Explore advanced pandas techniques for reshaping, manipulating, and handling data. Gain insights into using unconventional features and extended libraries to streamline workflows and enhance data analysis.

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

Which of the `DatetimeIndex` attributes can we use to obtain the week number in the year for a given date?

Which of the <code>DatetimeIndex</code> attributes can we use to obtain the week number in the year for a given date?

**(True or False)** The `DateOffset` objects follow specific calendar duration rules, thereby enabling us to handle more complex business analyses and financial calculations. 

(True or False) The <code>DateOffset</code> objects follow specific calendar duration rules, thereby enabling us to handle more complex business analyses and financial calculations.

`BusinessHour` is different from business dates because it doesn’t take into account weekends and holidays. 

Both `pd.offsets.BDay()` and `pd.offsets.BusinessDay()` refer to the same thing and are interchangeable.

When we pass the list `[1,1,1,1,0,0,1]` into the `weekmask` parameter of `bdate_range()`, it means we’re defining the weekend as Friday and Saturday.

We can use numerous pre-defined native holiday classes in `pandas`, such as the US Federal Holiday calendar, the UK Holiday calendar, and the Chinese Lunar Holiday calendar.

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

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

Upsampling involves reducing the frequency of the data, such as converting daily data to monthly data.

The `interpolate()` method only performs linear interpolation when filling missing values.

When performing resampling, we can use the `agg()` method to apply multiple aggregations at once.

When we apply a shift with  `df.shift(periods=3)`, the time series values shift three periods forward but the index remains the same.

**(True or False)** We can use either `tz_localize()` or `tz_convert()` to change the time zone of an object that has a time zone already assigned.

(True or False) We can use either <code>tz_localize()</code> or <code>tz_convert()</code> to change the time zone of an object that has a time zone already assigned.

When we set `closed='right'`, it means that the right end of the window is closed. It also means that the rolling window aggregates the current observation and N-1 previous rows where N is the window size.

When we apply window operations and aggregation functions, we can’t incorporate the `Groupby` operations.

The larger the `alpha` smoothing factor in `ewm()`, the greater the weights assigned to the more recent observations.

We use the `com` parameter in `ewm()` to specify the window size that the exponential decay should consider.

Handling time series data

Handling time series data

Check your understanding of handling, manipulating, and computing time series 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