Articles
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 asterisk Iterating with dictionaries Iterating with file connections Using enumerate() enumerate() and unpack Using zip() zip() and unpack Print zip with asterisk 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? Using timeit timeit output Specifying number loops Using timeit in line magic mode Using timeit in cell magic mode Saving output Comparing times Code profiling for runtime %lprun output Code profilling for memory usage %mprun output Efficiently Combining, Counting, and iterating Combining objects Combining objects with zip Counting with loop collections.Counter() The itertools module Combinations with loop itertools.combinations() Comparing objects with loops Set method difference Set method symmetric difference Set method union Uniques with sets Beneifits of eleiminating loops Eliminate loops with NumPy Moving calculations above a loop Using holistic conversions Introduction to pandas DataFrame iteration Calculating win percentage Adding win percentage to DataFrame Iterating with .iloc Iterating with .iterrows() .itertuples() Iterating with .itertuples() pandas .apply() method Dates in Python Attributes of a date Finding the weekday of a date Math with Dates Incrementing variables += Turning dates into strings ISO 8601 format with Exmples Format strftime Adding time to the mix Replacing parts of a datetime Printing datetimes Parsing datetimes with strptime Working with durations Creating timedeltas Negative timedeltas UTC offsets Adjusting timezone vs changing tzinfo Time zone database Starting Daylight Saving Time Ending Daylight Saving Time Reading date and time data in Pandas Loading datetimes with parse_dates Timezone-aware arithmetic Summarizing datetime data in pandas Additional datetime methods in Pandas Timezones in Pandas All datetime operations in Pandas All parts of Pandas Introduction to string manipulation Concatenation Indexing Slicing Stride String operations Adjusting cases Splitting Joining Stripping characters Finding and replacing Finding substrings Index function Counting occurrences Replacing substrings Positional formatting string formatting Methods for formatting Reordering values Named placeholders Format specifier Formatting datetime Formatted string literal f-strings Type conversion Index lookups Escape sequences Inline operations Calling functions Template method Substitution The re module Supported metacharacters Repeated characters Quantifiers in re module Regex metacharacters Special characters OR operator in re Module OR operand in re module Greedy vs. nongreedy matching Grouping and capturing re module Pipe re module Non-capturing groups Backreferences Numbered groups Named groups Lookaround Look-ahead Positive look-ahead Negative look-ahead Look-behind Positive look-behind Negative look-behind Web Scraping With Python Slashes and Brackets in web scrapping Introduction to the scrapy Selector Setting up a Selector Selecting Selectors Extracting Data from a SelectorList CSS Locators Attributes in CSS Selectors with CSS Text Extraction Crawl A Classy Spider Docstrings Docstring formats Don't repeat yourself (DRY) Pass by assignment Immutable or Mutable Using context managers The yield keyword Nested contexts Two ways to define a context manager Handling errors Functions as objects Functions as variables Lists and dictionaries of functions Referencing a function Functions as arguments Defining a function inside another function Functions as return values The global keyword The nonlocal keyword Attaching nonlocal variables to nested functions Closures and deletion Closures and overwriting Definitions - nested function Definitions - nonlocal variables Decorators decorator look like The double_args decorator Time a function Using timer() When to use decorators with timer() Decorators and metadata The timer decorator Access to the original function Decorators that take arguments run_n_times() A decorator factory Timeout(): a real world example Querying Python interpreter's memory usage Allocating memory for an array Allocating memory for a computation Querying array memory Usage Querying DataFrame memory usage Using pd.read_csv() with chunksize Examining a chunk Filtering a chunk Chunking & filtering together Using pd.concat() Plotting the filtered results Managing Data with Generators Filtering in a list comprehension Filtering & summing with generators Examining consumed generators Reading many files Examining a sample DataFrame Aggregating with Generators Computing the fraction of long trips Delaying Computation with Dask Composing functions Deferring computation with `delayed` Visualizing a task graph Renaming decorated functions Using decorator @-notation Deferring Computation with Loops Aggregating with delayed Functions Computing fraction of long trips with `delayed` functions Chunking Arrays in Dask Working with Numpy arrays Working with Dask arrays Aggregating in chunks Aggregating with Dask arrays Dask array methods/attributes Timing array computations Computing with Multidimensional Arrays A Numpy array of time series data Reshaping time series data Reshaping: Getting the order correct! Using reshape: Row- & column-major ordering Indexing in multiple dimensions Aggregating multidimensional arrays Broadcasting rules Connecting with Dask HDF5 format (Hierarchical Data Format version 5) Extracting Dask array from HDF5 Aggregating while ignoring NaNs Producing a visualization of data_dask Stacking arrays Stacking one-dimensional arrays Stacking two-dimensional arrays Putting array blocks together Analyzing Earthquake Data Using HDF5 files for analyzing earthquake data Extracting Dask array from HDF5 for Analyzing Earthquake Data Aggregating while ignoring NaNs for Analyzing Earthquake Data Producing a visualization of data_dask for Analyzing Earthquake Data Stacking arrays for Analyzing Earthquake Data Stacking one-dimensional arrays for Analyzing Earthquake Data Stacking two-dimensional arrays for Analyzing Earthquake Data Putting array blocks together for Analyzing Earthquake Data Using Dask DataFrames Reading CSV For Dask DataFrames Reading multiple CSV files For Dask DataFrames Building delayed pipelines Compatibility with Pandas API Timing DataFrame Operations Timing I-O & computation: Pandas Is Dask or Pandas appropriate? Building Dask Bags & Globbing Sequences to bags Reading text files Glob expressions Using Python's glob module Functional Approaches using Dask Bags Functional programming Functional programming Using map Functional programming Using Filter Functional Approaches Using dask.bag.map Functional Approaches Using dask.bag.filter Functional Approaches Using .str & string methods JSON data files Using json module JSON Files into Dask Bags Plucking values Merging DataFrames Dask DataFrame pipelines Repeated reads & performance Using persistence Python, data science, & software engineering Software engineering concepts Django Introduction Datatypes Lists Combining Lists Finding and Removing Elements in a List Iterating and Sorting Tuples Zipping and Unpacking More Unpacking in Loops Enumerating positions Sets for Unordered and Unique Data with Tuples in Python Set Creating Sets in Python: Harnessing the Power of Unique Collections Modifying Sets in Python: Adding and Removing Elements with Ease Removing Data from Sets in Python: Streamlining Set Operations Exploring Set Operations in Python: Uncovering Similarities among Sets Set Operations in Python: Unveiling Differences among Sets Exploring Dictionaries in Python: A Key-Value Data Structure Creating and Looping Through Dictionaries in Python: A Comprehensive Guide Safely Finding Values in Python Dictionaries: A Guide to Avoiding Key Errors Safely Finding Values in Python Dictionaries: Advanced Techniques for Key Lookup Dictionaries-Working with Nested Data in Python: Exploring Hierarchical Structures Adding and Extending Python Dictionaries: Flexible Data Manipulation Popping and Deleting from Python Dictionaries: Managing Key-Value Removal Working with Dictionaries More Pythonically: Efficient Data Manipulation Checking Dictionaries for Data: Effective Data Validation in Python Working with CSV Files in Python: Simplify Data Processing and Analysis Creating a Dictionary from a File in Python: Simplify Data Mapping and Access Counting Made Easy in Python: Harness the Power of Counting Techniques Exploring the Collections Module in Python: Enhance Data Structures and Operations Understanding the Counter Class in Python: Simplify Counting and Frequency Analysis Working with Dictionaries of Unknown Structure using defaultdict in Python Advanced Usage of defaultdict in Python for Flexible Data Handling Maintaining Dictionary Order with OrderedDict in Python Harnessing the Power of OrderedDict's Advanced Features in Python Unleashing the Power of namedtuple in Python Leveraging the Power of namedtuples in Python Working with Datetime Components and Current Time in Python Exploring Datetime Components in Python Understanding now in Python's Datetime Module Exploring Timezones in Python's Datetime Module Time Travel in Python: Adding and Subtracting Time HELP! 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
(Living .low%255B%255D + sliding is a power comb)
Working With CSV

