Data Analysis From Scratch With Python: Beginner Guide

Data Analysis From Scratch With Python: Beginner Guide: Python is a popular programming language that can be used for data analysis. It provides a wide range of libraries and frameworks that enable you to easily perform data analysis tasks. Some of the popular libraries that you can use for data analysis with Python include Pandas, NumPy, Scikit-Learn, and IPython. In this beginner’s guide, we’ll explore how to use these libraries for data analysis.

Data Analysis From Scratch With Python: Beginner Guide
Data Analysis From Scratch With Python: Beginner Guide
  1. Installing Python and Required Libraries

Before we get started with data analysis, we need to install Python and the required libraries. You can download Python from the official website and install it on your computer. Once you have installed Python, you can install the required libraries using pip, which is the package manager for Python. You can install libraries like Pandas, NumPy, Scikit-Learn, and IPython by running the following commands in your terminal or command.

pip install pandas pip install numpy pip install scikit-learn pip install ipython 
  1. Loading and Inspecting Data with Pandas

Once you have installed the required libraries, you can start with data analysis. Pandas is a powerful library that is used for data manipulation and analysis. You can load data into Pandas using various methods such as reading from CSV files, Excel files, and databases. Let’s take a look at how to load a CSV file using Pandas:

import pandas as pd

data = pd.read_csv('data.csv')

In this example, we are using the read_csv method to load a CSV file named ‘data.csv’. The head() method is used to print the first few rows of the data. This will help us to get an idea of the structure of the data.

  1. Data Cleaning and Preprocessing with Pandas

Once we have loaded the data, we need to clean and preprocess it before we can perform analysis. Pandas provide various methods to clean and preprocess data such as removing missing values, dropping duplicates, and converting data types. Let’s take a look at some examples:

# Removing missing values
data = data.dropna()

# Dropping duplicates
data = data.drop_duplicates()

# Converting data types
data['age'] = data['age'].astype(int)

In this example, we use the dropna() method to remove missing values from the data. The drop_duplicates() method is used to drop duplicate rows from the data. The astype() method is used to convert the data type of the ‘age’ column to integer.

  1. Exploratory Data Analysis with Pandas

Exploratory Data Analysis (EDA) is an important step in data analysis that helps us to understand the data better. Pandas provides various methods to perform EDA such as summary statistics, correlation analysis, and visualization. Let’s take a look at some examples:

# Summary statistics

# Correlation analysis

# Visualization
import matplotlib.pyplot as plt
data.plot(kind='scatter', x='age', y='income')

In this example, we are using the describe() method to print summary statistics of the data. The corr() method is used to compute the correlation between the columns. The plot() method is used to visualize the relationship between the ‘age’ and ‘income’ columns.

  1. Machine Learning with Scikit-Learn

Scikit-Learn is a popular library that is used for machine learning in Python. It provides various algorithms for classification, regression, and clustering. Let’s take a look at how to use Scikit-Learn for machine learning:

# Splitting the data into training and testing sets
from sklearn.model_selection import train_test_split

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