Heatmap Plot in Python

 

import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

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

sns.heatmap(data, cmap='Reds')

plt.show()

#source code -- clcoding.com

Read More

Density plot using Python

 import seaborn as sns

import matplotlib.pyplot as plt

import numpy as np

data = np.random.normal(size=1000)

sns.kdeplot(data, fill=True, color="blue")

plt.title("Density Plot")

plt.xlabel("Value")

plt.ylabel("Density")

plt.show()

#source code --> clcoding.com

Read More

Waterfall Chart using Python

 

import plotly.graph_objects as go

fig = go.Figure(go.Waterfall(

    name = "20", orientation = "v",

    measure = ["relative", "relative", "total", "relative",

               "relative", "total"],

    x = ["Sales", "Consulting", "Net revenue", "Purchases",

         "Other expenses", "Profit before tax"],

    textposition = "outside",

    text = ["+60", "+80", "", "-40", "-20", "Total"],

    y = [60, 80, 0, -40, -20, 0],

    connector = {"line":{"color":"rgb(63, 63, 63)"}},

))

fig.update_layout(

        title = "Profit and loss statement 2024",

        showlegend = True

)

fig.show()

#source code --> clcoding.com

Read More

Pareto Chart using Python

 

import pandas as pd

import matplotlib.pyplot as plt

data = {'Category': ['A', 'B', 'C', 'D', 'E'],

        'Frequency': [50, 30, 15, 5, 2]}

df = pd.DataFrame(data)

df = df.sort_values('Frequency', ascending=False)

df['Cumulative %'] = df['Frequency'].cumsum() / df['Frequency'].sum() * 100

fig, ax1 = plt.subplots()

ax1.bar(df['Category'], df['Frequency'], color='C4')

ax1.set_ylabel('Frequency')

ax2 = ax1.twinx()

ax2.plot(df['Category'], df['Cumulative %'], 'C1D')

ax2.set_ylabel('Cumulative %')

plt.title('Pareto Chart')

plt.show()

#source code --> clcoding.com

Read More

Python For Data Analysis: Unlocking The Power Of Data Analysis With Python Programming And Hands-On Projects (complete python programming handbooks)

 

Are you ready to unlock the power of data analysis and harness Python’s potential to turn raw data into valuable insights? Python Programming for Data Analysis: Unlocking the Power of Data Analysis with Python Programming and Hands-On Projects is your comprehensive guide to mastering data analysis techniques and tools using Python.

Whether you're a beginner eager to dive into the world of data or a professional looking to enhance your skills, this hands-on guide will equip you with everything you need to analyze, visualize, and interpret data like never before.

Why this book is essential for data enthusiasts:

•  Learn how to use Python programming to handle, clean, and analyze large datasets with ease. From basic techniques to advanced methods, this book covers everything you need to know to excel in data analysis.
•  Apply your learning with real-world projects that provide a practical understanding of data analysis in action. You'll work through examples in finance, healthcare, marketing, and more to deepen your skills.
•  Discover the power of Python libraries like Pandas, NumPy, Matplotlib, and Seaborn to transform raw data into meaningful insights. Learn how to manipulate data efficiently, perform statistical analysis, and visualize results beautifully.
•  Understand how to create stunning visualizations that communicate your findings effectively. Learn best practices for visualizing data in a way that tells compelling stories and drives decisions.
•  Gain experience in applying Python to solve real-world data challenges, whether it's analyzing sales trends, predicting customer behavior, or optimizing business processes through data-driven insights.
•  Whether you're just starting out or refining your data skills, this book provides a clear, step-by-step approach to understanding the principles of data analysis and how to apply them in Python.

By the end of Python Programming for Data Analysis, you’ll have the confidence and capability to tackle any data analysis challenge, backed by a solid foundation in Python programming. This is your gateway to becoming a data-driven problem solver in any field.

Unlock the potential of your data—click the "Buy Now" button and start your journey into Python-powered data analysis today.

Hard Copy : Python For Data Analysis: Unlocking The Power Of Data Analysis With Python Programming And Hands-On Projects (complete python programming handbooks)

Read More

Prepare Data for Exploration

 

Mastering Data Preparation: A Review of Coursera's "Data Preparation" Course

In today’s data-driven world, the ability to handle and prepare data is a vital skill. Coursera’s Data Preparation course offers an excellent introduction to this fundamental process, providing learners with hands-on experience and practical knowledge in preparing data for analysis.

Why Data Preparation Matters

Before any analysis can begin, data must be cleaned, formatted, and organized. Messy or incomplete data can lead to inaccurate results and poor decisions. Proper data preparation ensures that your data is reliable and ready for analysis, making it one of the most important steps in the data science workflow.

What the Course Covers

The Data Preparation course on Coursera, part of a broader data science specialization, covers essential techniques to ensure that your data is in prime shape for analysis. Whether you’re working with large datasets or trying to make sense of small, incomplete ones, the course provides the tools needed to:

• Clean and format data: You’ll learn how to deal with missing values, outliers, and inconsistent formatting—common issues when working with raw data.
• Handle different data types: Learn how to work with various data types such as text, numeric, categorical, and date/time data.
• Data transformation: You’ll explore techniques for transforming data, such as normalization, standardization, and encoding categorical variables, making the data suitable for algorithms and further analysis.
• Explore datasets: The course also emphasizes the importance of exploratory data analysis (EDA), where you’ll learn to visualize and summarize data to uncover patterns, correlations, and trends.

