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loc gets rows (or columns) with particular labels from the index. iloc gets rows (or columns) at particular positions in the index (so it only takes integers). ix usually tries to behave like loc but falls back to behaving like iloc if a label is not present in the index. Pandas loc and iloc | Pandas loc tutorial | Pandas iloc slicing | loc vs iloc Pandas loc iloc documentation: 🤍
.loc[ ] - Label based data selecting method .iloc[ ] - Index based data selecting method
Pandas is mainly used for machine learning in the form of dataframes. Once we use panda functions to extract data from our text file or binary file, the data will be formed as a dataframe. And Pandas further allows us to perform various data manipulation operations. There are two commonly used methods to extract data in pandas: .loc and .iloc methods.
In this video, you will learn to fetch rows and columns from a dataframe using the iloc function About CampusX: CampusX is an online mentorship program for engineering students. We offer a 6-month long mentorship to students in the latest cutting - edge technologies like Machine Learning, Python, Web Development, and Deep Learning & Neural networks. At its core, CampusX aims to change this education system of India. We believe that high-quality education is not just for the privileged few. It is the right of everyone who seeks it. Through our mentorship program, we aim to bring quality education to every single student. A mentored student is provided with guidance on how to ace a technology through 24x7 mentorship, live and recorded video lectures, daily skill-building activities, project assignments, and evaluation, hackathons, interactions with industry experts, soft skill training, personal counseling, and comprehensive reports. All we need from you is intent, a ray of passion to learn. Connect with us: Website: 🤍 Medium Blog: 🤍 Facebook: 🤍 Linkedin: linkedin.com/company/campusx-official Instagram: 🤍 Github: 🤍 Email: support🤍campusx.in
Hi everyone! In this video, I'll explain the difference between the methods loc and iloc in Pandas. We'll compare them and see some examples with code. ⭐️ Get access to thousands of Python tutorials, data science guides and more on Medium for $5/month using this link: 🤍 😉 If you'd like to read a Medium guide, check this guide I made before publishing this YouTube video 👉 🤍 👨🏻💻 Code/Dataset used in this video: 🤍 📩 Download the Python Cheat Sheet I use in all my videos (Free PDF): 🤍 🔥 Join My Python for Data Science Bootcamp: 🤍 🔥 8-hour Web Scraping Course in Python: 🤍 ✅ Follow me on Medium: 🤍 Content: 0:00 loc vs iloc 6:06 loc - examples with code 12:28 iloc - examples with code
Want to learn more? Take the full course at 🤍 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. - Welcome back! In the previous lesson, we studied the basics of timing, how to time a chunk of code, and why speed efficiency matters with pandas and in general. In this lesson, we will look at the loc[] and iloc[] pandas functions, and find out which one is the most efficient to select columns and rows in a pandas DataFrame. Let's look at the main dataset we will use in this lesson, which derives from the famous poker card game. In each round, each player has five cards in hand, each one characterized by its symbol, which can be either hearts, diamonds, clubs or spades, and its rank, which ranges from 1 to 13. The dataset consists of every possible combination of five cards one person can posses. Let's take for example the first combination, which corresponds to the first row. We have a 10 of diamonds, a Jack and a King of clubs, a 4 of spades and an ace of hearts. If you are still not completely sure about the dataset, pause the video and look the bottom part of this slide carefully. One of the most useful features of the pandas library is the ease of convenience of selecting specific rows of a Pandas DataFrame. We're going to use iloc[], the index number locator, and loc[], the index name locator. In this example, we want to select the first 500 rows of the poker dataset. Firstly by using the loc[] function, and then by using the iloc[] function. While these two methods have the same syntax, iloc[] performs almost 200% faster than loc[]. iloc[] takes advantage of the order of the indices, which are already sorted, and is therefore faster. We used iloc[] and loc[] to target rows, but we can also use them to locate different features in a pandas DataFrame. In this example, we want to select the first three columns of the poker dataset: the symbol and the rank of the cards that came first in each hand, and the rank of the cards that came second in each