Creating Stunning Visualization Charts with pyMarketLab¶

We are excited to announce the upcoming launch of pyMarketLab, a comprehensive and cutting-edge Python package designed to revolutionize market data analysis. pyMarketLab is meticulously crafted for professionals who seek to harness the power of data to drive insightful business decisions.

Key Features:¶

1. Pricing Analytics:

  • Optimize pricing strategies with robust analytics tools.
  • Analyze historical pricing data to uncover trends and forecast future pricing.

2. Product Analytics:

  • Conduct detailed cohort and funnel analyses to understand product performance and customer journeys.
  • Identify key metrics and track product lifecycles to enhance product management.

3. Customer Analytics:

  • Predict customer lifetime value (CLV) to maximize customer relationships and revenue.
  • Analyze churn rates and identify at-risk customers to improve retention strategies.

4. Market Basket Analysis:

  • Uncover hidden patterns and associations in purchase data.
  • Enhance cross-selling and up-selling strategies through actionable insights.

5. Product Recommendation Systems:

  • Implement advanced algorithms to provide personalized product recommendations.
  • Increase customer engagement and satisfaction with tailored suggestions.

6. Market Data Analysis with GIS:

  • Integrate Geographic Information System (GIS) capabilities for spatial data analysis.
  • Visualize and analyze market data geographically to identify regional trends and opportunities.

7. Aesthetic and Stunning Data Visualization:

  • Create visually appealing and informative data visualizations.
  • Transform complex data into clear, actionable insights with customizable charts and graphs.

Why Choose pyMarketLab?¶

pyMarketLab combines the power of advanced analytics with the ease of Python, offering a user-friendly interface that caters to both novice and experienced analysts. Our package is designed to be intuitive yet powerful, ensuring that you can quickly derive meaningful insights from your market data.

Stay tuned for the official launch of pyMarketLab and be among the first to experience the future of market data analysis. Transform your data into actionable intelligence with pyMarketLab!

For more information and updates, visit our website [Your Website] and follow us on [Your Social Media Channels].

Embrace the power of data with pyMarketLab – your partner in advanced market analytics.¶

pyMarketLab's Data Visualization: Captivating Insights for Winning Reports¶

Data is powerful, but it can also be overwhelming. pyMarketLab cuts through the noise with its aesthetic and stunning data visualization capabilities, designed to transform complex marketing data into clear, impactful stories that resonate with any audience.

Imagine crafting business reports or marketing reports that don't just inform, they impress. pyMarketLab empowers you to do just that.

Effortless Chart Creation: Generate a wide range of charts with ease, from classic bar and line graphs to heatmaps and interactive scatter plots.

Customization Power:¶

Take full control over the look and feel of your visualizations. Tailor colors, fonts, and layouts to match your branding or create a specific mood for your report.

Interactive Insights:¶

Go beyond static images. Leverage interactive elements to allow viewers to explore your data in more depth, fostering deeper engagement with your findings.

Stunning Visual Storytelling:¶

pyMarketLab helps you weave data into a compelling narrative. Use charts to showcase trends, highlight key metrics, and bring insights to life in a way that static text simply can't.

The Benefits for Businesses:¶

Clearer Communication: Visualizations can explain complex information in a way that is easier to understand for both technical and non-technical audiences.

Enhanced Persuasion:¶

Captivating visuals grab attention and leave a lasting impression, making your reports more persuasive.

Actionable Insights:¶

Data visualizations can help identify trends and patterns that might otherwise be missed, leading to more informed business decisions.

