Getting Started with PivotPal

A comprehensive guide to set up, understand, and utilize the functionalities of the PivotPal Python package.

Table of Contents

  1. 1. Installing the Package: Get PivotPal ready in your notebook.

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  2. 2. Importing PivotPal: Access the functionalities of PivotPal in your workspace.

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  3. 3. The 'helper' Function: A guide to the myriad of functions in PivotPal.

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  4. 4. Massive Dataset Analysis: A deep dive into a dataset with over 19 million entries.

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  5. 5. Titanic Dataset: Exploring missing data within the Titanic dataset.

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  6. 6. Airbnb Dataset: Analyzing the distribution of 'Room Type'.

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1. First install the package to your notebook:

!pip install pivotpal

2. Then import the PivotPal Package:

import pivotpal as pp

3. Understanding the 'helper' Function in the Pivot Pal Package

Overview: A detailed explanation of the 'helper' function designed to assist users in understanding the functionalities of the 'Pivot Pal' package.

Explanation: The 'helper' function provides descriptions of various functions available in the 'Pivot Pal' package. It offers guidance on how to use them based on a keyword provided by the user. If no keyword is provided, it displays a list of all available functions.

Function SignatureDescription
pp.distribution(df, "column_name")Displays the distribution of values for a given column.
pp.range(df)Shows the minimum and maximum values for each column in the dataset.
pp.unique(df)Provides a count of unique values for each column.
pp.summarise(df)Summarizes numeric columns with count, sum, mean, median, max, and min values.
pp.missing(df)Provides a summary of missing values for each column in the dataset.
pp.zeros(df)Summarizes columns with zero values and their respective counts.

The table showcases some of the functions available in the 'Pivot Pal' package and their descriptions. The 'helper' function can provide details on these functions and more based on user input.

4. Understanding Our Massive Dataset

Overview: Diving into the specifics of our dataset that boasts over 19 million entries.

Explanation: Handling big datasets can be daunting. It's essential to get a clear picture of its structure and peculiarities. Here, we're taking a closer look at the various aspects of our dataset, from the types of data it contains to the presence of any missing values.

Total Entries19,269,992
Number of Features12
Features with Missing Data7
Repeated Entries1,455,794
Most Common Data Typetext
Features with Only Yes/No Data0
Features with Only Zeroes0
Different Types of Data2
Features with Numbers3
Features with Text9

This table gives a quick overview of our dataset. It's quite large with 19 million entries. Most of the data is textual, but we also have some numerical features. Notably, none of the features have just binary or zero values, which adds to the dataset's richness.

5. Exploring Missing Data in the Titanic Dataset

Overview: A deep dive into the missing data within the Titanic dataset using the PivotPal Python package.

Explanation: The Titanic dataset is one of the most popular datasets used in data science. It contains information about the passengers onboard the Titanic, including their age, cabin, and embarkation point. In this exploration, we'll focus on identifying and understanding the missing data within this dataset.

Column NameMissing CountMissing %

The table above showcases the columns in the Titanic dataset with missing values. The 'Cabin' column has the highest number of missing values, with 687 missing entries, accounting for 77% of the total data. The 'Age' column has 177 missing values, which is 20% of the data. Lastly, the 'Embarked' column has only 2 missing values, making up 0% of the dataset.

6. Distribution Analysis of 'Room Type' in the Airbnb Dataset

Overview: A comprehensive look at the distribution of different room types within the Airbnb dataset.

Explanation: The Airbnb dataset provides insights into various listings and their attributes. One of the key attributes is the 'Room Type'. In this exploration, we'll focus on understanding the distribution of different room types available in the dataset.

pp.distribution(airbnb_data, 'Room Type')
Room TypeCount%
Entire Home/Apt518651.76
Private Room460745.98
Shared Room2262.26

The table above showcases the distribution of room types in the Airbnb dataset. 'Entire Home/Apt' is the most preferred room type, accounting for 51.76% of the dataset. This is closely followed by 'Private Room' with 45.98%. 'Shared Room' is the least common, making up only 2.26% of the dataset.