Python provides built-in functionality to read and write CSV (comma-separated values) files. CSV files are commonly used to store data in a tabular format, with each row representing a data record and each column representing a data field.


Counter built-in class

Counter is a built-in class in Python's collections module that is used to count the occurrences of elements in an iterable object. It is a dictionary subclass that takes an iterable object as input and returns a dictionar
most_common() - collections module

In Python's collections module, most_common() is a method of the Counter class that returns a list of the n most common elements and their counts in the Counter object. The elements a
OrderedDict power feature - subclass

OrderedDict is a subclass of Python's built-in dict type that maintains the order of the keys in the dictionary. It was added to the collections module in Python 2.7 and provides some powerful features that th
namedtuple is a powerful tool

Python's namedtuple is a powerful tool that allows you to create lightweight classes that are similar to tuples, but with named fields. This can be useful in a variety of situations where you need to represent a collection of related v
From String to datetime

In Python, you can easily convert a string to a datetime object using the datetime module. The datetime module provides a datetime class that represents a date and time, and a strptime() function that
DateTime Components

In Python's datetime module, a datetime object represents a date and time. It has several components or attributes that allow you to access and manipulate different parts of the date and time. Here are some of the key comp
TimeZone in Action

Time zones are an important consideration when working with dates and times in Python. Python's datetime module provides several classes and functions for working with time zones, including timezone, datetime, and pytz. Here's an examp
TimeDelta - Time Travel with timedelta

In Python, timedelta is a class in the datetime module that represents a duration of time, such as the difference between two dates or times. A timedelta object is created using the timedelta() constr
Parsing time with pendulum

Pendulum is a Python library for working with dates, times, and timezones. It provides a simple and intuitive API for parsing, manipulating, and formatting dates and times. In this article, we'll explore how to use Pendulum to work with time.


Data Manipulation with Pandas

Pandas is a popular open-source data analysis and manipulation library for Python. It provides powerful tools for working with structured data, such as data frames and series. In this article, we will explore some of the fundamental data manipulation techniqu
Creating DataFrames with Pandas

Pandas is a popular open-source data analysis and manipulation library for Python. It provides powerful tools for working with structured data, such as data frames and series. In this article, we will explore how to create data frames in Pandas.


Creating DataFrames with Dictionaries in Pandas

Pandas is a powerful data analysis and manipulation library for Python. One of the core data structures in Pandas is the DataFrame, a two-dimensional table-like structure that contains rows and columns of data. In this article, we will explore how to create d
DataFrame With CSV File

Reading a CSV file and performing data manipulation is a common task in data analysis and machine learning. Here are maximum steps to help you get started:

servers_info.csv

Summary statistics

Summary statistics are numerical values that provide a brief summary of the features of a dataset. In Python, you can use various libraries such as NumPy, Pandas, and SciPy to calculate summary statistics for your data.

Summarizing numerical data

Summarizing numerical data is an important part of data analysis, and there are many ways to do it in Python. Here are a few common techniques:

1.    Mean: The mean is the average of all
Summarizing dates

Summarizing dates is an important part of data analysis, and there are many ways to do it in Python. Here are a few common techniques:

  1. Count: The count is the number of dates in the dataset.
    The .agg() method

    The .agg() method in Python is used to aggregate or summarize data in a DataFrame. It is a versatile method that allows you t
    Summaries on multiple columns

    To summarize multiple columns of a DataFrame in Python, you can use the .agg() method with a dictionary. The keys of the dictionary should be the column names, and the values should be the summary statistics you want to calculate.

    <
    Multiple summaries

    To perform multiple summaries on a single column of a Pandas DataFrame, you can use the .agg() method with a list of summary

