Advanced Pandas - Going Beyond the Basics 3.png

Educative_pandas.tar.gz

default

runJupyter

runJupyter2

runJupyter3

runJupyter4

Pandas is an easy-to-use and performant Python library for data manipulation and analysis. It is an important toolkit in a world where the volume of data created and captured is growing exponentially.

In this course, you’ll discover an in-depth exploration of the pandas library. The course covers advanced  techniques to reshape data, manipulate data, and handle various data types. You will also learn how to use less conventional pandas features to streamline workflows, while also leveraging extended pandas libraries for enhanced data analysis.

Whether you are an aspiring data scientist, a researcher, or a seasoned professional looking to sharpen your skills, this course is your pathway to becoming proficient in using pandas to analyze and manipulate data effectively and efficiently.

Roshan

This course was transformative for my data science skills. It offered a deep dive into complex data manipulation techniques, making advanced concepts easily understandable. The practical exercises were directly applicable to real-world data challenges

Data Science Analyst

Advanced pandas—Going Beyond the Basics

**(True or False)** Combining data from multiple sources allows for a more comprehensive and accurate representation of a particular subject or topic. 

(True or False) Combining data from multiple sources allows for a more comprehensive and accurate representation of a particular subject or topic.

Instead of using `concat()` for joins, the `merge()` function is better because it offers greater flexibility in implementing database-like Join operations.

In the `concat()` function, the `axis=0` implements row-wise concatenation while `axis=1` implements column-wise concatenation.

The `append()` function is great for concatenating DataFrames vertically.

The `concat()` function does not allow for hierarchical indexing.

**(Select all that apply.)** Which statements are *true*? 

(Select all that apply.) Which statements are true?

INNER JOIN gives us all the rows from the right table and the matching rows from the left table.

INNER JOIN gives us all rows from both tables, including the ones that do not match.

INNER JOIN gives us all the rows from the left table and the matching rows from the right table.

INNER JOIN gives us all the matching rows from the left and right tables.

INNER JOIN gives us a collection of paired combinations of each row of the left table with each row of the right table. 

Which statement accurately describes the INNER JOIN operation?

Which statement accurately describes the INNER JOIN operation?

Compares the three DataFrames to identify rows where differences exist.

Performs an INNER JOIN of the three DataFrames based on the indexes.

Creates a MultiIndex DataFrame based on the three DataFrames.

Performs a row-wise concatenation of the three DataFrames.

What does this `pandas` code do?

```Python
pd.merge(pd.merge(df1, df2, left_index=True, right_index=True), df3, left_index=True, right_index=True)
```

What does this <code>pandas</code> code do?
<pre><code class="language-Python">pd.merge(pd.merge(df1, df2, left_index=True, right_index=True), df3, left_index=True, right_index=True)
</code></pre>

`merge()` is used for joining DataFrames on their index, while `join()` is used for merging DataFrames using a common column as a key.

`merge()` is used for merging DataFrames using a common column as a key, while `join()` is used for joining DataFrames on their index.

There is no distinct difference between `merge()` and `join()`.

`merge()` can be used for the different types of JOIN, whereas `join()` is limited to INNER JOIN only.

What is the difference between `merge()` and `join()`?

What is the difference between <code>merge()</code> and <code>join()</code>?

Combining data

Combining data

Check your understanding of the different techniques used for combining data.

Before We Begin

Reading Data into pandas

Combining Data

Reshaping and Manipulating Data

Encoding Data Types

Handling Numerical Data

Handling Categorical Data

Handling Text Data

Handling Time Series Data

Handling Sparse Data Structures

Handling Missing Data

Leveraging Further Features of pandas

Utilizing Extended Libraries

Wrap Up

Appendix

Quiz