Exploratory Data Analysis

Exploratory Data Analysis (EDA) is essential in any data science project. The primary objective is to explore and understand the dataset at hand.

In this process, data analysts use various statistical and visualization techniques to uncover patterns, identify trends, and extract valuable insights from the data. EDA enables analysts to deeply understand the data, including its distribution, correlation, and other relevant characteristics. It also helps them identify potential outliers, missing values, and other anomalies that could affect the quality of the analysis.

EDA is a crucial step that helps data scientists lay the groundwork for building predictive models and making informed business decisions.

How To

import pandas as pd
df = pd.read_csv("data/housing.csv")
df.head()

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity
0	-122.23	37.88	41.0	880.0	129.0	322.0	126.0	8.3252	452600.0	NEAR BAY
1	-122.22	37.86	21.0	7099.0	1106.0	2401.0	1138.0	8.3014	358500.0	NEAR BAY
2	-122.24	37.85	52.0	1467.0	190.0	496.0	177.0	7.2574	352100.0	NEAR BAY
3	-122.25	37.85	52.0	1274.0	235.0	558.0	219.0	5.6431	341300.0	NEAR BAY
4	-122.25	37.85	52.0	1627.0	280.0	565.0	259.0	3.8462	342200.0	NEAR BAY

from ydata_profiling import ProfileReport

profile = ProfileReport(df)

profile

profile.to_widgets()

profile = ProfileReport(df, explorative=True)
profile.to_widgets()

profile.to_file("pandas_profile.html")