Calculating Business Day Vacancy in a Python DataFrame

In this article, we will explore how to calculate business day vacancy in a pandas DataFrame. This is a common problem in data analysis where you need to find the number of business days between two dates.

Introduction

Business day vacancy refers to the number of days between two dates when there are no occupied or available business days. In this article, we will use Python and the pandas library to calculate business day vacancy.

Background

The problem described in the question can be solved using the date_range function from pandas. This function generates a sequence of dates from a start date to an end date with a specified frequency.

Step 1: Import Libraries and Load Data

To solve this problem, we need to import the necessary libraries and load our data into a DataFrame.

import pandas as pd

# Load data into a DataFrame
df = pd.DataFrame({
    'Cust_Name': ['APPT1', 'APPT1','APPT2','APPT2'],
    'Move_In': ['2013-02-01','2019-02-01','2019-02-04','2019-02-19'],
    'Move_Out': ['2019-01-31','','2019-02-15','']
})

# Convert data types
df['Move_In'] = df['Move_In'].astype('datetime64')
df['Move_Out'] = df['Move_Out'].astype('datetime64')

Step 2: Calculate Previous Move-Out Date

We need to calculate the previous move-out date for each row in our DataFrame. We can do this by shifting the ‘Move_Out’ column up by one row using the shift function.

df['Prev_Move_Out'] = df['Move_Out'].shift()

Step 3: Define Function to Calculate Business Day Vacancy

We need a function that calculates the number of business days between two dates. This can be done using the date_range function from pandas.

def calculate_business_day_vacancy(df):
    try:
        return len(pd.date_range(start=df['Prev_Move_Out'], end=df['Move_In'], freq='B')) - 2
    except ValueError:
        # Consider instead running the function only on rows that do not contain NaT.
        return 0

Step 4: Apply Function to Each Row

We need to apply our calculate_business_day_vacancy function to each row in our DataFrame using the apply method.

df['Vacancy_BDays'] = df.apply(calculate_business_day_vacancy, axis=1)

Step 5: Print Results

Finally, we can print our results to see how many business day vacancies there are for each customer.

print(df)

Conclusion

In this article, we have explored how to calculate business day vacancy in a pandas DataFrame. This is a common problem in data analysis where you need to find the number of business days between two dates.

By following these steps, you can easily calculate business day vacancy for any DataFrame with ‘Move_In’ and ‘Move_Out’ columns.

References

• pandas Documentation
• date_range Function

Step 6: Handling Missing Values and Data Type

Handling missing values is a common problem in data analysis. Pandas provides several ways to handle missing values.

Handling Missing Values

# Drop rows with missing values
df.dropna()

# Replace missing values with a specific value
df.fillna(value)

# Interpolate missing values
df.interpolate()

Data types are also important when working with data analysis. Pandas provides several data types, including integer, float, and datetime.

Data Type

# Convert to integer
df['column_name'].astype(int)

# Convert to float
df['column_name'].astype(float)

Step 7: Handling Different Date Formats

Handling different date formats is a common problem in data analysis. Pandas provides several ways to handle different date formats.

Handling Different Date Formats

# Convert to datetime
pd.to_datetime('2022-01-01', format='%Y-%m-%d')

# Convert from string to datetime
df['column_name'].str.strip('%Y-%m-%d').astype(pd.Timestamp)

# Convert from datetime to string
df['column_name'].dt.strftime('%Y-%m-%d')

Step 8: Optimizing Business Day Vacancy Calculation

The business day vacancy calculation can be optimized by reducing the number of date range calculations.

Optimizing Business Day Vacancy Calculation

def calculate_business_day_vacancy(df):
    # Sort the DataFrame by Move_In date
    df.sort_values('Move_In', inplace=True)
    
    # Calculate the business day vacancies for each customer
    df['Business_Day_Vacancies'] = 0
    
    for i in range(len(df)):
        if df['Prev_Move_Out'][i] is not pd.NaT:
            start_date = pd.to_datetime(df['Move_In'][i])
            end_date = pd.to_datetime(df['Prev_Move_Out'][i])
            
            # Calculate the business day vacancies
            vacancies = (start_date - end_date).days
            
            if vacancies > 0:
                df['Business_Day_Vacancies'][i] = vacancies
    
    return df

Step 9: Grouping by Customer

Grouping by customer can help to calculate the total number of business day vacancies for each customer.

Grouping by Customer

def calculate_total_business_day_vacancy(df):
    # Group by customer and sum the business day vacancies
    total_vacancies = df.groupby('Cust_Name')['Business_Day_Vacancies'].sum().reset_index()
    
    return total_vacancies

Step 10: Visualizing Business Day Vacancies

Visualizing business day vacancies can help to better understand the data.

Visualizing Business Day Vacancies

import matplotlib.pyplot as plt

# Plot the business day vacancies
plt.figure(figsize=(10,6))
plt.bar(df['Cust_Name'], df['Business_Day_Vacancies'])
plt.xlabel('Customer')
plt.ylabel('Business Day Vacancies')
plt.title('Business Day Vacancies')
plt.show()

Step 11: Conclusion

In this article, we have explored how to calculate business day vacancy in a pandas DataFrame. We have also discussed ways to optimize the calculation and visualize the results.

By following these steps, you can easily calculate business day vacancy for any DataFrame with ‘Move_In’ and ‘Move_Out’ columns.

Last modified on 2024-10-22