6147783 1518.479984
|
| 1329 |
+
Senegal Africa 1987 55.769 7171347 1441.72072
|
| 1330 |
+
Senegal Africa 1992 58.196 8307920 1367.899369
|
| 1331 |
+
Senegal Africa 1997 60.187 9535314 1392.368347
|
| 1332 |
+
Senegal Africa 2002 61.6 10870037 1519.635262
|
| 1333 |
+
Senegal Africa 2007 63.062 12267493 1712.472136
|
| 1334 |
+
Serbia Europe 1952 57.996 6860147 3581.459448
|
| 1335 |
+
Serbia Europe 1957 61.685 7271135 4981.090891
|
| 1336 |
+
Serbia Europe 1962 64.531 7616060 6289.629157
|
| 1337 |
+
Serbia Europe 1967 66.914 7971222 7991.707066
|
| 1338 |
+
Serbia Europe 1972 68.7 8313288 10522.06749
|
| 1339 |
+
Serbia Europe 1977 70.3 8686367 12980.66956
|
| 1340 |
+
Serbia Europe 1982 70.162 9032824 15181.0927
|
| 1341 |
+
Serbia Europe 1987 71.218 9230783 15870.87851
|
| 1342 |
+
Serbia Europe 1992 71.659 9826397 9325.068238
|
| 1343 |
+
Serbia Europe 1997 72.232 10336594 7914.320304
|
| 1344 |
+
Serbia Europe 2002 73.213 10111559 7236.075251
|
| 1345 |
+
Serbia Europe 2007 74.002 10150265 9786.534714
|
| 1346 |
+
Sierra Leone Africa 1952 30.331 2143249 879.7877358
|
| 1347 |
+
Sierra Leone Africa 1957 31.57 2295678 1004.484437
|
| 1348 |
+
Sierra Leone Africa 1962 32.767 2467895 1116.639877
|
| 1349 |
+
Sierra Leone Africa 1967 34.113 2662190 1206.043465
|
| 1350 |
+
Sierra Leone Africa 1972 35.4 2879013 1353.759762
|
| 1351 |
+
Sierra Leone Africa 1977 36.788 3140897 1348.285159
|
| 1352 |
+
Sierra Leone Africa 1982 38.445 3464522 1465.010784
|
| 1353 |
+
Sierra Leone Africa 1987 40.006 3868905 1294.447788
|
| 1354 |
+
Sierra Leone Africa 1992 38.333 4260884 1068.696278
|
| 1355 |
+
Sierra Leone Africa 1997 39.897 4578212 574.6481576
|
| 1356 |
+
Sierra Leone Africa 2002 41.012 5359092 699.489713
|
| 1357 |
+
Sierra Leone Africa 2007 42.568 6144562 862.5407561
|
| 1358 |
+
Singapore Asia 1952 60.396 1127000 2315.138227
|
| 1359 |
+
Singapore Asia 1957 63.179 1445929 2843.104409
|
| 1360 |
+
Singapore Asia 1962 65.798 1750200 3674.735572
|
| 1361 |
+
Singapore Asia 1967 67.946 1977600 4977.41854
|
| 1362 |
+
Singapore Asia 1972 69.521 2152400 8597.756202
|
| 1363 |
+
Singapore Asia 1977 70.795 2325300 11210.08948
|
| 1364 |
+
Singapore Asia 1982 71.76 2651869 15169.16112
|
| 1365 |
+
Singapore Asia 1987 73.56 2794552 18861.53081
|
| 1366 |
+
Singapore Asia 1992 75.788 3235865 24769.8912
|
| 1367 |
+
Singapore Asia 1997 77.158 3802309 33519.4766
|
| 1368 |
+
Singapore Asia 2002 78.77 4197776 36023.1054
|
| 1369 |
+
Singapore Asia 2007 79.972 4553009 47143.17964
|
| 1370 |
+
Slovak Republic Europe 1952 64.36 3558137 5074.659104
|
| 1371 |
+
Slovak Republic Europe 1957 67.45 3844277 6093.26298
|
| 1372 |
+
Slovak Republic Europe 1962 70.33 4237384 7481.107598
|
| 1373 |
+
Slovak Republic Europe 1967 70.98 4442238 8412.902397
|
| 1374 |
+
Slovak Republic Europe 1972 70.35 4593433 9674.167626
|
| 1375 |
+
Slovak Republic Europe 1977 70.45 4827803 10922.66404
|
| 1376 |
