Delve into effective data manipulation with pandas. Discover filtering, merging, grouping, and reshaping techniques through hands-on examples, best practices, and performance tips for real-world analysis.

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The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

Type conversion is not allowed in pandas. 

Suppose we have a Series `S` of type `object`. Which method can be used to change the type to string?


Suppose we have a Series <code>S</code> of type <code>object</code>. Which method can be used to change the type to string?

Suppose we have a Series `S` of type `object` and we want all of the data in the Series to be in lowercase. What can help us do that? 


Suppose we have a Series <code>S</code> of type <code>object</code> and we want all of the data in the Series to be in lowercase. What can help us do that?

Suppose we have a Series `date` that contains dates in m/d/y format. We want to change the format to `d-m-y` format. Which method can help us do that?

Suppose we have a Series <code>date</code> that contains dates in m/d/y format. We want to change the format to <code>d-m-y</code> format. Which method can help us do that?

The command will give us the maximum number of sales from April to June for the last year. 


The command will give us the maximum number of sales from April to June for all the years.

The command will give us the maximum number of sales annually, ending in June.

Suppose we have a Series `sales` that contains monthly sales data for 10 years. What does the following command do? 

```python
sales.resample('A-Jun').max()
```



Suppose we have a Series <code>sales</code> that contains monthly sales data for 10 years. What does the following command do?
<pre><code class="language-python">sales.resample('A-Jun').max()
</code></pre>

```
['2015-03-01','2016-03-01','2017-03-01','2018-03-01','2019-03-01','2020-03-01']
```

```python
['2015-06-01','2016-06-01','2017-06-01','2018-06-01','2019-06-01','2020-06-01']  
```

```python
['2015-12-31','2016-12-31','2017-12-31','2018-12-31','2019-12-31'] 
```


```python
['2016-01-01','2017-01-01','2018-01-01','2019-01-01','2020-01-01'] 
```

Given the following command, what date-time index will we get?

```python
pd.date_range('2015-03-01', '2020-06-01', freq = 'Y')
```

Given the following command, what date-time index will we get?
<pre><code class="language-python">pd.date_range('2015-03-01', '2020-06-01', freq = 'Y')
</code></pre>

Quiz yourself on string manipulation, date-time manipulation, and DT indexing.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: String Manipulation, Date-Time Manipulation, and DT Indexing