How does a lambda function work in Pandas?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

# How does a lambda function work in Pandas?

How does a lambda function work in Pandas

## Recipe Objective

Suppose we wish to perform any operation on row or column of dataset. Now we can do it through a loop or set up a lambda function for the same.

So this recipe is a short example on How does a lambda function work in Pandas. Let's get started.

## Step 1 - Import the library

``` import pandas as pd import seaborn as sb ```

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays. Seaborn will help us in importing dataset.

## Step 2 - Setup the Data

``` df = sb.load_dataset('tips') print(df.head()) ```

Here we have imported datset from seaborn library.

## Step 3 - Applying lambda function

``` df = df.assign(Percentage = lambda x: (x['tip'] /x['total_bill'] * 100)) print(df.head()) ```

Now we are creating a newcolumn of percentage as tip vs total bill.

## Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

```Scroll down the ipython file to visualize the final output.
```

We can see a new column of Percentage being created with the formula as mentioned above.

#### Relevant Projects

##### Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

##### Time Series Python Project using Greykite and Neural Prophet
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

##### Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

##### Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

##### Census Income Data Set Project - Predict Adult Census Income
Use the Adult Income dataset to predict whether income exceeds 50K yr based on census data.

##### Deep dive into BERT & transformers - Part 1
In this project, we will cover in detail the architecture of a transformer used in natural language processing use cases. We will go through the key nlp areas in the pre-transformer stage like bow, word2vec...and then the origin and gradual refinement of transformers. Finally, we will study one of the most popular state of the art transformer models, called BERT and use it for text classification on a large dataset.

##### Time Series LSTM forecasting
In this project, we will use time-series forecasting to predict the values of a sensor using multiple dependent variables. A variety of machine learning models are applied in this task of time series forecasting. We will see a comparison between the LSTM, ARIMA and Regression models. Classical forecasting methods like ARIMA are still popular and powerful but they lack the overall generalizability that memory-based models like LSTM offer. Every model has its own advantages and disadvantages and that will be discussed. The main objective of this article is to lead you through building a working LSTM model and it's different variants such as Vanilla, Stacked, Bidirectional, etc. There will be special focus on customized data preparation for LSTM.

##### Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

##### Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

##### Classification - Zero to hero - Part 1
Classification is one of the basic things in ML and most of us jump to Neural networks or boosting to predict classes. But more often than not, to make the other person understand how the classification is happening, we need to use basic models like Logistic, decision trees etc. In this project we talk about you can apply various basic techniques, the maths and intuition behind them and how they paved way to bagging and boosting of the world