Data Analytics using Python has become most preferred and popular mode of data analysis sinnce it does provides a range of libraries.
NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform.
Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data.
Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data.
SciPy: SciPy library is used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing.
Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models.
Data Analytics Using the Python Library, NumPy
Various numerical analysis uses and data manipulation using the NumPy library.
1. Create a NumPy array.
2. Access and manipulate elements in the array.
3. Create a 2-dimensional array and check the shape of the array.
4. Access elements from the 2D array using index positions.
5. Create an array of type string.
Data Analytics Using Python Libraries, Pandas and Matplotlib
Use a car.csv dataset and perform exploratory data analysis using Pandas and Matplotlib library functions to manipulate and visualize the data and find insights.
1. Import the libraries.
2. Load the dataset using pandas read_csv() function.
3. Display the head of the dataset using the head() function.
4. Display the bottom 5 rows from the dataset using the tail() function.
5. Print summary statistics of the dataset using the describe() function.
6.Plot a histogram for all the variables.
7. Box plot to visualize the relationship between vehicle size and engine hp.