Hands-on Learning Experience

What sets this course apart is the practical, hands-on learning experience. Using real-world datasets, you’ll get to apply the techniques you learn, ensuring you leave the course not only with theoretical knowledge but also the skills to execute data preparation in practice.

The exercises include working with Python libraries like pandas, numpy, and matplotlib—key tools for data manipulation and visualization.

Who Should Take This Course?

This course is designed for beginners in data science and those with some basic programming skills who want to strengthen their data preparation abilities. If you're familiar with Python and want to develop your data handling skills further, this course is a perfect fit.

Whether you’re a budding data scientist, a business analyst, or a professional looking to enhance your data analysis skills, this course will equip you with the essential knowledge needed to prepare data for any data analysis or machine learning project.

Final Thoughts

Data preparation is often an overlooked but crucial step in the data science process. Coursera’s Data Preparation course offers a structured, in-depth introduction to this essential skill, ensuring that your data is clean, organized, and ready for analysis. With a mix of theory and hands-on practice, this course is an excellent choice for anyone looking to improve their data-handling skills.

Join Free: Prepare Data for Exploration

Read More

5 Hidden Gems in Pandas You Should Start Using Today

1. query() Method for Filtering Data

What it is: The query() method allows you to filter data in a DataFrame using a more readable and concise string-based expression.

Why it's useful: It avoids the verbosity of standard indexing and makes the code more readable, especially for complex conditions.

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3, 4], 

                   'B': [10, 20, 30, 40]})

result = df.query('A > 2 & B < 40')

print(result)

#clcoding.com

   A   B

2  3  30

2. eval() Method for Efficient Calculations

What it is: The eval() method evaluates a string expression within the context of a DataFrame, allowing for efficient computation.

Why it's useful: It can speed up operations involving arithmetic or logical operations on DataFrame columns, especially with large datasets.

df['C'] = df.eval('A + B')

print(df)

#clcoding.com

   A   B   C

0  1  10  11

1  2  20  22

2  3  30  33

3  4  40  44

3. at and iat for Fast Access

What it is: at and iat are optimized methods for accessing scalar values in a DataFrame.

Why it's useful: These methods are much faster than using .loc[] or .iloc[] for individual cell access, making them ideal for performance-critical code.

value = df.at[2, 'B']  

print(value)

#clcoding.com

30

4. pipe() Method for Method Chaining

What it is: The pipe() method allows you to apply a function or sequence of functions to a DataFrame within a method chain.

Why it's useful: It improves code readability by keeping the DataFrame operations within a single fluent chain.

def add_constant(df, value):

    return df + value

df = df.pipe(add_constant, 10)

print(df)

#clcoding.com

    A   B   C

0  11  20  21

1  12  30  32

2  13  40  43

3  14  50  54

5. explode() for Expanding Lists in Cells

What it is: The explode() method expands a list-like column into separate rows.

Why it's useful: This is particularly useful when working with data that has embedded lists within cells and you need to analyze or visualize each item individually.

df = pd.DataFrame({'A': [1, 2], 

                   'B': [[10, 20, 30], [40, 50]]})

df_exploded = df.explode('B')

print(df_exploded)

#clcoding.com

   A   B

0  1  10

0  1  20

0  1  30

1  2  40

1  2  50

Read More

Demonstrating different types of colormaps

 

import matplotlib.pyplot as plt

import numpy as np

# Generate sample data

data = np.random.rand(10, 10)

# List of colormaps to demonstrate

colormaps = [

    'viridis',      # Sequential

    'plasma',       # Sequential

    'inferno',      # Sequential

    'magma',        # Sequential

    'cividis',      # Sequential

    'PiYG',         # Diverging

    'PRGn',         # Diverging

    'BrBG',         # Diverging

    'PuOr',         # Diverging

    'Set1',         # Qualitative

    'Set2',         # Qualitative

    'tab20',        # Qualitative

    'hsv',          # Cyclic

    'twilight',     # Cyclic

    'twilight_shifted' # Cyclic

]

# Create subplots to display colormaps

fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

# Flatten axes array for easy iteration

axes = axes.flatten()

# Loop through colormaps and plot data

for ax, cmap in zip(axes, colormaps):

    im = ax.imshow(data, cmap=cmap)

    ax.set_title(cmap)

    plt.colorbar(im, ax=ax)

# Adjust layout to prevent overlap

plt.tight_layout()

# Show the plot

plt.show()

Explanation:

1. Generate Sample Data:

   data = np.random.rand(10, 10)

   This creates a 10x10 array of random numbers.

2. List of Colormaps:

   • A list of colormap names is defined. Each name corresponds to a different colormap in Matplotlib.

3. Create Subplots:

   fig, axes = plt.subplots(nrows=5, ncols=3, figsize=(15, 20))

   This creates a 5x3 grid of subplots to display multiple colormaps.

