Survey data analysis pattern in Data Analysis Python - Step-by-Step Execution

Concept Flow - Survey data analysis pattern

Load survey data

↓

Clean data: handle missing, fix types

↓

Explore data: summary stats, counts

↓

Analyze patterns: group, aggregate

↓

Visualize results: charts, tables

↓

Interpret insights and report

This flow shows the main steps to analyze survey data: load, clean, explore, analyze, visualize, and interpret.

Execution Sample

Data Analysis Python

import pandas as pd

# Load data
survey = pd.DataFrame({
    'Age': [25, 30, None, 22],
    'Satisfaction': [4, 5, 3, None]
})

# Clean data
survey = survey.fillna(survey.mean())

# Analyze average satisfaction by age group
survey['AgeGroup'] = pd.cut(survey['Age'], bins=[20, 25, 30, 35], labels=['20-25', '26-30', '31-35'])
result = survey.groupby('AgeGroup')['Satisfaction'].mean()

This code loads survey data, fills missing values, groups by age ranges, and calculates average satisfaction.

Execution Table

Step	Action	Data State	Result
1	Create DataFrame with missing values	{'Age': [25,30,None,22], 'Satisfaction':[4,5,3,None]}	DataFrame with NaNs
2	Fill missing values with column mean	{'Age': [25,30,25.6667,22], 'Satisfaction':[4,5,3,4]}	No missing values
3	Create AgeGroup bins	AgeGroup assigned as ['20-25', '26-30', '26-30', '20-25']	New column AgeGroup added
4	Group by AgeGroup and calculate mean Satisfaction	{'20-25': 4.0, '26-30': 4.0, '31-35': NaN}	Series with average satisfaction
5	End of analysis	Final grouped averages	Ready for visualization or reporting

💡 All steps completed; missing values handled; grouped averages calculated.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
survey	{'Age': [25,30,None,22], 'Satisfaction':[4,5,3,None]}	{'Age': [25,30,25.6667,22], 'Satisfaction':[4,5,3,4]}	{'Age': [...], 'Satisfaction': [...], 'AgeGroup': ['20-25','26-30','26-30','20-25']}	Grouped by AgeGroup	Series with mean satisfaction per AgeGroup

Key Moments - 3 Insights

Why do we fill missing values before grouping?

Filling missing values ensures groups have complete data for accurate averages, as shown in step 2 of the execution_table.

How does pd.cut assign age groups?

pd.cut divides ages into bins with labels; step 3 shows ages mapped to '20-25' or '26-30' groups.

Why is there NaN for '31-35' group in results?

No ages fall into '31-35' bin, so mean satisfaction is NaN, as seen in step 4 of the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the value of 'Age' for the third entry after filling missing values?

A 30

B 25.6667

C None

D 22

💡 Hint

Check the 'Data State' column at step 2 in execution_table.

At which step is the 'AgeGroup' column added to the data?

A Step 3

B Step 1

C Step 4

D Step 5

💡 Hint

Look for the action mentioning 'Create AgeGroup bins' in execution_table.

If we did not fill missing values, what would likely happen to the average satisfaction calculation?

A It would ignore missing values and calculate correctly

B It would cause an error and stop execution

C The averages might be incorrect or NaN for groups with missing data

D It would fill missing values automatically

💡 Hint

Refer to key_moments about filling missing values before grouping.

Concept Snapshot

Survey Data Analysis Pattern:
1. Load data (e.g., CSV, DataFrame)
2. Clean data (handle missing values, fix types)
3. Explore data (summary stats, counts)
4. Analyze patterns (group by categories, aggregate)
5. Visualize results (charts, tables)
6. Interpret insights for decisions

Full Transcript

This visual execution shows how to analyze survey data step-by-step. First, we load data with some missing values. Next, we fill missing values with the column mean to avoid errors in calculations. Then, we create age groups using pd.cut to categorize ages. After that, we group data by these age groups and calculate the average satisfaction score for each group. Finally, we have a summary of average satisfaction by age group ready for visualization or reporting. Key points include why filling missing values is important before grouping, how age groups are assigned, and why some groups may have no data resulting in NaN averages.

Practice

1. What does the value_counts() function do when analyzing survey data in Python?

easy

A. Calculates the average of numerical answers

B. Groups data by categories for comparison

C. Counts how many times each unique answer appears

D. Removes missing values from the dataset

Solution

1. Step 1: Understand the purpose of value_counts()

   This function counts the frequency of each unique value in a column, showing how many times each answer appears.

