Pandas Exercise: Player Scores Analysis

:bulb: What you'll learn

• How to create DataFrames from lists and dictionaries
• How to access DataFrame columns and rows
• How to use basic DataFrame methods (sum, max, describe)
• How to work with labeled data using loc[] and iloc[]

:computer: Activity Type: Coding Exercise
⏱️ Estimated Time: 30-40 minutes

Getting Started

1. Click the link here
2. Make a copy of the Colab Notebook into your Google Drive
3. Follow the steps below to complete the exercise

Setup Your Environment ⏱️ (2 minutes)

Import Required Packages

1. Import the packages numpy and pandas:

import numpy as np
import pandas as pd

:bulb: Always import pandas as pd and numpy as np - this is the standard convention that makes your code easier for others to read!

Exercise Steps

Step One: Create the Data ⏱️ (3 minutes)

1. Copy and paste the data below into your notebook:

python

data = [
        ["Jane",10,9,10,29],
        ["Mark",9,9,9,27],
        ["Bill",7,10,9,26],
        ["Jack",8,9,7,24],
        ["Ray",8,9,6,23],
        ["Harry",5,5,10,20],
        ["Louis",6,6,5,17],
        ["Prince",5,4,6,15],
        ]
columns = ["Player", "Round 1", "Round 2", "Round 3", "Total"]
index = ["1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th"]

Step 2: Create the DataFrame ⏱️ (5 minutes)

1. Create a Pandas DataFrame named df using the DataFrame() function
2. Use the data, columns, and index variables you created in Step 1
3. Your DataFrame should look like this:

Expected output:

text

     Player  Round 1  Round 2  Round 3  Total
1st    Jane       10        9       10     29
2nd    Mark        9        9        9     27
3rd    Bill        7       10        9     26
4th    Jack        8        9        7     24
5th     Ray        8        9        6     23
6th   Harry        5        5       10     20
7th   Louis        6        6        5     17
8th  Prince        5        4        6     15

Step 3: Explore DataFrame Information ⏱️ (5 minutes)

1. Display the column names using df.columns
2. Display the row index using df.index
3. Show the first 5 rows using df.head()
4. Display detailed information using df.info()

:bulb: Tip The info() method is very useful - it shows you the data type of each column and whether there are any missing values!

Expected outputs:

Index(['Player', 'Round 1', 'Round 2', 'Round 3', 'Total'], dtype='object')

Index(['1st', '2nd', '3rd', '4th', '5th', '6th', '7th', '8th'], dtype='object')

     Player  Round 1  Round 2  Round 3  Total
1st    Jane       10        9       10     29
2nd    Mark        9        9        9     27
3rd    Bill        7       10        9     26
4th    Jack        8        9        7     24
5th     Ray        8        9        6     23

<class 'pandas.core.frame.DataFrame'>
Index: 8 entries, 1st to 8th
Data columns (total 5 columns):
 #   Column   Non-Null Count  Dtype
---  ------   --------------  -----
 0   Player   8 non-null      object
 1   Round 1  8 non-null      int64
 2   Round 2  8 non-null      int64
 3   Round 3  8 non-null      int64
 4   Total    8 non-null      int64
dtypes: int64(4), object(1)
memory usage: 384.0+ bytes

Step 4: Access Column Data ⏱️ (3 minutes)

1. Access the Round 2 column using bracket notation: df['Round 2']
2. Display the scores from Round 2

Expected output:

1st     9
2nd     9
3rd    10
4th     9
5th     9
6th     5
7th     6
8th     4
Name: Round 2, dtype: int64

Step 5: Access Multiple Columns ⏱️ (3 minutes)

1. Access both the Player and Total columns
2. Use double brackets: df[['Player', 'Total']]

Expected output:

     Player  Total
1st    Jane     29
2nd    Mark     27
3rd    Bill     26
4th    Jack     24
5th     Ray     23
6th   Harry     20
7th   Louis     17
8th  Prince     15

Step 6: Access Row by Label ⏱️ (3 minutes)

1. Use loc[] to access the data for the 5th ranked player
2. Syntax: df.loc['5th']

:bulb: Tip loc[] uses the actual index labels (like '1st', '2nd', etc.), not numbers!

Expected output:

Player      Ray
Round 1       8
Round 2       9
Round 3       6
Total        23
Name: 5th, dtype: object

Step 7: Access Multiple Rows by Position ⏱️ (4 minutes)

1. Use iloc[] to access rows for 2nd to 5th ranked players
2. Remember: iloc[] uses integer positions (0-based indexing)
3. Think: If '1st' is at position 0, what positions are '2nd' through '5th'?

Expected output:

     Player  Round 1  Round 2  Round 3  Total
2nd    Mark        9        9        9     27
3rd    Bill        7       10        9     26
4th    Jack        8        9        7     24
5th     Ray        8        9        6     23

Step 8: Calculate Total Score ⏱️ (2 minutes)

1. Calculate the total score earned by all players
2. Use the sum() method on the 'Total' column
3. Syntax: df['Total'].sum()

Expected Answer: 181

Step 9: Find Maximum Score ⏱️ (2 minutes)

1. Find the highest score in Round 3
2. Use the max() method on the 'Round 3' column

Expected Answer: 10

Step 10: Statistical Summary ⏱️ (3 minutes)

1. Display a statistical summary using describe()
2. This will show count, mean, std, min, 25%, 50%, 75%, and max for numeric columns

Expected output:

         Round 1    Round 2    Round 3      Total
count  8.000000  8.000000  8.000000   8.000000
mean   7.250000  7.625000  7.750000  22.625000
std    1.832251  2.263846  1.982062   4.926242
min    5.000000  4.000000  5.000000  15.000000
25%    5.750000  5.750000  6.000000  19.250000
50%    7.500000  9.000000  8.000000  23.500000
75%    8.250000  9.000000  9.250000  26.250000
max   10.000000 10.000000 10.000000  29.000000

:dart: Troubleshooting Tips

• DataFrame not displaying? Make sure you're using the variable name df or printing it
• Getting errors with loc/iloc? Remember:
  • loc[] uses labels (like '5th')
  • iloc[] uses integer positions (like 4 for the 5th item)
• Column not found? Check your spelling and capitalization - Python is case-sensitive!

:emoji: Submission

:warning: Warning Important: Make sure to review all your answers before submitting!

• Check that all outputs match the expected results
• Ensure your code runs without errors
• Save your notebook before submitting

Submit Your Work

:link: Submit your exercise here

:information_source: Info Your submission helps us track your progress and provide feedback. Make sure to submit even if you couldn't complete all steps - partial credit is given!