hand. We can use the iloc[] function to locate a feature by index. Alternatively, we can simply select one or several columns by name. The syntax of iloc[] for that purpose is simple: we denote with a colon that we want all the rows of the DataFrame and then after the comma, we use a colon followed by a 3 to denote that we want all the columns until the 3rd one. To select columns by name, we simply include the name of the columns we want in double square brackets. In terms of speed, for the task of locating features, loc[] performs 30% faster. In general, the loc[] function works better for for selecting columns, while iloc[] is faster for selecting specific rows. Now that we explored the differences between .iloc() and .loc(), it's your turn to target rows and columns and evaluate performance yourself! #DataCamp #PythonTutorial #Writing #Efficient #Code #pandas #Python
In this Pandas tutorial we will learn how to slice & index a dataframe using loc & iloc. For a tutorial, in text, see this blog post: 🤍 There are several methods to select, slice and index rows and columns from Pandas Dataframes. In this tutorial we will learn to select data by row numbers (.iloc) by label (.loc). The iloc method for slicing Pandas dataframe is used for integer-location based indexing / selection by position whereas the loc method for slicing Pandas dataframe can select rows & column by label/index. We will also cover some of the differences between loc and iloc. Make sure you subscribe to the channel if you haven't: 🤍 » Jupyter Notebook code example: 🤍 » Blogpost about working with Pandas Dataframe: 🤍 » Pandas Documentation: 🤍 Other great videos about working with Pandas dataframe: Head, Tail, and Query Tutorial: 🤍 » How to install Pandas: 🤍 » Creating dataframes (empty, from dictionaries, and arrays): 🤍 » How to read Excel files using Pandas read_excel: 🤍 » Pandas loc documentation: 🤍 » Pandas iloc documentation: 🤍
In this video, we will learn about loc and iloc functions of Pandas. We will learn about the difference between loc and iloc function also their uses. A number of beginners in the field of data science, find loc and iloc functions to be very confusing. So, in this video, we will cut through this confusion and will understand the usage, function and utility of these Pandas function.
In this video, we discuss some ways to access subsets of rows and columns of a Pandas DataFrame using .loc and .iloc
Have you ever been confused about the "right" way to select rows and columns from a DataFrame? pandas gives you an incredible number of options for doing so, but in this video, I'll outline the current best practices for row and column selection using the loc, iloc, and ix methods. SUBSCRIBE to learn data science with Python: 🤍 JOIN the "Data School Insiders" community and receive exclusive rewards: 🤍 RESOURCES GitHub repository for the series: 🤍 "loc" documentation: 🤍 "iloc" documentation: 🤍 "ix" documentation: 🤍 Summary of the pandas API for selection: 🤍 LET'S CONNECT! Newsletter: 🤍 Twitter: 🤍 Facebook: 🤍 LinkedIn: 🤍
In this tutorial, we are gonna learn about Pandas iloc and loc in the greater details. As we know these two functions are used for selecting data from a Pandas Data Frame. Really, when it comes to select data on a DataFrame, Pandas loc and iloc are two top favorites, They are very quick, fast, easy to read and access the data very efficiently. This video tutorial will cover complete understanding of below points: 1.) What is difference between ILOC and LOC in pandas? 2.) How do I use LOC and ILOC in Python? 3.) Find all the rows based on any condition in a column. 4.) Find all the rows with more than one condition. 5.) Select rows with indices using iloc. 6.) Select rows with particular indices and particular columns. 7.) How get specific rows and column using loc and iloc? 8.) How to apply filter using loc on specific column? 9.) What is the use of index and index label in loc and iloc? 10.) Select rows whose column values equals a scalar. 11.) Select rows that satisfy multiple boolean conditions; keep selected columns 12.) More examples... Jupyter Notebook: 🤍 Python Teaser: 🤍 Python Pandas Tutorial: 🤍 Python Playlist: 🤍 Python Data Structure Playlist: 🤍 Python OOPs Playlist: 🤍 Python Excel Automation: 🤍
Here is the video that shows how to create a multi-indexed DataFrame: 🤍 Here is the template to start with this video: 🤍 Feel free to download the student's performance dataset here: 🤍 Please feel free to check out my Data Science blog: 🤍 Twitter profile: 🤍 #datascience #dataanalytics #datavisualization #pandas #python
Pandas loc function tutorial. The loc function is used for extracting row and column data at the same time.