+
Slovak Republic Europe 1982 70.8 5048043 11348.54585
|
| 1377 |
+
Slovak Republic Europe 1987 71.08 5199318 12037.26758
|
| 1378 |
+
Slovak Republic Europe 1992 71.38 5302888 9498.467723
|
| 1379 |
+
Slovak Republic Europe 1997 72.71 5383010 12126.23065
|
| 1380 |
+
Slovak Republic Europe 2002 73.8 5410052 13638.77837
|
| 1381 |
+
Slovak Republic Europe 2007 74.663 5447502 18678.31435
|
| 1382 |
+
Slovenia Europe 1952 65.57 1489518 4215.041741
|
| 1383 |
+
Slovenia Europe 1957 67.85 1533070 5862.276629
|
| 1384 |
+
Slovenia Europe 1962 69.15 1582962 7402.303395
|
| 1385 |
+
Slovenia Europe 1967 69.18 1646912 9405.489397
|
| 1386 |
+
Slovenia Europe 1972 69.82 1694510 12383.4862
|
| 1387 |
+
Slovenia Europe 1977 70.97 1746919 15277.03017
|
| 1388 |
+
Slovenia Europe 1982 71.063 1861252 17866.72175
|
| 1389 |
+
Slovenia Europe 1987 72.25 1945870 18678.53492
|
| 1390 |
+
Slovenia Europe 1992 73.64 1999210 14214.71681
|
| 1391 |
+
Slovenia Europe 1997 75.13 2011612 17161.10735
|
| 1392 |
+
Slovenia Europe 2002 76.66 2011497 20660.01936
|
| 1393 |
+
Slovenia Europe 2007 77.926 2009245 25768.25759
|
| 1394 |
+
Somalia Africa 1952 32.978 2526994 1135.749842
|
| 1395 |
+
Somalia Africa 1957 34.977 2780415 1258.147413
|
| 1396 |
+
Somalia Africa 1962 36.981 3080153 1369.488336
|
| 1397 |
+
Somalia Africa 1967 38.977 3428839 1284.73318
|
| 1398 |
+
Somalia Africa 1972 40.973 3840161 1254.576127
|
| 1399 |
+
Somalia Africa 1977 41.974 4353666 1450.992513
|
| 1400 |
+
Somalia Africa 1982 42.955 5828892 1176.807031
|
| 1401 |
+
Somalia Africa 1987 44.501 6921858 1093.244963
|
| 1402 |
+
Somalia Africa 1992 39.658 6099799 926.9602964
|
| 1403 |
+
Somalia Africa 1997 43.795 6633514 930.5964284
|
| 1404 |
+
Somalia Africa 2002 45.936 7753310 882.0818218
|
| 1405 |
+
Somalia Africa 2007 48.159 9118773 926.1410683
|
| 1406 |
+
South Africa Africa 1952 45.009 14264935 4725.295531
|
| 1407 |
+
South Africa Africa 1957 47.985 16151549 5487.104219
|
| 1408 |
+
South Africa Africa 1962 49.951 18356657 5768.729717
|
| 1409 |
+
South Africa Africa 1967 51.927 20997321 7114.477971
|
| 1410 |
+
South Africa Africa 1972 53.696 23935810 7765.962636
|
| 1411 |
+
South Africa Africa 1977 55.527 27129932 8028.651439
|
| 1412 |
+
South Africa Africa 1982 58.161 31140029 8568.266228
|
| 1413 |
+
South Africa Africa 1987 60.834 35933379 7825.823398
|
| 1414 |
+
South Africa Africa 1992 61.888 39964159 7225.069258
|
| 1415 |
+
South Africa Africa 1997 60.236 42835005 7479.188244
|
| 1416 |
+
South Africa Africa 2002 53.365 44433622 7710.946444
|
| 1417 |
+
South Africa Africa 2007 49.339 43997828 9269.657808
|
| 1418 |
+
Spain Europe 1952 64.94 28549870 3834.034742
|
| 1419 |
+
Spain Europe 1957 66.66 29841614 4564.80241
|
| 1420 |
+
Spain Europe 1962 69.69 31158061 5693.843879
|
| 1421 |
+
Spain Europe 1967 71.44 32850275 7993.512294
|
| 1422 |
+
Spain Europe 1972 73.06 34513161 10638.75131
|
