Discovering Data Insights: 12 Advanced Python Packages for Efficient Data Exploration
Exploratory data analysis (EDA) is a critical step in the data science process. It involves analyzing and summarizing data to gain insights and understand its underlying patterns, relationships, and distributions. EDA can help you to identify outliers, missing values, and other data quality issues. It can also help you to identify relationships between different features in your data. This information can be used to improve the performance of machine learning models and other data science tasks.
Benefits of Exploratory Data Analysis (EDA)
- It can help you to identify outliers and missing values. Outliers are data points that are significantly different from the rest of the data. Missing values are data points that are not present in the dataset. Both outliers and missing values can impact the performance of machine learning models. EDA can help you to identify these issues so that you can take steps to address them.
import vaex
# Load data
df = vaex.read_csv(‘data.csv’)
# Compute on-the-fly statistics
df.describe()
import dtale
import pandas as pd
# Load data
df = pd.read_csv(‘data.csv’)
# Launch D-Tale interface
dtale.show(df)
import sweetviz
# Compare two dataframes
report = sweetviz.compare([train_df, test_df], ‘Train’, ‘Test’)
report.show_html(‘report.html’)