Chet  Lubowitz

Chet Lubowitz

1598682780

4 Must-Know Libraries in Pandas Ecosystem

Pandas is a very powerful and versatile Python data analysis library that expedites the preprocessing steps of data science projects. It provides numerous functions and methods that are quite useful in data analysis.

Although the built-in functions of Pandas are capable of performing efficient data analysis, custom made tools or libraries add value to Pandas. In this post, we will explore 4 tools that enhance the data analysis process with Pandas.


Missingno

Pandas provides functions to check the number of missing values in the dataset. **Missingno **library takes it one step further and provides the distribution of missing values in the dataset by informative visualizations.

Using the plots of Missingno, we are able to see where the missing values are located in each column and if there is a correlation between missing values of different columns. Before handling missing values, it is very important to explore them in the dataset. Thus, I consider **Missingno **as a highly valuable asset in data cleaning and preprocessing steps.

Let’s first try to explore a dataset about the movies on streaming platforms. The dataset is available here on Kaggle.

The dataset contains 16744 movies and 17 features that describe each movie. Pandas **isna **function combined with sum() gives us the number of missing values in each column. But, we need more than the count in some cases. Let’s explore the missing values with Missingno.

import missingno as msno
%matplotlib inline #render plots within jupyter notebook

The first tool we will use is the missing value matrix.

msno.matrix(df)

Image for post

White lines indicate missing values. “Age” and “Rotten Tomatoes” columns are dominated by white lines. But, there is an interesting trend in the other columns that have missing values. They mostly have missing values in common rows. If a row has a missing value in “Directors” columns, it is likely to have missing values in “Genres”, “Country”, “Language”, and “Runtime” columns. This is highly valuable information when handling missing values

#machine-learning #data-science #programming #pandas #artificial-intelligence

What is GEEK

Buddha Community

4 Must-Know Libraries in Pandas Ecosystem
Kasey  Turcotte

Kasey Turcotte

1623140954

Playing with Pandas library

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.

#pandas #data-science #python #artificial-intelligence #playing with pandas library #pandas library

Chet  Lubowitz

Chet Lubowitz

1598682780

4 Must-Know Libraries in Pandas Ecosystem

Pandas is a very powerful and versatile Python data analysis library that expedites the preprocessing steps of data science projects. It provides numerous functions and methods that are quite useful in data analysis.

Although the built-in functions of Pandas are capable of performing efficient data analysis, custom made tools or libraries add value to Pandas. In this post, we will explore 4 tools that enhance the data analysis process with Pandas.


Missingno

Pandas provides functions to check the number of missing values in the dataset. **Missingno **library takes it one step further and provides the distribution of missing values in the dataset by informative visualizations.

Using the plots of Missingno, we are able to see where the missing values are located in each column and if there is a correlation between missing values of different columns. Before handling missing values, it is very important to explore them in the dataset. Thus, I consider **Missingno **as a highly valuable asset in data cleaning and preprocessing steps.

Let’s first try to explore a dataset about the movies on streaming platforms. The dataset is available here on Kaggle.

The dataset contains 16744 movies and 17 features that describe each movie. Pandas **isna **function combined with sum() gives us the number of missing values in each column. But, we need more than the count in some cases. Let’s explore the missing values with Missingno.

import missingno as msno
%matplotlib inline #render plots within jupyter notebook

The first tool we will use is the missing value matrix.

msno.matrix(df)

Image for post

White lines indicate missing values. “Age” and “Rotten Tomatoes” columns are dominated by white lines. But, there is an interesting trend in the other columns that have missing values. They mostly have missing values in common rows. If a row has a missing value in “Directors” columns, it is likely to have missing values in “Genres”, “Country”, “Language”, and “Runtime” columns. This is highly valuable information when handling missing values

#machine-learning #data-science #programming #pandas #artificial-intelligence

August  Larson

August Larson

1625100480

4 Cool Python Libraries That You Should Know About

Discover useful Python libraries that you should try out in your next project

Some of my most popular blogs are about Python libraries. I believe that they are so popular because Python libraries have the power to save us a lot of time and headaches. The problem is that most people focus on those most popular libraries but forget that multiple less-known Python libraries are just as good as their most famous cousins.

Finding new Python libraries can also be problematic. Sometimes we read about these great libraries, and when we try them, they don’t work as we expected. If this has ever happened to you, fear no more. I got your back!

In this blog, I will show you four Python libraries and why you should try them. Let’s get started.

#python #coding #programming #cool python libraries #python libraries #4 cool python libraries

Paula  Hall

Paula Hall

1623389988

4 Pandas GroupBy Tricks You Should Know

Use Pandas GroupBy more flexibly and creatively

As one of the most popular libraries in Python, Pandas has been utilised very commonly especially in data EDA (Exploratory Data Analysis) jobs. Very typically, it can be used for filtering and transforming dataset just like what we usually do using SQL queries. They share a lot of similar concepts such as joining tables. However, some features from them have the same names but different concepts. “Group By” is one of them.

In this article, I’ll introduce some tricks for the Pandas group by function, which could improve our productivity in EDA jobs. Hopefully at least one is something you never familiar with so that it could help you.

I’m sure that you know how to import Pandas in Python, but still, let me put it here. All the rest of the code in this article assume Pandas has been imported as follows.

import pandas as pd

#python #technology #data-science #programming #4 pandas groupby tricks you should know #pandas groupby tricks

Udit Vashisht

1586702221

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

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

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.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial