Python

Data cleaning FIG : Data Cleaning Data cleaning, also known as data cleansing or data pre-processing, is the process of detecting and correcting or removing corrupt, inaccurate, or incomplete records from a dataset. It is a crucial step in machine learning as the accuracy and effectiveness of a model depend on the quality of the data that it is trained on. The data cleaning process typically involves several steps: Handling missing data: Incomplete data records can lead to biased or incorrect results. One way to handle missing data is to delete records with missing values. Another approach is to fill in the missing values with reasonable estimates, such as the mean or median of the available data. Handling outliers: Outliers are data points that deviate significantly from the rest of the data. They can distort the results and should be handled carefully. One approach is to remove them from the dataset, but in some cases, they may be important and should be kept. Handling duplicates: Du

 MOST USED FUNCTIONS IN  PANDAS  read_csv(): reads a CSV file into a DataFrame. read_excel(): reads an Excel file into a DataFrame. read_sql(): reads a SQL query or database table into a DataFrame. read_json(): reads a JSON file into a DataFrame. read_html(): reads an HTML file or URL into a list of DataFrames. read_stata(): reads a Stata file into a DataFrame. read_clipboard(): reads text from the clipboard into a DataFrame. read_pickle(): reads a pickled object into a DataFrame. read_feather(): reads a Feather file into a DataFrame. read_parquet(): reads a Parquet file into a DataFrame. read_hdf(): reads an HDF5 file into a DataFrame. DataFrame(): creates a new DataFrame object. Series(): creates a new Series object. concat(): concatenates two or more DataFrames. merge(): merges two DataFrames based on a common column. append(): appends rows to a DataFrame. pivot_table(): creates a pivot table from a DataFrame. groupby(): groups data by one or more columns. apply():

Pandas Introduction: Pandas is a widely used open-source data manipulation library for Python. It was created by Wes McKinney in 2008 to provide efficient, flexible, and easy-to-use data analysis and manipulation tools. The name "pandas" is derived from "panel data," a term used in statistics for multidimensional data sets. Features: Pandas offers a wide range of features and capabilities, including: Data structures for efficiently storing and manipulating labeled data: Pandas provides two main data structures: Series (one-dimensional) and DataFrame (two-dimensional). These structures are highly optimized for efficient data manipulation and analysis. Data cleaning and preprocessing: Pandas makes it easy to clean and preprocess data by providing methods for handling missing values, transforming data, and more. Data visualization: Pandas includes built-in visualization tools that allow users to create informative and visually appealing charts and graphs. Integration w

   NUMPY The Introduction: NumPy is a powerful and popular Python library used for numerical computing. It is a fundamental library in the scientific computing ecosystem of Python, providing support for array computing, mathematical operations, linear algebra, Fourier transforms, and more. In this blog, we will explore NumPy in detail, discussing its features, syntax, and benefits. Features of NumPy: NumPy provides a powerful array data structure that can hold homogeneous data types. These array are more efficient and powerful than the built-in Python lists. Arrays can be one-dimensional, two-dimensional, or multidimensional. NumPy provides a wide range of mathematical functions that are optimized for array. These functions include basic arithmetic operations, statistical functions, linear algebra functions and more. Broadcasting: NumPy provides a powerful feature called broadcasting that allows for performing arithmetic operations on array of different sizes. Indexing and Slicing: Num