Pandas tips and tricks | in Urdu/Hindi | Day-9

#datascience
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[@salmatahira4865]

Bht informative lectures h apk sary mashallah

[@umesalmahyder8529]

A great Teacher. Excellent work Ustaad. excellent step by step guide to learn pandas easily. u have extremely simplified all the concepts. & best part is that you start the class with Bismillah.. a Super Power Word

[@zohaib.qureshi]

Bro you are an excellent teacher.

[@salmatahira4865]

Jazak Allah

[@fitfaizan1]

Thank you so much sir for this series

[@desiikhayal]

i have watched may videos on data science but you are doing marvelous. I really appreciate your effort .I m subscribing you

[@azamiqbal8792]

Great 👍

[@alpha7101]

break for gap achi thi ...

[@StarGazeUniverse]

23:43 The Iris flower dataset is a widely used dataset in the field of data science and machine learning. It's often employed for tasks such as classification, clustering, and data exploration. This dataset contains information about different species of iris flowers. Here's an explanation of the Iris flower dataset and its columns: **Columns in the Iris Dataset**: 1. **Sepal Length (cm)**: This column represents the length of the sepals, which are the outermost whorls of the flower. Sepals are usually green and protect the inner flower parts. 2. **Sepal Width (cm)**: This column represents the width of the sepals. 3. **Petal Length (cm)**: This column represents the length of the petals. Petals are the colorful, inner parts of the flower. 4. **Petal Width (cm)**: This column represents the width of the petals. 5. **Species**: This column indicates the species of the iris flower. It's the target variable, and there are three species in the dataset: setosa, versicolor, and virginica. The Iris dataset is typically used for supervised learning tasks, where the goal is to predict the species of the iris flower (the target variable) based on the measurements of sepal and petal length and width. It is a small, well-understood dataset, making it an excellent choice for educational purposes, testing and demonstrating various machine learning algorithms, and practicing data analysis techniques. The Iris dataset is available in many machine learning libraries and is commonly used for tasks such as classification, clustering, and dimensionality reduction. It's often one of the first datasets that data scientists and machine learning practitioners work with to learn the ropes of data analysis and model building.

[@MDJUNAIDALAM-i4i]

love from india

[@wasimakram1891]

❤

[@monicagarg6691]

@41:46 nan means not a number. It represents a missing value in data.

[@muhammadnouman3175]

done

[@shafiullahmarwat7165]

Columns Description: You can check what actually the 'titanic' dataset cols mean: campus.lakeforest.edu/frank/FILES/MLFfiles/Bio150/Titanic/TitanicMETA.pdf

[@abdullahsajid9373]

Survived: Binary variable indicating whether a passenger survived (1) or not (0). Pclass: Ticket class, representing the socio-economic status of the passenger (1st, 2nd, or 3rd class). Sex: Gender of the passenger. Age: Age of the passenger. SibSp: Number of siblings/spouses aboard the Titanic. Parch: Number of parents/children aboard the Titanic. Fare: Fare paid by the passenger. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton). Class: Same as Pclass but with a different naming convention. Who: Describes the passenger as a man, woman, or child. Adult_male: Boolean variable indicating whether the passenger is an adult male. Deck: Deck on which the passenger's cabin is located. Embark_town: Town from which the passenger embarked. Alive: Survival status (yes or no). Alone: Boolean variable indicating whether the passenger was traveling alone (without siblings, spouses, parents, or children).

[@Arslanvlog0]

survival - Survival (0 = No; 1 = Yes) class - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) name - Name sex - Sex age - Age sibsp - Number of Siblings/Spouses Aboard parch - Number of Parents/Children Aboard ticket - Ticket Number fare - Passenger Fare cabin - Cabin embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) boat - Lifeboat (if survived) body - Body number (if did not survive and body was recovered)

[@SelroosirSirsir]

Sir i am following ur series on yt but you skipped the day 7 and 8. SO now can you please tell how i follow?

[@rehanahmed7072]

sir ye sara code please github ma upload krdy udr sa pratice krna ma bhot east ho jata ha app sara days ka code github ma uplaod krdy thanks very much

[@saeedahmadldcqurantafseer1855]

32 pounds

[@muhammadihsan8177]

nan is not a number

[@muhammadtahir4832]

phool.to_excel("IRIS.xlsx")

[@monicagarg6691]

@15:56 sns.get_dataset_names() . there are 22 datasets in seaborn library

[@Khanacade]

done

[@ahmaddureedalvi2057]

Done