Learn how to use the pandas dataframe loc method for python programming twitter: 🤍python_basics
The video discusses slicing, indexing, .loc and .iloc for dataframes in Python. Update: Please check out the 'Important_Note' below. Timeline & Exercise (Python 3.7) 00:00 - Welcome 00:09 - Outline of video 00:43 - Open Jupyter Notebook 00:56 - Create data 01:42 - How to pull data from dataframe? 03:15 - .loc() to select rows or columns 04:51 - ——————Correction comment —————— 06:50 - .iloc() to select rows or columns 07:40 - ——————Correction comment —————— 15:05 - End Exercise: learndataa exercise for this video can be found at link below: 🤍 Important_Note: # -run the code below)- # Example DataFrame df = pd.DataFrame({'dogs':[3,2,999],'cats':[1,5,1000]}, index=[99,98,0]) # .iloc is purely positional by number only print('df.iloc[0,:]:\n',df.iloc[0,:]) print("Notice!!! that .iloc returns the 1st row in df and NOT row labeled as '0' in index column.") print('\n') # .loc is by given index or label print('df.loc[0,:]:\n',df.loc[0,:]) # -# -# - Thank you.
When it comes to selecting data on your DataFrame, Pandas loc and iloc are two top favorites. They are quick, fast, and easy to read when reviewing code later. 🤍 🤍 Pandas loc will select data based off of the label of your index (row/column labels) whereas Pandas iloc will select data based off of the position of your index (position 1, 2, 3, etc.)
In this video, we're going to discuss how to select multiple rows and columns in Pandas DataFrame. There are various methods for doing it such as loc[], iloc[], ix[], etc. Let's understand each of them with the help of live examples. Download the DataSet [🤍 Check Out the Related Article: Pandas DataFrame.loc[] [🤍 Pandas .iloc[] [🤍 Pandas DataFrame.ix[ ] [🤍 Apply For Video Internship Program - 🤍 Our courses: 🤍 This video is contributed by Akshit Madan. Please Like, Comment, and Share the Video with your friends. #python #pandas #geeksforgeeks #dataframe #datascience #pythonpandas #eda Install our Android App: 🤍 If you wish, translate into the local language and help us reach millions of other geeks: 🤍 Follow us on our Social Media Handles - Twitter- 🤍 LinkedIn- 🤍 Facebook- 🤍 Instagram- 🤍 Reddit- 🤍 Telegram- 🤍 Also, Subscribe if you haven't already! :)
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In this session of 🤍analyticsschool , we will discuss about how to filter the dataframe in pandas. As a data analyst, we provide the analysis to the stakeholders on the basis of the questions that are asked by them to achieve their business objectives. In that scenario, we filter the data on the basis of business objectives and provide the insights to the stakeholders. In this session, we will use LOC method to filter the pandas dataframe using multiple logical conditions. We will also discuss the major advantages of LOC method over square bracket notation method. Chapters: 00:00 - Introduction 00:55 - Square Bracket Example & Disadvantages 03:15 - LOC Method Example & Advantages #analytics #datascience #dataanalytics #pandas #dataanalysis #dataanalyst 🤍analyticsschool
We can get rows and data based on column labels of a DataFrame by using loc(). Difference between iloc and loc loc is label based so we have to use column names. iloc is position based ( integer ). WE can get first row , last row, any row based on various column labels . Similarly we can get the rows using a range of column values of the DataFrame. WE can get value at any position also. We can use callable function with loc to use any logic to get the records. Download the source code from here 🤍 Create sample student DataFrame 🤍 #loc #pandasloc #CallbackFunction #pandas #python #plus2net #dataframeloc #dataframe #ColumnLabel
In this video, I have Compared loc Vs. iloc very clearly with many practical examples. loc Vs. iloc Pandas DataFrame | Python Pandas Tutorial (2020) Data Frame 1 - import pandas as pd dict1 ={'Name':['Priyang','Aadhya','Krisha','Vedant','Parshv', 'Mittal','Archana'], 'Marks':[98,89,99,87,90,83,82], 'Grades':['AA','AB','AA','AB','AC','BA','BB'] } df1=pd.DataFrame(dict1) - Data Frame 2 - import pandas as pd dict1 ={'Name':['Priyang','Aadhya','Krisha','Vedant','Parshv', 'Mittal','Archana'], 'Marks':[98,89,99,87,90,83,82], 'Grades':['AA','AB','AA','AB','AC','BA','BB'] } df1=pd.DataFrame(dict1,index=['stu1','stu2','stu3','stu4','stu5', 'stu6','stu7']) - loc Vs. iloc Pandas DataFrame | Python Pandas Tutorial (2020) If you enjoy these tutorials, like the video, and give it a thumbs up and also share these videos with your friends and families if you think these videos would help him. Please consider clicking the SUBSCRIBE button to be notified of future videos. Thank you all for watching
