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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pandas

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Python

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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

```python
     a    b    
0    1    2    
1    2    2
2    2    1    
3    10   20    
4    20   20    
5    20   10    
```

```python
     a    b        
0    1    2    
1    2    2
2    2    1    
0    10   20
1    20   20
2    20   10
```

```python
     a     b     c     d    
0    1     2     3     NaN    
1    2     2     4     NaN
2    2     1     3     NaN    
3    10    20    NaN   30
4    20    20    NaN   40
5    20    10    NaN   30
```

```python
     a     b     c     d    
0    1     2     3     NaN
1    2     2     4     NaN
2    2     1     3     NaN
0    10    20    NaN   30
1    20    20    NaN   40
2    20    10    NaN   30 
```

Look at the two DataFrames `df1` and `df2` below:

```python
     a    b    c    
0    1    2    3    
1    2    2    4
2    2    1    3
```

and

```python
     a     b     d    
0    10    20    30    
1    20    20    40
2    20    10    30    
```

We want to concatenate the two DataFrames into one. 
What will be the output of the following command?

```python
pd.concat([df1, df2], ignore_index = True, join = 'inner')
```

Look at the two DataFrames <code>df1</code> and <code>df2</code> below:
<pre><code class="language-python"> a b c 
0 1 2 3 
1 2 2 4
2 2 1 3
</code></pre>
and
<pre><code class="language-python"> a b d 
0 10 20 30 
1 20 20 40
2 20 10 30 
</code></pre>
We want to concatenate the two DataFrames into one.
What will be the output of the following command?
<pre><code class="language-python">pd.concat([df1, df2], ignore_index = True, join = 'inner')
</code></pre>

Full join (known as outer join in pandas)

What is not a join type in Pandas?

Which join results in instances of `NAN` when there isn’t a perfect match?

Which join results in instances of <code>NAN</code> when there isn’t a perfect match?

Assume two DataFrames `df1` and `df2`. What is the correct syntax of using an inner join in pandas?


Assume two DataFrames <code>df1</code> and <code>df2</code>. What is the correct syntax of using an inner join in pandas?

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: Joining in pandas