Create label.yml

[bryanbasor53]

8243208e61deb26a0615cec6841c8250b0cd5e69

[bryanbasor53]

import pandas as pd

Load the dataset

For this example, we'll use a sample CSV file. Replace the file path with your dataset.

file_path = 'sample_dataset.csv'
data = pd.read_csv(file_path)

Display the first few rows of the dataset

print("First few rows of the dataset:")
print(data.head())

Display summary statistics of the dataset

print("\nSummary statistics:")
print(data.describe())

Display information about the dataset

print("\nDataset information:")
print(data.info())

Check for missing values

print("\nMissing values in each column:")
print(data.isnull().sum())

Perform some basic analysis

Example: Calculate the mean of a specific column (replace 'column_name' with an actual column name)

column_name = 'age' # Replace this with the name of the column you want to analyze
mean_value = data[column_name].mean()
print(f"\nMean value of {column_name}: {mean_value}")

Example: Group by a categorical column and calculate the mean of another column

Replace 'category_column' and 'numeric_column' with actual column names

category_column = 'gender' # Replace this with the name of the categorical column
numeric_column = 'salary' # Replace this with the name of the numeric column
grouped_mean = data.groupby(category_column)[numeric_column].mean()
print(f"\nMean {numeric_column} by {category_column}:")
print(grouped_mean)

Example: Create a new column based on existing data

Replace 'existing_column' with an actual column name

data['new_column'] = data['existing_column'] * 2 # Modify this based on your analysis needs
print("\nDataset with new column:")
print(data.head())

Save the modified dataset to a new CSV file

output_file_path = 'modified_dataset.csv'
data.to_csv(output_file_path, index=False)
print(f"\nModified dataset saved to {output_file_path}")