Mastering Pandas: Adding Total Rows and Creating Team Stats Data Frames

Adding a Total Row to a Pandas DataFrame

1. Introduction

Data frames are fundamental tools in data science and analysis. They help organize data in a structured format, facilitating manipulation and analysis. One common task in data analysis is adding a total row to a data frame, which provides the sum or mean of numerical values in a column.

2. Syntax for Adding a Total Row

The following syntax adds a total row to a pandas data frame:

df.loc['Total'] = df.mean()

In this code, ‘df’ represents the data frame name, ‘loc’ is a function for selecting and adding a new row, ‘Total’ is the label for the new row, and ‘df.mean()’ calculates the mean of all numerical values in each column.

To add a row with the sum of values, replace ‘df.mean()’ with ‘df.sum()’.

3. Example of Adding a Total Row

Consider this example:

import pandas as pd
data = {'Name': ['John','Mary','Peter','Samantha'],
        'Age': [28, 24, 32, 29],
        'Score': [70, 80, 90, 85]}
df = pd.DataFrame(data)
df.loc['Total'] = df.mean()

Resulting Data Frame:

Creating a Pandas DataFrame with Team Stats

1. Introduction

Data frames can be used to organize team-related data, such as sports, project, or business teams. This section demonstrates creating a data frame with basketball team statistics.

2. Creating a Pandas DataFrame

Create a dictionary containing column data. The keys represent column names, and the values represent data within each column. For example:

import pandas as pd
data = {'Player Name': ['LeBron James', 'Anthony Davis', 'Russell Westbrook', 'Carmelo Anthony', 'Dwight Howard'],
        'Points per Game': [25.3, 22.5, 19.6, 14.2, 6.8],
        'Rebounds per Game': [7.8, 9.5, 9.2, 4.5, 4.0],
        'Assists per Game': [7.7, 2.5, 7.1, 1.5, 0.3],
        'Steals per Game': [1.2, 1.2, 1.7, 1.0, 0.2],
        'Blocks per Game': [0.6, 1.8, 0.7, 0.3, 0.6]}
team_stats = pd.DataFrame(data)

The data frame now displays the team statistics in table format, with ‘Player Name’ and average statistics for each player.

Conclusion

Pandas data frames are powerful tools in data science and analysis. Adding a total row offers a quick overview and summary statistics for large datasets. Creating a data frame with team statistics is useful for analyzing performance in various domains.

Adding a Total Row to a DataFrame: Example

1. Example DataFrame

import pandas as pd
data = {'Name': ['John', 'Jane', 'Chris', 'Mark', 'Alex'],
        'Experience': ['3 years', '4 years', '2 years', '5 years', '1 year'],
        'Sales': [120000, 88000, 132000, 190000, 65000]}
sales_data = pd.DataFrame(data)

2. Code for Adding a Total Row

total_sales = sales_data['Sales'].sum()
total_row = {'Name':'Total', 'Experience': '', 'Sales': total_sales}
sales_data = sales_data.append(total_row, ignore_index=True)

This code calculates the total sales, creates a dictionary with the total row data, and appends it to the data frame.

3. Viewing Updated DataFrame with Total Row

print(sales_data)

Output:

4. Notes on Character Columns in Total Row

Adding a total row to a character column raises a TypeError because it’s not possible to calculate a sum or average for non-numeric values. To address this, you can set the last value of the column to a blank string or null value.

5. Setting Last Value in Team Column to Be Blank

data = {'Name': ['John', 'Jane', 'Chris', 'Mark', 'Alex'],
        'Team': ['A', 'B', 'C', 'A', 'D']}
sales_data = pd.DataFrame(data)
sales_data.loc[sales_data.index[-1], 'Team'] = ''

This code sets the last value in the ‘Team’ column to an empty string, allowing you to add a total row without errors.

Conclusion

Adding a total row to a DataFrame provides a quick summary of data statistics. For numeric columns, use ‘sum’ or ‘mean’. For character columns, handle it by setting the last value to a blank string. Pandas is a versatile library for data analysis, offering numerous functionalities for data cleaning, visualization, aggregation, merging, joining, and time-series analysis.

Resources for Performing Common Tasks in Pandas

• Data Cleaning:
  • Official Pandas Documentation
  • Online courses: ‘Data Cleaning with Pandas,’ ‘Data Wrangling with Pandas,’ ‘Pandas Data Cleaning and Preparation’
• Data Visualization:
  • Official Pandas Documentation (‘Plotting with Pandas’)
  • Online courses: ‘Data Visualization with Pandas’
• Data Aggregation:
  • Official Pandas Documentation (‘Group By: split-apply-combine’)
  • Online courses: ‘Data Aggregation with Pandas’
• Merging and Joining Data:
  • Official Pandas Documentation (‘Merge, join, and concatenate’)
  • Online courses: ‘Data manipulation in Pandas: Merge, Join, and Concatenate’
• Time Series Analysis:
  • Official Pandas Documentation (‘DatetimeIndex’, ‘Time Series Analysis’)
  • Online courses: ‘Time Series Analysis with Pandas,’ ‘Python Time Series Analysis with Financial Data’

Final Conclusion

Pandas is a powerful data analysis library. Mastering its features enhances data handling and analysis skills, essential for various career paths.