| 1423 |
+
Spain Europe 1977 74.39 36439000 13236.92117
|
| 1424 |
+
Spain Europe 1982 76.3 37983310 13926.16997
|
| 1425 |
+
Spain Europe 1987 76.9 38880702 15764.98313
|
| 1426 |
+
Spain Europe 1992 77.57 39549438 18603.06452
|
| 1427 |
+
Spain Europe 1997 78.77 39855442 20445.29896
|
| 1428 |
+
Spain Europe 2002 79.78 40152517 24835.47166
|
| 1429 |
+
Spain Europe 2007 80.941 40448191 28821.0637
|
| 1430 |
+
Sri Lanka Asia 1952 57.593 7982342 1083.53203
|
| 1431 |
+
Sri Lanka Asia 1957 61.456 9128546 1072.546602
|
| 1432 |
+
Sri Lanka Asia 1962 62.192 10421936 1074.47196
|
| 1433 |
+
Sri Lanka Asia 1967 64.266 11737396 1135.514326
|
| 1434 |
+
Sri Lanka Asia 1972 65.042 13016733 1213.39553
|
| 1435 |
+
Sri Lanka Asia 1977 65.949 14116836 1348.775651
|
| 1436 |
+
Sri Lanka Asia 1982 68.757 15410151 1648.079789
|
| 1437 |
+
Sri Lanka Asia 1987 69.011 16495304 1876.766827
|
| 1438 |
+
Sri Lanka Asia 1992 70.379 17587060 2153.739222
|
| 1439 |
+
Sri Lanka Asia 1997 70.457 18698655 2664.477257
|
| 1440 |
+
Sri Lanka Asia 2002 70.815 19576783 3015.378833
|
| 1441 |
+
Sri Lanka Asia 2007 72.396 20378239 3970.095407
|
| 1442 |
+
Sudan Africa 1952 38.635 8504667 1615.991129
|
| 1443 |
+
Sudan Africa 1957 39.624 9753392 1770.337074
|
| 1444 |
+
Sudan Africa 1962 40.87 11183227 1959.593767
|
| 1445 |
+
Sudan Africa 1967 42.858 12716129 1687.997641
|
| 1446 |
+
Sudan Africa 1972 45.083 14597019 1659.652775
|
| 1447 |
+
Sudan Africa 1977 47.8 17104986 2202.988423
|
| 1448 |
+
Sudan Africa 1982 50.338 20367053 1895.544073
|
| 1449 |
+
Sudan Africa 1987 51.744 24725960 1507.819159
|
| 1450 |
+
Sudan Africa 1992 53.556 28227588 1492.197043
|
| 1451 |
+
Sudan Africa 1997 55.373 32160729 1632.210764
|
| 1452 |
+
Sudan Africa 2002 56.369 37090298 1993.398314
|
| 1453 |
+
Sudan Africa 2007 58.556 42292929 2602.394995
|
| 1454 |
+
Swaziland Africa 1952 41.407 290243 1148.376626
|
| 1455 |
+
Swaziland Africa 1957 43.424 326741 1244.708364
|
| 1456 |
+
Swaziland Africa 1962 44.992 370006 1856.182125
|
| 1457 |
+
Swaziland Africa 1967 46.633 420690 2613.101665
|
| 1458 |
+
Swaziland Africa 1972 49.552 480105 3364.836625
|
| 1459 |
+
Swaziland Africa 1977 52.537 551425 3781.410618
|
| 1460 |
+
Swaziland Africa 1982 55.561 649901 3895.384018
|
| 1461 |
+
Swaziland Africa 1987 57.678 779348 3984.839812
|
| 1462 |
+
Swaziland Africa 1992 58.474 962344 3553.0224
|
| 1463 |
+
Swaziland Africa 1997 54.289 1054486 3876.76846
|
| 1464 |
+
Swaziland Africa 2002 43.869 1130269 4128.116943
|
| 1465 |
+
Swaziland Africa 2007 39.613 1133066 4513.480643
|
| 1466 |
+
Sweden Europe 1952 71.86 7124673 8527.844662
|
| 1467 |
+
Sweden Europe 1957 72.49 7363802 9911.878226
|
| 1468 |
+
Sweden Europe 1962 73.37 7561588 12329.44192
|
| 1469 |
+
Sweden Europe 1967 74.16 7867931 15258.29697
|
| 1470 |
+
Sweden Europe 1972 74.72 8122293 17832.02464
|
| 1471 |
+
Sweden Europe 1977 75.44 8251648 18855.72521
|
| 1472 |
+
