# Introduction to Data Visualization in Python

Data visualization is a crucial skill in data science that allows for the graphical representation of data. It helps in understanding data distributions, patterns, and trends. Various libraries in Python can facilitate data visualization, such as Matplotlib, Seaborn, Plotly, and Bokeh. This section provides a practical implementation setting up these libraries for an introductory visualization task.

## Setup Instructions

First, ensure you have Python installed. Most data visualization libraries require packages that can be installed via pip. Use the following commands to set up your environment:

pip install matplotlib seaborn plotly bokeh

Next, open a Python script or Jupyter notebook to begin implementing the visualizations.

## Example Data Visualization

We’ll use the Matplotlib and Seaborn libraries to create a simple visualization showcasing trends in a dataset.

### Import Necessary Libraries

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

### Create a Sample Dataset

For demonstration purposes, let’s create a mock dataset:

# Create sample data
np.random.seed(0)
data = pd.DataFrame({
'A': np.random.rand(100),
'B': np.random.rand(100)
})

### Basic Matplotlib Plot

# Basic scatter plot using Matplotlib
plt.figure(figsize=(8, 6))
plt.scatter(data['A'], data['B'])
plt.title('Scatter Plot of A vs B')
plt.xlabel('A')
plt.ylabel('B')
plt.show()

### Basic Seaborn Plot

# Basic scatter plot using Seaborn
plt.figure(figsize=(8, 6))
sns.scatterplot(x='A', y='B', data=data)
plt.title('Seaborn Scatter Plot of A vs B')
plt.show()

### Interactive Plotly Plot

import plotly.express as px

# Interactive scatter plot using Plotly
fig = px.scatter(data, x='A', y='B', title='Plotly Scatter Plot of A vs B')
fig.show()

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

# Inline visualization for Bokeh
output_notebook()

# Create a new plot with a title and axis labels
p = figure(title="Bokeh Scatter Plot of A vs B", x_axis_label='A', y_axis_label='B')

# Add a scatter renderer with legend and size
p.circle(data['A'], data['B'], size=8, color="navy", alpha=0.5)

# Show the results
show(p)

## Conclusion

This section introduced the fundamental setup and basic examples of visualizations using Matplotlib, Seaborn, Plotly, and Bokeh. These libraries provide a foundation for further exploration and can be customized for more complex and meaningful visual displays of data. Each library has unique strengths and capabilities, allowing for a wide range of use cases in data visualization.

## Overview of Popular Python Visualization Libraries

In this section, we present an overview of popular Python visualization libraries. We will explore the key features, strengths, and example codes for each library. The libraries covered are Matplotlib, Seaborn, Plotly, and Bokeh.

### Matplotlib

Matplotlib is one of the oldest and most widely used Python visualization libraries. It provides a robust foundation for creating static, animated, and interactive visualizations.

#### Key Features:

• Highly customizable plots
• Support for a wide range of plot types (line, bar, scatter, histogram, etc.)
• Detailed control over plot elements (axes, labels, colors, etc.)

#### Example Code:

import matplotlib.pyplot as plt

# Sample Data
x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 17, 14]

# Creating a line plot
plt.plot(x, y, label='Line 1', color='blue', marker='o')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')
plt.title('Line Plot Example')
plt.legend()
plt.grid(True)
plt.show()

### Seaborn

Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics.

#### Key Features:

• Simplifies complex visualizations
• Built-in themes and color palettes for aesthetically pleasing plots
• Seamless integration with Pandas DataFrames

#### Example Code:

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

# Sample Data
data = {'day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
'value': [10, 15, 13, 17, 14]}
df = pd.DataFrame(data)

# Creating a bar plot
sns.barplot(x='day', y='value', data=df, palette='viridis')
plt.title('Bar Plot Example')
plt.show()

### Plotly

Plotly is known for its ability to create interactive plots that can be easily shared and embedded. It supports a wide range of chart types and offers various interactive functionalities.

#### Key Features:

• Interactive graphs with zoom, hover, and clickable legends
• Wide range of chart types (3D, geographical maps, etc.)
• Easy export to web formats

#### Example Code:

import plotly.graph_objects as go

# Sample Data
x = ['A', 'B', 'C', 'D']
y = [10, 15, 13, 17]

# Creating a bar chart
fig = go.Figure(data=[go.Bar(x=x, y=y, marker_color='indigo')])
fig.update_layout(title='Bar Chart Example',
xaxis_title='Category',
yaxis_title='Values')
fig.show()

### Bokeh

Bokeh is designed for creating interactive visualizations for modern web browsers. It emphasizes interactivity and provides elegant and concise construction of versatile graphics.

