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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NumPy array boolean indexing

NumPy array boolean indexing allows you to select elements from an array based on a condition expressed as a boolean expression. Boolean indexing can be a powerful tool for filtering, masking, and modifying arrays.

Here are some tips for using NumPy array boolean indexing:

  1. Understand boolean expressions: Boolean expressions are expressions that evaluate to either True or False. In NumPy, you can use boolean expressions to create masks, which are arrays of the same shape as the input array, where each element is either True or False depending on whether the corresponding element in the input array satisfies the condition.

  2. Use boolean expressions to create masks: You can create a mask by applying a boolean expression to an array. For example, mask = arr > 0 creates a mask where each element is True if the corresponding element in arr is greater than 0, and False otherwise.

  3. Use masks for filtering: You can use a mask to select elements from an array that satisfy the condition. For example, arr[mask] selects all elements of arr that are greater than 0.

  4. Use masks for masking: You can use a mask to mask or remove elements from an array that do not satisfy the condition. For example, arr[mask] = 0 sets all elements of arr that are greater than 0 to 0.

  5. Combine masks with logical operators: You can combine masks using logical operators, such as & (and), | (or), and ~ (not), to create more complex conditions. For example, mask = (arr > 0) & (arr < 10) creates a mask where each element is True if the corresponding element in arr is between 0 and 10.

  6. Use numpy.where for conditional operations: You can use the numpy.where function to perform conditional operations based on a mask. For example, np.where(mask, arr1, arr2) returns an array where each element is taken from arr1 if the corresponding element in mask is True, and from arr2 otherwise.

By using boolean indexing, you can efficiently filter, mask, and modify arrays based on conditions expressed as boolean expressions.

Efficient Python code Pythonic building with builtins. Efficient code using Pythons built-in range() function. Pythonic efficiency with the built-in enumerate() function. Pythonic efficiency with the built-in map() function. Pythonic efficiency with the power of NumPy arrays. Broadcasting NumPy arrays for pythonic data manipulation. Boolean indexing for pythonic data manipulation with NumPy arrays. In Python How to use %timeit in Python for performance testing Interpreting the %timeit output in Python Python loop optimization: Specifying the number of loops Using %timeit in Pythons line magic mode for code optimization Measuring Python code performance with %timeit in cell magic mode Saving Python code output to a file How to compare Python code execution times Optimizing Python code with runtime code profiling Measuring Python code line by line with %lprun Optimizing Python code with memory usage code profiling Python code optimization with memory profiler %mprun Tips and tricks for efficient combining Efficient ways to combine objects in Python Using zip for efficient object combination in Python Python techniques for efficient counting with loops Efficient counting with collections.Counter() in Python Efficient iteration with the itertools module in Python Python techniques for efficient combination with loops Efficient combination with itertools.combinations() in Python Python techniques for efficient object comparison with loops Python techniques for efficient set differences using the difference() method Python techniques for efficient symmetric differences using the symmetric_difference() method Python techniques for efficient set unions using the union() method Python techniques for finding unique elements using sets Improving Python code performance with loop elimination techniques How NumPy can help eliminate loops in Python code The benefits of moving calculations above loops in Python Simplifying data transformations with holistic conversions in Python Exploring DataFrame Iteration in pandas Determining the success rate: Calculating win percentage Enhancing DataFrame with win percentage calculation Exploring data with iterative access using .iloc Traversing DataFrame rows with .iterrows() iterator Traversing DataFrame rows with .itertuples() iterator Traversing DataFrame rows using .itertuples() iterator Applying a function to pandas data with .apply()