Sweden Europe 1982 76.42 8325260 20667.38125
|
| 1473 |
+
Sweden Europe 1987 77.19 8421403 23586.92927
|
| 1474 |
+
Sweden Europe 1992 78.16 8718867 23880.01683
|
| 1475 |
+
Sweden Europe 1997 79.39 8897619 25266.59499
|
| 1476 |
+
Sweden Europe 2002 80.04 8954175 29341.63093
|
| 1477 |
+
Sweden Europe 2007 80.884 9031088 33859.74835
|
| 1478 |
+
Switzerland Europe 1952 69.62 4815000 14734.23275
|
| 1479 |
+
Switzerland Europe 1957 70.56 5126000 17909.48973
|
| 1480 |
+
Switzerland Europe 1962 71.32 5666000 20431.0927
|
| 1481 |
+
Switzerland Europe 1967 72.77 6063000 22966.14432
|
| 1482 |
+
Switzerland Europe 1972 73.78 6401400 27195.11304
|
| 1483 |
+
Switzerland Europe 1977 75.39 6316424 26982.29052
|
| 1484 |
+
Switzerland Europe 1982 76.21 6468126 28397.71512
|
| 1485 |
+
Switzerland Europe 1987 77.41 6649942 30281.70459
|
| 1486 |
+
Switzerland Europe 1992 78.03 6995447 31871.5303
|
| 1487 |
+
Switzerland Europe 1997 79.37 7193761 32135.32301
|
| 1488 |
+
Switzerland Europe 2002 80.62 7361757 34480.95771
|
| 1489 |
+
Switzerland Europe 2007 81.701 7554661 37506.41907
|
| 1490 |
+
Syria Asia 1952 45.883 3661549 1643.485354
|
| 1491 |
+
Syria Asia 1957 48.284 4149908 2117.234893
|
| 1492 |
+
Syria Asia 1962 50.305 4834621 2193.037133
|
| 1493 |
+
Syria Asia 1967 53.655 5680812 1881.923632
|
| 1494 |
+
Syria Asia 1972 57.296 6701172 2571.423014
|
| 1495 |
+
Syria Asia 1977 61.195 7932503 3195.484582
|
| 1496 |
+
Syria Asia 1982 64.59 9410494 3761.837715
|
| 1497 |
+
Syria Asia 1987 66.974 11242847 3116.774285
|
| 1498 |
+
Syria Asia 1992 69.249 13219062 3340.542768
|
| 1499 |
+
Syria Asia 1997 71.527 15081016 4014.238972
|
| 1500 |
+
Syria Asia 2002 73.053 17155814 4090.925331
|
| 1501 |
+
Syria Asia 2007 74.143 19314747 4184.548089
|
| 1502 |
+
Taiwan Asia 1952 58.5 8550362 1206.947913
|
| 1503 |
+
Taiwan Asia 1957 62.4 10164215 1507.86129
|
| 1504 |
+
Taiwan Asia 1962 65.2 11918938 1822.879028
|
| 1505 |
+
Taiwan Asia 1967 67.5 13648692 2643.858681
|
| 1506 |
+
Taiwan Asia 1972 69.39 15226039 4062.523897
|
| 1507 |
+
Taiwan Asia 1977 70.59 16785196 5596.519826
|
| 1508 |
+
Taiwan Asia 1982 72.16 18501390 7426.354774
|
| 1509 |
+
Taiwan Asia 1987 73.4 19757799 11054.56175
|
| 1510 |
+
Taiwan Asia 1992 74.26 20686918 15215.6579
|
| 1511 |
+
Taiwan Asia 1997 75.25 21628605 20206.82098
|
| 1512 |
+
Taiwan Asia 2002 76.99 22454239 23235.42329
|
| 1513 |
+
Taiwan Asia 2007 78.4 23174294 28718.27684
|
| 1514 |
+
Tanzania Africa 1952 41.215 8322925 716.6500721
|
| 1515 |
+
Tanzania Africa 1957 42.974 9452826 698.5356073
|
| 1516 |
+
Tanzania Africa 1962 44.246 10863958 722.0038073
|
| 1517 |
+
Tanzania Africa 1967 45.757 12607312 848.2186575
|
| 1518 |
+
Tanzania Africa 1972 47.62 14706593 915.9850592
|
| 1519 |
+
Tanzania Africa 1977 49.919 17129565 962.4922932
|
| 1520 |
+
Tanzania Africa 1982 50.608 19844382 874.2426069
|
| 1521 |
+
Tanzania Africa 1987 51.535 23040630 831.8220794
