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Tools - 20th November 2025

Visualization dataset

Revision of the last class

Mr. Jullian show how to rename the files, usuage of gitlibs and pushing icons

Modules imported

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd
datasets = pd.read_excel("datasets/student_data_analysis_filled.xlsx")
datasets.head()

	Student ID	Student Name	Class	Assignment 1	Assignment 2	Midterm	Final Exam	Total Marks	Percentage	Grade
0	S001	Sonam Wangchuk	VIII	70	65	60	75	270	67.5	C
1	S002	Dechen Lhamo	VIII	71	66	61	76	274	68.5	C
2	S003	Karma Dorji	VIII	72	67	62	77	278	69.5	C
3	S004	Thinley Om	VIII	73	68	63	78	282	70.5	B
4	S005	Pema Choden	VIII	74	69	64	79	286	71.5	B

datasets = pd.read_excel("datasets/student_data_analysis_filled.xlsx")
print("Data shape:", df.shape)
df.head()

Data shape: (20, 10)

	Student ID	Student Name	Class	Assignment 1	Assignment 2	Midterm	Final Exam	Total Marks	Percentage	Grade
0	S001	Sonam Wangchuk	VIII	70	65	60	75	270	67.5	C
1	S002	Dechen Lhamo	VIII	71	66	61	76	274	68.5	C
2	S003	Karma Dorji	VIII	72	67	62	77	278	69.5	C
3	S004	Thinley Om	VIII	73	68	63	78	282	70.5	B
4	S005	Pema Choden	VIII	74	69	64	79	286	71.5	B

• While importing my datasets, it only appear limited rows and columns. so i tried to display all the rows and colums. I explore youtubes and also used chatgpt.

pd.set_option('display.max_rows',None)
pd.set_option('display.max_columns',None)
datasets

	Student ID	Student Name	Class	Assignment 1	Assignment 2	Midterm	Final Exam	Total Marks	Percentage	Grade
0	S001	Sonam Wangchuk	VIII	70	65	60	75	270	67.50	C
1	S002	Dechen Lhamo	VIII	71	66	61	76	274	68.50	C
2	S003	Karma Dorji	VIII	72	67	62	77	278	69.50	C
3	S004	Thinley Om	VIII	73	68	63	78	282	70.50	B
4	S005	Pema Choden	VIII	74	69	64	79	286	71.50	B
5	S006	Jigme Tenzin	VIII	75	70	65	80	290	72.50	B
6	S007	Kinley Yangzom	VIII	76	71	66	81	294	73.50	B
7	S008	Tshering Dorji	VIII	77	72	67	75	291	72.75	B
8	S009	Ugyen Wangmo	VIII	78	65	68	76	287	71.75	B
9	S010	Sangay Phuntsho	VIII	79	66	69	77	291	72.75	B
10	S011	Kezang Dema	VIII	70	67	70	78	285	71.25	B
11	S012	Choki Gyeltshen	VIII	71	68	71	79	289	72.25	B
12	S013	Phub Zam	VIII	72	69	60	80	281	70.25	B
13	S014	Tandin Wangdi	VIII	73	70	61	81	285	71.25	B
14	S015	Passang Lham	VIII	74	71	62	75	282	70.50	B
15	S016	Namgay Dorji	VIII	75	72	63	76	286	71.50	B
16	S017	Chimi Yangzom	VIII	76	65	64	77	282	70.50	B
17	S018	Ngawang Tshering	VIII	77	66	65	78	286	71.50	B
18	S019	Dawa Tshering	VIII	78	67	66	79	290	72.50	B
19	S020	Pelden Lhamo	VIII	79	68	67	80	294	73.50	B

Bargraph

import matplotlib.pyplot as plt
import numpy as np

# Example: multiple subjects per student
students = df["Student Name"]
subjects = df.columns[1:]  # assuming first column is "Student Name"