4. Loop Through Colormaps:

   • The loop iterates through each colormap, applying it to the sample data and plotting it in a subplot.

5. Add Colorbar:

   plt.colorbar(im, ax=ax)
   

   This adds a colorbar to each subplot to show the mapping of data values to colors.

6. Adjust Layout and Show Plot:

   plt.tight_layout() plt.show()

   These commands adjust the layout to prevent overlap and display the plot.

Choosing Colormaps

• Sequential: Good for data with a clear order or progression.
• Diverging: Best for data with a critical midpoint.
• Qualitative: Suitable for categorical data.
• Cyclic: Ideal for data that wraps around, such as angles.

By selecting appropriate colormaps, you can enhance the visual representation of your data, making it easier to understand and interpret.

Read More

Matrix in Python

 

Rank of Matrix

import numpy as np

x = np.matrix("4,5,16,7;2,-3,2,3;,3,4,5,6;4,7,8,9")

print(x)

[[ 4  5 16  7]

 [ 2 -3  2  3]

 [ 3  4  5  6]

 [ 4  7  8  9]]

#numpy.linalg.matrix_rank() - return a rank of a matrix

# Syntax: numpy.linalg.matrix_rank(matrix)

rank_matrix = np.linalg.matrix_rank(x)

print(rank_matrix)

4

Determinant of Matrix

import numpy as np

x = np.matrix("4,5,16,7;2,-3,2,3;,3,4,5,6;4,7,8,9")

print(x)

[[ 4  5 16  7]

 [ 2 -3  2  3]

 [ 3  4  5  6]

 [ 4  7  8  9]]

det_matrix = np.linalg.det(x)

print(det_matrix)

128.00000000000009

Inverse of a Matrix

inverse formula = A-1 = (1/determinant of A) * adj A

numpy.linalg.inv() - return the multiplicative inverse of a matrix Syntax: numpy.linalg.inv(matrix)

A = np.matrix("3,1,2;3,2,5;6,7,8")

print(A)

[[3 1 2]

 [3 2 5]

 [6 7 8]]

Inv_matrix = np.linalg.inv(A)

print(Inv_matrix)

[[ 0.57575758 -0.18181818 -0.03030303]

 [-0.18181818 -0.36363636  0.27272727]

 [-0.27272727  0.45454545 -0.09090909]]

Read More

Data Science Basics to Advance Course Syllabus

 

Week 1: Introduction to Data Science and Python Programming

• Overview of Data Science
  • Understanding what data science is and its importance.
• Python Basics
  • Introduction to Python, installation, setting up the development environment.
• Basic Python Syntax
  • Variables, data types, operators, expressions.
• Control Flow
  • Conditional statements, loops.
• Functions and Modules
  • Defining, calling, and importing functions and modules.
• Hands-on Exercises
  • Basic Python programs and assignments.

---

Week 2: Data Structures and File Handling in Python

• Data Structures
  • Lists, tuples, dictionaries, sets.
• Manipulating Data Structures
  • Indexing, slicing, operations.
• File Handling
  • Reading from and writing to files, file operations.
• Error Handling
  • Using try-except blocks.
• Practice Problems
  • Mini-projects involving data structures and file handling.

---

Week 3: Data Wrangling with Pandas

• Introduction to Pandas
  • Series and DataFrame objects.
• Data Manipulation
  • Indexing, selecting data, filtering.
• Data Cleaning
  • Handling missing values, data transformations.
• Data Integration
  • Merging, joining, concatenating DataFrames.
• Hands-on Exercises
  • Data wrangling with real datasets.

---

Week 4: Data Visualization

• Introduction to Matplotlib
  • Basic plotting, customization.
• Advanced Visualization with Seaborn
  • Statistical plots, customization.
• Interactive Visualization with Plotly
  • Creating interactive plots.
• Data Visualization Projects
  • Creating visualizations for real datasets.

---

Week 5: Exploratory Data Analysis (EDA) - Part 1

• Importance of EDA
  • Understanding data and deriving insights.
• Descriptive Statistics
  • Summary statistics, data distributions.
• Visualization for EDA
  • Histograms, box plots.
• Correlation Analysis
  • Finding relationships between variables.
• Hands-on Projects
  • Conducting EDA on real-world datasets.

---

Week 6: Exploratory Data Analysis (EDA) - Part 2

• Visualization for EDA
  • Scatter plots, pair plots.
• Handling Missing Values and Outliers
  • Techniques for dealing with incomplete data.
• Feature Engineering
  • Creating new features, transforming existing features.
• Hands-on Projects
  • Advanced EDA techniques on real datasets.

---

Week 7: Data Collection and Preprocessing Techniques

• Data Collection Methods
  • Surveys, web scraping, APIs.
• Data Cleaning
  • Handling missing data, outliers, and inconsistencies.
• Data Transformation
  • Normalization, standardization, encoding categorical variables.
• Hands-on Projects
  • Collecting and preprocessing real-world data.