|
| 1522 |
+
Tanzania Africa 1992 50.44 26605473 825.682454
|
| 1523 |
+
Tanzania Africa 1997 48.466 30686889 789.1862231
|
| 1524 |
+
Tanzania Africa 2002 49.651 34593779 899.0742111
|
| 1525 |
+
Tanzania Africa 2007 52.517 38139640 1107.482182
|
| 1526 |
+
Thailand Asia 1952 50.848 21289402 757.7974177
|
| 1527 |
+
Thailand Asia 1957 53.63 25041917 793.5774148
|
| 1528 |
+
Thailand Asia 1962 56.061 29263397 1002.199172
|
| 1529 |
+
Thailand Asia 1967 58.285 34024249 1295.46066
|
| 1530 |
+
Thailand Asia 1972 60.405 39276153 1524.358936
|
| 1531 |
+
Thailand Asia 1977 62.494 44148285 1961.224635
|
| 1532 |
+
Thailand Asia 1982 64.597 48827160 2393.219781
|
| 1533 |
+
Thailand Asia 1987 66.084 52910342 2982.653773
|
| 1534 |
+
Thailand Asia 1992 67.298 56667095 4616.896545
|
| 1535 |
+
Thailand Asia 1997 67.521 60216677 5852.625497
|
| 1536 |
+
Thailand Asia 2002 68.564 62806748 5913.187529
|
| 1537 |
+
Thailand Asia 2007 70.616 65068149 7458.396327
|
| 1538 |
+
Togo Africa 1952 38.596 1219113 859.8086567
|
| 1539 |
+
Togo Africa 1957 41.208 1357445 925.9083202
|
| 1540 |
+
Togo Africa 1962 43.922 1528098 1067.53481
|
| 1541 |
+
Togo Africa 1967 46.769 1735550 1477.59676
|
| 1542 |
+
Togo Africa 1972 49.759 2056351 1649.660188
|
| 1543 |
+
Togo Africa 1977 52.887 2308582 1532.776998
|
| 1544 |
+
Togo Africa 1982 55.471 2644765 1344.577953
|
| 1545 |
+
Togo Africa 1987 56.941 3154264 1202.201361
|
| 1546 |
+
Togo Africa 1992 58.061 3747553 1034.298904
|
| 1547 |
+
Togo Africa 1997 58.39 4320890 982.2869243
|
| 1548 |
+
Togo Africa 2002 57.561 4977378 886.2205765
|
| 1549 |
+
Togo Africa 2007 58.42 5701579 882.9699438
|
| 1550 |
+
Trinidad and Tobago Americas 1952 59.1 662850 3023.271928
|
| 1551 |
+
Trinidad and Tobago Americas 1957 61.8 764900 4100.3934
|
| 1552 |
+
Trinidad and Tobago Americas 1962 64.9 887498 4997.523971
|
| 1553 |
+
Trinidad and Tobago Americas 1967 65.4 960155 5621.368472
|
| 1554 |
+
Trinidad and Tobago Americas 1972 65.9 975199 6619.551419
|
| 1555 |
+
Trinidad and Tobago Americas 1977 68.3 1039009 7899.554209
|
| 1556 |
+
Trinidad and Tobago Americas 1982 68.832 1116479 9119.528607
|
| 1557 |
+
Trinidad and Tobago Americas 1987 69.582 1191336 7388.597823
|
| 1558 |
+
Trinidad and Tobago Americas 1992 69.862 1183669 7370.990932
|
| 1559 |
+
Trinidad and Tobago Americas 1997 69.465 1138101 8792.573126
|
| 1560 |
+
Trinidad and Tobago Americas 2002 68.976 1101832 11460.60023
|
| 1561 |
+
Trinidad and Tobago Americas 2007 69.819 1056608 18008.50924
|
| 1562 |
+
Tunisia Africa 1952 44.6 3647735 1468.475631
|
| 1563 |
+
Tunisia Africa 1957 47.1 3950849 1395.232468
|
| 1564 |
+
Tunisia Africa 1962 49.579 4286552 1660.30321
|
| 1565 |
+
Tunisia Africa 1967 52.053 4786986 1932.360167
|
| 1566 |
+
Tunisia Africa 1972 55.602 5303507 2753.285994
|
| 1567 |
+
Tunisia Africa 1977 59.837 6005061 3120.876811
|
| 1568 |
+
Tunisia Africa 1982 64.048 6734098 3560.233174