# Set figure size dynamically
plt.figure(figsize=(len(students)*0.5 + 2, len(subjects)*1 + 2))  # width, height

# Plot multiple columns for each student
x = np.arange(len(students))  # the label locations
width = 0.1  # width of each bar

for i, subject in enumerate(subjects):
    plt.bar(x + i*width, df[subject], width=width, label=subject)

plt.xticks(x + width*len(subjects)/2, students, rotation=45)
plt.xlabel("Students")
plt.ylabel("Marks")
plt.title("Students' Marks in Different Subjects")
plt.legend()
plt.tight_layout()
plt.show()

Sankey graph

First i should what is sankey graph is meant for "Sankey diagrams are powerful visual tools used to show flows, connections, and how something moves from one stage to another. They are especially useful when the size of the flow matters."(OpenAI, 2025) - Target - Source and values

import pandas as pd
import plotly.graph_objects as go

df = pd.read_excel("datasets/student_data_analysis_filled.xlsx")
df.head()

	Student ID	Student Name	Class	Assignment 1	Assignment 2	Midterm	Final Exam	Total Marks	Percentage	Grade
0	S001	Sonam Wangchuk	VIII	70	65	60	75	270	67.5	C
1	S002	Dechen Lhamo	VIII	71	66	61	76	274	68.5	C
2	S003	Karma Dorji	VIII	72	67	62	77	278	69.5	C
3	S004	Thinley Om	VIII	73	68	63	78	282	70.5	B
4	S005	Pema Choden	VIII	74	69	64	79	286	71.5	B

import pandas as pd
import plotly.graph_objects as go

# Step 1: Create the DataFrame
data = {
    "Student ID": ["S001","S002","S003","S004","S005","S006","S007","S008","S009","S010",
                   "S011","S012","S013","S014","S015","S016","S017","S018","S019","S020"],
    "Student Name": ["Sonam Wangchuk","Dechen Lhamo","Karma Dorji","Thinley Om","Pema Choden",
                     "Jigme Tenzin","Kinley Yangzom","Tshering Dorji","Ugyen Wangmo","Sangay Phuntsho",
                     "Kezang Dema","Choki Gyeltshen","Phub Zam","Tandin Wangdi","Passang Lham",
                     "Namgay Dorji","Chimi Yangzom","Ngawang Tshering","Dawa Tshering","Pelden Lhamo"],
    "Grade": ["C","C","C","B","B","B","B","B","B","B","B","B","B","B","B","B","B","B","B","B"]
}

df = pd.DataFrame(data)

# Step 2: Define nodes for Sankey (Students + Grades)
labels = df["Student Name"].tolist() + df["Grade"].unique().tolist()
label_indices = {label: i for i, label in enumerate(labels)}

# Step 3: Define source, target, and values
source = [label_indices[name] for name in df["Student Name"]]  # Students
target = [label_indices[grade] for grade in df["Grade"]]       # Grades
value = [1]*len(df)  # Each student counts as 1

# Step 4: Create the Sankey diagram
fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.5),
        label=labels,
        color="skyblue"
    ),
    link=dict(
        source=source,
        target=target,
        value=value,
        color="lightgreen"
    )
)])

fig.update_layout(title_text="Student to Grade Sankey Diagram", font_size=12)
fig.show()

(Bhatt, 2022)

Click here

Video guides for Sankey diagram

Headmap

A heatmap is a visual representation of data where values are represented by colors, and it’s commonly used in data analysis and visualization. In Jupyter Notebook, you can create heatmaps using Python libraries like Seaborn, Matplotlib, or Plotly.

Purpose of a Heatmap

Show patterns in data – e.g., which students scored higher or lower.

Compare multiple variables at once – e.g., Assignment 1 vs Assignment 2 vs Midterm.

Highlight high or low values – colors make differences easy to see.

