Data Analysis with Python
Data analysis with Python involves using Python programming language and its libraries to extract insights and knowledge from data. Python is a popular language for data analysis due to its simplicity, flexibility, and powerful libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.
There are several methods to perform data analysis with Python, some of which are:
Data Cleaning: This involves cleaning and preparing the data for analysis. It includes removing duplicates, handling missing values, and converting data types. For example, using Pandas library, we can remove duplicates using the
drop_duplicates() function and handle missing values using the
Data Visualization: This involves creating visual representations of the data to better understand it. It includes creating plots, charts, and graphs. For example, using Matplotlib library, we can create a line plot using the
plot() function and a scatter plot using the
Exploratory Data Analysis (EDA): This involves exploring the data to understand its characteristics and relationships between variables. It includes calculating summary statistics, identifying patterns, and detecting outliers. For example, using Pandas library, we can calculate summary statistics using the
describe() function and identify patterns using the
Machine Learning: This involves using algorithms to build predictive models from the data. It includes data preprocessing, feature selection, model selection, and evaluation. For example, using Scikit-learn library, we can preprocess data using the
StandardScaler() function, select features using the
SelectKBest() function, and build a model using the
Overall, data analysis with Python involves a combination of these methods to extract insights and knowledge from data.