How to do Exploratory Data Analysis in Python?

How to do Exploratory Data Analysis in Python?
Exploratory Data Analysis (EDA) is an essential step in the data analysis process to understand the structure, patterns, and relationships within your dataset. Python offers various libraries, including Pandas, Matplotlib, Seaborn, and Plotly, to perform EDA effectively.
How to do Exploratory Data Analysis in Python?

Here’s a step-by-step guide on how to do EDA in Python using sample code

Import Libraries: Start by importing the necessary Python libraries

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns

Load Your Dataset and Data Inspection: Read your dataset into a Pandas DataFrame. Replace "your_data.csv" with the path to your dataset.

df = pd.read_csv("your_data.csv")
print(df.head())
print(df.info())
print(df.describe())
print(df.describe())

Handling Missing Data: Check for missing values and decide how to handle them (e.g., fill, drop, or interpolate).

# Check for missing values 
print(df.isnull().sum()) 
# Fill missing values 
df.fillna(method='ffill', inplace=True) # Example: Forward fill

dropna(): Removing rows or columns with missing values.
fillna(): Filling missing values with specified values or methods.
interpolate(): Interpolating missing values.
replace(): Replacing values with other values.

Data Visualization

df.hist(figsize=(10, 8))
plt.show()

corr_matrix = df.corr()
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.show()

sns.boxplot(data=df[['column_name']])
plt.show()

sns.countplot(data=df, x='categorical_column')
plt.show()

Feature Engineering: Create new features or transform existing ones to make them more suitable for analysis.

Example: Creating a new feature from existing columns
df['new_feature'] = df['feature1'] + df['feature2']

Let’s look at the top two techniques of feature engineering

Binning/Discretization: Convert continuous features into categorical bins or intervals. This can be useful when the relationship between the feature and the target variable is non-linear.

df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, np.inf], labels=['child', 'young_adult', 'adult', 'senior'])

One-Hot Encoding: Convert categorical variables into binary (0/1) columns, one for each category. This is necessary for many machine learning algorithms.

df = pd.get_dummies(df, columns=['gender'], drop_first=True)

Data Insights: Based on your visualizations and data exploration, draw initial insights and hypotheses about your data.

Further Analysis: Depending on your dataset and objectives, you may want to perform additional analyses such as time series analysis, clustering, or predictive modeling.

Documentation: Document your findings, code, and visualizations in a clear and organized manner, which can be shared with others or used for reference in the future.

EDA is an iterative process, and you may need to revisit previous steps as you gain more insights and refine your analysis. The above steps provide a basic framework for EDA in Python, but the specific analysis and visualizations will vary depending on your dataset and research questions.

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