---

Week 8: Database Management and SQL

• Introduction to Databases
  • Relational databases, database design.
• SQL Basics
  • SELECT, INSERT, UPDATE, DELETE statements.
• Advanced SQL
  • Joins, subqueries, window functions.
• Connecting Python to Databases
  • Using libraries like SQLAlchemy.
• Hands-on Exercises
  • SQL queries and database management projects.

---

Week 9: Introduction to Time Series Analysis

• Time Series Concepts
  • Understanding time series data, components of time series.
• Time Series Visualization
  • Plotting time series data, identifying patterns.
• Basic Time Series Analysis
  • Moving averages, smoothing techniques.
• Hands-on Exercises
  • Working with time series data.

---

Week 10: Advanced Time Series Analysis

• Decomposition
  • Breaking down time series into trend, seasonality, and residuals.
• Forecasting Methods
  • Introduction to ARIMA and other forecasting models.
• Model Evaluation
  • Assessing forecast accuracy.
• Practical Application
  • Time series forecasting projects.

---

Week 11: Advanced Data Wrangling with Pandas

• Advanced Data Manipulation
  • Pivot tables, groupby operations.
• Time Series Manipulation
  • Working with date and time data in Pandas.
• Merging and Joining DataFrames
  • Advanced techniques for combining datasets.
• Practical Exercises
  • Complex data wrangling tasks.

---

Week 12: Advanced Data Visualization Techniques

• Interactive Dashboards
  • Creating dashboards with Dash and Tableau.
• Geospatial Data Visualization
  • Mapping data with libraries like Folium.
• Storytelling with Data
  • Effective communication of data insights.
• Practical Projects
  • Building interactive and compelling data visualizations.

Read More

Box and Whisker plot using Python Libraries

Step 1: Install Necessary Libraries

First, make sure you have matplotlib and seaborn installed. You can install them using pip:

pip install matplotlib seaborn

#clcoding.com

Step 2: Import Libraries

Next, import the necessary libraries in your Python script or notebook.

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

Step 3: Create Sample Data

Create some sample data to plot. This can be any dataset you have, but for demonstration purposes, we will create a simple dataset using NumPy.

# Generate sample data

np.random.seed(10)

data = [np.random.normal(0, std, 100) for std in range(1, 5)]

Step 4: Create the Box and Whisker Plot

Using matplotlib and seaborn, you can create a basic Box and Whisker plot.

# Create a boxplot

plt.figure(figsize=(10, 6))

plt.boxplot(data, patch_artist=True)

# Add title and labels

plt.title('Box and Whisker Plot')

plt.xlabel('Category')

plt.ylabel('Values')

# Show plot

plt.show()

Step 5: Enhance the Plot with Seaborn

For more advanced styling, you can use seaborn, which provides more aesthetic options.

# Set the style of the visualization

sns.set(style="whitegrid")

# Create a boxplot with seaborn

plt.figure(figsize=(10, 6))

sns.boxplot(data=data)

# Add title and labels

plt.title('Box and Whisker Plot')

plt.xlabel('Category')

plt.ylabel('Values')

# Show plot

plt.show()

Read More

Donut Charts using Python

 

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

# Plot

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

plt.axis('equal')

plt.title('Basic Donut Chart')

plt.show()

#clcoding.com

Explanation:

In this code snippet, we're using the matplotlib.pyplot module, which is a powerful library in Python for creating static, animated, and interactive visualizations. We're importing it using the alias plt, which is a common convention for brevity.

Here's a breakdown of the code:

Importing matplotlib.pyplot: import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt, allowing us to reference it with the shorter name plt throughout the code.

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

These lines define the data we want to visualize. labels contains the labels for each segment of the pie chart, and sizes contains the corresponding sizes or values for each segment.

Plotting the pie chart:

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)

Here, we use the plt.pie() function to create a pie chart. We pass sizes as the data to plot, labels to label each segment, autopct='%1.1f%%' to display the percentage for each segment, and startangle=140 to rotate the pie chart to start from the angle 140 degrees.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

These lines draw a white circle at the center of the pie chart, creating a donut-like appearance. The plt.Circle() function creates a circle with the specified parameters: center (0,0) and radius 0.70.

Setting equal aspect ratio:

plt.axis('equal')

This line ensures that the plot is displayed with equal aspect ratio, so the pie chart appears as a circle rather than an ellipse.

Adding a title and displaying the plot:

plt.title('Basic Donut Chart')

plt.show()

Here, we set the title of the plot to 'Basic Donut Chart' using plt.title(), and then plt.show() displays the plot on the screen.

This code generates a basic donut chart with four segments labeled A, B, C, and D, where the size of each segment is determined by the values in the sizes list.

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice

# Plot

plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=140)

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

plt.axis('equal')

plt.title('Donut Chart with Exploded Slices')

plt.show()

#clcoding.com

Explanation: 

This code snippet is similar to the previous one, but it adds exploding effect to one of the slices in the pie chart. Let's break down the code:

Importing matplotlib.pyplot:

import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt, allowing us to reference it with the shorter name plt throughout the code.