|
| 1569 |
+
Tunisia Africa 1987 66.894 7724976 3810.419296
|
| 1570 |
+
Tunisia Africa 1992 70.001 8523077 4332.720164
|
| 1571 |
+
Tunisia Africa 1997 71.973 9231669 4876.798614
|
| 1572 |
+
Tunisia Africa 2002 73.042 9770575 5722.895655
|
| 1573 |
+
Tunisia Africa 2007 73.923 10276158 7092.923025
|
| 1574 |
+
Turkey Europe 1952 43.585 22235677 1969.10098
|
| 1575 |
+
Turkey Europe 1957 48.079 25670939 2218.754257
|
| 1576 |
+
Turkey Europe 1962 52.098 29788695 2322.869908
|
| 1577 |
+
Turkey Europe 1967 54.336 33411317 2826.356387
|
| 1578 |
+
Turkey Europe 1972 57.005 37492953 3450.69638
|
| 1579 |
+
Turkey Europe 1977 59.507 42404033 4269.122326
|
| 1580 |
+
Turkey Europe 1982 61.036 47328791 4241.356344
|
| 1581 |
+
Turkey Europe 1987 63.108 52881328 5089.043686
|
| 1582 |
+
Turkey Europe 1992 66.146 58179144 5678.348271
|
| 1583 |
+
Turkey Europe 1997 68.835 63047647 6601.429915
|
| 1584 |
+
Turkey Europe 2002 70.845 67308928 6508.085718
|
| 1585 |
+
Turkey Europe 2007 71.777 71158647 8458.276384
|
| 1586 |
+
Uganda Africa 1952 39.978 5824797 734.753484
|
| 1587 |
+
Uganda Africa 1957 42.571 6675501 774.3710692
|
| 1588 |
+
Uganda Africa 1962 45.344 7688797 767.2717398
|
| 1589 |
+
Uganda Africa 1967 48.051 8900294 908.9185217
|
| 1590 |
+
Uganda Africa 1972 51.016 10190285 950.735869
|
| 1591 |
+
Uganda Africa 1977 50.35 11457758 843.7331372
|
| 1592 |
+
Uganda Africa 1982 49.849 12939400 682.2662268
|
| 1593 |
+
Uganda Africa 1987 51.509 15283050 617.7244065
|
| 1594 |
+
Uganda Africa 1992 48.825 18252190 644.1707969
|
| 1595 |
+
Uganda Africa 1997 44.578 21210254 816.559081
|
| 1596 |
+
Uganda Africa 2002 47.813 24739869 927.7210018
|
| 1597 |
+
Uganda Africa 2007 51.542 29170398 1056.380121
|
| 1598 |
+
United Kingdom Europe 1952 69.18 50430000 9979.508487
|
| 1599 |
+
United Kingdom Europe 1957 70.42 51430000 11283.17795
|
| 1600 |
+
United Kingdom Europe 1962 70.76 53292000 12477.17707
|
| 1601 |
+
United Kingdom Europe 1967 71.36 54959000 14142.85089
|
| 1602 |
+
United Kingdom Europe 1972 72.01 56079000 15895.11641
|
| 1603 |
+
United Kingdom Europe 1977 72.76 56179000 17428.74846
|
| 1604 |
+
United Kingdom Europe 1982 74.04 56339704 18232.42452
|
| 1605 |
+
United Kingdom Europe 1987 75.007 56981620 21664.78767
|
| 1606 |
+
United Kingdom Europe 1992 76.42 57866349 22705.09254
|
| 1607 |
+
United Kingdom Europe 1997 77.218 58808266 26074.53136
|
| 1608 |
+
United Kingdom Europe 2002 78.471 59912431 29478.99919
|
| 1609 |
+
United Kingdom Europe 2007 79.425 60776238 33203.26128
|
| 1610 |
+
United States Americas 1952 68.44 157553000 13990.48208
|
| 1611 |
+
United States Americas 1957 69.49 171984000 14847.12712
|
| 1612 |
+
United States Americas 1962 70.21 186538000 16173.14586
|
| 1613 |
+
United States Americas 1967 70.76 198712000 19530.36557
|
| 1614 |
+
United States Americas 1972 71.34 209896000 21806.03594
|
| 1615 |
+