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Published on Sep 07, 2019: In this video, we will learn to access rows from a dataframe using the .loc function. We can specify the row range we want to access from the dataframe using this. This is a very useful function for exploratory data analysis. In the previous video, we learnt to append two dataframes using python. This is like a union clause in sql that is used to join two resultsets. We will use pandas concat function for the same. Previous Video: 🤍 Additional Reading material: Link to the python in the repository - 🤍 File name is -Row Indexing using .loc.pynb You can download the dataset from the below location: 🤍 File name-test.csv,train.csv SUBSCRIBE to learn more about Power BI,Power Query, Power Pivot, Excel,SQL Server and Python!! 🤍 Our Playists: SQL Playlist :🤍 DAX PlayList : 🤍 Power BI PlayList: 🤍 Power Query Playlist: 🤍 Getting Started with Power BI:🤍 Getting Started with Python: 🤍 Data Science With Python:🤍 ABOUT DAGDOO: Website: Home Page: 🤍 Power BI Tutorials: 🤍 Questions? Comments and SUGESTIONS? You will find me here: Twitter: 🤍dagdooe Category: Science & Technology License: Standard YouTube License
In Video #2 of this Python Pandas Tutorial Series, you'll learn what all about the Pandas Dataframe index and how to select data in Pandas. Check out Video #1: 🤍 Download the dataset here: 🤍 0:00 Introduction 0:30 Recap of Video 1 1:28 Pandas Index Overview 2:00 Set Index Function in Pandas 2:54 Setting Index in Place 4:07 Selecting a Single Column in Pandas 5:15 Iloc and loc functions in Pandas 7:48 Accessing Slices of Data with iloc 9:36 Selecting Data based on Text 11:08 Selecting Conditionally 11:29 And and Or Selections Pandas 12:55 Pandas .isin to Select a List of Values 14:35 Challenge for the Video Learn Python programming the right way! ✅ Check out my eBook to get started with Python for Data Science: 🤍 ✅ Follow me on other platforms: Website: 🤍 Twitter: 🤍 Mailing List: 🤍 (and get a FREE Pandas tip and tricks book and a FREE Guide to SQL)
Watch Part 2: 🤍 This video covers .iloc[], iloc[], and simple index filtering in Pandas. Contact me: alex.sington🤍gmail.com 🤍 Data set: 🤍 Intro: 0:00 Simple index filtering: 0:46 .iloc[]: 6:17 .loc[]: 13:47
In this session, we will learn how to extract a subset of a Pandas DataFrame. This is very useful because we often want to perform operations on subsets of our data. There are many different ways of sub-setting a Pandas DataFrame. You may need to select specific columns with all rows. Sometimes, you want to select specific rows with all columns or select rows and columns that meet a specific criterion, etc. We will learn how to use square bracket notation, loc method and iloc method to extract a subset of a Pandas DataFrame. Chapters: 00:00 - Introduction 01:21- Load The Superstore Dataset 01:36 - Inspect The Dataset 02:55 - Selection Of A Column/Multiple Columns Using [] Method 07:00 - Difference Between LOC & ILOC Method 08:04 - Selection Of A Column/Multiple Columns Using LOC Method 11:30 - Selection Of A Column/Multiple Columns Using ILOC Method 14:23 - Slicing In Pandas DataFrame Using [], LOC, ILOC Method 22:11 - Indexing In Pandas DataFrame Using [], LOC, ILOC Method #analyticsschool #dataanalysis #dataanalyst #dataanalytics #pandaspythontutorial #datascience #locmethod #ilocmethod
In this video, we will be learning how to filter our Pandas dataframes using conditionals. This video is sponsored by Brilliant. Go to 🤍 to sign up for free. Be one of the first 200 people to sign up with this link and get 20% off your premium subscription. In this Python Programming video, we will be learning how to write conditionals in or to filter our data within our Pandas dataframes. This is a fundamental skill to have when using Pandas because it is one of the first things most people do when starting a new Pandas project. Let's get started... The code for this video can be found at: 🤍 StackOverflow Survey Download Page - 🤍 ✅ Support My Channel Through Patreon: 🤍 ✅ Become a Channel Member: 🤍 ✅ One-Time Contribution Through PayPal: 🤍 ✅ Cryptocurrency Donations: Bitcoin Wallet - 3MPH8oY2EAgbLVy7RBMinwcBntggi7qeG3 Ethereum Wallet - 0x151649418616068fB46C3598083817101d3bCD33 Litecoin Wallet - MPvEBY5fxGkmPQgocfJbxP6EmTo5UUXMot ✅ Corey's Public Amazon Wishlist 🤍 ✅ Equipment I Use and Books I Recommend: 🤍 ▶️ You Can Find Me On: My Website - 🤍 My Second Channel - 🤍 Facebook - 🤍 Twitter - 🤍 Instagram - 🤍 #Python #Pandas
