First data analysis walkthrough in Data Analysis Python

Introduction

We do a first data analysis to understand what the data looks like and find useful information.

When you get a new dataset and want to know what it contains.

Before making decisions based on data, to check its quality and patterns.

To find simple summaries like averages or counts.

To spot missing or strange values in the data.

To prepare for more complex analysis or modeling.

Syntax

Data Analysis Python

import pandas as pd

# Load data
df = pd.read_csv('file.csv')

# See first rows
df.head()

# Get summary info
df.info()

df.describe()

pandas is a popular Python library for data analysis.

head() shows first rows, info() shows data types and missing values, describe() gives basic statistics.

Examples

Load data from a CSV file and show the first 5 rows.

Data Analysis Python

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())

Show information about columns, data types, and missing values.

Data Analysis Python

print(df.info())

Get basic statistics like mean, min, max for numeric columns.

Data Analysis Python

print(df.describe())

Sample Program

This code creates a small dataset, loads it into a table, and prints the first rows, info, and summary statistics.

Data Analysis Python

import pandas as pd

# Create a small example dataset
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 28],
    'Salary': [50000, 60000, 70000, 80000, 55000]
}

# Load data into DataFrame
df = pd.DataFrame(data)

# Show first rows
print('First rows:')
print(df.head())

# Show info
print('\nData info:')
df.info()

# Show summary statistics
print('\nSummary statistics:')
print(df.describe())

Important Notes

Always check the first few rows to understand the data structure.

Look for missing values or wrong data types with info().

Summary statistics help find outliers or unusual values.

Summary

First data analysis helps you understand your data quickly.

Use head(), info(), and describe() to explore data.

This step is important before any deeper analysis or modeling.

Practice

1. What is the main purpose of using head() on a DataFrame during the first data analysis walkthrough?

easy

A. To check the data types of each column

B. To get summary statistics of numerical columns

C. To quickly see the first few rows of the data

D. To remove missing values from the data

Solution

1. Step 1: Understand the function head()

   The head() function shows the first few rows of a DataFrame, usually the first 5 rows by default.

2. Step 2: Purpose in data analysis walkthrough

   Seeing the first few rows helps you quickly understand what kind of data you have and check if it loaded correctly.

3. Final Answer:

   To quickly see the first few rows of the data -> Option C

4. Quick Check:

   head() shows first rows [OK]

Hint: Remember: head() shows top rows fast [OK]

Common Mistakes:

• Confusing head() with describe()
• Thinking head() gives statistics
• Assuming head() cleans data

2. Which of the following is the correct syntax to get a summary of data types and non-null counts in a DataFrame named df?

easy

A. df.describe()

B. df.info()

C. df.head()

D. df.summary()

Solution

1. Step 1: Identify the function for data types and non-null counts

   The info() method on a DataFrame shows data types and counts of non-null values per column.

2. Step 2: Match syntax with DataFrame df

   Calling df.info() correctly applies this method to the DataFrame named df.

3. Final Answer:

   df.info() -> Option B

4. Quick Check:

   info() shows data types and counts [OK]

Hint: Use info() to check data types and missing data [OK]

Common Mistakes:

• Using describe() to check data types
• Trying summary() which does not exist
• Confusing head() with info()

3. Given the following code:

import pandas as pd
data = {'age': [25, 30, 22, 40, 28], 'salary': [50000, 60000, 45000, 80000, 52000]}
df = pd.DataFrame(data)
summary = df.describe()

What will be the output of summary.loc['mean', 'salary']?

medium

A. 52000.0

B. SyntaxError

C. 60000.0

D. 57400.0

Solution

1. Step 1: Calculate the mean of the 'salary' column

   Sum salaries: 50000 + 60000 + 45000 + 80000 + 52000 = 287000. Number of entries: 5. Mean = 287000 / 5 = 57400.0.

2. Step 2: Understand describe() output

   The describe() method returns a DataFrame with statistics including 'mean' as an index row. Accessing summary.loc['mean', 'salary'] gives the mean salary value.

3. Final Answer:

   57400.0 -> Option D

4. Quick Check:

   Mean salary = 57400.0 [OK]

Hint: Sum values and divide by count for mean [OK]

Common Mistakes:

• Adding incorrectly or missing one value
• Confusing mean with median or mode
• Using wrong index or column name

4. You run the following code but get an error:

import pandas as pd
data = {'name': ['Alice', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data)
print(df.describe())

What is the most likely reason for the error or unexpected output?

medium

A. The describe() method cannot handle string columns by default

B. The DataFrame is missing a numeric column

C. The DataFrame is empty

D. The describe() method requires a parameter

Solution

1. Step 1: Check DataFrame columns and types

   The DataFrame has a string column 'name' and a numeric column 'age'.

2. Step 2: Understand describe() behavior

   By default, describe() summarizes only numeric columns. It ignores string columns unless specified.

3. Final Answer:

   The describe() method cannot handle string columns by default -> Option A

4. Quick Check:

   describe() skips strings by default [OK]

Hint: describe() defaults to numeric columns only [OK]

Common Mistakes:

• Expecting describe() to summarize strings automatically
• Thinking DataFrame is empty
• Assuming describe() needs parameters always

5. You have a DataFrame df with columns ['product', 'price', 'quantity']. You want to quickly find the average price of products that have quantity greater than 10. Which code snippet correctly performs this first data analysis step?

hard

A. df[df['quantity'] > 10]['price'].mean()

B. df['price'].mean(df['quantity'] > 10)

C. df.mean()['price'] > 10

D. df.describe('price' > 10)

Solution

1. Step 1: Filter rows where quantity > 10

   Use df[df['quantity'] > 10] to select only rows with quantity greater than 10.

2. Step 2: Calculate mean of 'price' column on filtered data

   Access the 'price' column of filtered rows and call mean() to get average price.

3. Final Answer:

   df[df['quantity'] > 10]['price'].mean() -> Option A

4. Quick Check:

   Filter then mean() gives correct average [OK]

Hint: Filter first, then calculate mean [OK]

Common Mistakes:

• Passing condition inside mean() incorrectly
• Using mean() on entire DataFrame without filtering
• Wrong syntax for describe()