2. Step 2: Compare with other functions

   Calculating averages is done by mean(), grouping is done by groupby(), and removing missing values uses dropna().

3. Final Answer:

   Counts how many times each unique answer appears -> Option C

4. Quick Check:

   value_counts() = count unique answers [OK]

Hint: Remember: value_counts counts answers, not averages [OK]

Common Mistakes:

• Confusing value_counts with mean()
• Thinking value_counts groups data
• Assuming it removes missing data

2. Which of the following is the correct syntax to get the average rating from a survey column named rating in a pandas DataFrame df?

easy

A. df['rating'].mean()

B. df['rating'].count()

C. df.groupby('rating').sum()

D. df['rating'].value_counts()

Solution

1. Step 1: Identify the function for average calculation

   The mean() function calculates the average of numerical values in a column.

2. Step 2: Check other options

   count() counts non-missing values, groupby().sum() sums grouped data, and value_counts() counts unique values.

3. Final Answer:

   df['rating'].mean() -> Option A

4. Quick Check:

   Average = mean() [OK]

Hint: Use mean() to get average of numeric columns [OK]

Common Mistakes:

• Using count() instead of mean()
• Confusing value_counts() with average
• Trying to sum without grouping

3. Given the following code, what will be the output?

import pandas as pd
data = {'gender': ['M', 'F', 'F', 'M', 'F'], 'score': [5, 3, 4, 2, 5]}
df = pd.DataFrame(data)
result = df.groupby('gender')['score'].mean()
print(result)

medium

A. gender
F 3.5
M 4.0
Name: score, dtype: float64

B. gender
F 4.0
M 3.5
Name: score, dtype: float64

C. gender
M 4.0
F 3.5
Name: score, dtype: float64

D. gender
F 3.0
M 4.0
Name: score, dtype: float64

Solution

1. Step 1: Group scores by gender

   Group 'score' values by 'gender': For 'F' scores are [3,4,5], for 'M' scores are [5,2].

2. Step 2: Calculate mean for each group

   Mean for 'F' = (3+4+5)/3 = 4.0; Mean for 'M' = (5+2)/2 = 3.5.

3. Final Answer:

   gender F 4.0 M 3.5 Name: score, dtype: float64 -> Option B

4. Quick Check:

   Group mean = correct averages [OK]

Hint: Calculate group means by averaging grouped values [OK]

Common Mistakes:

• Mixing up group labels and values
• Calculating sums instead of means
• Ignoring group sizes

4. The following code is intended to count how many times each answer appears in the 'response' column, but it raises an error. What is the error and how to fix it?

import pandas as pd
df = pd.DataFrame({'response': ['Yes', 'No', 'Yes', 'Maybe']})
counts = df.response.value_count()
print(counts)

medium

A. KeyError because 'response' column does not exist

B. SyntaxError due to missing parentheses in print statement

C. TypeError because value_count() requires an argument

D. AttributeError because the method name should be value_counts()

Solution

1. Step 1: Identify the method name error

   The method value_count() is incorrect; the correct method is value_counts() with an 's' at the end.

2. Step 2: Correct the method name

   Changing value_count() to value_counts() fixes the AttributeError and allows counting unique values.

3. Final Answer:

   AttributeError because the method name should be value_counts() -> Option D

4. Quick Check:

   Correct method is value_counts() [OK]

Hint: Remember the method ends with 's': value_counts() [OK]

Common Mistakes:

• Misspelling method name without 's'
• Confusing print syntax errors
• Assuming column name is missing

5. You have a survey DataFrame df with columns age_group and satisfaction_score. How would you calculate the average satisfaction score for each age group and then find which age group has the highest average score?

hard

A. Use df.groupby('age_group')['satisfaction_score'].mean().idxmax()

B. Use df['satisfaction_score'].max() directly

C. Use df.value_counts('age_group') and pick max

D. Use df.groupby('satisfaction_score')['age_group'].mean()

Solution

1. Step 1: Group by age group and calculate average satisfaction

   Use groupby('age_group')['satisfaction_score'].mean() to get average scores per group.

2. Step 2: Find the age group with the highest average

   Use idxmax() on the resulting Series to get the group label with the highest average score.

3. Final Answer:

   Use df.groupby('age_group')['satisfaction_score'].mean().idxmax() -> Option A

4. Quick Check:

   Group mean + idxmax() finds highest average [OK]

Hint: Chain mean() and idxmax() to find top group quickly [OK]

Common Mistakes:

• Using max() on scores without grouping
• Confusing value_counts() with averages
• Grouping by wrong column