Data to plot:

labels = ['A', 'B', 'C', 'D']

sizes = [15, 30, 45, 10]

explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice

Here, labels contains the labels for each segment of the pie chart, sizes contains the corresponding sizes or values for each segment, and explode contains the magnitude of the explosion for each slice. In this case, we're exploding the second slice ('B') by 0.1.

Plotting the pie chart:

plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', startangle=140)

This line creates a pie chart using plt.pie(). The explode parameter is used to specify the amount by which to explode each slice. Here, we're exploding only the second slice ('B') by 0.1. Other parameters are similar to the previous example.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.70,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

This part is the same as before. It draws a white circle at the center of the pie chart, creating a donut-like appearance.

Setting equal aspect ratio:

plt.axis('equal')

This line ensures that the plot is displayed with an equal aspect ratio, so the pie chart appears as a circle.

Adding a title and displaying the plot:

plt.title('Donut Chart with Exploded Slices')

plt.show()

Here, we set the title of the plot to 'Donut Chart with Exploded Slices' and then display the plot.

This code generates a donut chart with four segments labeled A, B, C, and D, where the second slice ('B') is exploded outwards. The size of each segment is determined by the values in the sizes list.

Code:

import matplotlib.pyplot as plt

# Data to plot

labels = ['A', 'B', 'C', 'D']

sizes1 = [25, 30, 35, 10]

sizes2 = [20, 40, 20, 20]

# Plot

fig, ax = plt.subplots()

ax.pie(sizes1, radius=1.2, labels=labels, autopct='%1.1f%%', startangle=140)

ax.pie(sizes2, radius=1, startangle=140, colors=['red', 'green', 'blue', 'yellow'])

# Draw a circle at the center of pie to make it look like a donut

centre_circle = plt.Circle((0,0),0.8,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

# Equal aspect ratio ensures that pie is drawn as a circle

ax.set(aspect="equal")

plt.title('Donut Chart with Multiple Rings')

plt.show()

#clcoding.com

Explanation: 

This code snippet creates a donut chart with multiple rings, demonstrating the capability to display more than one dataset in the same chart. Let's dissect the code:

Importing matplotlib.pyplot:

import matplotlib.pyplot as plt

This line imports the matplotlib.pyplot module and assigns it the alias plt.

Data to plot:

labels = ['A', 'B', 'C', 'D']

sizes1 = [25, 30, 35, 10]

sizes2 = [20, 40, 20, 20]

Two sets of data are defined here: sizes1 and sizes2. Each set represents the values for different rings of the donut chart.

Plotting the donut chart:

fig, ax = plt.subplots()

ax.pie(sizes1, radius=1.2, labels=labels, autopct='%1.1f%%', startangle=140)

ax.pie(sizes2, radius=1, startangle=140, colors=['red', 'green', 'blue', 'yellow'])

This code creates a subplot (fig, ax = plt.subplots()) and then plots two pie charts on the same subplot using ax.pie().

The first ax.pie() call plots the outer ring (sizes1) with a larger radius (radius=1.2), while the second call plots the inner ring (sizes2) with a smaller radius (radius=1).

labels, autopct, and startangle parameters are used to configure the appearance of the pie charts.

Different colors are specified for the inner ring using the colors parameter.

Drawing a circle to create a donut effect:

centre_circle = plt.Circle((0,0),0.8,fc='white')

fig = plt.gcf()

fig.gca().add_artist(centre_circle)

This part is similar to previous examples. It draws a white circle at the center of the pie chart to create the donut-like appearance.

Setting equal aspect ratio:

ax.set(aspect="equal")

This line sets the aspect ratio of the subplot to 'equal', ensuring that the pie charts are displayed as circles.

Adding a title and displaying the plot:

plt.title('Donut Chart with Multiple Rings')

plt.show()

Finally, the title of the plot is set to 'Donut Chart with Multiple Rings', and the plot is displayed.

This code generates a donut chart with two rings, each representing different datasets (sizes1 and sizes2). Each ring has its own set of labels and colors, and they are displayed concentrically to create the donut chart effect.

Read More

Data Science: The Hard Parts: Techniques for Excelling at Data Science

 

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you will:

Understand how data science creates value

Deliver compelling narratives to sell your data science project

Build a business case using unit economics principles

Create new features for a ML model using storytelling

Learn how to decompose KPIs

Perform growth decompositions to find root causes for changes in a metric

Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

PDF: Data Science: The Hard Parts: Techniques for Excelling at Data Science

Hard Copy: Data Science: The Hard Parts: Techniques for Excelling at Data Science

Read More

Streamgraphs using Python

 

Code:

import matplotlib.pyplot as plt

import numpy as np

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

plt.stackplot(x, y1, y2, baseline='wiggle')

plt.title('Streamgraph')

plt.show()

Explanation: 

This code snippet creates a streamgraph using Matplotlib, a popular plotting library in Python. Let's break down the code:

Importing Libraries:

import matplotlib.pyplot as plt

import numpy as np

matplotlib.pyplot as plt: This imports the pyplot module of Matplotlib and assigns it the alias plt, which is a common convention.

numpy as np: This imports the NumPy library and assigns it the alias np. NumPy is commonly used for numerical computing in Python.

