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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

len()

Python is a popular programming language that offers a wide range of built-in functions to perform various tasks. One of the most commonly used built-in functions is "len()", which returns the length of an object.

The "len()" function can be used with many data types, such as strings, lists, tuples, and dictionaries. The function counts the number of elements in the object and returns the result as an integer.

Syntax: The syntax for the "len()" function is as follows:

len(object)

where:

object: The object for which you want to find the length.

Example 1:

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

print(len(my_list))

In this example, we have created a list "my_list" with 5 elements. We then use the "len()" function to find the length of the list and print the result. The output of this code will be:

Output:

Example 2:

my_string = "Hello World"

print(len(my_string))

In this example, we have created a string "my_string" with 11 characters. We then use the "len()" function to find the length of the string and print the result. The output of this code will be:

Output:

Example 3:

my_tuple = (1, 2, 3, 4, 5)

print(len(my_tuple))

In this example, we have created a tuple "my_tuple" with 5 elements. We then use the "len()" function to find the length of the tuple and print the result. The output of this code will be:

Output:

Example 4:

my_dict = {"name": "John", "age": 25, "gender": "Male"}

print(len(my_dict))

In this example, we have created a dictionary "my_dict" with 3 key-value pairs. We then use the "len()" function to find the length of the dictionary and print the result. The output of this code will be:

Output:

In conclusion, the "len()" function is a very useful built-in function in Python that returns the length of an object. It can be used with many data types and is commonly used in many Python programs to find the length of strings, lists, tuples, and dictionaries.