SciPy with Pandas for data handling - Mini Project: Build & Apply

SciPy with Pandas for data handling

📖 Scenario: You work as a data analyst for a small company. You have collected sales data for different products over several months. You want to use Pandas to organize the data and SciPy to calculate some statistics like the mean and median sales.

🎯 Goal: Build a simple program that creates a Pandas DataFrame with sales data, sets a threshold for filtering, uses SciPy to calculate statistics on filtered data, and finally prints the results.

📋 What You'll Learn

Create a Pandas DataFrame with exact sales data

Define a sales threshold variable

Use SciPy to calculate mean and median sales above the threshold

Print the calculated mean and median values

💡 Why This Matters

🌍 Real World

Data analysts often combine Pandas for data handling and SciPy for statistical calculations to understand business data better.

💼 Career

Knowing how to filter data and calculate statistics is essential for roles like data analyst, business analyst, and data scientist.

1

Create the sales data DataFrame

Create a Pandas DataFrame called sales_data with these exact columns and values:
'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
'Month': ['Jan', 'Jan', 'Feb', 'Feb', 'Mar'],
'Sales': [150, 200, 50, 300, 120].

SciPy

import pandas as pd
# Create the DataFrame sales_data with the given columns and values
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set the sales threshold

Create a variable called sales_threshold and set it to 100.

SciPy

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Month': ['Jan', 'Jan', 'Feb', 'Feb', 'Mar'],
    'Sales': [150, 200, 50, 300, 120]
})
# Create the variable sales_threshold and set it to 100
# Your code here

Hint

Just assign the number 100 to the variable sales_threshold.

3

Calculate mean and median sales above threshold

Import scipy.stats as stats. Then create a variable called filtered_sales that contains only the 'Sales' values from sales_data where sales are greater than sales_threshold. Use stats.tmean to calculate the mean of filtered_sales and store it in mean_sales. Use stats.tmedian to calculate the median of filtered_sales and store it in median_sales.

SciPy

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Month': ['Jan', 'Jan', 'Feb', 'Feb', 'Mar'],
    'Sales': [150, 200, 50, 300, 120]
})
sales_threshold = 100
# Import scipy.stats as stats
# Filter sales above sales_threshold into filtered_sales
# Calculate mean_sales using stats.tmean
# Calculate median_sales using stats.tmedian
# Your code here

Hint

Use sales_data.loc to filter rows where 'Sales' is greater than sales_threshold. Then use stats.tmean and stats.tmedian on the filtered sales.

4

Print the mean and median sales

Write two print statements to display the mean and median sales. Use print(f"Mean sales: {mean_sales}") and print(f"Median sales: {median_sales}").

SciPy

import pandas as pd
import scipy.stats as stats

sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Month': ['Jan', 'Jan', 'Feb', 'Feb', 'Mar'],
    'Sales': [150, 200, 50, 300, 120]
})
sales_threshold = 100

filtered_sales = sales_data.loc[sales_data['Sales'] > sales_threshold, 'Sales']
mean_sales = stats.tmean(filtered_sales)
median_sales = stats.tmedian(filtered_sales)
# Print the mean and median sales
# Your code here

Hint

Use print with f-strings to show the values of mean_sales and median_sales.

Practice

1. What is the main reason to use SciPy together with Pandas in data analysis?

easy

A. SciPy provides advanced math and stats functions, while Pandas organizes data in tables.

B. Pandas is used only for visualization, SciPy handles all data storage.

C. SciPy replaces Pandas for data cleaning tasks.

D. Pandas is used to write code, SciPy runs the code faster.

Solution

1. Step 1: Understand roles of Pandas and SciPy

   Pandas organizes data into tables called DataFrames, making it easy to handle data.

2. Step 2: Identify SciPy's role

   SciPy offers math and statistics tools to analyze data prepared by Pandas.