Generating Data:

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

np.linspace(0, 10, 100): This creates an array x of 100 evenly spaced numbers between 0 and 10.

np.sin(x): This calculates the sine of each value in x, resulting in an array y1.

np.cos(x): This calculates the cosine of each value in x, resulting in an array y2.

Creating the Streamgraph:

plt.stackplot(x, y1, y2, baseline='wiggle')

plt.stackplot(x, y1, y2, baseline='wiggle'): This function creates a stack plot (streamgraph) with the x-values from x and the y-values from y1 and y2. The baseline='wiggle' argument specifies that the baseline for the stacked areas should be wiggled, which can help to visually separate the layers in the streamgraph.

Setting Title:

plt.title('Streamgraph')

plt.title('Streamgraph'): This sets the title of the plot to "Streamgraph".

Displaying the Plot:

plt.show()

plt.show(): This command displays the plot on the screen. Without this command, the plot would not be shown.

Overall, the code generates a streamgraph showing the variations of sine and cosine functions over the range of 0 to 10. The streamgraph visually represents how these functions change over the given range, with the wiggled baseline helping to distinguish between the layers.

Read More

Statistical Inference and Probability

 

An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including:

·       Probability and Sampling distributions

·       Inference and regression

·       Power, effect size and inverse probability

Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

Hard Copy: Statistical Inference and Probability

PDF: Statistical Inference and Probability (The SAGE Quantitative Research Kit)

Read More

Top 4 free Mathematics course for Data Science !

• Introduction to Statistics

In the age of big data, understanding statistics and data science concepts is becoming increasingly crucial across various industries. From finance to healthcare, businesses are leveraging data-driven insights to make informed decisions and gain a competitive edge. In this blog post, we'll embark on a journey through fundamental statistical concepts, explore the powerful technique of K-Means Clustering in Python, delve into the realm of probability, and demystify practical time series analysis.

• Foundations of Data Science: K-Means Clustering in Python

In our tutorial, we'll walk through the implementation of K-Means clustering using Python, focusing on the following steps:

Understanding the intuition behind K-Means clustering.

Preprocessing the data and feature scaling.

Choosing the optimal number of clusters using techniques like the Elbow Method or Silhouette Score.

Implementing K-Means clustering using scikit-learn.

Visualizing the clustering results to gain insights into the underlying structure of the data.

• An Intuitive Introduction to Probability

Probability theory is the mathematical framework for analyzing random phenomena and quantifying uncertainty. Whether you're predicting the outcome of a coin toss or estimating the likelihood of a stock market event, probability theory provides the tools to make informed decisions in the face of uncertainty.

In this section, we'll provide an intuitive introduction to probability, covering essential concepts such as:

Basic probability terminology: events, sample space, and outcomes.

Probability axioms and rules: addition rule, multiplication rule, and conditional probability.

Probability distributions: discrete and continuous distributions.

Common probability distributions: Bernoulli, binomial, normal, and Poisson distributions.

Applications of probability theory in real-world scenarios.

• Practical Time Series Analysis

Time series analysis is a crucial technique for analyzing data points collected over time and extracting meaningful insights to make forecasts and predictions. From stock prices to weather patterns, time series data is ubiquitous in various domains.

In our practical guide to time series analysis, we'll cover the following topics:

Introduction to time series data: components, trends, seasonality, and noise.

Preprocessing time series data: handling missing values, detrending, and deseasonalizing.

Exploratory data analysis (EDA) techniques for time series data visualization.

Time series forecasting methods: moving averages, exponential smoothing, and ARIMA models.

Implementing time series analysis in Python using libraries like pandas, statsmodels, and matplotlib.

Read More

Practical Time Series Analysis

 

There are 6 modules in this course

Welcome to Practical Time Series Analysis!

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training.  This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. 

In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.  We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.

Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.

You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!

Join Free: Practical Time Series Analysis

Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

Read More

Meta Data Analyst Professional Certificate

 

Why Take a Meta Data Analyst Professional Certificate? 

Collect, clean, sort, evaluate, and visualize data

Apply the Obtain, Sort, Explore, Model, Interpret (OSEMN) framework to guide the data analysis process

Learn to use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions

Develop an understanding of the foundational principles underpinning effective data management and usability of data assets within organizational context

Aquire the confidence to add the following skills to add to your resume: 

Data analysis

Python Programming

Statistics

Data management

Data-driven decision making

Data visualization

Linear Regression

Hypothesis testing

Data Management

Tableau

Join Free: Meta Data Analyst Professional Certificate

What you'll learn

Collect, clean, sort, evaluate, and visualize data

Apply the OSEMN, framework to guide the data analysis process, ensuring a comprehensive and structured approach to deriving actionable insights

Use statistical analysis, including hypothesis testing, regression analysis, and more, to make data-driven decisions

Develop an understanding of the foundational principles of effective data management and usability of data assets within organizational context

Professional Certificate - 5 course series

Prepare for a career in the high-growth field of data analytics. In this program, you’ll build in-demand technical skills like Python, Statistics, and SQL in spreadsheets to get job-ready in 5 months or less, no prior experience needed.