Sharpened Reports:¶

Professional-looking reports polished with high-quality visualizations build trust and credibility with stakeholders. pyMarketLab empowers you to transform raw data into captivating stories that will take your business reports and marketing reports to the next level.

pyMarketLab Data Visualization Demo: Himanshu Bhardwaj¶

In [1]:
import json
import base64
from IPython.display import display, HTML
import pandas as pd
import random
import numpy as np
from pyMarketLab import VisualizationLab as VizLab
In [2]:
# Example usage with a DataFrame:
data = {
    "Month": ["January", "February", "March", "April", "May", "June", "July"],
    "Dataset 1": [12, 19, 3, 5, 2, 3, 7],
    "Dataset 2": [2, 3, 20, 5, 1, 4, 6],
    "Dataset 3": [10, 17, 6, 8, 4, 6, 9]
}
df = pd.DataFrame(data)
In [3]:
VizLab.unstacked_bar_chart(df, value_columns=["Dataset 1", "Dataset 2", "Dataset 3"],chart_title="Unstacked Bar Chart", category_column="Month", border_radius=10)
In [4]:
VizLab.stacked_bar_chart(df, value_columns=["Dataset 1", "Dataset 2", "Dataset 3"],chart_title="Stacked Bar Chart", category_column="Month")
In [5]:
# Example usage:
labels = ["January", "February", "March", "April", "May"]
data = [15, 25, 30, 20, 35]

df = pd.DataFrame({"labels": labels, "data": data})

VizLab.bar_chart(df,labels = 'labels', data = 'data', chart_title="Bar Chart with Border Radius", border_radius=17)
In [6]:
VizLab.horizontal_bar_chart(df,labels = 'labels', data = 'data', chart_title="Bar Chart with Border Radius", border_radius=17)
In [7]:
# Example usage with a DataFrame:
data = {
    "Month": ["January", "February", "March", "April", "May", "June", "July"],
    "Dataset 1": [12, 19, 3, 5, 2, 3, 7],
    "Dataset 2": [2, 3, 20, 5, 1, 4, 6],
    "Dataset 3": [10, 17, 6, 8, 4, 6, 9]
}
df = pd.DataFrame(data)
In [8]:
VizLab.horizontal_unstacked_bar_chart(df, value_columns=["Dataset 1", "Dataset 2", "Dataset 3"],chart_title="Unstacked Bar Chart", category_column="Month", border_radius=10)
In [9]:
VizLab.horizontal_stacked_bar_chart(df, value_columns=["Dataset 1", "Dataset 2", "Dataset 3"],chart_title="Horizontal Stacked Bar Chart", category_column="Month")
In [10]:
df = pd.read_csv('product_analytics_data.csv')
df.head()
Out[10]:
Customer State Customer Lifetime Value Response Coverage Education Effective To Date EmploymentStatus Gender Income ... Months Since Policy Inception Number of Open Complaints Number of Policies Policy Type Policy Renew Offer Type Sales Channel Total Claim Amount Vehicle Class Vehicle Size
0 BU79786 Washington 2763.519279 No Basic Bachelor 2/24/11 Employed F 56274 ... 5 0 1 Corporate Auto Corporate L3 Offer1 Agent 384.811147 Two-Door Car Medsize
1 QZ44356 Arizona 6979.535903 No Extended Bachelor 1/31/11 Unemployed F 0 ... 42 0 8 Personal Auto Personal L3 Offer3 Agent 1131.464935 Four-Door Car Medsize
2 AI49188 Nevada 12887.431650 No Premium Bachelor 2/19/11 Employed F 48767 ... 38 0 2 Personal Auto Personal L3 Offer1 Agent 566.472247 Two-Door Car Medsize
3 WW63253 California 7645.861827 No Basic Bachelor 1/20/11 Unemployed M 0 ... 65 0 7 Corporate Auto Corporate L2 Offer1 Call Center 529.881344 SUV Medsize
4 HB64268 Washington 2813.692575 No Basic Bachelor 2/3/11 Employed M 43836 ... 44 0 1 Personal Auto Personal L1 Offer1 Agent 138.130879 Four-Door Car Medsize

