Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA)
What is Exploratory Data Analysis?
Exploratory Data Analysis means eDA is the process of understanding data before building a model. It checks structure, missing values, summary and patterns.
In real programs, this topic helps in understanding data before modelling. Learn the idea first, then type the program yourself and compare the output.
| Point | Details |
|---|---|
| Course Area | Data Science Tools and concepts used to analyse, clean and present data. |
| Main Use | understanding data before modelling |
| Example File | exploratory-data-analysis.py |
| Practice Focus | Run, change values, and explain the output line by line. |
Why should you learn this?
- It is useful for understanding data before modelling.
- It connects with finding patterns.
- It improves your ability to read, write and debug Python programs.
Important Terms
These terms are used directly in this lesson. Understand them before memorising the code.
| Term | Meaning |
|---|---|
| shape | Dimensions of an array. |
| describe | describe is an important term in this topic. |
| info | info is an important term in this topic. |
| missing values | Blank or unavailable data values. |
| patterns | patterns is an important term in this topic. |
Syntax / Basic Pattern
The simple pattern is: prepare data, apply the concept, then show the result.
import pandas as pd
df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})
print(df.shape)
print(df.describe())
print(df.isnull().sum())Complete Example Program
import pandas as pd
df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})
print(df.shape)
print(df.describe())
print(df.isnull().sum())Expected Output
Program Explanation
import pandas as pdimports ready-made features from a module/library.df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})stores a value in df.print(df.shape)displays information or calculated result on the screen.print(df.describe())displays information or calculated result on the screen.print(df.isnull().sum())displays information or calculated result on the screen.
Where will you use it?
- Understanding data before modelling.
- Finding patterns.
- Detecting outliers.
Common Mistakes
- Analysing data before checking missing values, duplicates and data types.
- Changing original data without keeping a clean copy.
- Creating charts without title, labels or explanation.
Practice Tasks
- Type the program in
exploratory-data-analysis.pyand run it. - Change input values or sample data and observe the new output.
- Create one example related to understanding data before modelling.
- Write 5 lines explaining the logic in your own words.
Summary
Exploratory Data Analysis is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.
Exploratory Data Analysis क्या है?
Exploratory Data Analysis ka matlab hai: EDA is the process of understanding data before building a model. It checks structure, missing values, summary and patterns. Simple words me, ye topic practical Python programs likhne me direct use hota hai.
Is topic ko sirf definition ke liye nahi, balki understanding data before modelling jaise real examples ke liye practice karein.
यह क्यों सीखना जरूरी है?
- Ye understanding data before modelling me kaam aata hai.
- Ye finding patterns se bhi connected hai.
- Isse aap code ka output aur errors better samajh paate hain.
Important Terms
| Term | Meaning |
|---|---|
| shape | Dimensions of an array. |
| describe | describe is an important term in this topic. |
| info | info is an important term in this topic. |
| missing values | Blank or unavailable data values. |
| patterns | patterns is an important term in this topic. |
Syntax / Basic Pattern
Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.
import pandas as pd
df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})
print(df.shape)
print(df.describe())
print(df.isnull().sum())Complete Example Program
import pandas as pd
df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})
print(df.shape)
print(df.describe())
print(df.isnull().sum())Expected Output
Program Explanation
import pandas as pdimports ready-made features from a module/library.df = pd.DataFrame({"Age": [15, 16, 17], "Marks": [80, 90, 85]})stores a value in df.print(df.shape)displays information or calculated result on the screen.print(df.describe())displays information or calculated result on the screen.print(df.isnull().sum())displays information or calculated result on the screen.
Practical Uses
- Understanding data before modelling.
- Finding patterns.
- Detecting outliers.
Common Mistakes
- Analysing data before checking missing values, duplicates and data types.
- Changing original data without keeping a clean copy.
- Creating charts without title, labels or explanation.
Practice Tasks
- Program ko
exploratory-data-analysis.pyfile me type karke run karein. - Values change karke output compare karein.
- understanding data before modelling par ek छोटा example banayen.
- Logic ko apne words me 5 lines me likhein.
सारांश
Exploratory Data Analysis ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.