Data analysis involves collecting, processing, and analyzing data to extract insights that can inform decision-making and strategy across an organization.

In this program, you’ll learn basic data analysis principles, how data informs decisions, and how to apply the OSEMN framework to approach common analytics questions. You’ll also learn how to use essential tools like SQL, Python, and Tableau to collect, connect, visualize, and analyze relevant data.

You’ll learn how to apply common statistical methods to writing hypotheses through project scenarios to gain practical experience with designing experiments and analyzing results. 

When you complete this full program, you’ll have a portfolio of hands-on projects and a Professional Certificate from Meta to showcase your expertise. 

Applied Learning Project

Throughout the program, you’ll get to practice your new data analysis skills through hands-on projects including: 

Identifying data sources

Using spreadsheets to clean and filter data

Using Python to sort and explore data

Using Tableau to visualize results

Using statistical analyses

By the end, you’ll have a professional portfolio that you can show to prospective employers or utilize for your own business.

Read More

do you know difference between Data Analyst , Data Scientist and Data Engineer?

Data Analyst

A data analyst sits between business intelligence and data science. They provide vital information to business stakeholders.

Data Management in SQL (PostgreSQL)

Data Analysis in SQL (PostgreSQL)

Exploratory Analysis Theory

Statistical Experimentation Theory

Free Certification : Data Analyst Certification 

Data Scientist Associate 

A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data.

R / Python Programming

Data Manipulation in R/Python

1.1 Calculate metrics to effectively report characteristics of data and relationships between

features

● Calculate measures of center (e.g. mean, median, mode) for variables using R or Python.

● Calculate measures of spread (e.g. range, standard deviation, variance) for variables

using R or Python.

● Calculate skewness for variables using R or Python.

● Calculate missingness for variables and explain its influence on reporting characteristics

of data and relationships in R or Python.

● Calculate the correlation between variables using R or Python.

1.2 Create data visualizations in coding language to demonstrate the characteristics of data

● Create and customize bar charts using R or Python.

● Create and customize box plots using R or Python.

● Create and customize line graphs using R or Python.

● Create and customize histograms graph using R or Python.

1.3 Create data visualizations in coding language to represent the relationships between

features

● Create and customize scatterplots using R or Python.

● Create and customize heatmaps using R or Python.

● Create and customize pivot tables using R or Python.

1.4 Identify and reduce the impact of characteristics of data

● Identify when imputation methods should be used and implement them to reduce the

impact of missing data on analysis or modeling using R or Python.

● Describe when a transformation to a variable is required and implement corresponding

transformations using R or Python.

● Describe the differences between types of missingness and identify relevant approaches

to handling types of missingness.

● Identify and handle outliers using R or Python.

Statistical Fundamentals in R/Python

2.1 Perform standard data import, joining and aggregation tasks

● Import data from flat files into R or Python.

● Import data from databases into R or Python

● Aggregate numeric, categorical variables and dates by groups using R or Python.

● Combine multiple tables by rows or columns using R or Python.

● Filter data based on different criteria using R or Python.

2.2 Perform standard cleaning tasks to prepare data for analysis

● Match strings in a dataset with specific patterns using R or Python.

● Convert values between data types in R or Python.

● Clean categorical and text data by manipulating strings in R or Python.

● Clean date and time data in R or Python.

2.3 Assess data quality and perform validation tasks

● Identify and replace missing values using R or Python.

● Perform different types of data validation tasks (e.g. consistency, constraints, range

validation, uniqueness) using R or Python.

● Identify and validate data types in a data set using R or Python.

2.4 Collect data from non-standard formats by modifying existing code

● Adapt provided code to import data from an API using R or Python.

● Identify the structure of HTML and JSON data and parse them into a usable format for

data processing and analysis using R or Python

Importing & Cleaning in R/Python

3.1 Prepare data for modeling by implementing relevant transformations.

● Create new features from existing data (e.g. categories from continuous data, combining

variables with external data) using R or Python.

● Explain the importance of splitting data and split data for training, testing, and validation

using R or Python.

● Explain the importance of scaling data and implement scaling methods using R or Python.

● Transform categorical data for modeling using R or Python.

3.2 Implement standard modeling approaches for supervised learning problems.

● Identify regression problems and implement models using R or Python.

● Identify classification problems and implement models using R or Python.

3.3 Implement approaches for unsupervised learning problems.

● Identify clustering problems and implement approaches for them using R or Python.

● Explain dimensionality reduction techniques and implement the techniques using R or

Python.

3.4 Use suitable methods to assess the performance of a model.

● Select metrics to evaluate regression models and calculate the metrics using R or Python.

● Select metrics to evaluate classification models and calculate the metrics using R or

Python.

● Select metrics and visualizations to evaluate clustering models and implement them

using R or Python.

Machine Learning Fundamentals in R/Python

4.2 Demonstrates best practices in production code including version control, testing, and

package development.

● Describe the basic flow and structures of package development in R or Python.

● Explain how to document code in packages, or modules in R or Python.

● Explain the importance of the testing and write testing statements in R or Python.

