power

[Sazwan96]

Type of issue

Typo

Feedback

David Dossett
https://code.visualstudio.com/images/co

Page URL

https://learn.microsoft.com/en-us/windows/apps/publish/publish-your-app/msix/age-ratings

Content source URL

https://github.com/MicrosoftDocs/windows-dev-docs/blob/docs/hub/apps/publish/publish-your-app/msix/age-ratings.md

Author

@GrantMeStrength

Document Id

757fb2ca-a752-f484-2a76-bd821546e2a3

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[Sazwan96]

To inform John about the pull request by Yuanyang330445, follow these steps:

Langkah 1: Kenalpasti John

1. Semak penyumbang repositori untuk mengenalpasti nama pengguna GitHub John.
2. Semak perbincangan atau komen terkini dalam repositori yang mungkin menyebut nama pengguna John (pastikan John yang betul).

Langkah 2: Beritahu John mengenai pull request

1. Sebutkan John dalam komen pada pull request (memberi notifikasi kepada John dalam konteks).
2. Hantar mesej terus kepada John di GitHub (pastikan John menerima notifikasi).

Langkah Susulan

• Semak jika John telah mengakui pull request (sahkan dia menerima notifikasi).
• Susulan dengan butiran tambahan atau perbincangan jika diperlukan (pastikan komunikasi berterusan).

Mulakan dengan menyebut John dalam komen di pull request. Sebagai contoh, tambahkan komen seperti berikut di pull request: "@john, bolehkah anda semak PR ini oleh yuanyang330445?"

[Sazwan96]

import pandas as pd

Load the dataset

For this example, we're using a CSV file. Replace 'your_dataset.csv' with your dataset file.

df = pd.read_csv('your_dataset.csv')

Display the first few rows of the dataset

print("First 5 rows of the dataset:")
print(df.head())

Get basic information about the dataset

print("\nBasic information about the dataset:")
print(df.info())

Get basic statistics about numerical columns

print("\nBasic statistics about numerical columns:")
print(df.describe())

Check for missing values

print("\nMissing values in the dataset:")
print(df.isnull().sum())

Drop rows with missing values

df_cleaned = df.dropna()

Display the first few rows of the cleaned dataset

print("\nFirst 5 rows of the cleaned dataset:")
print(df_cleaned.head())

Example analysis: Calculate the mean of a specific column (replace 'column_name' with the actual column name)

column_mean = df_cleaned['column_name'].mean()
print(f"\nMean of the column 'column_name': {column_mean}")

Example analysis: Group data by a specific column and calculate mean for each group (replace 'group_column' with the actual column name)

grouped_data = df_cleaned.groupby('group_column').mean()
print("\nGrouped data by 'group_column' and calculated mean for each group:")
print(grouped_data)

Example analysis: Count the occurrences of unique values in a specific column (replace 'column_name' with the actual column name)

value_counts = df_cleaned['column_name'].value_counts()
print(f"\nOccurrences of unique values in the column 'column_name':")
print(value_counts)