Hits: 91
Applied Data Science Coding in Python: How to get descriptive statistics of Dataset
Descriptive statistics is a branch of statistics that deals with summarizing and describing a dataset. Descriptive statistics helps to understand the characteristics of the data, such as its central tendency, spread, shape, and so on.
There are several ways to get the descriptive statistics of a dataset in Python:
Using the describe()
function in the pandas
library: The describe()
function can be used to calculate various summary statistics of a dataset. It takes a Pandas DataFrame or Series as an input, and returns the count, mean, standard deviation, minimum, and maximum values, as well as the 25th, 50th (median), and 75th percentiles.
Using the mean()
, std()
, min()
, max()
, median()
, and other functions in the numpy
library: The mean()
, std()
, min()
, max()
, median()
, and other functions can be used to calculate specific summary statistics of a dataset. They take a Pandas DataFrame or Series as an input, and return the corresponding summary statistic.
Using the scipy
library: The scipy.stats
module provides a wide range of statistical functions, including scipy.stats.describe()
function which provides a detailed summary of statistics of a dataset.
In summary, you can use the describe()
function from pandas
library, mean()
, std()
, min()
, max()
, median()
functions from numpy
library or scipy.stats.describe()
function from scipy
library to get the descriptive statistics of a dataset in Python. Descriptive statistics can provide a good overview of the data, and also helps to identify outliers, missing values and other characteristics of the dataset.
In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get descriptive statistics of Dataset.
Applied Data Science Coding in Python: How to get descriptive statistics of Dataset
Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding