Unlocking the Hidden Mysteries of Data with Seaborn’s Visual Storytelling
Seaborn is a powerful data visualization library built on top of Matplotlib, a widely used Python library for creating plots and graphs. It provides a high-level interface for creating beautiful and informative statistical graphics, making data visualization more accessible and intuitive for Python users.
Here are some of the key advantages of using Seaborn for data visualization:
- Simplified Plot Creation: Seaborn offers a concise and user-friendly interface for generating common statistical plots, reducing the amount of code required compared to Matplotlib.
- Seamless Matplotlib Integration: Seaborn seamlessly integrates with Matplotlib, allowing you to leverage Matplotlib’s extensive customization options while benefiting from Seaborn’s higher-level abstractions.
- Aesthetically Appealing Plots: Seaborn produces visually appealing plots with default styling guidelines that enhance data comprehension and readability.
- Statistical Functions and Tools: Seaborn incorporates statistical functions and tools, such as correlation analysis and linear regression, within the visualization context, facilitating data exploration and understanding.
- Thematic Styling and Consistency: Seaborn provides a variety of themes for consistent styling across plots, ensuring visual coherence and enhancing the overall presentation of your data.
- Faceting and Subplotting: Seaborn supports faceting and subplotting, enabling you to visualize data by subgroups or multiple plots within a single figure, providing a more comprehensive view of your data.
- Statistical Analysis Integration: Seaborn seamlessly integrates statistical analysis with visualization, allowing you to explore and understand data patterns simultaneously, facilitating a deeper understanding of your data.
In summary, Seaborn’s versatility, ease of use, and ability to produce aesthetically pleasing and informative statistical graphics make it an invaluable tool for data scientists, analysts, and anyone who wants to effectively communicate data insights through compelling visualizations.
- Installation: Install Seaborn using pip: pip install seaborn
- Import Seaborn: Import Seaborn in your Python script: import seaborn as sns
- Explore Data and Create Plots: Use Seaborn functions to explore your data and generate plots:
# Load data data = ... # Create a histogram sns.histplot(data['column_name'])
Univariate Distribution Plots: Histograms, kernel density plots, violin plots
Histograms:
Histograms are bar charts that represent the frequency distribution of a quantitative variable. They divide the data into bins or intervals and count the number of data points that fall within each bin. Histograms provide a visual representation of the data’s distribution, revealing its shape, central tendency, and spread.Kernel Density Plots:
Kernel density plots, also known as density curves, provide a more continuous representation of the data’s distribution compared to histograms. They estimate the probability density function (PDF) of the data using a kernel function, which is a smooth bell-shaped curve. Kernel density plots effectively capture the underlying shape of the data, including its modes, skewness, and kurtosis.Violin Plots:
Violin plots combine box plots with kernel density estimates to provide a comprehensive overview of the distribution of a quantitative variable. They display the median, quartiles, and extreme values using a boxplot-like structure, while the kernel density estimate around the boxplot represents the distribution of the data within each quartile. Violin plots are particularly useful for comparing the distributions of a variable across different groups or categories. Choosing the Right Univariate Distribution Plot: The choice of univariate distribution plot depends on the specific characteristics of the data and the desired level of detail.- Histograms: Histograms are suitable for exploring the frequency distribution and identifying potential outliers.
- Kernel Density Plots: Kernel density plots are effective for capturing the overall shape and density of the data, revealing its underlying structure.
- Violin Plots: Violin plots are useful for comparing distributions across groups, providing a combined view of central tendency, spread, and density.
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# Generate some sample data
data = np.random.randn(100)
# Create a list of plot types
plot_types = ['histogram', 'kernel_density', 'violin']
# Iterate through the list of plot types and create the corresponding plots
for plot_type in plot_types:
if plot_type == 'histogram':
sns.histplot(data)
plt.title('Histogram')
plt.show()
elif plot_type == 'kernel_density':
sns.kdeplot(data)
plt.title('Kernel Density Plot')
plt.show()
elif plot_type == 'violin':
sns.violinplot(x=data)
plt.title('Violin Plot')
plt.show()
Bivariate Relationship Plots: Scatterplots, line plots, correlation matrices
Scatterplots:
Purpose:
A scatterplot is used to visualize the relationship between two continuous variables. It displays individual data points on a two-dimensional graph, where each point represents a pair of values for the two variables.
Representation:
Points are scattered on the graph, and the position of each point corresponds to the values of the two variables. The pattern of points can provide insights into the nature and strength of the relationship between the variables (e.g., positive correlation, negative correlation, or no correlation).
Seaborn Function:
In Seaborn, the scatterplot function is commonly used for creating scatterplots.
Line Plots:
Purpose:
Line plots (or line charts) are used to visualize the trend or pattern of a variable over a continuous interval or time. They are especially useful for displaying changes in a variable’s value over a sequential range.
Representation: A line is drawn connecting points corresponding to the variable’s values. This helps in identifying trends, patterns, or fluctuations in the data.
Seaborn Function:
In Seaborn, the lineplot function is commonly used for creating line plots.
Correlation Matrices:
Purpose:
A correlation matrix is used to examine the strength and direction of the linear relationship between multiple variables. It shows pairwise correlations between variables, providing insights into how changes in one variable relate to changes in another.
Representation:
The matrix is a square grid where each cell represents the correlation coefficient between two variables. Values close to 1 indicate a strong positive correlation, values close to -1 indicate a strong negative correlation, and values close to 0 indicate a weak or no correlation.
Seaborn Function:
In Seaborn, the heatmap function is often used to create a visually appealing representation of the correlation matrix.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Example data
data = {‘variable1’: [1, 2, 3, 4, 5],
‘variable2’: [5, 4, 3, 2, 1],
‘variable3′: [2, 3, 1, 4, 5]}
df = pd.DataFrame(data)
# Scatterplot
sns.scatterplot(x=’variable1′, y=’variable2’, data=df)
plt.title(‘Scatterplot’)
plt.xlabel(‘Variable 1’)
plt.ylabel(‘Variable 2′)
plt.show()
# Line Plot
sns.lineplot(x=’variable1′, y=’variable2’, data=df)
plt.title(‘Line Plot’)
plt.xlabel(‘Variable 1’)
plt.ylabel(‘Variable 2′)
plt.show()
# Correlation Matrix
correlation_matrix = df.corr()
sns.heatmap(correlation_matrix, annot=True, cmap=’coolwarm’, fmt=”.2f”)
plt.title(‘Correlation Matrix’)
plt.show()
Categorical Data Plots: Bar charts, boxplots, boxenplots
Bar Charts:
Purpose:
Bar charts are used to display the distribution of a categorical variable by representing the frequencies or proportions of each category.
Representation:
Categories are shown on one axis (usually the x-axis for vertical bars) and their corresponding frequencies or proportions on the other axis.
Seaborn Function:
The countplot function is commonly used for creating bar charts.
Boxplots:
Purpose:
Boxplots (or box-and-whisker plots) are useful for visualizing the distribution of a continuous variable within different categories. They show the median, quartiles, and potential outliers of the data.
Representation:
A box is drawn representing the interquartile range (IQR), with a line inside indicating the median. Whiskers extend to the minimum and maximum values within a certain range.
Seaborn Function:
The boxplot function can be used for creating boxplots.
Boxenplots:
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Purpose:
Boxenplots (or letter-value plots) are similar to boxplots but are more detailed, especially for larger datasets. They show additional information about the tails of the distribution.
Representation:
Similar to boxplots, but with more notches and detailed information about the tails of the distribution.
Seaborn Function:
The boxenplot function is used for creating boxenplots.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Example data
data = {'category': ['A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'C'],
'value': [10, 15, 8, 20, 12, 18, 22, 14, 25]}
df = pd.DataFrame(data)
# Bar Chart
plt.figure(figsize=(8, 6))
sns.countplot(x='category', data=df)
plt.title('Bar Chart')
plt.xlabel('Category')
plt.ylabel('Count')
plt.show()
# Boxplot
plt.figure(figsize=(8, 6))
sns.boxplot(x='category', y='value', data=df)
plt.title('Boxplot')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
# Boxenplot
plt.figure(figsize=(8, 6))
sns.boxenplot(x='category', y='value', data=df)
plt.title('Boxenplot')
plt.xlabel('Category')
plt.ylabel('Value')
plt.show()
Pair plots, joint plots, clustermaps
Pair Plots:
Purpose:
Pair plots are used to visualize pairwise relationships between multiple variables in a dataset. It shows scatterplots for each pair of variables along the diagonal and histograms on the off-diagonal to represent the distribution of each variable.
Representation:
The diagonal shows the univariate distribution of each variable, and the scatterplots on the lower and upper triangles show bivariate relationships.
Seaborn Function: The pairplot function is commonly used for creating pair plots.
Joint Plots:
Purpose:
Joint plots are used to visualize the relationship between two variables, including the distribution of each variable and a scatterplot of the two variables.
Representation:
It combines a scatterplot with histograms along the axes, providing a comprehensive view of the relationship and marginal distributions.
Seaborn Function: The jointplot function is used for creating joint plots.
Clustermaps:
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Purpose:
Clustermaps are used to visualize the relationships between variables and cluster them based on similarity. It’s particularly useful for exploring patterns in large datasets
.
Representation:
The clustermap arranges variables in rows and columns and colors cells based on the similarity of values. Hierarchical clustering is often applied to group similar variables together.
Seaborn Function:
The clustermap function is used for creating clustermaps.
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Example data
data = {‘variable1’: [1, 2, 3, 4, 5],
‘variable2’: [5, 4, 3, 2, 1],
‘variable3’: [2, 3, 1, 4, 5]}
df = pd.DataFrame(data)
# Pair Plot
plt.figure(figsize=(10, 8))
sns.pairplot(df)
plt.suptitle(‘Pair Plot’, y=1.02)
plt.show()
# Joint Plot
plt.figure(figsize=(8, 6))
sns.jointplot(x=’variable1′, y=’variable2′, data=df, kind=’scatter’)
plt.suptitle(‘Joint Plot’, y=1.02)
plt.show()
# Clustermap
plt.figure(figsize=(8, 6))
sns.clustermap(df.corr(), annot=True, cmap=’coolwarm’, fmt=”.2f”)
plt.suptitle(‘Clustermap’, y=1.02)
plt.show()
The Big Data Revolution with Data Analytics
Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continue to grow exponentially over time. It is characterized by the three Vs: volume, velocity, and variety. Volume refers to the amount of data, velocity refers to the speed at which data is generated and processed, and variety refers to the different types of data, including structured, semi-structured, and unstructured data. Big data can be analyzed for insights that improve decisions and give confidence for making strategic business moves. Big data can help organizations to identify patterns, trends, and insights that can be used to improve business operations, customer experiences, and decision-making processes. It can also help organizations to identify new opportunities for growth and innovation.
The world is awash in data, and businesses are increasingly recognizing the value of this vast resource. Big data, which refers to the massive and complex datasets that are too large to be processed by traditional methods, holds the key to unlocking new insights and opportunities. Data analytics, the process of extracting meaningful information from big data, is the engine that drives this revolution.
Imagine the world as a vast library, filled with books on every imaginable topic. Each book represents a piece of data, and the library as a whole represents the vast sea of data that exists in our world today. This is what we call “big data.”
But just as a library without librarians would be useless, big data without data analytics would be nothing more than a pile of information. Data analytics is like the librarian, the expert who can sift through the mountains of data, organize it, and extract meaningful insights.
the Supermarket Detective
Imagine you’re a supermarket manager, your store is bustling with activity, and shelves are lined with products. But behind the scenes, you’re facing a challenge: understanding which products are selling well and which ones are just taking up space. That’s where data analytics comes in, acting as your very own supermarket detective.
Data analysts can sift through your sales data, customer behavior, and market trends like a seasoned detective examining clues at a crime scene. They’ll uncover hidden patterns, identify popular items, and predict future demand. With these insights, you can:
- Stock up on the right products, ensuring your customers always have what they’re looking for.
- Avoid overstocking slow-moving items, saving valuable shelf space and reducing inventory costs.
- Make informed pricing decisions, optimizing profits and customer satisfaction.
The Netflix Recommendation Guru
Ever wondered how Netflix knows that romantic comedy you watched last night is exactly the kind of movie you’re in the mood for today? It’s not magic, it’s data analytics, your very own Netflix recommendation guru.
Netflix collects a massive amount of data about your viewing habits, analyzing every pause, rewind, and skip. Data analysts use this information to create a personalized profile of your viewing preferences, like a detective building a psychological profile of a criminal.
- With this profile in hand, they can:
- Recommend movies and shows that align with your tastes and interests.
- Suggest new genres and hidden gems you might not have discovered on your own.
- Keep you engaged and entertained, increasing your satisfaction and loyalty to Netflix.
The Fraud-Fighting Bank
In the world of finance, fraudsters are like cunning spies, constantly trying to infiltrate and steal money. But data analytics is the ultimate counterintelligence weapon, your very own fraud-fighting bank.
Data analysts can analyze vast amounts of financial transactions, scrutinizing every purchase, transfer, and withdrawal like a detective examining fingerprints at a crime scene. They’ll identify unusual patterns or anomalies that might indicate fraudulent activity.
With these insights, they can:
- Detect fraudulent transactions in real-time, preventing financial losses and protecting customer accounts.
- Identify potential fraudsters and take proactive measures to prevent future attacks.
- Safeguard the integrity of the financial system and build trust among customers.
The Traffic-Taming City Planner
Ever felt trapped in a sea of cars, navigating through a city choked with traffic congestion? Data analytics is the key to untangling this mess, your very own traffic-taming city planner.
Data analysts can analyze traffic patterns, sensor readings, and travel behavior like a detective investigating a complex traffic accident. They’ll identify bottlenecks, optimize traffic signals, and suggest alternative routes.
With these insights, they can:
- Reduce traffic congestion, saving commuters time and fuel.
- Improve travel times, making cities more livable and businesses more accessible.
- Plan for future infrastructure needs, ensuring smooth traffic flow as cities grow.
The Disease-Detecting Doctor
In the fight against disease, data analytics is the ultimate diagnostic tool, your very own disease-detecting doctor.
Doctors can analyze medical records, patient data, and genetic information like a detective examining a patient’s medical history. They’ll identify patterns and correlations that might indicate underlying diseases or potential health risks.
With these insights, they can:
- Diagnose diseases more accurately and efficiently, providing timely and effective treatment.
- Develop personalized treatment plans tailored to each patient’s unique genetic makeup and medical history.
- Predict and prevent potential health risks, promoting preventive care and improving overall health outcomes.
These examples illustrate how data analytics is transforming our lives in countless ways. It’s not just about numbers and spreadsheets; it’s about using data to make a difference, solve problems, and improve the world around us. Data analytics is the detective, the guru, the fraud-fighter, the traffic-tamer, and the disease-detector, working tirelessly behind the scenes to make our lives better.
Data Analytics: A Journey of Discovery
Imagine you’re an archaeologist, uncovering hidden treasures buried beneath layers of dust and time. Data analytics is like your very own archaeological tool, unearthing valuable insights from the vast troves of data that surround us.
In recent years, data analytics has made remarkable strides, opening up new frontiers in understanding and utilizing data. Let’s delve into some of these exciting discoveries and advancements, presented in a way that even the most data-savvy layman can appreciate:
1. XAI: Unlocking the Mysteries of AI Decisions
Have you ever wondered how AI makes its decisions? Sometimes, even the creators of AI models struggle to explain their inner workings. That’s where Explainable AI (XAI) comes in, like a translator bridging the gap between AI and human understanding.
XAI techniques are like having a magnifying glass for AI models, allowing us to see how they arrive at their conclusions. This newfound transparency is crucial for ensuring that AI decisions are fair, unbiased, and accountable.
2. Federated Learning: Keeping Data Privacy in the Spotlight
In today’s data-driven world, privacy concerns are paramount. Federated learning is like a privacy-preserving cloak for data analytics, enabling researchers and organizations to collaborate on training machine learning models without sharing sensitive data.
Imagine multiple hospitals working together to improve disease detection algorithms without sharing their patient records directly. Federated learning makes this possible, allowing researchers to harness the power of collective data while safeguarding individual privacy.
3. Real-Time Analytics: Capturing the Pulse of Data
Data is like a river, constantly flowing and evolving. Real-time analytics is like a dam that captures this dynamic flow, providing insights into data as it happens.
Imagine fraud detection systems that can identify and flag suspicious transactions in real time, saving banks millions of dollars. Or traffic management systems that can adjust traffic signals based on real-time traffic patterns, reducing congestion and improving commute times.
4. Edge Computing: Data Analytics at the Forefront
Edge computing is like bringing the power of data analytics closer to the action, where data is generated. It’s like having a mini data center embedded in devices and sensors, crunching numbers right at the source.
Imagine sensors in industrial machinery analyzing data in real time, predicting equipment failures before they occur. Or smart home devices optimizing energy consumption based on real-time usage patterns.
5. Graph Analytics: Navigating the Complexities of Relationships
Data isn’t always a straightforward list of numbers; it can be a network of interconnected entities, like a tangled web of relationships. Graph analytics is like a powerful microscope for these complex data structures, allowing us to identify patterns and connections that would otherwise remain hidden.
Imagine social media platforms using graph analytics to understand how information spreads and how communities form. Or supply chain managers using graph analytics to optimize logistics and identify potential disruptions.
6. DataOps: Bridging the Divide Between Data and Operations
DataOps is like a bridge connecting data teams, development teams, and operations teams, ensuring that data is seamlessly flowing and being used effectively throughout the organization.
Imagine a company where data is not siloed in isolated departments but is readily accessible and integrated into decision-making processes at all levels. DataOps makes this possible, fostering a culture of collaboration and data-driven decision-making.
7. AutoML: Democratizing Machine Learning
Machine learning has revolutionized many industries, but building and training machine learning models can be complex and time-consuming. AutoML is like a helping hand, making machine learning more accessible to a wider range of users.
Imagine non-experts being able to build and train machine learning models with just a few clicks. AutoML is making this a reality, empowering individuals to harness the power of machine learning without extensive technical expertise.
8. NLP: Decoding the Language of Humans
Data isn’t always numbers and figures; it can be the rich tapestry of human language. Natural Language Processing (NLP) is like a universal translator, enabling computers to understand and process the nuances of human language.
Imagine customer service chatbots that can provide personalized support, understanding the sentiment and intent behind customer queries. Or social media analysis tools that can extract valuable insights from public conversations, revealing trends and opinions.
These new discoveries and advancements in data analytics are just the beginning of an exciting journey. As data continues to grow and evolve, data analytics will play an increasingly crucial role in shaping our world, providing insights that will transform businesses, industries, and society as a whole
the future of data analytics in India from reports:
The Indian Data Analytics Market is Expected to Reach $36.6 Billion by 2027:
According to a report by Market Research Future, the Indian data analytics market is expected to reach $36.6 billion by 2027, growing at a CAGR of 18.7% from 2022 to 2027. This growth is being driven by factors such as the increasing adoption of data-driven decision-making in Indian businesses, the government’s focus on promoting data analytics, and the growing demand for skilled data analytics professionals.
2. The Demand for Data Analytics Professionals in India is Expected to Grow by 35% by 2025:
A report by NASSCOM and SkillsCircle found that the demand for data analytics professionals in India is expected to grow by 35% by 2025. This means that there will be a significant need for data analytics professionals in the coming years.
3. The Indian Government is Actively Promoting Data Analytics:
The Indian government is actively promoting data analytics through initiatives such as the National Data Policy and the National Artificial Intelligence Strategy. These initiatives are aimed at creating a data-driven economy and fostering innovation in the field of data analytics.
4. India’s IT Industry is a Major Driver of Data Analytics Growth:
India’s IT industry is a major driver of data analytics growth. The country has a large pool of skilled IT professionals who are able to develop and implement data analytics solutions.
5. India is Emerging as a Global Data Analytics Hub:
India is emerging as a global data analytics hub, with several data analytics startups emerging in recent years. These startups are developing innovative solutions for various industries, such as healthcare, finance, and retail.
6. India is Focusing on Data Privacy and Security:
The Indian government is taking steps to ensure data privacy and security in the country. The Personal Data Protection Bill (PDP Bill), which is currently under consideration, aims to regulate the collection, use, and storage of personal data.
7. India is Participating in Global Data Analytics Initiatives:
India is actively participating in global data analytics initiatives, such as the Global Partnership on Artificial Intelligence (GPAI) and the Open Data Institute (ODI). This collaboration is helping India to stay up-to-date with the latest advancements in data analytics and share best practices with other countries.
8. Data Analytics is Playing a Crucial Role in India’s COVID-19 Response:
Data analytics is playing a crucial role in India’s COVID-19 response. Data is being used to track the spread of the virus, identify hotspots, and develop targeted interventions.
Overall, India is well-positioned to play a leading role in the future of data analytics. The country’s strong IT industry, growing demand for data analytics professionals, and government initiatives are all creating a favorable environment for the development and adoption of data analytics solutions.
AUTOMATION IN JOBS AND DATA ANALYTICS
Yes, it is true that automation is likely to play an increasing role in data science and analytics in the future. Many data science and analytics tasks, such as data cleaning, data preparation, and feature engineering, can be automated using machine learning and artificial intelligence. This will free up data scientists and analysts to focus on more creative and strategic tasks, such as developing predictive models and generating insights from data.
However, automation is not likely to replace the need for human data scientists and analysts. Data scientists and analysts will still be needed to:
- Understand the business context and objectives of data analytics projects.
- Collect and curate the right data for the project.
- Interpret the results of data analysis and communicate them to stakeholders.
- Develop and implement data governance and privacy policies.
- In addition, data scientists and analysts will need to stay up-to-date on the latest advancements in machine learning and artificial intelligence in order to effectively use these technologies to automate tasks.
Overall, the automation of data science and analytics tasks is likely to have a positive impact on the field. It will free up data scientists and analysts to focus on more strategic tasks, and it will make data analytics more accessible to a wider range of users. However, it is important to remember that automation is not a replacement for human expertise. Data scientists and analysts will still be needed to provide the context, interpretation, and leadership that is essential for successful data analytics projects.
Here are some specific examples of how automation is being used in data science and analytics:
- Data cleaning and preparation: Machine learning algorithms can be used to automatically identify and correct errors in data, such as missing values and outliers.
- Feature engineering: Machine learning algorithms can be used to automatically generate new features from existing data. This can be helpful for improving the performance of predictive models.
- Model development and selection: Machine learning algorithms can be used to automatically develop and select predictive models. This can save time and effort for data scientists and analysts.
- Model deployment and monitoring: Machine learning models can be automatically deployed to production and monitored for performance. This can help to ensure that models are performing as expected and are not generating biased or unfair results.
Overall, automation is a powerful tool that can be used to improve the efficiency and effectiveness of data science and analytics. However, it is important to use automation responsibly and to ensure that it is not used to replace human judgment and expertise.
“Mastering Charts with Matplotlib in Python Data Analytics
Data visualization plays a crucial role in understanding and interpreting complex datasets. Python, with its powerful libraries, provides an excellent environment for data analytics and visualization. One such library, Matplotlib, is a go-to tool for creating a wide variety of charts and plots. In this tutorial, we will explore the fundamentals of Matplotlib and learn how to create and customize different types of charts for effective data analysis.
Matplotlib is a versatile and widely-used plotting library in the Python ecosystem. Whether you’re a data scientist, analyst, or enthusiast, mastering Matplotlib empowers you to communicate insights visually. From simple line plots to sophisticated 3D visualizations, Matplotlib has you covered.
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Exploring Line Plots with Matplotlib
Basic Line Plot:
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Basic Line Plot
plt.plot(x, y, label='Line 1')
# Customize the plot
plt.title('Basic Line Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
# Show the plot
plt.show()
Multiple Lines on One Plot:
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
y2 = [1, 2, 1, 2, 1]
# Multiple Lines on One Plot
plt.plot(x, y1, label='Line 1')
plt.plot(x, y2, label='Line 2')
# Customize the plot
plt.title('Multiple Lines on One Plot')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
# Show the plot
plt.show()
3. Line Style and Color:
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Line Style and Color
plt.plot(x, y, linestyle='--', color='red', marker='o', label='Dashed Line')
# Customize the plot
plt.title('Line Style and Color')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
# Show the plot
plt.show()
types of label and marker in line chart
Line Styles:
SOLID LINEplt.plot(x, y, linestyle='-', label='Solid Line')Dashed Line:
plt.plot(x, y, linestyle='--', label='Dashed Line')Dotted Line:
plt.plot(x, y, linestyle=':', label='Dotted Line')Dash-Dot Line:
plt.plot(x, y, linestyle='-.', label='Dash-Dot Line')Line Colors:
plt.plot(x, y, color='red', label='Red Line')Markers Markers indicate specific data points on the line. You can use various markers:
Circle:
plt.plot(x, y, marker='o', label='Circle Marker')
Square:
plt.plot(x, y, marker='s', label='Square Marker')
Triangle Up
plt.plot(x, y, marker='^', label='Triangle Up Marker')
Bar Charts
Basic Bar Chart:
import matplotlib.pyplot as plt
# Sample data
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values = [30, 50, 20, 40]
# Basic Bar Chart
plt.bar(categories, values, color='blue')
# Customize the plot
plt.title('Basic Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
# Show the plot
plt.show()
Stacked Bar Chart:
import matplotlib.pyplot as plt
# Sample data
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values1 = [30, 50, 20, 40]
values2 = [10, 20, 30, 40]
# Stacked Bar Chart
plt.bar(categories, values1, color='blue', label='Group 1')
plt.bar(categories, values2, bottom=values1, color='orange', label='Group 2')
# Customize the plot
plt.title('Stacked Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.legend()
# Show the plot
plt.show()
Group bar chart
import numpy as np
import matplotlib.pyplot as plt
# Sample data
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values1 = [30, 50, 20, 40]
values2 = [10, 20, 30, 40]
# Grouped Bar Chart
bar_width = 0.35 # Width of each bar
index = np.arange(len(categories)) # Generating an array of evenly spaced values representing the categories
# Creating the first set of bars (Group 1)
plt.bar(index, values1, width=bar_width, color='blue', label='Group 1')
# Creating the second set of bars (Group 2), shifted to the right by bar_width
plt.bar(index + bar_width, values2, width=bar_width, color='orange', label='Group 2')
# Customize the plot
plt.title('Grouped Bar Chart')
plt.xlabel('Categories')
plt.ylabel('Values')
plt.xticks(index + bar_width / 2, categories) # Setting the x-axis ticks at the center of each group
plt.legend() # Displaying the legend
# Show the plot
plt.show()
Creating Grouped Bar Chart:
plt.bar(index, values1, width=bar_width, color=’blue’, label=’Group 1′): Creates the first set of bars (Group 1).
plt.bar(index + bar_width, values2, width=bar_width, color=’orange’, label=’Group 2′): Creates the second set of bars (Group 2), shifted to the right by bar_width
Scatter Plots: Unveiling Relationships
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
# Scatter Plot
plt.scatter(x, y, color='red', marker='o', label='Data Points')
# Customize the plot
plt.title('Scatter Plot: Unveiling Relationships')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
# Show the plot
plt.show()
Adding Text Annotations Scatter Plotter
import matplotlib.pyplot as plt
# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
data_labels = ['Point 1', 'Point 2', 'Point 3', 'Point 4', 'Point 5']
# Scatter Plot with Text Annotations
plt.scatter(x, y, color='purple', marker='o', label='Data Points')
# Adding text annotations
for i, label in enumerate(data_labels):
plt.annotate(label, (x[i], y[i]), textcoords="offset points", xytext=(0,5), ha='center')
# Customize the plot
plt.title('Scatter Plot with Text Annotations')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
# Show the plot
plt.show()
Histrogram
import matplotlib.pyplot as plt
import numpy as np
# Sample data
data = [2, 5, 7, 10, 5, 8, 3, 7, 6, 9, 11, 5, 7]
# Create histogram
plt.hist(data, bins=10, edgecolor='black', color='skyblue')
# Customize the plot
plt.title('Histogram Example')
plt.xlabel('Values')
plt.ylabel('Frequency')
# Show the plot
plt.show()
import matplotlib.pyplot as plt
import numpy as np
# Sample data
data1 = np.random.randn(1000)
data2 = np.random.randn(1000) + 2 # Shift the second dataset
# Create multiple histograms
plt.hist(data1, bins=30, edgecolor='black', color='skyblue', alpha=0.7, label='Dataset 1')
plt.hist(data2, bins=30, edgecolor='black', color='orange', alpha=0.7, label='Dataset 2')
# Customize the plot
plt.title('Multiple Histograms Example')
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.legend()
# Show the plot
plt.show()
Pie Chart
import matplotlib.pyplot as plt
# Sample data
labels = ['Category A', 'Category B', 'Category C', 'Category D']
sizes = [30, 20, 15, 35]
# Create a pie chart
plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=['skyblue', 'orange', 'lightgreen', 'lightcoral'])
# Customize the plot
plt.title('Pie Chart Example')
# Show the plot
plt.show() - plt.pie(sizes, labels=labels, autopct=’%1.1f%%’, startangle=90, colors=[‘skyblue’, ‘orange’, ‘lightgreen’, ‘lightcoral’]): This line creates a pie chart. sizes is a list of data values, labels is a list of category labels, autopct adds percentage labels, startangle rotates the pie chart, and colors specifies the color for each category.
- plt.title(‘Pie Chart Example’): Adds a title to the plot.
- plt.show(): Displays the pie chart.
Blox plot
import matplotlib.pyplot as plt # Dataset scores = [78, 82, 85, 90, 91, 93, 95, 97, 100, 102, 105, 108] # Creating the box plot plt.boxplot(scores) plt.title(‘Test Scores Box Plot’) plt.ylabel(‘Scores’) # Display the plot plt.show()
Mastering Data Analytics with pandas: A Comprehensive Guide to DataFrame Joins
Data analytics is an integral part of any data-driven organization. To extract valuable insights and make data-driven decisions, you often need to combine and analyze data from different sources. One of the essential skills in this domain is mastering the art of DataFrame joins using pandas, a popular Python library for data manipulation and analysis.
In this comprehensive guide, we will dive into the world of DataFrame joins, exploring different types of joins and demonstrating how to perform them with pandas. By the end of this blog, you’ll have a solid understanding of how to leverage pandas to combine, merge, and analyze data effectively.
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Data analysis frequently involves working with multiple datasets that need to be combined to gain meaningful insights or perform advanced analysis. DataFrame joins are a fundamental technique in data manipulation, especially when using pandas in Python. This introductory section will provide an overview of DataFrame joins, highlighting their importance and the types of joins available.
