Python - Data Analysis

Python has become a dominant language for data analysis, thanks to its powerful libraries and ease of use. Here’s a concise guide to get you started with data analysis using Python.

Key Libraries for Data Analysis

Pandas: Provides data structures like DataFrames for manipulating and analyzing data.

NumPy: Offers support for large, multi-dimensional arrays and matrices, along with mathematical functions.

Matplotlib: A plotting library for creating static, animated, and interactive visualizations.

Seaborn: Built on Matplotlib, it simplifies creating attractive and informative statistical graphics.

SciPy: Includes modules for optimization, integration, and other advanced mathematical operations.

Basic Steps in Data Analysis

Data Loading:

Use Pandas to load data from various sources, such as CSV files, Excel spreadsheets, or SQL databases.

import pandas as pd

# Load data from a CSV file

d = pd.read_csv('data_1.csv')

Data Cleaning:

Handle missing values, duplicates, and incorrect data types.

# Remove duplicates

d.drop_duplicates(inplace=True)

# Fill missing values

d.fillna(method='ffill', inplace=True)

Exploratory Data Analysis (EDA):

Use summary statistics and visualizations to understand data patterns and distributions.

import matplotlib.pyplot as plt

import seaborn as sns

# Summary statistics

print(data.describe())

# Visualization

sns.histplot(data['column_name'])

plt.show()