EDA (Exploratory Data Analysis) is one of the first steps performed on a given dataset. It helps us to understand more about our data and gives us an idea of manipulations and cleaning we might have to do. EDA can take anywhere from a few lines to a few hundred lines. In this tutorial, we will look at libraries which help us perform EDA in a few lines
We will use the Titanic Dataset provide by Kaggle. Using Panda’s describe() method, we get the below output
Screenshot by Author
As you can see the Age Column has missing values. The below libraries are basically describe() on steroids.
Screencast of EDA Report Generated by Pandas Profiling
First, we will instal the library
pip install pandas-profiling
Next, we will import the library and generate the report
import pandas_profiling prof_report = pandas_profiling.ProfileReport(df , title = 'Titanic Report')
To display it inside the notebook
To generate it as an HTML file
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The techniques for Reshaping, Grouping, and Pivoting the data
Python has turned the world just in a decade with its popularity and efficiency. Python has followed offering a reliable trend of Data Science which comprises of:
· Data Gathering
· Data Cleaning
· Machine Learning models
· Visualization of Data
Pandas is a very fundamental inbuilt library in Python uptakes a lot of the area. It is an open-source library that is easy to use, providing high efficiency and many tools used in the analysis of data for Python programming.
Pandas is an in-memory no SQL type database providing a helping hand for basic SQL constructs, statistical methods, and the capability of graphing. As it was built on top of Cython, it runs quicker along with consuming less time to access some memory within a machine.
→Pandas have a very advanced feature of carrying out some operations on the group of data frames.
→Data Frame: A 2D data that is labeled. It contains different columns and rows.
So, in this article, we’re going to have our quick eyes on some methods of grouping, reshaping, and pivoting the data.
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Use the right tool for Exploratory Data Analysis (EDA)
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In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-
Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.
Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.
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Are you looking for best web application business ideas that make money in 2021?
There are lots of simple web app ideas but all those web application business ideas do not make money.
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Analyzing and Visualizing the data is the most important and time taking process. We need to invest a lot of time in order to clearly analyze what the data is all about and what it is trying to tell. We use different types of python libraries and functions to visualize the patterns and anomalies in the dataset in order to get familiar with the dataset.
Bamboolib is GUI for pandas DataFrames that enables anyone to work with python in Jupyter Notebook or JupyterLab. Bamboolib is a highly interactive and extensively helpful library in order to analyze, visualize, and manipulate the data. Even a person with a non-technical background can use it to draw insights from data because it does not require any coding experience.
Bamboolib is used by more than 100 companies and it allows data analysts to work with python even without writing code. Bamboolib is not open-source which means that you need to buy bamboolib in order to use it, but it provides a 14-day free trial version so that you can fully explore it and see how it can be useful for you.
In this article, we will explore different uses of bamboolib and see how it saves time and effort. We will explore different functions that bamboolib provides and also export the code used for that functionality.
For exploring bamboolib we first need to register on their website for a 14 days free trial. After registering you will receive an email with the activation key on registered email-id. Like any other python library, we need to install bamboolib using pip install bamboolib.
We will need to import pandas for loading the dataset and bamboolib for visualizing the dataset.
import bamboolib as bam
import pandas as pd
We will be using a car design dataset here, which contains different attributes related to Automobile Manufacturing companies. You can download this dataset from Kaggle. We will use pandas to load this dataset.
df = pd.read_csv(‘car_design.csv’)
This is the main step where we will analyze and visualize the dataset using bamboolib.
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