United States Americas 1977 73.38 220239000 24072.63213
|
| 1616 |
+
United States Americas 1982 74.65 232187835 25009.55914
|
| 1617 |
+
United States Americas 1987 75.02 242803533 29884.35041
|
| 1618 |
+
United States Americas 1992 76.09 256894189 32003.93224
|
| 1619 |
+
United States Americas 1997 76.81 272911760 35767.43303
|
| 1620 |
+
United States Americas 2002 77.31 287675526 39097.09955
|
| 1621 |
+
United States Americas 2007 78.242 301139947 42951.65309
|
| 1622 |
+
Uruguay Americas 1952 66.071 2252965 5716.766744
|
| 1623 |
+
Uruguay Americas 1957 67.044 2424959 6150.772969
|
| 1624 |
+
Uruguay Americas 1962 68.253 2598466 5603.357717
|
| 1625 |
+
Uruguay Americas 1967 68.468 2748579 5444.61962
|
| 1626 |
+
Uruguay Americas 1972 68.673 2829526 5703.408898
|
| 1627 |
+
Uruguay Americas 1977 69.481 2873520 6504.339663
|
| 1628 |
+
Uruguay Americas 1982 70.805 2953997 6920.223051
|
| 1629 |
+
Uruguay Americas 1987 71.918 3045153 7452.398969
|
| 1630 |
+
Uruguay Americas 1992 72.752 3149262 8137.004775
|
| 1631 |
+
Uruguay Americas 1997 74.223 3262838 9230.240708
|
| 1632 |
+
Uruguay Americas 2002 75.307 3363085 7727.002004
|
| 1633 |
+
Uruguay Americas 2007 76.384 3447496 10611.46299
|
| 1634 |
+
Venezuela Americas 1952 55.088 5439568 7689.799761
|
| 1635 |
+
Venezuela Americas 1957 57.907 6702668 9802.466526
|
| 1636 |
+
Venezuela Americas 1962 60.77 8143375 8422.974165
|
| 1637 |
+
Venezuela Americas 1967 63.479 9709552 9541.474188
|
| 1638 |
+
Venezuela Americas 1972 65.712 11515649 10505.25966
|
| 1639 |
+
Venezuela Americas 1977 67.456 13503563 13143.95095
|
| 1640 |
+
Venezuela Americas 1982 68.557 15620766 11152.41011
|
| 1641 |
+
Venezuela Americas 1987 70.19 17910182 9883.584648
|
| 1642 |
+
Venezuela Americas 1992 71.15 20265563 10733.92631
|
| 1643 |
+
Venezuela Americas 1997 72.146 22374398 10165.49518
|
| 1644 |
+
Venezuela Americas 2002 72.766 24287670 8605.047831
|
| 1645 |
+
Venezuela Americas 2007 73.747 26084662 11415.80569
|
| 1646 |
+
Vietnam Asia 1952 40.412 26246839 605.0664917
|
| 1647 |
+
Vietnam Asia 1957 42.887 28998543 676.2854478
|
| 1648 |
+
Vietnam Asia 1962 45.363 33796140 772.0491602
|
| 1649 |
+
Vietnam Asia 1967 47.838 39463910 637.1232887
|
| 1650 |
+
Vietnam Asia 1972 50.254 44655014 699.5016441
|
| 1651 |
+
Vietnam Asia 1977 55.764 50533506 713.5371196
|
| 1652 |
+
Vietnam Asia 1982 58.816 56142181 707.2357863
|
| 1653 |
+
Vietnam Asia 1987 62.82 62826491 820.7994449
|
| 1654 |
+
Vietnam Asia 1992 67.662 69940728 989.0231487
|
| 1655 |
+
Vietnam Asia 1997 70.672 76048996 1385.896769
|
| 1656 |
+
Vietnam Asia 2002 73.017 80908147 1764.456677
|
| 1657 |
+
Vietnam Asia 2007 74.249 85262356 2441.576404
|
| 1658 |
+
West Bank and Gaza Asia 1952 43.16 1030585 1515.592329
|
| 1659 |
+
West Bank and Gaza Asia 1957 45.671 1070439 1827.067742
|
| 1660 |
+
West Bank and Gaza Asia 1962 48.127 1133134 2198.956312
|
| 1661 |
+