The Need for DataFrame Joins
In the real world, data often resides in multiple datasets. For example, you may have one dataset containing customer information and another containing their purchase history. To analyze customer behavior or preferences, you need to combine these datasets. This is where DataFrame joins come into play.
DataFrame joins enable you to bring together data from different sources, linking them based on a common attribute, such as a unique identifier, key, or column. The result is a merged dataset that provides a comprehensive view of the information you require for analysis.
Types of DataFrame Joins
There are several types of DataFrame joins, each serving a specific purpose:
Inner Join: This type of join returns only the rows where there is a match in both DataFrames based on the specified key. It filters the data to show only the common elements between the two datasets.
Left Join: A left join includes all rows from the left DataFrame and only the matching rows from the right DataFrame. If there’s no match in the right DataFrame, it still includes the data from the left DataFrame.
Right Join: The right join is the opposite of the left join. It includes all rows from the right DataFrame and the matching rows from the left DataFrame. Non-matching rows from the left DataFrame are excluded.
Outer Join: An outer join combines all rows from both DataFrames. It includes matching rows from both DataFrames and fills in missing values with NaN for columns that don’t have a match.
Practical Use Cases
DataFrame joins are a versatile tool for data analysts and scientists in various industries. Here are some common use cases:
Combining Sales Data: Merge sales transactions with customer information to analyze customer demographics and purchasing behavior.
Matching Employee Records: Join employee data with department data to assign department information to employees for organizational analysis.
Merging Time Series Data: Combine time series data with weather data to analyze the impact of weather on certain events or activities.
Data Enrichment: Augmenting existing data with additional information, such as adding geographical data to customer addresses for mapping and geospatial analysis.
DataFrame joins are an essential skill for data analysts and data scientists, and mastering them allows you to unlock the full potential of your data for informed decision-making. In the following sections of this guide, we will explore each type of join in detail and provide practical examples to help you become proficient in using pandas for data analysis.
sales_data:
- order_id: The order ID for each sale.
- product_id: The ID of the product sold.
- quantity: The quantity of the product sold.
- total_price: The total price for the sale.
customer_data:
- customer_id: The customer’s unique identifier.
- customer_name: The customer’s name.
- customer_email: The customer’s email address.
import pandas as pd # Read the customer data from the first Excel file # Read customer data from Excel customer_data = pd.read_excel(r"E:\JOINS\CUSTOMERDATA.xlsx") sales_data = pd.read_excel(r"E:\JOINS\SALESDATA.xlsx") print(customer_data) print(sales_data)
OUTPUT
customer_id customer_name customer_email 0 101 Alice alice@example.com 1 102 Bob bob@example.com 2 103 Charlie charlie@example.com 3 104 David david@example.com 4 105 Eve eve@example.com order_id customer_id quantity total_price 0 1 101 3 150 1 2 101 2 120 2 3 103 5 250 3 4 103 1 50 4 5 105 4 200
The code you provided will perform a left join on the ‘customer_id’ column in the “customer_data” and “sales_data” DataFrames. Here’s what the code will do:
- It will merge the two DataFrames based on the ‘customer_id’ column.
- Since you specified how=’left’, the result will include all rows from the “customer_data” DataFrame and only the matching rows from the “sales_data” DataFrame. Any unmatched rows from the “customer_data” DataFrame will still be included in the result.
The result will be a new DataFrame containing all the columns from both the “customer_data” and “sales_data” DataFrames, with additional rows where “customer_id” matches between the two DataFrames. If there is no match for a specific ‘customer_id’ in the “sales_data” DataFrame, the corresponding columns for that row in the “sales_data” DataFrame will contain NaN (missing values) in the result.
LEFT Join
# Perform a left join on 'customer_id' in customer_data and 'customer_id' in sales_data result_df = pd.merge(customer_data, sales_data, left_on='customer_id', right_on='customer_id', how='left') # Display the result print(result_df)outcome
customer_id customer_name customer_email order_id quantity \ 0 101 Alice alice@example.com 1.0 3.0 1 101 Alice alice@example.com 2.0 2.0 2 102 Bob bob@example.com NaN NaN 3 103 Charlie charlie@example.com 3.0 5.0 4 103 Charlie charlie@example.com 4.0 1.0 5 104 David david@example.com NaN NaN 6 105 Eve eve@example.com 5.0 4.0 total_price 0 150.0 1 120.0 2 NaN 3 250.0 4 50.0 5 NaN 6 200.0The code you provided will perform a left join on the ‘customer_id’ column in the “customer_data” and “sales_data” DataFrames. Here’s what the code will do:
- It will merge the two DataFrames based on the ‘customer_id’ column.
- Since you specified how=’left’, the result will include all rows from the “customer_data” DataFrame and only the matching rows from the “sales_data” DataFrame. Any unmatched rows from the “customer_data” DataFrame will still be included in the result.
Right join
# Perform a right join on 'customer_id' in customer_data and 'customer_id' in sales_data result_df = pd.merge(customer_data, sales_data, left_on='customer_id', right_on='customer_id', how='right') # Display the result print(result_df)
OUTPUT
customer_id customer_name customer_email order_id quantity \ 0 101 Alice alice@example.com 1 3 1 101 Alice alice@example.com 2 2 2 103 Charlie charlie@example.com 3 5 3 103 Charlie charlie@example.com 4 1 4 105 Eve eve@example.com 5 4 total_price 0 150 1 120 2 250 3 50 4 200
The code you provided performs a right join between two DataFrames, customer_data and sales_data, on the ‘customer_id’ column. Here’s an explanation of each part of the code:
- pd.merge(customer_data, sales_data, left_on=’customer_id’, right_on=’customer_id’, how=’right’): This line of code uses the pd.merge() function to perform the right join. Here’s a breakdown of the parameters used:
- customer_data and sales_data are the two DataFrames you want to join.
left_on=’customer_id’ specifies that you want to use the ‘customer_id’ column from the customer_data DataFrame as the key for the left DataFrame.
right_on=’customer_id’ specifies that you want to use the ‘customer_id’ column from the sales_data DataFrame as the key for the right DataFrame. - how=’right’ specifies the type of join, in this case, it’s a right join. This means that all rows from the right DataFrame (sales_data) will be included in the result, and matching rows from the left DataFrame (customer_data) will be included as well.
The result of the merge operation is stored in the result_df DataFrame. - print(result_df): This line of code prints the resulting DataFrame, which is the result of the right join between customer_data and sales_data.
The resulting DataFrame, result_df, will contain all the rows from the sales_data DataFrame and additional columns from the customer_data DataFrame where there is a matching ‘customer_id’. Rows from customer_data that don’t have a matching ‘customer_id’ in sales_data will be included with NaN values in the columns from sales_data.
In summary, this code is performing a right join between customer data and sales data using the ‘customer_id’ column as the key, and the result will include all sales data and associated customer data where applicable.
Inner Join
result_df = pd.merge(customer_data, sales_data, on='customer_id', how='inner') # Display the result print(result_df)Output
customer_id customer_name customer_email order_id quantity \ 0 101 Alice alice@example.com 1 3 1 101 Alice alice@example.com 2 2 2 103 Charlie charlie@example.com 3 5 3 103 Charlie charlie@example.com 4 1 4 105 Eve eve@example.com 5 4 total_price 0 150 1 120 2 250 3 50 4 200
- pd.merge(customer_data, sales_data, on=’customer_id’, how=’inner’): This line of code performs an inner join between customer_data and sales_data on the ‘customer_id’ column. The on=’customer_id’ parameter specifies that the ‘customer_id’ column is used as the key for the join, and how=’inner’ specifies that it’s an inner join. This means that only rows with matching ‘customer_id’ values in both DataFrames will be included in the result.
- The result of the inner join is stored in the result_df DataFrame.
- print(result_df): This line of code prints the resulting DataFrame, which contains only the rows with matching ‘customer_id’ values from both customer_data and sales_data.
OUTER JOIN
result_df = pd.merge(customer_data, sales_data, on=’customer_id’, how=’outer’) # Display the result print(result_df)output
customer_id customer_name customer_email order_id quantity \ 0 101 Alice alice@example.com 1.0 3.0 1 101 Alice alice@example.com 2.0 2.0 2 102 Bob bob@example.com NaN NaN 3 103 Charlie charlie@example.com 3.0 5.0 4 103 Charlie charlie@example.com 4.0 1.0 5 104 David david@example.com NaN NaN 6 105 Eve eve@example.com 5.0 4.0 total_price 0 150.0 1 120.0 2 NaN 3 250.0 4 50.0 5 NaN 6 200.0
Data Analytics Insight: The Magic of groupby in Pandas in Python
Data analytics is a vital component of modern businesses and research. Python, with its powerful libraries like Pandas, has become a go-to tool for data analysts and scientists. In this blog, we will delve into one of the most powerful and versatile features of Pandas: the GroupBy operation. GroupBy is a fundamental tool for data aggregation, transformation, and exploration. We will explore the magic of GroupBy in Pandas and how it can help you gain valuable insights from your data.
What is GroupBy?
GroupBy in Pandas is similar to the SQL GROUP BY statement. It allows you to group data based on one or more columns, enabling you to perform operations on these groups separately. This functionality is invaluable when dealing with large datasets, as it simplifies data summarization and analysis.
GroupBy is a fundamental operation in data analysis and is a feature commonly found in data manipulation libraries like Pandas in Python, as well as in database systems like SQL. It allows you to group rows of data based on the values in one or more columns, and then perform various operations within each group.
Here’s a breakdown of how GroupBy works:
Grouping: You start by specifying one or more columns by which you want to group your data. These columns contain categorical data, and rows with the same values in these columns are grouped together. Think of it as if you’re partitioning your dataset into smaller, separate subsets.
Aggregation: Once the data is grouped, you can apply aggregation functions to these groups. Aggregation functions perform calculations on each group separately, such as sum, mean, median, count, etc. The result is a summary statistic or a reduced representation of each group.
Transformation: You can also transform the data within each group. This means applying a function to each group that can modify the data within the group. For instance, you could standardize the values within each group or replace missing data with the group’s mean.
Filtering: GroupBy allows you to filter groups based on some condition. For example, you could filter out groups that don’t meet a certain criteria, like groups with a total sum above a certain threshold.
Iteration: You can iterate over the groups, which is useful if you want to perform custom operations for each group.
GroupBy is an essential tool in data analysis, as it enables you to gain insights from your data by exploring patterns and summarizing information within subgroups. It’s especially useful when dealing with large datasets and is widely used in a variety of domains, including business analysis, scientific research, and machine learnin
The GroupBy operation in Python,
Step 1: Import the Required Library
First, make sure you have Pandas installed. If not, you can install it using pip:
pip install pandas
Then, import Pandas in your Python script:
import pandas as pd
import pandas as pd
Step 2: Create a DataFrame
Create a DataFrame
with your data. In this example, we’ll use a simple dictionary to create a DataFrame:
data = {
'Category': ['A', 'B', 'A', 'B', 'A', 'C'],
'Value': [10, 15, 20, 25, 30, 35]
}
df = pd.DataFrame(data)
Step 3: Grouping by a Column
Use the groupby method to group the data based on a specific column. In this case, we’ll group the data by the ‘Category’ column:
grouped = df.groupby('Category')
Step 4: Aggregation or Transformation
You can apply various operations to the groups. Let’s calculate the sum of the ‘Value’ within each group:
result = grouped['Value'].sum()
The result will contain the sum of ‘Value’ for each unique category in the ‘Category’ column.
Step 5: Viewing the Results
print(result)
Output
Category A 60 B 40 C 35 Name: Value, dtype: int64
This output shows the result of grouping and summing the ‘Value’ column based on the ‘Category’ column. It displays the sum of ‘Value’ for each unique category in the ‘Category’ column. In this example:
The sum of ‘Value’ for category ‘A’ is 60.
The sum of ‘Value’ for category ‘B’ is 40.
The sum of ‘Value’ for category ‘C’ is 35.
This is a simple illustration of how the GroupBy operation works in Pandas, allowing you to perform aggregation operations on groups within your dataset.
Example of Group By
import pandas as pd
# Load the data from the Excel file
df = pd.read_excel('E:\DATA ANALYTICS\DATASET.xlsx') # Replace 'sample_data.xlsx' with the actual filename
print(df)This line of Python code is using the Pandas library to read data from an Excel file named ‘DATASET.xlsx’ located at the file path ‘E:/DATA ANALYTICS/’.