    Built-in Functions
    Using the memoryview() function Understanding the get() built-in method in Python Understanding the abs() function in Python for computing the absolute value of a number Understanding the aiter() function in Python for asynchronous iteration over an asynchronous iterable Understanding the all() function in Python for checking if all elements in an iterable are true Understanding the any() function in Python for checking if any element in an iterable is true Understanding the anext() function in Python for asynchronous iteration and retrieval of the next value from an asynchronous iterator Understanding the ascii() function in Python for generating ASCII representations of objects Understanding the bin() function in Python for converting an integer to a binary string representation Understanding the bool() function in Python for converting a value to its corresponding boolean representation Understanding the breakpoint() function in Python for setting a debugging breakpoint in code Understanding the bytearray() function in Python for creating a mutable sequence of bytes Understanding the bytes() function in Python for creating an immutable sequence of bytes Understanding the callable() function in Python for checking if an object is callable or not Understanding the chr() function in Python for converting an integer Unicode code point to its corresponding character Understanding the classmethod() function in Python for defining a method that operates on the class rather than an instance Understanding the compile() function in Python for compiling source code into a code object or AST (Abstract Syntax Tree) Understanding the complex() function in Python for creating a complex number from real and imaginary parts Understanding the delattr() function in Python for deleting an attribute from an object Understanding the dict() function in Python for creating a new dictionary object Understanding the dir() function in Python for retrieving the list of names in the current namespace or an object's attributes Understanding the divmod() function in Python for performing integer division and obtaining the remainder in a single operation Understanding the enumerate() function in Python for iterating over a sequence while keeping track of the index and value Understanding the eval() function in Python for dynamically evaluating and executing a string as Python code Understanding the exec() function in Python for dynamically executing Python code stored in a string or code object Understanding the filter() function in Python for selectively filtering elements from an iterable based on a given condition or predicate function Understanding the float() function in Python for converting a string or number into a floating-point number Understanding the format() function in Python for formatting values into strings based on a specified format specification Understanding the frozenset() function in Python for creating an immutable set object Understanding the getattr() function in Python for retrieving the value of an attribute from an object based on its name Understanding the globals() function in Python for accessing the global namespace as a dictionary Understanding the hasattr() function in Python for checking if an object has a particular attribute Understanding the hash() function in Python for generating a hash value for an object Understanding the help() function in Python for accessing the built-in help system and obtaining documentation for objects and modules Understanding the hex() function in Python for converting an integer to its hexadecimal representation Understanding the id() function in Python for obtaining the unique identifier of an object Understanding the input() function in Python for receiving user input from the console Understanding the int() function in Python for converting a value to an integer Understanding the isinstance() function in Python for checking if an object is an instance of a specific class or type Understanding the issubclass() function in Python for checking if a class is a subclass of another class Understanding the iter() function in Python for creating an iterator object from an iterable Understanding the len() function in Python for obtaining the length or size of a sequence Understanding the list() function in Python for creating a list object from an iterable or converting other data types to a list Understanding the locals() function in Python for accessing the local namespace and retrieving local variables as a dictionary nderstanding the map() function in Python for applying a function to each element of an iterable and returning an iterator Understanding the max() function in Python for finding the maximum value among a collection of elements Understanding the memoryview() function in Python for accessing the memory of an object as a mutable sequence of bytes Understanding the min() function in Python for finding the minimum value among a collection of elements Understanding the next() function in Python for retrieving the next item from an iterator or generator Understanding the object() function in Python for creating a new object that serves as the base class for all other classes Understanding the oct() function in Python for converting an integer to its octal representation as a string Understanding the open() function in Python for opening files and retrieving a file object Understanding the ord() function in Python for converting a single character to its corresponding Unicode code point Understanding the pow() function in Python for exponentiation and modular exponentiation calculations Understanding the print() function in Python for displaying output to the console or other output streams Understanding the property() function in Python for creating getter Understanding the range() function in Python for generating a sequence of numbers Understanding the repr() function in Python for obtaining the string representation of an object Understanding the reversed() function in Python for reversing the order of elements in a sequence Understanding the round() function in Python for rounding numbers to a specified number of decimal places Understanding the set() function in Python for creating an unordered collection of unique elements Understanding the setattr() function in Python for dynamically setting attributes on objects Understanding the slice() function in Python for creating slice objects used to specify ranges or subsets of sequences Understanding the sorted() function in Python for sorting sequences or iterable objects Understanding the staticmethod() function in Python for defining static methods within a class Understanding the str() function in Python for converting objects to string representations Understanding the sum() function in Python for calculating the sum of elements in a sequence Understanding the super() function in Python for accessing methods and attributes of a superclass or parent class Understanding the tuple() function in Python for creating tuples Understanding the type() function in Python for determining the type of an object Understanding the vars() function in Python for retrieving the attributes and values of an object as a dictionary Understanding the zip() function in Python for combining multiple iterables into a single iterable of tuples Understanding the import() function in Python for dynamically importing modules at runtime

    Enjoyed this article? Share it!