Pandas Essentials for AI and Data Beginners

Pandas is the main library for data handling in Python. It gives you tools to load, clean, explore, and prepare datasets. AI and machine learning depend on clean data, and Pandas makes this process simple.

1. Importing Pandas

import pandas as pd

This is the standard import name. Always use pd.

2. Creating a DataFrame

data = {
    "name": ["Sara", "Ali", "Yassine"],
    "score": [85, 92, 78],
    "age": [21, 23, 22]
}

df = pd.DataFrame(data)
print(df)

A DataFrame is like an excel sheet. Rows and columns.

3. Loading Data From Files

CSV.
Excel.
JSON.

df = pd.read_csv("data.csv")
df = pd.read_excel("file.xlsx")
df = pd.read_json("info.json")

4. Inspecting Data

print(df.head())      
print(df.tail())      
print(df.info())      
print(df.describe())

head. first rows.
info. types and null values.
describe. stats for numeric columns.

5. Selecting Columns

print(df["name"])
print(df[["name", "score"]])

Always use brackets for lists of columns.

6. Selecting Rows

Use iloc for index based selection. Use loc for label based selection.

print(df.iloc[0])        
print(df.iloc[0:2])      
print(df.loc[0])

7. Filtering Rows With Conditions

high_scores = df[df["score"] > 80]
print(high_scores)

Filter two conditions.

f = df[(df["score"] > 80) & (df["age"] < 23)]
print(f)

8. Adding and Updating Columns

df["passed"] = df["score"] >= 80
print(df)

You can also update values.

df["score"] = df["score"] + 5

9. Handling Missing Data

Check missing values.

print(df.isnull().sum())

Fill missing values.

df["age"] = df["age"].fillna(df["age"].mean())

Drop rows with missing values.

df = df.dropna()

10. Sorting Data

df_sorted = df.sort_values("score", ascending=False)
print(df_sorted)

11. Grouping Data

Grouping helps with summarization.

grouped = df.groupby("age")["score"].mean()
print(grouped)

You can use many functions.

df.groupby("age").agg({"score": ["mean", "max"]})

12. Merging DataFrames

Pandas supports joins like SQL.

merged = pd.merge(df1, df2, on="id", how="inner")

inner.
left.
right.
outer.

13. Removing Columns or Rows

df = df.drop("age", axis=1)  
df = df.drop(0)

14. Converting Data Types

df["age"] = df["age"].astype(int)

Always check types before model training.

15. Exporting Data

df.to_csv("output.csv", index=False)
df.to_excel("output.xlsx", index=False)

16. Mini Projects With Pandas

Project 1. Sales Analysis

Load sales CSV.
Group by product.
Compute revenue.
Sort by top sellers.

Project 2. Student Grades Report

Load student data.
Fill missing grades.
Create pass or fail column.
Export results.

Project 3. Data Cleaning Script

Load messy dataset.
Drop duplicates.
Fix types.
Handle missing values.

Pandas in Moroccan Darija

Pandas kay3awnk t3alj data b tariqa sahl. Kat load data. Katcleani. Katsort. Katgroupi. W kat7ddarha l machine learning.

df.head() bach tchouf data.
df["col"] bach tjib column.
Filtering bach tselecti rows.
groupby bach tdir stats.
merge bach tjma3 datasets.

Ila t9der tkhddem b Pandas mzyan, t9der tbni projects dial AI bla t3qid.

Conclusion

Pandas offers strong tools for data loading, cleaning, filtering, and grouping. These essentials prepare your datasets for machine learning and deep learning. Learn them well to build strong AI workflows.

Ai With Darija