#### Key Features:

• Interactive plots with tools like pan, zoom, and hover
• Great for web applications
• Integration with Jupyter Notebooks

#### Example Code:

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

output_notebook()

# Sample Data
x = [1, 2, 3, 4, 5]
y = [10, 15, 13, 17, 14]

# Creating a scatter plot
p = figure(title='Scatter Plot Example', x_axis_label='X Axis', y_axis_label='Y Axis')
p.circle(x, y, size=10, color='navy', alpha=0.5)

show(p)

This implementation highlights key features and example applications of some of the most popular data visualization libraries in Python. Each library has its particular strengths and use cases, making it essential to choose the right one based on specific project requirements.

# Setting Up and Installing Visualization Libraries

To create a comprehensive comparative study of various Python data visualization libraries, you need to have all necessary libraries installed. The following steps outline the practical implementation of setting up and installing these libraries, including Matplotlib, Seaborn, Plotly, Bokeh, and Altair. Assuming you have a working Python environment set up, we will use pip for installation.

## Practical Steps

### 1. Creating a Virtual Environment

First, it’s good practice to create a virtual environment to manage dependencies.

# Create a virtual environment
python -m venv visualization-env

# Activate the virtual environment
# On Windows
visualization-envScriptsactivate

# On MacOS/Linux
source visualization-env/bin/activate

### 2. Installing Libraries

#### Install Matplotlib

pip install matplotlib

#### Install Seaborn

pip install seaborn

#### Install Plotly

pip install plotly

#### Install Bokeh

pip install bokeh

#### Install Altair

pip install altair

### 3. Verifying Installations

After installing, itâ€™s a good idea to verify that each library is correctly installed. This can be done by importing each library in a Python script or interactive session.

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import bokeh.plotting as bk
import altair as alt

print("All libraries imported successfully.")

### 4. Saving Dependencies

To save all the project dependencies, you should create a requirements.txt file.

pip freeze > requirements.txt

Contents of requirements.txt might look like this:

altair==4.1.0
bokeh==2.4.2
matplotlib==3.4.3
plotly==5.3.1
seaborn==0.11.2

### 5. Cleaning Up

Whenever you need to clean up the environment or deactivate it, use:

# Deactivate the virtual environment
deactivate

# Remove the virtual environment folder (if necessary)
rm -rf visualization-env

## Conclusion

Following these steps ensures that you have all the necessary visualization libraries set up correctly for your comprehensive study comparing them. This setup enables you to proceed with implementing and testing the visualizations using the aforementioned libraries.

## Creating Basic Visualizations

This section covers the implementation of basic visualizations using various Python libraries. We’ll demonstrate how to create simple plots, such as line plots, bar charts, and scatter plots, using Matplotlib, Seaborn, and Plotly.

### Matplotlib

#### Line Plot

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a line plot
plt.plot(x, y, label='Line')

plt.title('Simple Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

plt.legend()

# Show the plot
plt.show()

#### Bar Chart

import matplotlib.pyplot as plt

# Data
categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 2, 4, 6]

# Create a bar chart
plt.bar(categories, values)

plt.title('Simple Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')

# Show the plot
plt.show()

#### Scatter Plot

import matplotlib.pyplot as plt

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a scatter plot
plt.scatter(x, y)

plt.title('Simple Scatter Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')

# Show the plot
plt.show()

### Seaborn

#### Line Plot

import seaborn as sns
import pandas as pd

# Data
data = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [2, 3, 5, 7, 11]
})

# Create a line plot
sns.lineplot(x='x', y='y', data=data)

plt.title('Simple Line Plot with Seaborn')

# Show the plot
plt.show()

#### Bar Chart

import seaborn as sns
import pandas as pd

# Data
data = pd.DataFrame({
'categories': ['A', 'B', 'C', 'D', 'E'],
'values': [5, 7, 2, 4, 6]
})

# Create a bar chart
sns.barplot(x='categories', y='values', data=data)

plt.title('Simple Bar Chart with Seaborn')

# Show the plot
plt.show()

#### Scatter Plot

import seaborn as sns
import pandas as pd

# Data
data = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [2, 3, 5, 7, 11]
})

# Create a scatter plot
sns.scatterplot(x='x', y='y', data=data)

plt.title('Simple Scatter Plot with Seaborn')

# Show the plot
plt.show()

### Plotly

#### Line Plot

import plotly.graph_objs as go
from plotly.offline import plot

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a line plot
line = go.Scatter(x=x, y=y, mode='lines', name='Line')

# Layout
layout = go.Layout(title='Simple Line Plot')

# Figure
fig = go.Figure(data=[line], layout=layout)

# Show the plot
plot(fig)

#### Bar Chart

import plotly.graph_objs as go
from plotly.offline import plot

# Data
categories = ['A', 'B', 'C', 'D', 'E']
values = [5, 7, 2, 4, 6]

# Create a bar chart
bar = go.Bar(x=categories, y=values)

# Layout
layout = go.Layout(title='Simple Bar Chart')

# Figure
fig = go.Figure(data=[bar], layout=layout)

# Show the plot
plot(fig)

#### Scatter Plot

import plotly.graph_objs as go
from plotly.offline import plot

# Data
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

# Create a scatter plot
scatter = go.Scatter(x=x, y=y, mode='markers')

# Layout
layout = go.Layout(title='Simple Scatter Plot')

# Figure
fig = go.Figure(data=[scatter], layout=layout)

# Show the plot
plot(fig)

By following the above implementations, you can create basic visualizations with Matplotlib, Seaborn, and Plotly to visualize various types of data in Python.

## 5.1 Interactive Visualizations

###1. Plotly Example: Interactive Scatter Plot

import plotly.express as px

# Sample data
df = px.data.iris()

# Creating an interactive scatter plot
fig = px.scatter(
df, x='sepal_width', y='sepal_length',
color='species', size='petal_length',
hover_data=['petal_width']
)

# Display interactive plot
fig.show()

###2. Bokeh Example: Interactive Time Series Plot

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
from bokeh.io import output_notebook
import pandas as pd
import numpy as np

output_notebook()

# Sample data
date_range = pd.date_range(start='1/1/2022', periods=100)
data = pd.DataFrame({'date': date_range, 'values': np.random.randn(100).cumsum()})

# Creating a ColumnDataSource
source = ColumnDataSource(data)

# Creating an interactive time-series plot
p = figure(x_axis_type='datetime', title='Time Series Example', plot_height=350, plot_width=800)
p.line(x='date', y='values', source=source)
p.circle(x='date', y='values', source=source, fill_color="white", size=8)

# Display the plot
show(p)

## 5.2 Customizing Visualizations

###1. Matplotlib Example: Customized Bar Chart

import matplotlib.pyplot as plt

# Sample data
categories = ['Category A', 'Category B', 'Category C']
values = [10, 15, 7]

# Creating a customized bar chart
fig, ax = plt.subplots()

bars = ax.bar(categories, values, color=['turquoise', 'orange', 'gray'])

ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Customized Bar Chart')

for bar in bars:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2 - 0.1, yval + 0.5, yval)

# Customize the grid
ax.grid(True, which='both', linestyle='--', linewidth=0.5)

fig.patch.set_facecolor('whitesmoke')

# Display the plot
plt.show()

###2. Seaborn Example: Customized Heatmap

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

# Sample data
data = np.random.rand(10, 12)

# Creating a customized heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(
data, annot=True, fmt=".2f", linewidths=0.5,
cmap='coolwarm', cbar_kws={'label': 'Scale'}
)

plt.title('Customized Heatmap')
plt.xlabel('X Axis')
plt.ylabel('Y Axis')

# Display the plot
plt.show()

## 5.3 Animations in Visualizations

###1. Matplotlib Animation Example: Animated Line Plot

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

# Sample data
x = np.linspace(0, 2 * np.pi, 128)
y = np.sin(x)

fig, ax = plt.subplots()
line, = ax.plot(x, y)

# Animation function
def animate(i):
line.set_ydata(np.sin(x + i / 10.0))
return line,

ani = animation.FuncAnimation(
fig, animate, interval=100, blit=True
)

# Display the animation
plt.show()

###2. Plotly Animation Example: Animated Scatter Plot

import plotly.express as px
import plotly.graph_objects as go

# Sample data
df = px.data.gapminder()

# Creating an animated scatter plot
fig = px.scatter(
df, x="gdpPercap", y="lifeExp", animation_frame="year",
animation_group="country", size="pop", color="continent",
hover_name="country", log_x=True, size_max=55,
range_x=[100,100000], range_y=[25,90]
)

# Display interactive animation
fig.show()

## Conclusion

These advanced visualization techniques using various Python libraries will help in creating more interactive, customized, and animated visualizations. They are essential in presenting data more dynamically and engagingly, making the analysis more insightful and comprehensive.

## Performance and Scalability Analysis

### Objective

To compare the performance and scalability of various Python data visualization libraries, focusing on key metrics such as rendering speed, memory usage, and handling of large datasets.

