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This article will explain in detail how to use the exploratory data analysis tool easyeda. The content of the article is of high quality, so the editor will share it with you for reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.

Exploratory data analysis (Exploratory Data Analysis) is a common task in the daily work of algorithm engineers. By analyzing the missing data, distribution, and correlation with tags, data EDA can help algorithm engineers evaluate the quality of data, understand the characteristics of data, provide direction for feature engineering, and form preliminary expectations of the upper limit of effect that can be achieved by subsequent models.

I encapsulated the set of scripts I often use for data EDA into a library easyeda and opened it up in PyPI and github. This is the first open source Python package I have released personally.

First, brief introduction to easyeda

Easyeda is a simple but practical exploratory data analysis tool.

Easyeda can carry out exploratory data analysis on common binary classification problems, multi-classification problems, and regression problems.

Easyeda supports exploratory data analysis of all common numeric, string, and Bool data attributes.

Easyeda supports common missing value analysis, data distribution analysis, data and label correlation analysis, training set and test set data co-distribution analysis.

Second, use examples

First, you can install easyeda using pip.

Pip install easyeda

It can then be called with the following example code.

From easyeda import eda

Import pandas as pd

From sklearn import datasets

From sklearn.model_selection import train_test_split

Boston = datasets.load_boston ()

Df = pd.DataFrame (boston.data,columns = boston.feature_names)

Df ["label"] = boston.target

Dftrain,dftest = train_test_split (df,test_size = 0.3)

Dfeda = eda (dftrain,dftest,language= "Chinese")

There are only two lines of core code:

From easyeda import eda

Dfeda = eda (dftrain,dftest,language= "Chinese")

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