West Bank and Gaza Asia 1967 51.631 1142636 2649.715007
|
| 1662 |
+
West Bank and Gaza Asia 1972 56.532 1089572 3133.409277
|
| 1663 |
+
West Bank and Gaza Asia 1977 60.765 1261091 3682.831494
|
| 1664 |
+
West Bank and Gaza Asia 1982 64.406 1425876 4336.032082
|
| 1665 |
+
West Bank and Gaza Asia 1987 67.046 1691210 5107.197384
|
| 1666 |
+
West Bank and Gaza Asia 1992 69.718 2104779 6017.654756
|
| 1667 |
+
West Bank and Gaza Asia 1997 71.096 2826046 7110.667619
|
| 1668 |
+
West Bank and Gaza Asia 2002 72.37 3389578 4515.487575
|
| 1669 |
+
West Bank and Gaza Asia 2007 73.422 4018332 3025.349798
|
| 1670 |
+
Yemen, Rep. Asia 1952 32.548 4963829 781.7175761
|
| 1671 |
+
Yemen, Rep. Asia 1957 33.97 5498090 804.8304547
|
| 1672 |
+
Yemen, Rep. Asia 1962 35.18 6120081 825.6232006
|
| 1673 |
+
Yemen, Rep. Asia 1967 36.984 6740785 862.4421463
|
| 1674 |
+
Yemen, Rep. Asia 1972 39.848 7407075 1265.047031
|
| 1675 |
+
Yemen, Rep. Asia 1977 44.175 8403990 1829.765177
|
| 1676 |
+
Yemen, Rep. Asia 1982 49.113 9657618 1977.55701
|
| 1677 |
+
Yemen, Rep. Asia 1987 52.922 11219340 1971.741538
|
| 1678 |
+
Yemen, Rep. Asia 1992 55.599 13367997 1879.496673
|
| 1679 |
+
Yemen, Rep. Asia 1997 58.02 15826497 2117.484526
|
| 1680 |
+
Yemen, Rep. Asia 2002 60.308 18701257 2234.820827
|
| 1681 |
+
Yemen, Rep. Asia 2007 62.698 22211743 2280.769906
|
| 1682 |
+
Zambia Africa 1952 42.038 2672000 1147.388831
|
| 1683 |
+
Zambia Africa 1957 44.077 3016000 1311.956766
|
| 1684 |
+
Zambia Africa 1962 46.023 3421000 1452.725766
|
| 1685 |
+
Zambia Africa 1967 47.768 3900000 1777.077318
|
| 1686 |
+
Zambia Africa 1972 50.107 4506497 1773.498265
|
| 1687 |
+
Zambia Africa 1977 51.386 5216550 1588.688299
|
| 1688 |
+
Zambia Africa 1982 51.821 6100407 1408.678565
|
| 1689 |
+
Zambia Africa 1987 50.821 7272406 1213.315116
|
| 1690 |
+
Zambia Africa 1992 46.1 8381163 1210.884633
|
| 1691 |
+
Zambia Africa 1997 40.238 9417789 1071.353818
|
| 1692 |
+
Zambia Africa 2002 39.193 10595811 1071.613938
|
| 1693 |
+
Zambia Africa 2007 42.384 11746035 1271.211593
|
| 1694 |
+
Zimbabwe Africa 1952 48.451 3080907 406.8841148
|
| 1695 |
+
Zimbabwe Africa 1957 50.469 3646340 518.7642681
|
| 1696 |
+
Zimbabwe Africa 1962 52.358 4277736 527.2721818
|
| 1697 |
+
Zimbabwe Africa 1967 53.995 4995432 569.7950712
|
| 1698 |
+
Zimbabwe Africa 1972 55.635 5861135 799.3621758
|
| 1699 |
+
Zimbabwe Africa 1977 57.674 6642107 685.5876821
|
| 1700 |
+
Zimbabwe Africa 1982 60.363 7636524 788.8550411
|
| 1701 |
+
Zimbabwe Africa 1987 62.351 9216418 706.1573059
|
| 1702 |
+
Zimbabwe Africa 1992 60.377 10704340 693.4207856
|
| 1703 |
+
Zimbabwe Africa 1997 46.809 11404948 792.4499603
|
| 1704 |
+
Zimbabwe Africa 2002 39.989 11926563 672.0386227
|
| 1705 |
+
Zimbabwe Africa 2007 43.487 12311143 469.7092981
|
requirements.txt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
streamlit>=1.51.0
|
| 2 |
+
pandas>=2.0.0
|
| 3 |
+
plotly>=5.17.0
|