labrijisaad/Data-scientist-tools-Pandas

In this hands-on training notebook, we'll see all the basics of the Pandas library used for data science/data analysis and machine learning tasks.

🛠️ Data scientist tools - Pandas :

• 🎯 In this hands-on training notebook, we'll see the most important features of the Pandas library used for data science/data analysis and machine learning tasks in Python.
• 📫 Feel free to contact me if anything is wrong or if anything needs to be changed 😎! labrijisaad@gmail.com
• 🙌 Notebook made by @labriji_saad inspired by the work of ageron.

🕊️ General overview :

👣 Here are the steps we followed in this notebook :

1️⃣ Setup

2️⃣ Series objects

2️⃣1️⃣ Creating a Series

2️⃣2️⃣ Series are similar to a 1D ndarray!

2️⃣3️⃣ Index labels

2️⃣4️⃣ Init from dict

2️⃣5️⃣ Automatic alignment

2️⃣6️⃣ Init with a scalar

2️⃣7️⃣ Series name

2️⃣8️⃣ Plotting a Series

2️⃣9️⃣ Handling time

2️⃣1️⃣0️⃣ Time range

2️⃣1️⃣1️⃣ Resampling

2️⃣1️⃣2️⃣ Upsampling & interpolation

2️⃣1️⃣3️⃣ Timezones

2️⃣1️⃣4️⃣ Periods

3️⃣ DataFrame objects

3️⃣1️⃣ Creating a Dataframe

3️⃣2️⃣ Multi-indexing

3️⃣3️⃣ Dropping a level

3️⃣4️⃣ Transposing

3️⃣5️⃣ Stacking & unstacking levels

3️⃣6️⃣ Most methods return modified copies

3️⃣7️⃣ Accessing rows

3️⃣8️⃣ Adding & removing columns

3️⃣9️⃣ Assigning new columns

3️⃣1️⃣0️⃣ Evaluating an expression

3️⃣1️⃣1️⃣ Querying a DataFrame

3️⃣1️⃣2️⃣ Sorting a DataFrame

3️⃣1️⃣3️⃣ Plotting a DataFrame

3️⃣1️⃣4️⃣ Operations on DataFrames

3️⃣1️⃣5️⃣ Automatic alignment

3️⃣1️⃣6️⃣ Handling missing data

3️⃣1️⃣7️⃣ Aggregating with groupby

3️⃣1️⃣8️⃣ Pivot tables

3️⃣1️⃣9️⃣ Overview functions

3️⃣2️⃣0️⃣ Saving & loading

3️⃣2️⃣0️⃣1️⃣ Saving

3️⃣2️⃣0️⃣2️⃣ Loading

3️⃣2️⃣1️⃣ Combining DataFrames

3️⃣2️⃣2️⃣ Concatenation

3️⃣2️⃣3️⃣ Categories

4️⃣ What next ?