‘Pandas loc & iloc Method in Hindi | Python Pandas Part-15 in Hindi’ Course name: “Machine Learning – Beginner to Professional Hands-on Python Course in Hindi” In this tutorial we explain Pandas loc & iloc Method in Hindi and describe these: 1) What is Pandas loc Method 2) What is Pandas iloc method Python Pandas Tutorial Part-14 🤍 Course Playlists- Python Pandas Tutorial in Hindi: 🤍 Machine Learning Beginner to Professional Hands-on Python Course in Hindi: 🤍 Python NumPy Tutorial in Hindi: 🤍 Python Matplotlib Tutorial in Hindi: 🤍 Introduction of Machine Learning: 🤍 For more information: Contact Us: - -Website: 🤍 -YouTube: 🤍 -Facebook: 🤍 -Instagram: 🤍 -Twitter: 🤍 -LinkedIn: 🤍 #Pandasloc #PandasilocinHindi #MachineLearningTutorialinHindi #IndianAIProduction
Have you ever wondered how to do data selection with the Pandas library in Python? Well look no further because, I will demonstrate how to do just that in detail, in this programming tutorial. We will be using .loc, .iloc, and the indexing operator [] to select data on dataframes and series. Video links: numbers dataset: 🤍 Jupyter notebook: 🤍 Pandas documentation (v0.25.3 used): indexing and selecting data: 🤍 loc: 🤍 iloc: 🤍 My personal links: IG: 🤍 GitHub: 🤍
#etlqalabs #etl #sqlinterviewquestionsandanswers #pandas Read rows and columns from a dataframe in pandas | use of .loc and .iloc Playlists for your reference: ETL Testing Tutorial for begginers and advanced level : 🤍 SQL Tutorial for begginers and advanced level : 🤍 Linux Tutorial for beginners and advanced level: 🤍 Data Warehousing Tutorial for beginners and advanced level: 🤍 Hey Guys, If you’re enjoying this video and you’d like to support this channel, please do to consider subscribe this channel! It might seem small, but hitting that subscribe button helps alot and means more contents in future.
This video will show you usage of loc and iloc function in Pandas api. loc is a labe based acessing function whereas iloc used for access data using position.
Video will describe the basics of Python Pandas Indexing using the .loc and .iloc method and the differences between the two
In this video I have explained difference between loc and iloc in python pandas dataframe in hindi with example Label based slicing Position based slicing label indexing Integer indexing Accessing data frame CLASS 12TH IP REVISION TOUR LINK 🤍 Class 12 ip MCQ series playlist link 🤍 👆👆 👆 👆 👆 👆 👆 Telegram link for this pdf 🤍 👆 👆 👆 👆 👆 👆 Tags : difference between loc and iloc, pandas loc vs iloc, iloc vs loc, Pandas loc and iloc, pandas slicing, python, pandas tutorial, data analysis, pandas, loc in pandas, iloc in pandas, loc vs iloc, .loc, .iloc, main difference of loc and iloc, loc and iloc in pandas, pandas tutorial in urdu, pandas tutorial in hindi, pandas in hindi, pandas iloc in hindi, pandas loc in hindi, pandas loc #ipcsbyyogendrasir #humareacademy #pandasbyyogerndrasir 🤍hamariacademy #hamariacademy #ipcsbyyogendrasir
Python Class 12 Pandas | loc and iloc function | Python Pandas | Dataframe Object | In Hindi | Tutorial#12 In this video I have explained Python 12 IP and the topic is Pandas data structure which is series and dataframe. Series and dataframe are two data structure which is used for data analysis as per cbse class 12 syllabus. In this video, I have explained loc and iloc function which is used to extract specified row or specified column. Please help this channel to grow and I promise I will keep uploading quality videos for you Join this channel to get access to perks: 🤍 Link to Previous Video: 🤍 Link to next video: 🤍 Facebook Group: 🤍 Instagram: 🤍 Facebook Page: 🤍 Website: 🤍 Tags Used: pandas loc and iloc loc vs iloc pandas slicing python loc and iloc function python pandas dataframe object python class 12 loc iloc pandas indexing slicing difference between loc and iloc pandas loc vs iloc iloc vs loc python class 12 python class 12 pandas python class 12 ip #codeitup #python #pandas 🤍CBSE 🤍CBSE INDIA 🤍Unacademy CBSE Science 11 & 12 🤍CBSE Udaan XI 🤍Ncert Solutions CBSE ncerthelp.com
