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#### 1 : Data Analysis With Pandas And NumPy In Python

**NumPy **and **Pandas **for **Data Analysis** and Financial Applications, Examples in Trading Market Analysis

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This online course is designed to equip you with the skills and knowledge needed to efficiently and effectively manipulate and analyze data using two powerful Python libraries: Pandas and NumPy.

In this course, you will start by learning the fundamentals of data wrangling, including the different types of data and data cleaning techniques. You will then dive into the NumPy library, exploring its powerful features for working with N-dimensional arrays and universal functions.

Next, you will explore the Pandas library, which offers powerful tools for data manipulation, including data structures and data frame manipulation. You will learn how to use advanced Pandas functions, manipulate time and time series data, and read and write data with Pandas.

Throughout the course, you will engage in hands-on exercises and practice problems to reinforce your learning and build your skills. By the end of the course, you will be able to effectively wrangle and analyze data using Pandas and NumPy, and create compelling data visualizations using these tools.

Whether you're a data analyst, data scientist, or data enthusiast, this course will give you the skills you need to take your data wrangling and analysis to the next level.

**Content Table:**

**Lesson 1: Introduction to Data Wrangling**

**Lesson 2: Introduction to NumPy**

**Lesson 3: Data structure in Pandas**

**Lesson 4: Pandas DataFrame Manipulation**

**Lesson 5: Advanced Pandas Functions**

**Lesson 6: Time and Time Series in Pandas**

**Lesson 7: Reading and Writing Data with Pandas**

**Lesson 8: Data Visualization with Pandas**

**Practice Exercises**

#### Who this course is for:

- Beginner in Python building Data Science skills for real world applications

**What you'll learn**

- Data manipulation: working with data, filter, sort, and transform large datasets
- Data analysis: perform a wide range of data analysis tasks, including aggregating data, performing statistical calculations
- Data visualization: create a variety of visualizations to help understand data and communicate findings
- Data wrangling: cleaning and preparing data for analysis, handling missing data, merge datasets, and reshape data

**Requirements**

- Python basics, for loops, condition statements, python containers; lists, sets, tuples and dictionnaries.
- How to use Pandas and NumPy in Python?

**NumPy is a dependency of Pandas**.

**they are working together pretty good**. This is why people use them together.

**By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood**. NumPy was originally developed in the mid 2000s, and arose from an even older package called Numeric.

**use the ISNA() function to analyze and detect the missing values in the data**. This function looks at every value of the rows and columns. If the value is missing, it returns True, otherwise it returns False.

**you should learn Numpy**. It is the most fundamental module for scientific computing with Python. Numpy provides the support of highly optimized multidimensional arrays, which are the most basic data structure of most Machine Learning algorithms. Next, you should learn Pandas.

**The performance of Pandas is much better for about 500k rows or even more.**

**The performance of NumPy is better for about 50k rows or less**. This module consumes comparatively much larger memory than the NumPy module. This module consumes much less memory than the Pandas module.

**NumPy arrays have a fixed size at creation, unlike Python lists**(which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory.

**NumPy is commonly used within data science in order to work through numerical analyses and functions**, such as creating and working with arrays, returning descriptive statistics, and a variety of machine learning models and mathematical formulas. You can also access the NumPy library through the GitHub platform