Coding Class

Master Javascript :

The JavaScript Tree 👇

|

|── Variables

| ├── var

| ├── let

| └── const

|

|── Data Types

| ├── String

| ├── Number

| ├── Boolean

| ├── Object

| ├── Array

| ├── Null

| └── Undefined

|

|── Operators

| ├── Arithmetic

| ├── Assignment

| ├── Comparison

| ├── Logical

| ├── Unary

| └── Ternary (Conditional)

||── Control Flow

| ├── if statement

| ├── else statement

| ├── else if statement

| ├── switch statement

| ├── for loop

| ├── while loop

| └── do-while loop

|

|── Functions

| ├── Function declaration

| ├── Function expression

| ├── Arrow function

| └── IIFE (Immediately Invoked Function Expression)

|

|── Scope

| ├── Global scope

| ├── Local scope

| ├── Block scope

| └── Lexical scope

||── Arrays

| ├── Array methods

| | ├── push()

| | ├── pop()

| | ├── shift()

| | ├── unshift()

| | ├── splice()

| | ├── slice()

| | └── concat()

| └── Array iteration

| ├── forEach()

| ├── map()

| ├── filter()

| └── reduce()|

|── Objects

| ├── Object properties

| | ├── Dot notation

| | └── Bracket notation

| ├── Object methods

| | ├── Object.keys()

| | ├── Object.values()

| | └── Object.entries()

| └── Object destructuring

||── Promises

| ├── Promise states

| | ├── Pending

| | ├── Fulfilled

| | └── Rejected

| ├── Promise methods

| | ├── then()

| | ├── catch()

| | └── finally()

| └── Promise.all()

|

|── Asynchronous JavaScript

| ├── Callbacks

| ├── Promises

| └── Async/Await

|

|── Error Handling

| ├── try...catch statement

| └── throw statement

|

|── JSON (JavaScript Object Notation)

||── Modules

| ├── import

| └── export

|

|── DOM Manipulation

| ├── Selecting elements

| ├── Modifying elements

| └── Creating elements

|

|── Events

| ├── Event listeners

| ├── Event propagation

| └── Event delegation

|

|── AJAX (Asynchronous JavaScript and XML)

|

|── Fetch API

||── ES6+ Features

| ├── Template literals

| ├── Destructuring assignment

| ├── Spread/rest operator

| ├── Arrow functions

| ├── Classes

| ├── let and const

| ├── Default parameters

| ├── Modules

| └── Promises

|

|── Web APIs

| ├── Local Storage

| ├── Session Storage

| └── Web Storage API

|

|── Libraries and Frameworks

| ├── React

| ├── Angular

| └── Vue.js

||── Debugging

| ├── Console.log()

| ├── Breakpoints

| └── DevTools

|

|── Others

| ├── Closures

| ├── Callbacks

| ├── Prototypes

| ├── this keyword

| ├── Hoisting

| └── Strict mode

|

| END __

Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use.

1. Python Basics

- Variables:

x = 10

y = "Hello"

- Data Types:

  - Integers: x = 10

  - Floats: y = 3.14

  - Strings: name = "Alice"

  - Lists: my_list = [1, 2, 3]

  - Dictionaries: my_dict = {"key": "value"}

  - Tuples: my_tuple = (1, 2, 3)

- Control Structures:

  - if, elif, else statements

  - Loops: 

    for i in range(5):

        print(i)

  - While loop:

    while x < 5:

        print(x)

        x += 1

2. Importing Libraries

- NumPy:

  import numpy as np

- Pandas:

  import pandas as pd

- Matplotlib:

  import matplotlib.pyplot as plt

- Seaborn:

  import seaborn as sns

3. NumPy for Numerical Data

- Creating Arrays:

  arr = np.array([1, 2, 3, 4])

- Array Operations:

  arr.sum()

  arr.mean()

- Reshaping Arrays:

  arr.reshape((2, 2))

- Indexing and Slicing:

  arr[0:2]  # First two elements

4. Pandas for Data Manipulation

- Creating DataFrames:

  df = pd.DataFrame({

      'col1': [1, 2, 3],

      'col2': ['A', 'B', 'C']

  })

- Reading Data:

  df = pd.read_csv('file.csv')

- Basic Operations:

  df.head()          # First 5 rows

  df.describe()      # Summary statistics

  df.info()          # DataFrame info

- Selecting Columns:

  df['col1']

  df[['col1', 'col2']]

- Filtering Data:

  df[df['col1'] > 2]

- Handling Missing Data:

  df.dropna()        # Drop missing values

  df.fillna(0)       # Replace missing values

- GroupBy:

  df.groupby('col2').mean()

5. Data Visualization

- Matplotlib:

  plt.plot(df['col1'], df['col2'])

  plt.xlabel('X-axis')

  plt.ylabel('Y-axis')

  plt.title('Title')

  plt.show()

- Seaborn:

  sns.histplot(df['col1'])

  sns.boxplot(x='col1', y='col2', data=df)

6. Common Data Operations

- Merging DataFrames:

  pd.merge(df1, df2, on='key')

- Pivot Table:

  df.pivot_table(index='col1', columns='col2', values='col3')

- Applying Functions:

  df['col1'].apply(lambda x: x*2)

7. Basic Statistics

- Descriptive Stats:

  df['col1'].mean()

  df['col1'].median()

  df['col1'].std()

- Correlation:

  df.corr()

This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.

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