Visualize correlations – often used in statistics to show relationships between variables.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Step 1: Create the DataFrame
data = {
    "Student Name": ["Sonam Wangchuk","Dechen Lhamo","Karma Dorji","Thinley Om","Pema Choden",
                     "Jigme Tenzin","Kinley Yangzom","Tshering Dorji","Ugyen Wangmo","Sangay Phuntsho",
                     "Kezang Dema","Choki Gyeltshen","Phub Zam","Tandin Wangdi","Passang Lham",
                     "Namgay Dorji","Chimi Yangzom","Ngawang Tshering","Dawa Tshering","Pelden Lhamo"],
    "Assignment 1": [70,71,72,73,74,75,76,77,78,79,70,71,72,73,74,75,76,77,78,79],
    "Assignment 2": [65,66,67,68,69,70,71,72,65,66,67,68,69,70,71,72,65,66,67,68],
    "Midterm": [60,61,62,63,64,65,66,67,68,69,70,71,60,61,62,63,64,65,66,67],
    "Final Exam": [75,76,77,78,79,80,81,75,76,77,78,79,80,81,75,76,77,78,79,80],
    "Total Marks": [270,274,278,282,286,290,294,291,287,291,285,289,281,285,282,286,282,286,290,294]
}

df = pd.DataFrame(data)

# Step 2: Set student names as index
df_heatmap = df.set_index("Student Name")

# Step 3: Create the heatmap
plt.figure(figsize=(12,8))
sns.heatmap(df_heatmap, annot=True, cmap="YlGnBu", linewidths=.5)
plt.title("Heatmap of Student Scores")
plt.show()

1. Data

The most important component. Heatmaps require numerical data, often organized in a table or matrix.
Rows usually represent categories (e.g., students).
Columns usually represent variables or features (e.g., Assignment 1, Assignment 2, Midterm).
Data can also be a correlation matrix if you want to visualize relationships.

Example:
Student	Assignment 1	Assignment 2	Midterm	Final Exam
Sonam	70	65	60	75
Dechen	71	66	61	76
2. Python Libraries

You need libraries that can handle data visualization:
Pandas – for organizing and handling data in DataFrames.
Seaborn – easiest way to create heatmaps in Python.
Matplotlib – used by Seaborn to plot figures.
Optional: Plotly for interactive heatmaps.

3. Data Preprocessing
Before plotting:
Set row labels (e.g., student names) as the index.
Ensure all values are numeric.
Handle missing values (NaN) if necessary.
Normalize data if you want colors to be relative.

4. Heatmap Parameters
When creating a heatmap, you often customize:
Annotations (annot) – display numbers on cells.
Color map (cmap) – color scheme (e.g., YlGnBu).
Line widths (linewidths) – for separating cells.
Figure size – to make the heatmap readable.

5. Plotting
Finally, you use Seaborn or another library to render the heatmap. Example:
sns.heatmap(df, annot=True, cmap="YlGnBu", linewidths=0.5)
plt.title("Heatmap Title")
plt.show()

What it does

Each row = a student

Each column = an assessment

Colors represent scores (darker = higher, lighter = lower)

annot=True shows the numeric values in each cell

Functions

Linear

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# Step 1: Create the dataset
data = {
    "Student ID": ["S001","S002","S003","S004","S005","S006","S007","S008","S009","S010",
                   "S011","S012","S013","S014","S015","S016","S017","S018","S019","S020"],
    "Assignment 1": [70,71,72,73,74,75,76,77,78,79,70,71,72,73,74,75,76,77,78,79],
    "Assignment 2": [65,66,67,68,69,70,71,72,65,66,67,68,69,70,71,72,65,66,67,68],
    "Midterm": [60,61,62,63,64,65,66,67,68,69,70,71,60,61,62,63,64,65,66,67],
    "Final Exam": [75,76,77,78,79,80,81,75,76,77,78,79,80,81,75,76,77,78,79,80],
    "Total Marks": [270,274,278,282,286,290,294,291,287,291,285,289,281,285,282,286,282,286,290,294]
}

df = pd.DataFrame(data)

# Step 2: Features and target
X = df[["Assignment 1", "Assignment 2", "Midterm", "Final Exam"]]
y = df["Total Marks"]

# Step 3: Train Linear Regression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)

# Step 4: Predict
y_pred = model.predict(X)

# Step 5: Plot actual vs predicted Total Marks
plt.figure(figsize=(10,6))
plt.scatter(range(len(y)), y, color='blue', label='Actual Total Marks')
plt.plot(range(len(y)), y_pred, color='red', label='Predicted Total Marks', linewidth=2)
plt.xlabel('Student Index')
plt.ylabel('Total Marks')
plt.title('Linear Regression: Actual vs Predicted Total Marks')
plt.legend()
plt.grid(True)
plt.show()

Different Tools for Data visualization

• for my grade four children in different classes

Types of visualization tools: Matplotlib, Seaborn, Pandas Plotting, and Plotly.

I will first structure your dataset, then show visualizations for: Pass/Fail comparison

Pass % vs Fail % Mean Mark vs National Mean Bar charts and line charts Interactive charts

Import datasets

import pandas as pd

Pass % by section

Fail % by section

Passed vs Failed

Mean Mark vs National Mean

Distribution of marks (if available)

Import Seabon and Load Datasets

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid")

df = pd.read_excel("datasets/Cl IVABC ICT result Analysis Term 1 2025.xlsx")
df.head()

	Unnamed: 0	Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9
0	NaN	NaN	NaN	NaN	Class IVABC	Midterm 2025	NaN	NaN	NaN	NaN
1	NaN	Class	Section	Total Students	Total Stds Passed	Total Stds Fail	Total Pass %	Total Fail %	Mean Mark	National Mean Mark
2	NaN	IV	A	28	22	6	78.571429	21.428571	75.7	67.09
3	NaN	IV	B	28	24	5	85.714286	17.857143	71.7	75
4	NaN	IV	C	28	13	15	46.428571	53.571429	57.9	67.09

Tool- Matplotlib

Visualization Toolkit Setup

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px

plt.style.use('seaborn-v0_8') sns.set_theme()

# =========================================================
# SEABORN DASHBOARD FOR CLASS IVABC DATASET
# =========================================================

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid")  # Seaborn theme

# Load dataset
df = pd.read_excel("datasets/Cl IVABC ICT result Analysis Term 1 2025.xlsx")

# =========================================================
# CREATE DASHBOARD
fig, axes = plt.subplots(3, 2, figsize=(16, 15))
fig.suptitle("Class IVABC Term 1 2025 - Data Analysis Dashboard", fontsize=18)

# 1. Pass % by Section
sns.barplot(data=df, x="Section", y="Pass %", palette="Blues_d", ax=axes[0,0])
axes[0,0].set_title("Pass Percentage by Section")

# 2. Fail % by Section
sns.barplot(data=df, x="Section", y="Fail %", palette="Reds_d", ax=axes[0,1])
axes[0,1].set_title("Fail Percentage by Section")

# 3. Passed vs Failed Students
df_melted = df.melt(id_vars=["Section"], 
                    value_vars=["Total Passed", "Total Failed"],
                    var_name="Category",
                    value_name="Count")
sns.barplot(data=df_melted, x="Section", y="Count", hue="Category", palette="Set2", ax=axes[1,0])
axes[1,0].set_title("Passed vs Failed Students")

# 4. Mean Mark vs National Mean
sns.lineplot(data=df, x="Section", y="Mean Mark", marker="o", label="Mean Mark", ax=axes[1,1])
sns.lineplot(data=df, x="Section", y="National Mean", marker="o", label="National Mean", ax=axes[1,1])
axes[1,1].set_title("Mean Mark vs National Mean")
axes[1,1].set_ylabel("Marks")

# 5. Correlation Heatmap
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap="coolwarm", ax=axes[2,0])
axes[2,0].set_title("Correlation Heatmap")

# 6. Distribution of Mean Marks
sns.histplot(df["Mean Mark"], kde=True, color="skyblue", bins=10, ax=axes[2,1])
axes[2,1].set_title("Distribution of Mean Marks")

# Adjust layout
plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[16], line 20
     17 fig.suptitle("Class IVABC Term 1 2025 - Data Analysis Dashboard", fontsize=18)
     19 # 1. Pass % by Section
---> 20 sns.barplot(data=df, x="Section", y="Pass %", palette="Blues_d", ax=axes[0,0])
     21 axes[0,0].set_title("Pass Percentage by Section")
     23 # 2. Fail % by Section

File /opt/conda/lib/python3.13/site-packages/seaborn/categorical.py:2341, in barplot(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, seed, units, weights, orient, color, palette, saturation, fill, hue_norm, width, dodge, gap, log_scale, native_scale, formatter, legend, capsize, err_kws, ci, errcolor, errwidth, ax, **kwargs)
   2338 if estimator is len:
   2339     estimator = "size"
-> 2341 p = _CategoricalAggPlotter(
   2342     data=data,
   2343     variables=dict(x=x, y=y, hue=hue, units=units, weight=weights),
   2344     order=order,
   2345     orient=orient,
   2346     color=color,
   2347     legend=legend,
   2348 )
   2350 if ax is None:
   2351     ax = plt.gca()

File /opt/conda/lib/python3.13/site-packages/seaborn/categorical.py:67, in _CategoricalPlotter.__init__(self, data, variables, order, orient, require_numeric, color, legend)
     56 def __init__(
     57     self,
     58     data=None,
   (...)     64     legend="auto",
     65 ):
---> 67     super().__init__(data=data, variables=variables)
     69     # This method takes care of some bookkeeping that is necessary because the
     70     # original categorical plots (prior to the 2021 refactor) had some rules that
     71     # don't fit exactly into VectorPlotter logic. It may be wise to have a second
   (...)     76     # default VectorPlotter rules. If we do decide to make orient part of the
     77     # _base variable assignment, we'll want to figure out how to express that.
     78     if self.input_format == "wide" and orient in ["h", "y"]:

File /opt/conda/lib/python3.13/site-packages/seaborn/_base.py:634, in VectorPlotter.__init__(self, data, variables)
    629 # var_ordered is relevant only for categorical axis variables, and may
    630 # be better handled by an internal axis information object that tracks
    631 # such information and is set up by the scale_* methods. The analogous
    632 # information for numeric axes would be information about log scales.
    633 self._var_ordered = {"x": False, "y": False}  # alt., used DefaultDict
--> 634 self.assign_variables(data, variables)
    636 # TODO Lots of tests assume that these are called to initialize the
    637 # mappings to default values on class initialization. I'd prefer to
    638 # move away from that and only have a mapping when explicitly called.
    639 for var in ["hue", "size", "style"]:

File /opt/conda/lib/python3.13/site-packages/seaborn/_base.py:679, in VectorPlotter.assign_variables(self, data, variables)
    674 else:
    675     # When dealing with long-form input, use the newer PlotData
    676     # object (internal but introduced for the objects interface)
    677     # to centralize / standardize data consumption logic.
    678     self.input_format = "long"
--> 679     plot_data = PlotData(data, variables)
    680     frame = plot_data.frame
    681     names = plot_data.names

File /opt/conda/lib/python3.13/site-packages/seaborn/_core/data.py:58, in PlotData.__init__(self, data, variables)
     51 def __init__(
     52     self,
     53     data: DataSource,
     54     variables: dict[str, VariableSpec],
     55 ):
     57     data = handle_data_source(data)
---> 58     frame, names, ids = self._assign_variables(data, variables)
     60     self.frame = frame
     61     self.names = names

File /opt/conda/lib/python3.13/site-packages/seaborn/_core/data.py:232, in PlotData._assign_variables(self, data, variables)
    230     else:
    231         err += "An entry with this name does not appear in `data`."
--> 232     raise ValueError(err)
    234 else:
    235 
    236     # Otherwise, assume the value somehow represents data
    237 
    238     # Ignore empty data structures
    239     if isinstance(val, Sized) and len(val) == 0:

ValueError: Could not interpret value `Section` for `x`. An entry with this name does not appear in `data`.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Set Seaborn theme
sns.set_theme(style="whitegrid")

# Load Excel file
df = pd.read_excel("datasets/Cl IVABC ICT result Analysis Term 1 2025.xlsx")

# Display first 5 rows
df.head()

	Unnamed: 0	Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9
0	NaN	NaN	NaN	NaN	Class IVABC	Midterm 2025	NaN	NaN	NaN	NaN
1	NaN	Class	Section	Total Students	Total Stds Passed	Total Stds Fail	Total Pass %	Total Fail %	Mean Mark	National Mean Mark
2	NaN	IV	A	28	22	6	78.571429	21.428571	75.7	67.09
3	NaN	IV	B	28	24	5	85.714286	17.857143	71.7	75
4	NaN	IV	C	28	13	15	46.428571	53.571429	57.9	67.09

# Remove spaces and special characters from column names
df.columns = df.columns.str.strip().str.replace(' ', '_').str.replace('%','pct')

# Check the new column names
print(df.columns.tolist())

['Unnamed:_0', 'Unnamed:_1', 'Unnamed:_2', 'Unnamed:_3', 'Unnamed:_4', 'Unnamed:_5', 'Unnamed:_6', 'Unnamed:_7', 'Unnamed:_8', 'Unnamed:_9']

print("Dataset shape:", df.shape)
print("\nColumns:\n", df.columns)
print("\nFirst 5 rows:")
display(df.head())

Dataset shape: (5, 10)

Columns:
 Index(['Unnamed:_0', 'Unnamed:_1', 'Unnamed:_2', 'Unnamed:_3', 'Unnamed:_4',
       'Unnamed:_5', 'Unnamed:_6', 'Unnamed:_7', 'Unnamed:_8', 'Unnamed:_9'],
      dtype='object')

First 5 rows:

	Unnamed:_0	Unnamed:_1	Unnamed:_2	Unnamed:_3	Unnamed:_4	Unnamed:_5	Unnamed:_6	Unnamed:_7	Unnamed:_8	Unnamed:_9
0	NaN	NaN	NaN	NaN	Class IVABC	Midterm 2025	NaN	NaN	NaN	NaN
1	NaN	Class	Section	Total Students	Total Stds Passed	Total Stds Fail	Total Pass %	Total Fail %	Mean Mark	National Mean Mark
2	NaN	IV	A	28	22	6	78.571429	21.428571	75.7	67.09
3	NaN	IV	B	28	24	5	85.714286	17.857143	71.7	75
4	NaN	IV	C	28	13	15	46.428571	53.571429	57.9	67.09

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt

Load dataset (ensure the file path is correct)

df = pd.read_excel("datasets/Cl IVABC ICT result Analysis Term 1 2025.xlsx")

Clean column names

df.columns = df.columns.str.strip().str.replace(' ', '_').str.replace('%','pct')

Ensure the column is numeric

df['Total_Pass_pct'] = pd.to_numeric(df['Total_Pass_pct'], errors='coerce')

Plot Bar Chart

plt.figure(figsize=(7,5)) ax = sns.barplot(data=df, x="Section", y="Total_Pass_pct", palette="Blues_d")

Add values on top of bars

for p in ax.patches: height = p.get_height() ax.annotate(f"{height:.1f}", # Display value with 1 decimal (p.get_x() + p.get_width()/2, height), ha='center', va='bottom', fontsize=10)

plt.title("Pass Percentage by Section") plt.ylabel("Pass %") plt.xlabel("Section") plt.ylim(0, 100) # Optional: set y-axis from 0 to 100 plt.show()

Types of data visualizaiton tools

Reference (https://www.geeksforgeeks.org/r-language/types-of-data-visualization/)

Bhatt, B. (2022, March 18). Generating Sankey diagrams or alluvial diagrams with Python's Plotly library | Jupyter Notebook [Video]. YouTube. https://www.youtube.com/watch?v=yyVwvBUFRwY&t=213s

OpenAI. (2025, December 5). ChatGPT conversation. ChatGPT. https://chatgpt.com

import pandas as pd
df = pd.read_csv("datasets/StudentPerformance.csv")
df.head()

	Hours Studied	Previous Scores	Extracurricular Activities	Sleep Hours	Sample Question Papers Practiced	Performance Index
0	7	99	Yes	9	1	91.0
1	4	82	No	4	2	65.0
2	8	51	Yes	7	2	45.0
3	5	52	Yes	5	2	36.0
4	7	75	No	8	5	66.0