Step-by-Step Guide Automatic Exploratory Data Analysis in Python |

Step 1: Data Collection

Data collection is the first and essential step in the data analysis process. This

can be done through various means:

1. Manual Data Entry: Small datasets can be manually created in CSV, Excel,

or other formats.

2. APIs or Web Scraping: Automatically collect data using APls or scraping

websites.

3. Databases: Query structured data from following databases.

• Google Data Search
• Kaggle
• github.com
• UCI Machine Learning Repository
• To load datasets using seaborn's load_dataset function in Python, you can follow these steps. Seaborn is a popular data visualization library that also includes several built-in datasets which you can load and use directly for practice or analysis. Here's an example of how you can do it:

import seaborn as sns

# Load the 'iris' dataset
iris = sns.load_dataset('iris')

# Display the first few rows of the dataset
print(iris.head())        

In this example, sns.load_dataset('iris') fetches the "iris" dataset, and print(iris.head()) displays the first few rows of this dataset. You can load other datasets by changing the dataset name in the load_dataset function.

What other datasets can I load with sns.load_dataset?

Seaborn provides a variety of built-in datasets that you can load using the sns.load_dataset() function. Here are some of the datasets you can access:

1. iris: The famous Iris flower dataset.
2. tips: Data about restaurant tips.
3. titanic: Titanic passenger data.
4. flights: Monthly number of airline passengers.
5. diamonds: Prices and attributes of about 54,000 diamonds.
6. planets: Planets dataset from NASA Exoplanet Archive.
7. exercise: Data on exercise intensity and weight loss.
8. penguins: Data on penguin species in Antarctica.
9. mpg: Miles per gallon for various makes and models of cars.
10. fmri: Functional MRI data.

import seaborn as sns

# Load the 'titanic' dataset
titanic = sns.load_dataset('titanic')

# Display the first few rows of the dataset
print(titanic.head())        

Python Libraries:

· Pandas: Works with CSV, Excel, and databases.

· Requests / BeautifulSoup / Scrapy: Used for web scraping.

SQLAlchemy / PyMySQL / SQLite3: For database queries.

Step 2: Data Cleaning

Real-world data is often messy. Data cleaning involves handling missing values,

fixing inconsistent data, removing duplicates, and correcting incorrect data types.

Tasks in data cleaning:

1. Handling Missing Data: You can remove or fill missing values.

2. Removing Duplicates: Identify and remove duplicated entries.

3. Correcting Data Types: Convert columns to the correct data types (e.g ., numeric, categorical).

Python Techniques:

· Drop missing values: data.dropna()

· Fill missing values: data. fillna(value)

· Drop duplicates: data.drop_duplicates()

· Convert data types: data['column'] = data['column'].astype('int')

Today

Drop missing values: data.dropna()

Using Python to work with a DataFrame!

The dropna() method is indeed a handy way to remove missing values.

import pandas as pd

# Sample DataFrame
data = pd.DataFrame({
    'A': [1, 2, None],
    'B': [None, 2, 3],
    'C': [1, None, None]
})

# Dropping rows with any missing values
data_cleaned = data.dropna()
print(data_cleaned)
        

In this example, data.dropna() removes any rows that have at least one missing value. If you want to drop columns with missing values instead, you can set the axis parameter to 1:

# Dropping columns with any missing values
data_cleaned = data.dropna(axis=1)
print(data_cleaned)
        

What are alternative methods for handling missing values?

There are several alternative methods for handling missing values in your dataset, each with its own use case and impact on your analysis. Here are a few common techniques:

• Fill with a Constant (Imputation): Replace missing values with a specific constant (e.g., 0, or 'Unknown' for categorical data).

  data.fillna(0, inplace=True)  # For numerical data
data.fillna('Unknown', inplace=True)  # For categorical data
        

• Fill with Mean/Median/Mode: Replace missing values with the mean, median, or mode of the column.

# Fill with mean
data['column_name'].fillna(data['column_name'].mean(), inplace=True) 
# Fill with median 
data['column_name'].fillna(data['column_name'].median(), inplace=True) 
 # Fill with mode
data['column_name'].fillna(data['column_name'].mode()[0], inplace=True)  
        

Step 3: Data Exploration (Exploratory Data Analysis - EDA)

Exploratory Data Analysis (EDA) helps in understanding the main characteristics of the data. It includes descriptive statistics, data visualization, and identifying patterns or trends.

Key steps in EDA:

Summarizing Data: ·

1. Get an overview using data.describe() and data.info() .
2. View unique values for categorical variables: data ['column']. unique()

Data Visualization:

1. Visualize data distribution, correlations, and trends. ·
2. Matplotlib / Seaborn: Used for creating visualizations.

Step 4: Feature Engineering

Feature engineering involves creating new features or transforming existing ones to make the data more suitable for analysis.

Techniques:

1. Encoding Categorical Variables: Convert categorical variables to numerical form using one-hot encoding or label encoding.

· Use pd.get_dummies() for one-hot encoding.

2. Normalization / Standardization: Scale numerical features to a common

range using MinMaxScaler or StandardScaler .

Step 5: Data Modeling

This step involves selecting and applying statistical or machine learning models

to the data to make predictions, classify data, or find patterns.

Common Models:

Regression Models: Linear regression, logistic regression.

Classification Models: Decision trees, random forests, SVMs.

Clustering Models: K-means clustering, hierarchical clustering.

Python Libraries:

Scikit-learn: Provides tools for modeling and evaluation.

Step 6: Model Evaluation

After modeling, evaluate the performance of your model using metrics like accuracy, precision, recall, F1 score, or RMSE (for regression tasks).

Python Libraries:

Scikit-learn has built-in functions for evaluation.

Step 7: Drawing Conclusions & Insights

The final step is to interpret the results of your analysis, drawing actionable insights from the patterns, trends, and models.

1. Summarize findings: Based on EDA and model performance, identify key takeaways.

2. Decision-making: Use the insights to inform business decisions or make data-driven predictions.

3. Reporting: Create a final report with visualizations and explanations of your findings.

Step 8: Automating and Sharing Results

You can automate the analysis pipeline and share results with others using notebooks or dashboards.

Tools: ·

Jupyter Notebooks: To document and share code and visualizations.

Streamlit / Dash: For creating shareable web dashboards.