Don't Confuse to learn Python.
Learn This Concept to be proficient in Python.
𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages
𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy
𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)
𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables
𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization
𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas
𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets
𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files
𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays
𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting
𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions
𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing
Learn This Concept to be proficient in Python.
𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages
𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy
𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)
𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables
𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization
𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas
𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets
𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files
𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays
𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting
𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions
𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing
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Want to analyse data with Python?
Pandas is a must-know tool for data analysts:
- start with pandas
- read csv files
- check basic statistics
- group data
- pivot data
- sort data
- create a bar chart
Pandas is a must-know tool for data analysts:
- start with pandas
- read csv files
- check basic statistics
- group data
- pivot data
- sort data
- create a bar chart
👍22
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Starting your career with Python is an excellent choice due to its versatility and broad range of applications. As you advance, you might discover various specializations that align with your interests:
• Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models.
• Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit.
• Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions.
• Automation and Scripting: If you’re interested in automating repetitive tasks and creating noscripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling.
• Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful.
• DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for noscripting and integrating with tools like Docker and Kubernetes.
• Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills.
Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools.
The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications.
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• Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models.
• Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit.
• Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions.
• Automation and Scripting: If you’re interested in automating repetitive tasks and creating noscripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling.
• Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful.
• DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for noscripting and integrating with tools like Docker and Kubernetes.
• Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills.
Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools.
The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications.
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Pandas is a powerful and versatile library in Python, especially for data science tasks.
Here are some key Pandas methods that are widely used:
Data Loading and Creation
* read_csv(): Reads data from a CSV file into a DataFrame.
* read_excel(): Reads data from an Excel file into a DataFrame.
* DataFrame(): Creates a new DataFrame from a dictionary, list, or NumPy array.
Data Exploration and Selection
* head(): Returns the first few rows of a DataFrame.
* tail(): Returns the last few rows of a DataFrame.
* shape(): Returns the dimensions of a DataFrame (rows, columns).
* info(): Provides summary information about the DataFrame, including data types and missing values.
* describe(): Generates summary statistics for numerical columns.
* loc[]: Selects rows and columns by label.
* iloc[]: Selects rows and columns by integer position.
* filter(): Selects columns by name.
Data Cleaning and Transformation
* dropna(): Removes rows or columns with missing values.
* fillna(): Fills missing values with a specified value or strategy.
* drop_duplicates(): Removes duplicate rows.
* apply(): Applies a function to each element or row/column.
* groupby(): Groups data based on one or more columns and performs aggregate functions.
* pivot_table(): Creates a pivot table for data summarization.
* merge(): Merges DataFrames based on a common column.
Data Visualization
* plot(): Creates various types of plots (line, bar, scatter, etc.).
* hist(): Creates a histogram.
* boxplot(): Creates a box plot.
These are just a few examples of the many powerful methods that Pandas offers. By mastering these methods, you can efficiently load, clean, transform, analyze, and visualize data for your data science projects.
Example:
import pandas as pd
# Load data from a CSV file
df = pd.read_csv('data.csv')
# Select the first 5 rows
print(df.head())
# Group data by a column and calculate the mean
grouped_df = df.groupby('column_name').mean()
# Create a bar plot
grouped_df.plot(kind='bar')
Here are some key Pandas methods that are widely used:
Data Loading and Creation
* read_csv(): Reads data from a CSV file into a DataFrame.
* read_excel(): Reads data from an Excel file into a DataFrame.
* DataFrame(): Creates a new DataFrame from a dictionary, list, or NumPy array.
Data Exploration and Selection
* head(): Returns the first few rows of a DataFrame.
* tail(): Returns the last few rows of a DataFrame.
* shape(): Returns the dimensions of a DataFrame (rows, columns).
* info(): Provides summary information about the DataFrame, including data types and missing values.
* describe(): Generates summary statistics for numerical columns.
* loc[]: Selects rows and columns by label.
* iloc[]: Selects rows and columns by integer position.
* filter(): Selects columns by name.
Data Cleaning and Transformation
* dropna(): Removes rows or columns with missing values.
* fillna(): Fills missing values with a specified value or strategy.
* drop_duplicates(): Removes duplicate rows.
* apply(): Applies a function to each element or row/column.
* groupby(): Groups data based on one or more columns and performs aggregate functions.
* pivot_table(): Creates a pivot table for data summarization.
* merge(): Merges DataFrames based on a common column.
Data Visualization
* plot(): Creates various types of plots (line, bar, scatter, etc.).
* hist(): Creates a histogram.
* boxplot(): Creates a box plot.
These are just a few examples of the many powerful methods that Pandas offers. By mastering these methods, you can efficiently load, clean, transform, analyze, and visualize data for your data science projects.
Example:
import pandas as pd
# Load data from a CSV file
df = pd.read_csv('data.csv')
# Select the first 5 rows
print(df.head())
# Group data by a column and calculate the mean
grouped_df = df.groupby('column_name').mean()
# Create a bar plot
grouped_df.plot(kind='bar')
👍10❤4
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
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1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
👍7
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