3. Final Answer:

   SciPy provides advanced math and stats functions, while Pandas organizes data in tables. -> Option A

4. Quick Check:

   Data organization = Pandas, Analysis = SciPy [OK]

Hint: Remember: Pandas for tables, SciPy for math [OK]

Common Mistakes:

• Thinking Pandas does advanced stats alone
• Confusing SciPy as a data storage tool
• Believing SciPy replaces Pandas for cleaning

2. Which of the following is the correct way to import SciPy's stats module and Pandas in Python?

easy

A. from scipy import stats; import pandas as pd

B. import scipy.stats; import pandas as pandas

C. from scipy.stats import stats; import pandas as pd

D. import scipy.stats as sp; import pandas as pd

Solution

1. Step 1: Check common import styles

   Using 'from scipy import stats' imports the stats module directly, which is common and clear.

2. Step 2: Verify Pandas import

   Importing pandas as 'pd' is the standard alias used in data science.

3. Final Answer:

   from scipy import stats; import pandas as pd -> Option A

4. Quick Check:

   Standard imports = from scipy import stats, import pandas as pd [OK]

Hint: Use 'from scipy import stats' and 'import pandas as pd' [OK]

Common Mistakes:

• Using wrong alias for pandas
• Importing scipy.stats without alias or direct import
• Mixing import styles incorrectly

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

import pandas as pd
from scipy import stats

data = {'score': [10, 20, 20, 30, 40]}
df = pd.DataFrame(data)
mode_result = stats.mode(df['score'])
print(mode_result.mode[0])

medium

A. 10

B. 30

C. 20

D. 40

Solution

1. Step 1: Understand the data

   The 'score' column has values [10, 20, 20, 30, 40]. The number 20 appears twice, others once.

2. Step 2: Apply stats.mode

   stats.mode finds the most frequent value, which is 20 here.

3. Final Answer:

   20 -> Option C

4. Quick Check:

   Most frequent value = 20 [OK]

Hint: Mode is the most frequent value in the list [OK]

Common Mistakes:

• Choosing the first value instead of mode
• Confusing mean or median with mode
• Not accessing .mode[0] correctly

4. Identify the error in the following code snippet:

import pandas as pd
from scipy import stats

data = {'values': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
result = stats.mean(df['values'])
print(result)

medium

A. DataFrame creation syntax is incorrect.

B. stats.mean does not exist; use numpy.mean or pandas mean method instead.

C. The print statement is missing parentheses.

D. The import statement for pandas is wrong.

Solution

1. Step 1: Check function availability in SciPy

   SciPy's stats module does not have a 'mean' function; mean is in numpy or pandas.

2. Step 2: Identify correct function usage

   Use df['values'].mean() or numpy.mean(df['values']) instead.

3. Final Answer:

   stats.mean does not exist; use numpy.mean or pandas mean method instead. -> Option B

4. Quick Check:

   stats.mean missing, use pandas or numpy mean [OK]

Hint: Use pandas or numpy for mean, not stats.mean [OK]

Common Mistakes:

• Assuming all stats functions exist in SciPy
• Ignoring error messages about missing attributes
• Confusing pandas and SciPy function locations

5. You have a Pandas DataFrame with a column 'height' containing some missing values (NaN). You want to fill these missing values with the median height calculated using SciPy. Which code snippet correctly does this?

hard

A. from scipy import stats
median_height = stats.median(df['height'])
df['height'] = df['height'].fillna(median_height)

B. from scipy import stats
median_height = stats.median(df['height'].dropna())
df['height'] = df['height'].fillna(median_height)

C. from scipy import stats
median_height = stats.mode(df['height'].dropna()).mode[0]
df['height'] = df['height'].fillna(median_height)

D. from scipy import stats
median_height = stats.scoreatpercentile(df['height'].dropna(), 50)
df['height'] = df['height'].fillna(median_height)

Solution

1. Step 1: Identify correct SciPy function for median

   SciPy's stats module does not have 'median', but 'scoreatpercentile' can find the 50th percentile (median).

2. Step 2: Handle missing values correctly

   Drop NaN values before calculating median, then fill NaNs with this median.

3. Final Answer:

   from scipy import stats median_height = stats.scoreatpercentile(df['height'].dropna(), 50) df['height'] = df['height'].fillna(median_height) -> Option D

4. Quick Check:

   Median via scoreatpercentile, fillna with median [OK]

Hint: Use scoreatpercentile for median in SciPy [OK]

Common Mistakes:

• Using stats.median which does not exist
• Not dropping NaN before median calculation
• Using mode instead of median