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

`df.sort_values(by = 'Name', ascending = False)`

`df.sort_values(by = 'Name', ascending = True)`

`df.sort_values(by = 'Name', descending = False)`

`df.sort_values(by = 'Name', descending = True)`

Look at the given DataFrame `df`:

```python
     Name    col2  col3    
r1   Fred    1     5    
r2   Carl    2     6    
r3   Alice   3     7    
r4   BoB     4     8
```

We want to display the DataFrame in alphabetical order with respect to the column `Name` as shown below:

```python
     Name    col2    col3    
r3   Alice    3    7    
r4   BoB      4    8
r2   Carl     2    6    
r1   Fred     1    5
```

Which command will achieve this?

Look at the given DataFrame <code>df</code>:
<pre><code class="language-python"> Name col2 col3 
r1 Fred 1 5 
r2 Carl 2 6 
r3 Alice 3 7 
r4 BoB 4 8
</code></pre>
We want to display the DataFrame in alphabetical order with respect to the column <code>Name</code> as shown below:
<pre><code class="language-python"> Name col2 col3 
r3 Alice 3 7 
r4 BoB 4 8
r2 Carl 2 6 
r1 Fred 1 5
</code></pre>
Which command will achieve this?

Which command will sort the columns of DataFrame `df` based on their index labels?

Which command will sort the columns of DataFrame <code>df</code> based on their index labels?

Imagine a DataFrame `df` with columns `a`, `b`, and `c`. Which command will sort DataFrame `df` by columns `a` and `c`?


Imagine a DataFrame <code>df</code> with columns <code>a</code>, <code>b</code>, and <code>c</code>. Which command will sort DataFrame <code>df</code> by columns <code>a</code> and <code>c</code>?

Imagine a DataFrame `df` that contains student names (column name = `sname`)  and their marks (column name = `marks`). How can we print just the names of all the students whose marks are less than 40?

Imagine a DataFrame <code>df</code> that contains student names (column name = <code>sname</code>) and their marks (column name = <code>marks</code>). How can we print just the names of all the students whose marks are less than 40?

How can we extract the first three rows and the first three columns of a DataFrame given below?

```python
import pandas as pd
L = [ [ 'A', 'B', 'C', 'D'. 'E'], [ 'v', 'w', 'x', 'y', 'z'] ]
df = pd.DataFrame(L)

```


How can we extract the first three rows and the first three columns of a DataFrame given below?
<pre><code class="language-python">import pandas as pd
L = [ [ 'A', 'B', 'C', 'D'. 'E'], [ 'v', 'w', 'x', 'y', 'z'] ]
df = pd.DataFrame(L)

</code></pre>

Which plot can be used to give a statistical summary?

Which plot can be used to give a statistical summary?

Which plot can be used to compare data?

Quiz yourself on sorting columns, filtering, and indexing operations.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: Sorting Columns, Filtering, and Indexing Operations.