● Explain the importance of version control and describe key concepts of versioning

Free Certification : Data Science  

Data Engineer

A data engineer collects, stores, and pre-processes data for easy access and use within an organization. Associate certification is available.

Data Management in SQL (PostgreSQL)

Exploratory Analysis Theory

Free Certification : Data Science  

Read More

4 Free books to master Data Analytics

 Storytelling with Data: A Data Visualization Guide for Business Professionals  

Don't simply show your data - tell a story with it!

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory but made accessible through numerous real-world examples - ready for immediate application to your next graph or presentation.

Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:

Understand the importance of context and audience

Determine the appropriate type of graph for your situation

Recognize and eliminate the clutter clouding your information

Direct your audience's attention to the most important parts of your data

Think like a designer and utilize concepts of design in data visualization

Leverage the power of storytelling to help your message resonate with your audience

Together, the lessons in this book will help you turn your data into high-impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data - Storytelling with Data will give you the skills and power to tell it!

Fundamentals of Data Analytics: Learn Essential Skills, Embrace the Future, and Catapult Your Career in the Data-Driven World—A Comprehensive Guide to Data Literacy for Beginners

Gain a competitive edge in today’s data-driven world and build a rich career as a data professional that drives business success and innovation…

Today, data is everywhere… and it has become the essential building block of this modern society.

And that’s why now is the perfect time to pursue a career in data.

But what does it take to become a competent data professional?

This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.

Here is just a fraction of what you will discover:

A beginner-friendly 5-step framework to kickstart your journey into analyzing and processing data

How to get started with the fundamental concepts, theories, and models for accurately analyzing data

Everything you ever needed to know about data mining and machine learning principles

Why business run on a data-driven culture, and how you can leverage it using real-time business intelligence analytics

Strategies and techniques to build a problem-solving mindset that can overcome any complex and unique dataset

How to create compelling and dynamic visualizations that help generate insights and make data-driven decisions

The 4 pillars of a new digital world that will transform the landscape of analyzing data

And much more.

Believe it or not, you can be terrible in math or statistics and still pursue a career in data.

And this book is here to guide you throughout this journey, so that crunching data becomes second nature to you.

Ready to master the fundamentals and build a successful career in data analytics? Click the “Add to Cart” button right now.

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: Python for Data Science, Book 2

Make better decisions with this easy deconstructed guide to data analytics.

Want to add data analytics to your skill stack? Having trouble finding where to start?

Cell-by-cell, bit-by-bit, this audiobook teaches you the vocabulary, tools, and basic algorithms to think like a data scientist.

Like putting together a complex Lego set, each section connects and adds individual blocks of knowledge to build your data literacy. This linear structure to unpacking data analytics takes you from zero to confidently analyzing and discussing data problems.

Who is this audiobook for? This audiobook is ideal for anyone interested in making sense of data analytics without the assumption that you understand data science terminology or advanced math. If you've tried to learn data analytics before and failed, this audiobook is for you.

Practical approach. This audiobook takes a hands-on approach to learning. This includes practical examples, visual examples, as well as two bonus coding exercises in Python, including free video content to walk you through both exercises. By the end of the audiobook, you will have the practical knowledge to tackle real data problems in your organization or daily life.

What you will learn:

How to recognize the common data types every data scientist needs to master

Where to store your data, including big data

New trends in data analytics, including what is alternative data and why not many people know about it

How to explain the distinction between data mining, machine learning, and analytics to your colleagues

When and how to use regression analysis, classification, clustering, association analysis, and natural language processing

How to make better business decisions using data visualization and business intelligence

Data Analytics, Data Visualization & Communicating Data: 3 books in 1: Learn the Processes of Data Analytics and Data Science, Create Engaging Data Visualizations, and Present Data Effectively

Harvard Business Review called data science “the sexiest job of the 21st century,” so it's no surprise that data science jobs have grown up to 20 times in the last three years. With demand outpacing supply, companies are willing to pay top dollar for talented data professionals. However, to stand out in one of these positions, having foundational knowledge of interpreting data is essential. You can be a spreadsheet guru, but without the ability to turn raw data into valuable insights, the data will render useless. That leads us to data analytics and visualization, the ability to examine data sets, draw meaningful conclusions and trends, and present those findings to the decision-maker effectively.

Mastering this skill will undoubtedly lead to better and faster business decisions. The three audiobooks in this series will cover the foundational knowledge of data analytics, data visualization, and presenting data, so you can master this essential skill in no time. This series includes:

Everything data analytics: a beginner's guide to data literacy and understanding the processes that turns data into insights.

Beginner's guide to data visualization: how to understand, design, and optimize over 40 different charts.

How to win with your data visualizations: the five part guide for junior analysts to create effective data visualizations and engaging data stories.

These three audiobooks cover an extensive amount of information, such as:

Overview of the data collection, management, and storage processes.

Fundamentals of cleaning data.

Essential machine learning algorithms required for analysis such as regression, clustering, classification, and more....

The fundamentals of data visualization.

An in-depth view of over 40 plus charts and when to use them.

A comprehensive data visualization design guide.

Walkthrough on how to present data effectively.

And so much more!

Read More