⭕️⭕️نموذج نصفي ل د.منى⭕️⭕️

🔻ركزوا عليه ونذاكر على اساسه
احتمال كبير يكون النهائي قريب له.

⭕️نموذج نصفي اساسيات علوم بيانات

موقع ترجمة فخم جدا وبيفيدكم أثناء المذاكره لترجمة السلايدات.

https://www.onlinedoctranslator.com/ar/translationform

مُرسل من أ.جواد.

⭕️نموذج نصفي  اساسيات علوم بيانات....رقم 10و11 الإجابة الصحيحة رقم A

⭕️ بالنسبة لأسئلة الاكواد ⭕️

بتجيب اكواد واحنا نختار الأصح من الاختيارات حسب عمل الكود والداله الموجوده بالكود نفسه، يعني نذاكر كل شي ونركز ع الدوال وعملهن اكثر.

والاختبار بيجي نفس صياغه النصفي وبسيط للفاهم.

هذا على ما سألتها، والله يعدي الاختبار ع خير.🌚✨

💢💢ملخص للدوال وأهم المفاهيم💢💢

1. Basic Python & Data Type Functions
print()
Used to display outputs and check variable values.
Type Conversion
int(), float(), str(), bool()
Used to define or convert the data type of a variable.
type()
Identifies the data type of a given value (Integer, Float, String, Boolean).
2. Exploratory & Distribution Functions
head()
Displays the first rows of a dataset for quick inspection.
unique()
Returns all distinct values in a categorical column.
nunique()
Counts the number of unique values in a column.
value_counts()
Counts the frequency of each unique value in a categorical variable.
3. Aggregation Functions
These functions operate on a column and return a single aggregated value.
sum()
Calculates the total of numeric values.
mean()
Computes the arithmetic average.
median()
Returns the middle value (50th percentile).
max() and min()
Identify the largest and smallest values (used to compute range).
count()
Counts non-missing (non-null) values.
std()
Computes the standard deviation, measuring data spread.
mode()
Returns the most frequent value (commonly used for categorical data).
var()
Calculates variance.
4. Data Processing & Transformation
groupby()
Splits the DataFrame into groups based on one or more columns.
It does not produce output by itself and must be followed by an aggregation function.
aggregate() / agg()
Applies multiple aggregation functions at once (e.g., min, max, mean).
map()
Used in feature engineering to transform or reclassify values.
abs()
Returns absolute values, useful when calculating measures like Mean Absolute Deviation (MAD).
5. Relationship & Statistical Functions
cov()
Calculates covariance to determine the direction of a relationship between two variables.
corr()
Calculates correlation by normalizing covariance, producing values between -1 and +1 to represent relationship strength and direction.
6. Visualization Functions
Used to visually analyze data distributions and relationships.
plot.pie()
Visualizes categorical distributions as pie charts.
plot.bar()
Displays categorical frequencies using bar charts.
plot.line()
Shows trends over continuous or ordered numeric data.
plot.scatter()
Visualizes relationships between two numeric variables.
Connections Between Functions
Grouping and Aggregation
groupby() prepares the data, while aggregation functions (mean, sum, count, etc.) produce the actual results.
Covariance vs Correlation
corr() is a standardized form of cov().
Covariance depends on measurement units, while correlation is unit-free.
Operation Chaining
Functions can be chained together, such as:
df.groupby(...).mean().plot.bar()
This allows data processing, aggregation, and visualization in a single workflow.
Categorical vs Numeric Analysis
Categorical data: unique(), nunique(), value_counts()
Numeric data: mean(), median(), std(), var()