### Metrics for Analysis

1. Rendering Speed: Time taken to render a visualization.
2. Memory Usage: Memory consumption during the rendering process.
3. Handling Large Datasets: Ability to manage and visualize datasets of varying sizes.

### Experimental Setup

We will use three datasets:

• Small Dataset: ~1,000 data points.
• Medium Dataset: ~100,000 data points.
• Large Dataset: ~1,000,000 data points.

We will analyze three popular Python visualization libraries: Matplotlib, Seaborn, and Plotly.

### Pseudocode

The pseudocode below outlines the steps required to measure performance and scalability.

DEFINE datasets:
small_dataset = "path/to/small_dataset.csv"
medium_dataset = "path/to/medium_dataset.csv"
large_dataset = "path/to/large_dataset.csv"

DEFINE libraries:
libraries = ["Matplotlib", "Seaborn", "Plotly"]

FUNCTION measure_performance(library, dataset):
START timer
RENDER visualization using library
STOP timer
MEASURE memory usage
RETURN rendering time, memory usage

FOR EACH library IN libraries:
FOR EACH dataset IN datasets:
rendering_time, memory_usage = measure_performance(library, dataset)
PRINT library, dataset, rendering_time, memory_usage

### Implementation in Python

Below is the real implementation using Python.

import time
import pandas as pd
import tracemalloc
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Function to measure performance
def measure_performance(library, dataset_path):

tracemalloc.start()
start_time = time.time()

if library == "Matplotlib":
plt.plot(data['column_x'], data['column_y'])
plt.show()
elif library == "Seaborn":
sns.lineplot(data=data, x='column_x', y='column_y')
plt.show()
elif library == "Plotly":
fig = px.line(data, x='column_x', y='column_y')
fig.show()

end_time = time.time()
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()

rendering_time = end_time - start_time
memory_usage = peak - current

return rendering_time, memory_usage

# Datasets
datasets = {
"Small": "path/to/small_dataset.csv",
"Medium": "path/to/medium_dataset.csv",
"Large": "path/to/large_dataset.csv"
}

# Libraries
libraries = ["Matplotlib", "Seaborn", "Plotly"]

# Measure and print performance
for library in libraries:
for size, dataset_path in datasets.items():
rendering_time, memory_usage = measure_performance(library, dataset_path)
print(f"Library: {library}, Dataset: {size}, Rendering Time: {rendering_time:.4f} seconds, Memory Usage: {memory_usage / 1024:.2f} KB")

### Interpretation of Results

• Rendering Speed: Compare the time taken for rendering across different libraries and datasets.
• Memory Usage: Inspect the memory usage to understand each library’s efficiency.
• Handling of Large Datasets: Observe if any library struggles with larger datasets, indicated by increased rendering times or memory issues.

### Conclusion

This implementation provides a way to measure and compare the performance and scalability of different data visualization libraries in Python. Execute the script, gather data, and analyze the results to draw comprehensive conclusions on the most efficient library for your needs.

## Real-World Data Visualization Examples

### 1. Comparing Growth Rates of Tech Companies

Dataset: Quarterly revenue growth of top tech companies (e.g., Apple, Microsoft, Google, Amazon, Facebook)

Code:

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

# Sample data. In practice, load this from a CSV or database.
data = {
'Quarter': ['Q1', 'Q2', 'Q3', 'Q4'] * 5,
'Company': ['Apple'] * 4 + ['Microsoft'] * 4 + ['Google'] * 4 + ['Amazon'] * 4 + ['Facebook'] * 4,
'Revenue Growth (%)': [5, 6, 7, 8, 7, 6, 5, 4, 8, 9, 10, 12, 6, 8, 7, 9, 4, 5, 6, 7]
}
df = pd.DataFrame(data)

plt.figure(figsize=(10, 6))
sns.lineplot(data=df, x="Quarter", y="Revenue Growth (%)", hue="Company", marker='o')
plt.title("Quarterly Revenue Growth of Top Tech Companies")
plt.xlabel("Quarter")
plt.ylabel("Revenue Growth (%)")
plt.legend(title="Company")
plt.tight_layout()
plt.show()

### 2. Visualizing Population Density across States

Dataset: Population density of all states in the USA.

Code:

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

# Sample data. In practice, load this from a shapefile or GeoJSON.
pop_density = {
'density': [96.0, 1.3, ..., 5.9]
}
df = pd.DataFrame(pop_density)

# Merging data with geometry
states = states.merge(df, how='left', left_on='name', right_on='state')

# Plotting
fig, ax = plt.subplots(1, 1, figsize=(15, 10))
states.boundary.plot(ax=ax)
states.plot(column='density', ax=ax, legend=True, cmap='OrRd')
plt.title("Population Density across US States")
plt.show()

### 3. Sales Performance Dashboard

Dataset: Monthly sales data for multiple products.

