pandas.tar.gz

pandas

jupyter

jupyter-display

Python

pandas-with-sqlite3

jupyter-live

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

What is the correct syntax to create a pandas Series from a Python list?

What is the correct syntax to create a pandas Series from a Python list?

What is the correct syntax to return the first value of a pandas Series?

What is the correct syntax to return the first value of a pandas Series?

`pd.Series(data, label = ["a", "b", "c"])`

`pd.Series(data, index = ["a", "b", "c"])`

`pd.Series(data, name = ["a", "b", "c"])`

What is the correct syntax to add the labels "a," "b," and "c" to a pandas Series `data`?

What is the correct syntax to add the labels “a,” “b,” and “c” to a pandas Series <code>data</code>?

`S = pd.Series(data = A, index = B, dtype = np.float64) 
`

We want to create a Series with the given two sequences: 

```python3
A = [110, 220, 330, 440]
B = ['w', 'x', 'y', 'z']
```

Which command will _not_ create a Series?



We want to create a Series with the given two sequences:
<pre><code class="language-python3">A = [110, 220, 330, 440]
B = ['w', 'x', 'y', 'z']
</code></pre>
Which command will not create a Series?

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: pandas Series