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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

20 Must-Know Pandas Function for Exploratory Data Analysis

Chirag Goyal
Chirag Goyal Last Updated : 22 Apr, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Introduction

Exploratory Data Analysis(EDA) is an important component as well as one of the most under-estimated steps in any Data Science project. EDA is essential for well-defined and structured data analysis and should be performed before the machine learning modeling phase.

It involves finding insights from the data upon careful observation and further summarizing its main characteristics. Generally, the real-life data which we work upon contains a lot of ‘noise’, and therefore performing data analysis manually on such datasets becomes a complicated and tedious process.

EDA Pandas

Python Getting Started Tutorial: Scientific Calculation with Pandas | by Data Analysis Enthusiast | Medium

Python is one of the most widely used languages for Data Science particularly because of the presence of various libraries and packages that makes data analysis easier.

Accordingly, Pandas is one of the most popular libraries of Python that helps to present the data in a way which is suitable for analysis via its Series and DataFrame data structures. It provides various functions and methods to both simplify as well as expedite the data analysis process.

Here we use  “TITANIC” Dataset to do the practical implementation of all functions.

 

Firstly, we import Numpy and pandas library and then read the dataset.

import Numpy and pandas EDA

 

Now Let’s Get Started

 

1. df.head( ): By default, it returns the first 5 rows of the Dataframe. To change the default, you may insert a value between the parenthesis to change the number of rows returned.

head pandas eda

2. df.tail( ): By default, it returns the last 5 rows of the Dataframe. This function is used to get the last n rows. This function returns the last n rows from the object based on position.

tail pandas

3. df.info( ): It helps in getting a quick overview of the dataset. This function is used to get a brief summary of the dataframe. This method prints information about a DataFrame including the index dtype and column dtypes, non-null values, and memory usage.

info pandas

4. df.shape: It shows the number of dimensions as well as the size in each dimension. Since data frames are two-dimensional, what shape returns is the number of rows and columns.

shape pandas

5. df.size: Return an int representing the number of elements in this object. Return the number of rows if Series, otherwise returns the number of rows times the number of columns if DataFrame.

size pandas

6. df.ndim: Returns dimension of dataframe/series. 1 for one dimension (series), 2 for two dimensions (dataframe).

ndim pandas

7. df.describe( ): Return a statistical summary for numerical columns present in the dataset. This method calculates some statistical measures like percentile, mean and standard deviation of the numerical values of the Series or DataFrame.

describe

8. df.sample( ): Used to generate a sample randomly either row or column. It allows you to select values randomly from a Series or DataFrame. It is useful when we want to select a random sample from a distribution.

sample

9. df.isnull( ).sum( ): Return the number of missing values in each column.

isnull

10. df.nunique( ): Return number of unique elements in the object. It counts the number of unique entries over columns or rows. It is very useful in categorical features especially in cases where we do not know the number of categories beforehand.

nunique

11. df.index: This function searches for a given element from the start of the list and returns the lowest index where the element appears.

index

12. df.columns: Return the column labels of the dataframe.

columns

13. df.memory_usage( ): Returns how much memory each column uses in bytes. It is useful especially when we work with large data frames.

memory_usage

14. df.dropna( ): This function is used to remove a row or a column from a dataframe that has a NaN or missing values in it.

dropna

15. df.nlargest( ): Returns the first n rows ordered by columns in descending order.

nlargest

16. df.isna( ): This function returns a dataframe filled with boolean values with true indicating missing values.

isna

 

17. df.duplicated( ):  Returns a boolean Series denoting duplicate rows.

duplicated

18. value_counts( ): This function is used to get a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently occurring element. It excludes missing values by default. This function comes in handy when we want to check the problem of class imbalance for a categorical variable.

value_counts

19. df.corr( ): This function is used to find the pairwise correlation of all columns in the dataframe. Any missing values are automatically excluded. For any non-numeric data type columns in the dataframe, it is ignored. This function comes in handy while we doing the Feature Selection by observing the correlation between features and target variable or between variables.

corr

20. df.dtypes: This function shows the data type of each column.

End Notes

Thanks for reading!

If you liked this and want to know more, go visit my other articles on Data Science and Machine Learning by clicking on the Link

Please feel free to contact me on Linkedin, Email.

Something not mentioned or want to share your thoughts? Feel free to comment below And I’ll get back to you.

Till then Stay Home, Stay Safe to prevent the spread of COVID-19, and Keep Learning!

About the Author

Chirag Goyal

Currently, I pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

Chirag Goyal
Chirag Goyal

I am a B.Tech. student (Computer Science major) currently in the pre-final year of my undergrad. My interest lies in the field of Data Science and Machine Learning. I have been pursuing this interest and am eager to work more in these directions. I feel proud to share that I am one of the best students in my class who has a desire to learn many new things in my field.

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Ramesh
Ramesh

Amazing list Chirag. Best wishes for completing your studies and I am sure you will excel in this field.

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