Python | Creating charts and graphs with Python for data visualization

Python Why Python is best for Data Sciences Python Efficient Code Data Types For Data Science Working With CSV Counter built-in class most_common() - collections module OrderedDict power feature - subclass namedtuple is a powerful tool From String to datetime DateTime Components TimeZone in Action TimeDelta - Time Travel with timedelta Parsing time with pendulum Data Manipulation with Pandas Creating DataFrames with Pandas Creating DataFrames with Dictionaries in Pandas DataFrame With CSV File Summary statistics Summarizing numerical data Summarizing dates The .agg() method Summaries on multiple columns Multiple summaries Cumulative sum Cumulative statistics Dropping duplicate names Dropping duplicate pairs Summaries by group Multiple grouped summaries Grouping by multiple variables Many groups, many summaries Pivot tables Group by to pivot table Different statistics in a pivot table Multiple statistics in pivot table Pivot on two variables Filling missing values in pivot tables Summing with pivot tables Explicit indexes Slicing lists Sort the index before slice Slicing the outer index level Slicing the inner index levels badly Slicing the inner index levels correctly Slicing columns Slice twice Slicing by dates Slicing by partial dates Subsetting by row/column number Slicing - .loc[] + slicing is a power combo The axis argument Calculating summary stats across columns Visualizing data Histograms Bar plots Line plots Rotating axis labels Scatter plots Layering plots Plot with Legend Plot with Transparency Avocados Missing values Detecting missing values Detecting any missing values with .isna().any() Detecting any missing values Counting missing values Plotting missing values Removing missing values Replacing missing values List of dictionaries - by row Dictionary of lists - by column DataFrame manipulation Built-in functions Defining a function Function parameters Return values from functions Docstrings Multiple Parameters and Return Values Basic ingredients of a function Global vs. local scope Nested functions Returning functions Using nonlocal Default and flexible arguments Lambda functions Anonymous functions Introduction to error handling The float() function Passing an incorrect argument Passing valid arguments Passing invalid arguments Errors and exceptions Errors and exceptions - 2 What is iterate Iterating with a for loop Iterators vs. iterables Iterating over iterables: next() Iterating at once with * Iterating with dictionaries Iterating with file connections Using enumerate() enumerate() and unpack Using zip() zip() and unpack Print zip with * Using iterators to load large files into memory Loading data in chunks Iterating over data Populate a list with a for loop A list comprehension For loop And List Comprehension List comprehension with range() Nested loops Conditionals in comprehensions Dict comprehensions Generator expressions List comprehensions vs. generators Conditionals in generator expressions Build generator function Using generator function Generators for the large data limit Build a generator function Using pandas read_csv iterator for streaming data Building with builtins Built-in function: range() with Efficient Code Built-in function: enumerate() with Efficient Code Built-in function: map() with Efficient Code The power of NumPy arrays with Efficient Code NumPy array broadcasting NumPy array boolean indexing Why should we time our code? 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Libraries to Make Python Development Easier Parsing Time with Pendulum: Simplify Your Date and Time Operations Timezone Hopping with Pendulum: Seamlessly Manage Time across Different Timezones Humanizing Differences: Making Time Intervals More Readable with Pendulum

Visualizing data

Visualizing data is an important part of data analysis and Python provides several libraries for creating various types of visualizations. Here are some of the popular libraries for data visualization in Python:

Matplotlib: Matplotlib is a widely used library for creating static, interactive, and animated visualizations in Python. It provides a wide range of plot types and customization options.
Seaborn: Seaborn is a library built on top of Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics. It supports complex visualizations such as heatmaps, cluster maps, and categorical plots.
Plotly: Plotly is a powerful library for creating interactive visualizations in Python. It provides a variety of chart types, including scatter plots, line charts, and heatmaps, and allows for easy customization of visualizations.
Bokeh: Bokeh is a library for creating interactive visualizations in Python that are suitable for web applications. It provides a variety of chart types and interactive tools, including hover tools, pan and zoom tools, and selection tools.
Altair: Altair is a declarative visualization library for Python that allows you to create complex visualizations using concise, declarative syntax. It supports a wide range of data types and provides interactive features such as zooming and panning.

Here's an example of using Matplotlib to create a simple line chart:

import matplotlib.pyplot as plt

# Sample data

x = [1, 2, 3, 4, 5]

y = [2, 4, 6, 8, 10]

# Create a figure and axis

fig, ax = plt.subplots()

# Plot the data

ax.plot(x, y)

# Set the title and axis labels

ax.set_title('Simple Line Chart')

ax.set_xlabel('X-axis')

ax.set_ylabel('Y-axis')

# Show the plot

plt.show()

This will create a simple line chart with the given data. You can customize the appearance of the chart using various options such as changing the line style, color, and marker type.