In this video we will discuss #.loc and #.iloc in Pandas ▶Tutorial 5 : Different ways of Creating Data Frame - 🤍 ▶Tutorial 4 : How to Read Files Using Pandas ? -🤍 ▶Tutorial 3 : Pandas DataFrame || What is Data Frame ? - 🤍 ▶Tutorial 2 : Pandas Series - 🤍 ▶Tutorial 1 : Pandas Introduction-🤍 ▶ Python Playlist : 🤍 ▶Jupyter notebook tutorial -🤍 ▶Difference between AI vs ML vs DL vs DS-🤍 ▶Basic tools to know for Data Science-🤍 ⚡Channel Description⚡ Hi and Welcome to my channel. This channel focuses mainly on Data Science,any one from technical or non technical background who want to learn data science or to revise the concepts of python, maths,statistics, data science can follow the channel ........... Happy Learning ⭐Video Tags⭐ #PANDAS , #PYTHON BASICS,#PANDAS FOR BEGINEERS python,basics,fundamentals,programming concepts,object,programming fundamentals,PYTHON PROGRAMMING,PYTHON FUNDAMENTALS,interpreter,python libraries,libraries,numpy,pandas,numpy installation,python IDE,install and uninstall,PANDAS,idle,anaconda,series,dataframe,panel,csv filed,read_csv,read_excel,add column,drop column,manipulations,manipulating dataframe,loc in pandas,iloc in pandas,label based,index based,loc vs iloc,.loc,.iloc
In this video we discuss six different methods to filter a pandas dataframe. We go over how to filter a data frame based on a single condition, multiple conditions, multiple conditions and specific columns, based on rows, and missing data. Data can be found at 🤍 Timeline 0:00 Intro 0:07 Data 1:19 Boolean mask 2:30 Multiple conditions 4:50 ~ 6:45 loc 8:37 Query 10:14 Speed test 10:43 iloc 11:58 loc w/ rows 14:00 Missing data
Filtering Pandas Dataframe -1 | Slicing and Dicing a Pandas DataFrame | loc vs iloc vs ix | Selection using loc vs iloc vs ix No matter if you want to visualize the data, do the exploratory data analysis or whatever you want to do with data, selection of rows, columns or any element of data is always used. This video will provide you a step by step guide on how to make the selection using .loc and .iloc dataframe methods. In this video we have covered: 00:00 - Introduction 00:53 - Understanding elements of a dataframe 01:45 - Selecting rows or columns directly or normal rows and columns selection 06:59 - Selection using loc by providing list of rows index numbers and columns' name list 10:28 - Selection using loc by providing range of rows index numbers and a list of columns' name 13:12 - Selection using loc by providing Boolean condition on rows and a list of columns' name 17:06 - Selection using iloc by providing range of rows index numbers and columns position numbers 23:01 - Selection using iloc by providing list of rows index numbers and columns position numbers 23:44 - About .ix You can download the data, used in this video, from our GitHub page using the below: File Name :customers_unique.xlsx URL :🤍 #Learnerea #Python #Pandas #selection #SlicingAndDicing #SubsettingDataFrame #loc #iloc #LocVsIloc #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonMatplotlib
Download this code from 🤍 Certainly! In Pandas, cross-selection with .loc and slices allows you to select specific rows and columns based on both labels and positions. This is a powerful method for accessing data within a DataFrame. I'll provide a detailed tutorial with code examples to demonstrate how to perform cross-selection using .loc and slices in Python's Pandas library. Pandas provides the .loc accessor for label-based indexing and slicing, allowing you to select specific rows and columns using labels. Slicing, on the other hand, provides positional indexing, enabling you to select rows and columns based on their positions within the DataFrame. Let's create a sample DataFrame to demonstrate the cross-selection using .loc and slices. This creates a DataFrame df with labeled rows and columns: This code snippet demonstrates how to select specific rows ('Row2' and 'Row4') and columns ('B' and 'D') using the .loc accessor. The output will be: In this example, the code uses .iloc to select rows from index 1 to 4 (exclusive) and columns from index 1 to 3 (exclusive). The resulting output will be: In summary, using .loc and slices in Pandas allows you to perform cross-selection, selecting specific rows and columns using both labels and positional indices. These methods provide powerful ways to access and manipulate data within a DataFrame, offering flexibility in data analysis and manipulation. In Python, Pandas is a powerful library for data manipulation and analysis. It provides various methods to select and manipulate data within DataFrames, one of which is cross-selection. Cross-selection allows you to select data from a DataFrame using both .loc and slices. In this tutorial, we'll explore how to perform cross-selection in Pandas using .loc and slices with code examples. Cross-selection is the process of selecting a subset of data from a DataFrame based on both the row and column indices. You can think of it as a combination of using .loc for rows and slices for columns, enabling you to extract specific data from your DataFrame. First, let's see how you can use .loc for row selection. Here's the basic syntax: Let's take an example to illustrate this: In this example, we used .loc to select 'Row2' and 'Row4' from the DataFrame. Next, let's use slices to select specific columns. The basic syntax is: Here's an example: In this example, we used slices to select columns 'A' to 'B' from the DataFrame. Now, let's combine row and column selection to perform cross-selection. The syntax
In this video, we delve into one of the fundamental concepts of working with pandas DataFrames – the difference between loc and iloc. loc and iloc are essential methods for accessing and manipulating data in a DataFrame, but they have distinct approaches to locating and selecting data. Join us as we explore how to use loc for label-based indexing and iloc for integer-based indexing. We'll provide clear examples and explanations to help you grasp the key distinctions between these two methods and when to use each of them in your data analysis projects. In Pandas, `loc` and `iloc` are two important methods used to access and manipulate data in a DataFrame. They are used for indexing and selecting data, but they have some key differences in their behavior: 1. `loc` (Label-based indexing): `loc` is used for selection based on the labels of rows and columns. It means you refer to the rows and columns using their actual labels (index and column names). The syntax for using `loc` is `df.loc[row_label, column_label]`. Example: ```python import pandas as pd # Create a sample DataFrame data = { 'Name': ['John', 'Jane', 'Mike', 'Alice'], 'Age': [25, 30, 22, 28], 'City': ['New York', 'London', 'Paris', 'Tokyo'] } df = pd.DataFrame(data, index=['A', 'B', 'C', 'D']) # Using loc to access data print(df.loc['B', 'Age']) # Output: 30 print(df.loc['C', 'Name']) # Output: Mike ``` 2. `iloc` (Integer-based indexing): `iloc` is used for selection based on integer positions of rows and columns. It means you refer to the rows and columns using their numerical indices. The syntax for using `iloc` is `df.iloc[row_index, column_index]`. Example: ```python import pandas as pd # Create a sample DataFrame data = { 'Name': ['John', 'Jane', 'Mike', 'Alice'], 'Age': [25, 30, 22, 28], 'City': ['New York', 'London', 'Paris', 'Tokyo'] } df = pd.DataFrame(data) # Using iloc to access data print(df.iloc[1, 1]) # Output: 30 (Row at index 1, Column at index 1) print(df.iloc[2, 0]) # Output: Mike (Row at index 2, Column at index 0) ``` Key Differences: 1. Input type: - `loc` uses labels (index and column names) to access data. - `iloc` uses integer positions (numerical indices) to access data. 2. Slicing behavior: - When using `loc`, the end index is inclusive in slicing operations. - When using `iloc`, the end index is exclusive in slicing operations, just like Python's regular slicing. 3. Indexing Error: - Using `loc` with a label that doesn't exist in the DataFrame will raise a `KeyError`. - Using `iloc` with an index that is out of bounds (greater than the DataFrame size) will raise an `IndexError`. It's essential to understand the distinction between `loc` and `iloc` since using the wrong method can lead to unexpected results or errors when accessing data in Pandas DataFrames.
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