Code:

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

# Sample data. In practice, load this from a CSV or database.
data = {
'Month': ['January', 'February', 'March', 'April', 'May', 'June'] * 3,
'Product': ['A'] * 6 + ['B'] * 6 + ['C'] * 6,
'Sales': [100, 110, 120, 130, 125, 135, 70, 80, 75, 90, 85, 100, 50, 55, 65, 60, 70, 75]
}
df = pd.DataFrame(data)

plt.figure(figsize=(12, 8))
sns.barplot(data=df, x="Month", y="Sales", hue="Product")
plt.title("Monthly Sales Performance")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.legend(title="Product")
plt.tight_layout()
plt.show()

### 4. Correlation Matrix for Economic Indicators

Dataset: Correlation data between different economic indicators like GDP, Inflation Rate, Unemployment Rate etc.

Code:

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

# Sample data. In practice, load this from a CSV or database.
data = {
'GDP': [1, 2, 3, 4, 5],
'Inflation': [5, 6, 7, 8, 9],
'Unemployment': [9, 8, 7, 6, 5],
'Interest Rate': [1, 3, 5, 7, 9]
}
df = pd.DataFrame(data)

# Calculate correlation matrix
corr = df.corr()

plt.figure(figsize=(8, 6))
sns.heatmap(corr, annot=True, cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Correlation Matrix of Economic Indicators")
plt.show()

### 5. Distribution of Customer Ages

Dataset: Customer age distribution from an ecommerce platform.

Code:

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

# Sample data. In practice, load this from a CSV or database.
data = {
'Age': [22, 25, 29, 35, 38, 40, 44, 48, 51, 55, 22, 26, 30, 35, 39, 45, 49, 50, 60, 65]
}
df = pd.DataFrame(data)

plt.figure(figsize=(10, 6))
sns.histplot(df['Age'], bins=10, kde=True)
plt.title("Distribution of Customer Ages")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.tight_layout()
plt.show()

These examples illustrate real-world applications of data visualization using Python libraries. These visualizations can be adapted and utilized directly with relevant datasets.

## Comparison and Conclusion

In this section, we will summarize our findings on various data visualization libraries in Python. We will compare key attributes such as ease of use, customization, interactivity, and performance. Based on this comparison, we will draw conclusions on the suitability of each library for different types of projects.

### Comparison Matrix

Let’s construct a comparison matrix for the following libraries:

• Matplotlib
• Seaborn
• Plotly
• Bokeh
• Altair
• ggplot

### Key Observations

1. Ease of Use: Seaborn and Altair are particularly easy to use, with high-level interfaces that simplify complex visualizations.
2. Customization: Matplotlib, Plotly, and Bokeh offer extensive customization, enabling detailed and specific visual designs.
3. Interactivity: Plotly and Bokeh stand out for their interactive capabilities, making them suitable for dashboards and web applications.
4. Performance: Matplotlib generally offers high performance and is suitable for large datasets and complex visualizations, while other libraries like Plotly and Bokeh may have performance trade-offs for their interactivity features.
5. Suitable Use Cases:
• Matplotlib: Best for static, publication-quality plots.
• Seaborn: Ideal for statistical data visualizations.
• Plotly and Bokeh: Great for creating interactive visualizations and dashboards.
• Altair: Best for declarative visualization, where the focus is on ease of creating complex statistical graphics.
• ggplot: Excellent for those familiar with the Grammar of Graphics approach, providing a quick way to create plots.

### Conclusion

Each data visualization library in Python has its strengths and areas of applicability.

1. Matplotlib is a foundation library, robust for detailed and highly customized static visualizations.
2. Seaborn builds on Matplotlib, offering an easier interface for statistical plots.
3. Plotly and Bokeh shine in interactive visualizations suitable for web applications and dashboards.
4. Altair leverages a declarative approach, ideal for quickly creating sophisticated statistical visualizations.
5. ggplot provides a familiar syntax for those who appreciate the Grammar of Graphics principles.

Choosing the right library depends on your specific needs, including the necessity for interactivity, ease of use, and the depth of customization required. By leveraging the comparison matrix and key observations, you can make an informed decision regarding the best library for your data visualization projects.

### Practical Implementation Note

This comparison and conclusion process can be practically applied in your project by analyzing your specific use cases, performance requirements, and desired feature sets to choose the appropriate libraries effectively. Summarize these findings in your project documentation to guide future visualization efforts.

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