5 rows × 24 columns

In [11]:
df.dropna(inplace=True)
In [12]:
VizLab.category_barchart(df, variable = 'Customer Lifetime Value', category = 'Location Code', agg = 'sum')
In [13]:
VizLab.horizontal_category_barchart(df, variable = 'Customer Lifetime Value', category = 'Policy', agg = 'sum')
In [14]:
VizLab.category_unstacked_barchart(df, value_columns=['Monthly Premium Auto', 'Months Since Last Claim', 'Months Since Policy Inception'], chart_title="Unstacked Bar Chart", category_column="Location Code", border_radius=10, agg='sum')
In [15]:
VizLab.category_stacked_barchart(df, value_columns=['Monthly Premium Auto', 'Months Since Last Claim', 'Months Since Policy Inception'], category_column = "Location Code", chart_title="Stacked Bar Chart", agg='sum')
In [16]:
# Sample DataFrame
data = {
    'X': [1, 2, 3, 4, 5],
    'BarData': [10, 20, 15, 25, 30],
    'LineData': [5, 15, 10, 20, 25],
}
df = pd.DataFrame(data)

# Display bar line plot
VizLab.display_bar_line_plot(df, bar_column='BarData', line_column='LineData', chart_title="Bar Line Plot")
In [17]:
# Sample DataFrame
data = {
    'X': [1, 2, 3, 4, 5],
    'X1': [5, 8, 3, 14, 5],
    'X2': [15, 8, 13, 4, 15],
    'Y': [10, 20, 15, 25, 30],
    'Y1': [13, 2, 35, 5, 35],
    'Y2': [1, 50, 5, 65, 3],
    'Value1': [20, 30, 15, 35, 25],
    'Value2': [10, 25, 20, 15, 30],
    'Value3': [15, 18, 22, 27, 29]
}
df = pd.DataFrame(data)
In [18]:
# Display multi-value bubble scatter plot
VizLab.display_multi_value_bubble_scatter_plot(df, value_columns=['Value1', 'Value2', 'Value3'], x_columns=['X', 'X1', 'X2'], y_columns=['Y', 'Y1', 'Y2'], chart_title="Multi-Value Bubble Scatter Plot")
In [19]:
# Sample DataFrame
data = {
    'Label': ['A', 'B', 'C', 'D', 'E', 'F'],
    'Dataset1': [60, 20, 75, 45, 34, 55],
    'Dataset2': [15, 75, 20, 30, 65, 70],
    'Dataset3': [8, 18, 12, 72, 23, 14]
}
df = pd.DataFrame(data)

# Specify which columns to use as datasets
value_columns = ['Dataset1', 'Dataset2', 'Dataset3']

# Display multi-dataset radar plot
VizLab.display_multi_dataset_radar_plot(df, value_columns, chart_title="Multi-Dataset Radar Plot")
In [20]:
# Sample data
data = [
    {'label': 'Dataset 1', 'data': [10, 60, 20, 40, 15]},
    {'label': 'Dataset 2', 'data': [45, 25, 65, 45, 25]}
]

# Labels for the x-axis
labels = ['January', 'February', 'March', 'April', 'May']

# Display the filled line chart
VizLab.display_filled_line_chart(data, labels, chart_title="Example Filled Line Chart")
In [21]:
# Example data for the regression plot
np.random.seed(0)
x_data = np.random.rand(50) * 10
y_data = 2 * x_data + 1 + np.random.randn(50) * 2  # Example linear relationship with noise

# Display the interactive regression plot
VizLab.display_interactive_regression_plot(x_data, y_data)
In [22]:
# Example data for polar area plot
labels = ['Red', 'Blue', 'Yellow', 'Green', 'Purple', 'Orange']
values = [12, 19, 3, 5, 2, 3]  # Example data values

# Display the polar area plot
VizLab.display_polar_area_plot(labels, values, chart_title="Polar Area Plot")
In [23]:
# Example data for the donut chart
x_values = ['Red Wine', 'White Wine', 'Rose Wine', 'Sparkling Wine', 'Dessert Wine']
y_values = [30, 25, 20, 15, 10]  # percentages

# Display the donut chart
VizLab.display_donut_chart(x_values, y_values, chart_title="World Wide Wine Production")
In [24]:
# Example data for the donut chart
x_values = ['Red Wine', 'White Wine', 'Rose Wine', 'Sparkling Wine', 'Dessert Wine']
y_values = [30, 25, 20, 15, 10]  # percentages

# Display the donut chart
VizLab.display_pie_chart(x_values, y_values, chart_title="World Wide Wine Production")