Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

pandas contains extensive capabilities and features for time series data, so it provides a lot of functions to deal with datetime related requirements.

# Generate date range

`date_range` can generate a series of dates where the default frequency is `DAY`.

The example below generates a series of five days.
```
import pandas as pd
pd.date_range("1/1/2020", periods=5)
```
You can change the frequency from `DAY` to `WEEK` by passing `freq="W"`.
```
import pandas as pd
pd.date_range("1/1/2020", periods=5, freq="W")
```

`bdate_range` is a special function that also generates a series of dates, however, it excludes the weekend. The usage is the same as `date_range`.

Below is a table about the common abbreviation for frequency. 
| abbreviation  |  meaning |
|---|---|
| D  | day  |
| W  | week  | 
| M  | month  |
|B|work day|
|SM|half month|
|MS|month start|
|Q|end of quarter|
|H|hour|
|T|minute|

`date_range` also supports (start, end) mode, just like the Python function `range`.

Let's see how to deal with datetime in pandas.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Deal With datetime

Generate date range