Here’s a breakdown of the line:
df:
This is a variable name used to store the data that will be read from the Excel file. You can choose any valid variable name you prefer.
pd:
This is an alias for the Pandas library. Pandas is a popular data manipulation library in Python.
read_excel():
This is a Pandas function that reads data from an Excel file.
‘E:/DATA ANALYTICS/DATASET.xlsx’: This is the file path to the Excel file you want to read. In Python, you can use either double backslashes (\\) or a single forward slash (/) to specify the file path. In this case, the file ‘DATASET.xlsx’ is expected to be located in the ‘E:/DATA ANALYTICS/’ directory.
So, when you run this line of code, it will read the data from the Excel file and store it in the variable df, allowing you to work with the data in your Python program using Pandas functions and method
print(df.info())
- The line of code print(df.info()) in Python, when applied to a Pandas DataFrame, provides a concise summary of the DataFrame’s structure and content. Here’s what this code does:
- df is assumed to be a Pandas DataFrame, and you want to inspect its information.
- info() is a Pandas DataFrame method that generates a summary of the DataFrame’s metadata, including:
- The number of non-null (non-missing) values in each column.
- The data type of each column (e.g., int64, float64, object).
- The total memory usage of the DataFrame.
- print() is used to display the information summary on the console or output.
When you run print(df.info()), it will display something like:
Groupby department
mean_Department_salary = df.groupby('Department')['Salary'].mean()
median_Department_salary=df.groupby('Department')['Salary'].median()
std_Department_salary=df.groupby('Department')['Salary'].std()
print(mean_Department_salary)
print(median_Department_salary)
print(std_Department_salary)In the provided code, you are using Pandas to calculate various statistics related to salaries within different departments of your DataFrame. Let’s break down the code:
mean_Department_salary:
- df.groupby(‘Department’)[‘Salary’] groups the DataFrame df by the ‘Department’ column and selects the ‘Salary’ column.
- .mean() calculates the mean (average) salary within each department.
This code computes the mean salary for each department and stores the results in the mean_Department_salary Series.
median_Department_salary:
- Similar to the first line, df.groupby(‘Department’)[‘Salary’] groups the DataFrame by department and selects salaries.
.median() calculates the median salary within each department.
The results are stored in the median_Department_salary Series.
std_Department_salary:
- Once again, df.groupby(‘Department’)[‘Salary’] groups the DataFrame by department and selects salaries.
.std() computes the standard deviation of salaries within each department.
The results are stored in the std_Department_salary Series.
Finally, you print the results:
- print(mean_Department_salary) will display the mean salary for each department.
- print(median_Department_salary) will display the median salary for each department.
- print(std_Department_salary) will display the standard deviation of salaries for each department.
These statistics help you understand the distribution of salaries within different departments, providing insights into the central tendency (mean and median) and the variation (standard deviation) in salary data.
mean_gender_salary = df.groupby('Gender')['Salary'].mean()
median_Gender_salary=df.groupby('Gender')['Salary'].median()
std_Gender_salary=df.groupby('Gender')['Salary'].std()
print(mean_gender_salary)
print(median_Gender_salary)
print(std_Gender_salary)
mean_gender_salary:
- df.groupby(‘Gender’)[‘Salary’] groups the DataFrame df by the ‘Gender’ column and selects the ‘Salary’ column.
- .mean() calculates the mean (average) salary within each gender group.
- This code computes the mean salary for each gender group and stores the results in the mean_gender_salary Series.
median_Gender_salary:
- Similar to the first line, df.groupby(‘Gender’)[‘Salary’] groups the DataFrame by gender and selects salaries.
- .median() calculates the median salary within each gender group.
The results are stored in the median_Gender_salary Series.
std_Gender_salary:
- Once again, df.groupby(‘Gender’)[‘Salary’] groups the DataFrame by gender and selects salaries.
- .std() computes the standard deviation of salaries within each gender group.
- The results are stored in the std_Gender_salary Series.
Finally, you print the results:
- print(mean_gender_salary) will display the mean salary for each gender group.
- print(median_Gender_salary) will display the median salary for each gender group.
- print(std_Gender_salary) will display the standard deviation of salaries for each gender group.
- These statistics help you understand the distribution of salaries within different gender groups, providing insights into the central tendency (mean and median) and the variation (standard deviation) in salary data, with respect to gender.
Creating Bins in Pandas DataFrame and Exploring Statistics
print(df['Age'].max()) print(df['Age'].min()) # Define the bins for age age_bins = [20, 25, 30, 35, 40] # Create a new column for the age bins df['Age_Bin'] = pd.cut(df['Age'], bins=age_bins, labels=['20-25', '26-30', '31-35', '36-40']) print(df['Age_Bin'])
- print(df[‘Age’].max()): This line of code calculates and prints the maximum value in the ‘Age’ column of your DataFrame (‘df’). It finds the highest age among all the individuals in your dataset.
- print(df[‘Age’].min()): This line of code calculates and prints the minimum value in the ‘Age’ column of your DataFrame. It finds the lowest age among all the individuals in your dataset.
- age_bins = [20, 25, 30, 35, 40]: This line defines the bin boundaries for age. It specifies the age ranges for which you want to create bins. In this case, you’ve defined four bins: 20-25, 26-30, 31-35, and 36-40.
- df[‘Age_Bin’] = pd.cut(df[‘Age’], bins=age_bins, labels=[’20-25′, ’26-30′, ’31-35′, ’36-40′]): This line of code creates a new column in your DataFrame called ‘Age_Bin’. It uses the pd.cut function to bin the ‘Age’ column based on the defined ‘age_bins’ and assigns labels to the bins.
- df[‘Age’] is the source column containing individual ages.
bins=age_bins specifies the bin boundaries you defined earlier.
labels=[’20-25′, ’26-30′, ’31-35′, ’36-40′] assigns labels to the bins. These labels correspond to the age ranges you defined in the ‘age_bins’ list. - print(df[‘Age_Bin’]): This line prints the ‘Age_Bin’ column to the console, displaying the newly created age bins for each individual in your DataFrame. Each row in the ‘Age_Bin’ column will contain the age range label corresponding to the age of the respective individual.
Mean_Age_salary = df.groupby('Age_Bin')['Salary'].mean()
mode_Age_salary = df.groupby('Age_Bin')['Salary'].median()
print(Mean_Age_salary)
print(mode_Age_salary) 